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1 | alembic_stamp | def alembic_stamp(revision):
# lazy import for performance
import alembic.command
alembic.command.stamp(alembic_config(), revision=revision)
| 36e7e0838aeaffc9492b330297e4905f3ab4b11f | 9 | alembic_commands.py | 42 | code review revisions pt3 | 10,767 | 0 | 23 | 24 | 10 | 53,272 | 11 | prefect | 6 | src/prefect/orion/database/alembic_commands.py | Python | 3 | {
"docstring": "\n Stamp the revision table with the given revision; don’t run any migrations\n\n Args:\n revision: The revision passed to `alembic stamp`.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 20,
"vocab_size": 18
} | https://github.com/PrefectHQ/prefect.git |
|
2 | compat_system | def compat_system(source_dir):
try:
system = load_system(source_dir)
except (FileNotFoundError, KeyError):
system = {}
system.setdefault(
'build-backend',
'setuptools.build_meta:__legacy__',
)
system.setdefault('requires', ['setuptools', 'wheel'])
return system
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 10 | build.py | 87 | upd; format | 13,077 | 0 | 70 | 48 | 18 | 62,961 | 21 | transferlearning | 7 | .venv/lib/python3.8/site-packages/pip/_vendor/pep517/build.py | Python | 11 | {
"docstring": "\n Given a source dir, attempt to get a build system backend\n and requirements from pyproject.toml. Fallback to\n setuptools but only if the file was not found or a build\n system was not indicated.\n ",
"language": "en",
"n_whitespaces": 49,
"n_words": 33,
"vocab_size": 26
} | https://github.com/jindongwang/transferlearning.git |
|
2 | get_static_upper_page | def get_static_upper_page(with_shutdown):
template =
if with_shutdown:
shutdown_link = '<a href="SHUTDOWN THE SERVER">Shutdown</a>'
else:
shutdown_link = ""
return template % shutdown_link
| b0e85694107992e00a2f9fb48e6410c50fe1f1f6 | 10 | wordnet_app.py | 49 | Updated Copyright year to 2022 (#2928) | 7,529 | 0 | 50 | 24 | 15 | 42,431 | 20 | nltk | 4 | nltk/app/wordnet_app.py | Python | 30 | {
"docstring": "\n Return the upper frame page,\n\n If with_shutdown is True then a 'shutdown' button is also provided\n to shutdown the server.\n \n<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01//EN\" \"http://www.w3.org/TR/html4/strict.dtd\">\n<html>\n <!-- Natural Language Toolkit: Wordnet Interface: Graphical Wordnet Browser\n Copyright (C) 2001-2022 NLTK Project\n Author: Jussi Salmela <[email protected]>\n URL: <https://www.nltk.org/>\n For license information, see LICENSE.TXT -->\n <head>\n <meta http-equiv=\"Content-Type\" content=\"text/html; charset=iso-8859-1\" />\n <title>Untitled Document</title>\n </head>\n <body>\n <form method=\"GET\" action=\"search\" target=\"body\">\n Current Word: <input type=\"text\" id=\"currentWord\" size=\"10\" disabled>\n Next Word: <input type=\"text\" id=\"nextWord\" name=\"nextWord\" size=\"10\">\n <input name=\"searchButton\" type=\"submit\" value=\"Search\">\n </form>\n <a target=\"body\" href=\"web_help.html\">Help</a>\n %s\n\n</body>\n</html>\n",
"language": "en",
"n_whitespaces": 215,
"n_words": 91,
"vocab_size": 85
} | https://github.com/nltk/nltk.git |
|
1 | test_add_battery_later | async def test_add_battery_later(hass, aioclient_mock, mock_deconz_websocket):
data = {
"sensors": {
"1": {
"name": "Switch 1",
"type": "ZHASwitch",
"state": {"buttonevent": 1000},
"config": {},
"uniqueid": "00:00:00:00:00:00:00:00-00-0000",
},
"2": {
"name": "Switch 2",
"type": "ZHASwitch",
"state": {"buttonevent": 1000},
"config": {},
"uniqueid": "00:00:00:00:00:00:00:00-00-0001",
},
}
}
with patch.dict(DECONZ_WEB_REQUEST, data):
await setup_deconz_integration(hass, aioclient_mock)
assert len(hass.states.async_all()) == 0
event_changed_sensor = {
"t": "event",
"e": "changed",
"r": "sensors",
"id": "2",
"config": {"battery": 50},
}
await mock_deconz_websocket(data=event_changed_sensor)
await hass.async_block_till_done()
assert len(hass.states.async_all()) == 0
event_changed_sensor = {
"t": "event",
"e": "changed",
"r": "sensors",
"id": "1",
"config": {"battery": 50},
}
await mock_deconz_websocket(data=event_changed_sensor)
await hass.async_block_till_done()
assert len(hass.states.async_all()) == 1
assert hass.states.get("sensor.switch_1_battery").state == "50"
@pytest.mark.parametrize("model_id", ["0x8030", "0x8031", "0x8034", "0x8035"]) | 61ff52c93acd2f274d24469494b51356c88bb66f | @pytest.mark.parametrize("model_id", ["0x8030", "0x8031", "0x8034", "0x8035"]) | 15 | test_sensor.py | 466 | Normalize deCONZ sensor unique IDs (#76357)
* Normalize deCONZ sensor unique IDs
* Handle battery sensors properly
* Fix daylight sensor unique ID | 104,097 | 1 | 439 | 229 | 59 | 305,307 | 107 | core | 19 | tests/components/deconz/test_sensor.py | Python | 43 | {
"docstring": "Test that a battery sensor can be created later on.\n\n Without an initial battery state a battery sensor\n can be created once a value is reported.\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 26,
"vocab_size": 18
} | https://github.com/home-assistant/core.git |
6 | _get_text_value | def _get_text_value(self, row) -> Tuple[str, Dict[str, str]]:
# construct text query from the hf_text_fields specified
text_dict = {}
for col in self.hf_text_fields:
text_part = row.get(col)
if text_part is None:
raise KeyError(f'Feature "{col}" not found in data.')
text_dict[col] = text_part
query = '\n'.join(text_dict.values())
if hasattr(self, "hf_message_fields"):
for col in self.hf_message_fields:
text_part = row.get(col)
if text_part is None:
raise KeyError(f'Feature "{col}" not found in data.')
text_dict[col] = text_part
return query, text_dict
| 4f7e4b60975ff73be4227d7c8c428319366e1fe9 | 15 | agents.py | 189 | [tasks/huggingface] Way to add to message but not "text" (#4516)
I want to be able to add my own mutators to grab information from other fields out of a huggingface dataset. Since I don't want it to show up in the text itself, I do it in a new attribute.
I also delete some code that was written at some point but isn't used anywhere while I'm here.
Test Plan:
Run `parlai dd -t` with a new dataset that I'm working on that uses this.
Also run `parlai dd -t glue; parlai dd -t superglue` to validate that I don't break anything that already exists. (Former dies, but it's cause of a dataset issues on the HF side where the size of the datasets have changed, so not worrying about it) | 47,158 | 0 | 241 | 112 | 40 | 195,032 | 69 | ParlAI | 17 | parlai/tasks/huggingface/agents.py | Python | 18 | {
"docstring": "\n return the constructed text query and dict mapping text field names to values.\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 13,
"vocab_size": 12
} | https://github.com/facebookresearch/ParlAI.git |
|
2 | _postprocess | def _postprocess(self, inputs):
final_results = []
for text, similarity in zip(inputs['text'], inputs['result']):
result = {}
result['text1'] = text[0]
result['text2'] = text[1]
result['similarity'] = similarity
final_results.append(result)
return final_results
| 621357338437ee420eabbbf5ab19065bc85e73a5 | 10 | text_similarity.py | 112 | Update neural search readme and Add Paddle Serving Support (#1558)
* add recall inference similarity
* update examples
* updatea readme
* update dir name
* update neural search readme
* update milvus readme
* update domain adaptive pretraining readme
* fix the mistakes
* update readme
* add recall Paddle Serving Support
* update readme
* update readme and format the code
* reformat the files
* move the files
* reformat the code
* remove redundant code
Co-authored-by: Zeyu Chen <[email protected]>
Co-authored-by: tianxin <[email protected]> | 118,095 | 0 | 110 | 66 | 21 | 322,212 | 27 | PaddleNLP | 9 | paddlenlp/taskflow/text_similarity.py | Python | 9 | {
"docstring": "\n The model output is tag ids, this function will convert the model output to raw text.\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 16,
"vocab_size": 14
} | https://github.com/PaddlePaddle/PaddleNLP.git |
|
10 | _most_frequent | def _most_frequent(array, extra_value, n_repeat):
# Compute the most frequent value in array only
if array.size > 0:
if array.dtype == object:
# scipy.stats.mode is slow with object dtype array.
# Python Counter is more efficient
counter = Counter(array)
most_frequent_count = counter.most_common(1)[0][1]
# tie breaking similarly to scipy.stats.mode
most_frequent_value = min(
value
for value, count in counter.items()
if count == most_frequent_count
)
else:
mode = _mode(array)
most_frequent_value = mode[0][0]
most_frequent_count = mode[1][0]
else:
most_frequent_value = 0
most_frequent_count = 0
# Compare to array + [extra_value] * n_repeat
if most_frequent_count == 0 and n_repeat == 0:
return np.nan
elif most_frequent_count < n_repeat:
return extra_value
elif most_frequent_count > n_repeat:
return most_frequent_value
elif most_frequent_count == n_repeat:
# tie breaking similarly to scipy.stats.mode
return min(most_frequent_value, extra_value)
| 02a4b342181e5ff0226081691308414e53c3107b | 15 | _base.py | 221 | MAINT fix the way to call stats.mode (#23633)
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Meekail Zain <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]> | 76,401 | 0 | 366 | 137 | 67 | 260,662 | 121 | scikit-learn | 20 | sklearn/impute/_base.py | Python | 25 | {
"docstring": "Compute the most frequent value in a 1d array extended with\n [extra_value] * n_repeat, where extra_value is assumed to be not part\n of the array.",
"language": "en",
"n_whitespaces": 30,
"n_words": 25,
"vocab_size": 24
} | https://github.com/scikit-learn/scikit-learn.git |
|
2 | order | def order(self):
return reduce(lcm, [len(cycle) for cycle in self.cyclic_form], 1)
| 498015021131af4dbb07eb110e5badaba8250c7b | 10 | permutations.py | 41 | Updated import locations | 47,658 | 0 | 24 | 26 | 10 | 196,158 | 10 | sympy | 7 | sympy/combinatorics/permutations.py | Python | 2 | {
"docstring": "\n Computes the order of a permutation.\n\n When the permutation is raised to the power of its\n order it equals the identity permutation.\n\n Examples\n ========\n\n >>> from sympy.combinatorics import Permutation\n >>> from sympy import init_printing\n >>> init_printing(perm_cyclic=False, pretty_print=False)\n >>> p = Permutation([3, 1, 5, 2, 4, 0])\n >>> p.order()\n 4\n >>> (p**(p.order()))\n Permutation([], size=6)\n\n See Also\n ========\n\n identity, cardinality, length, rank, size\n ",
"language": "en",
"n_whitespaces": 181,
"n_words": 61,
"vocab_size": 47
} | https://github.com/sympy/sympy.git |
|
1 | test_filename_date_parse_valid_ymd | def test_filename_date_parse_valid_ymd(self, *args):
self.assertEqual(
parse_date("/tmp/Scan-2022-04-01.pdf", "No date in here"),
datetime.datetime(2022, 4, 1, 0, 0, tzinfo=tz.gettz(settings.TIME_ZONE)),
)
| 8a6aaf4e2d05021a14adc681c66dff6a815aa2a0 | 13 | test_date_parsing.py | 73 | Adds additional testing for both date parsing and consumed document created date | 116,952 | 0 | 59 | 47 | 15 | 319,512 | 16 | paperless-ngx | 11 | src/documents/tests/test_date_parsing.py | Python | 5 | {
"docstring": "\n GIVEN:\n - Date parsing from the filename is enabled\n - Filename date format is with Year Month Day (YMD)\n - Filename contains date matching the format\n\n THEN:\n - Should parse the date from the filename\n ",
"language": "en",
"n_whitespaces": 101,
"n_words": 35,
"vocab_size": 22
} | https://github.com/paperless-ngx/paperless-ngx.git |
|
8 | install_artifact | def install_artifact(b_coll_targz_path, b_collection_path, b_temp_path, signatures, keyring):
try:
with tarfile.open(b_coll_targz_path, mode='r') as collection_tar:
# Verify the signature on the MANIFEST.json before extracting anything else
_extract_tar_file(collection_tar, MANIFEST_FILENAME, b_collection_path, b_temp_path)
if signatures and keyring is not None:
manifest_file = os.path.join(to_text(b_collection_path, errors='surrogate_or_strict'), MANIFEST_FILENAME)
verify_artifact_manifest(manifest_file, signatures, keyring)
files_member_obj = collection_tar.getmember('FILES.json')
with _tarfile_extract(collection_tar, files_member_obj) as (dummy, files_obj):
files = json.loads(to_text(files_obj.read(), errors='surrogate_or_strict'))
_extract_tar_file(collection_tar, 'FILES.json', b_collection_path, b_temp_path)
for file_info in files['files']:
file_name = file_info['name']
if file_name == '.':
continue
if file_info['ftype'] == 'file':
_extract_tar_file(collection_tar, file_name, b_collection_path, b_temp_path,
expected_hash=file_info['chksum_sha256'])
else:
_extract_tar_dir(collection_tar, file_name, b_collection_path)
except Exception:
# Ensure we don't leave the dir behind in case of a failure.
shutil.rmtree(b_collection_path)
b_namespace_path = os.path.dirname(b_collection_path)
if not os.listdir(b_namespace_path):
os.rmdir(b_namespace_path)
raise
| 43e55db20821a1341d21ffa1e4e7e6185b244105 | 18 | __init__.py | 353 | ansible-galaxy - add signature verification of the MANIFEST.json (#76681)
* ansible-galaxy collection install|verify:
- Support verifying the origin of the MANIFEST.json when the Galaxy server has provided signatures.
- Allow supplemental signatures to use during verification on the CLI/requirements file.
* ansible-galaxy collection install:
- Support disabling signature verification. This silences the warning provided by ansible-galaxy if the Galaxy server provided signatures it cannot use because no keyring is configured.
- Store Galaxy server metadata alongside installed collections for provenance. This is used by 'ansible-galaxy collection verify --offline'.
* Add unit tests for method that gets signatures from a Galaxy server
* Add integration tests for user-provided signature sources
- Test CLI option combinations
- Test installing collections with valid/invalid signature sources
- Test disabling GPG verification when installing collections
- Test verifying collections with valid/invalid signature sources
* Make signature verification advisory-by-default if signatures are provided by the Galaxy server
- Make the default keyring None
- Warn if the keyring is None but the Galaxy server provided signatures
- Error if the keyring is None but the user supplied signatures
- Error if the keyring is not None but is invalid
* changelog
* add ansible-galaxy user documentation for new options
Co-authored-by: Matt Martz <[email protected]>
Co-authored-by: Sviatoslav Sydorenko <[email protected]>
Co-authored-by: Martin Krizek <[email protected]>
Co-authored-by: Sandra McCann <[email protected]>
Co-authored-by: Andy Mott <[email protected]>
Co-authored-by: John R Barker <[email protected]> | 78,488 | 0 | 446 | 216 | 83 | 266,590 | 108 | ansible | 39 | lib/ansible/galaxy/collection/__init__.py | Python | 26 | {
"docstring": "Install a collection from tarball under a given path.\n\n :param b_coll_targz_path: Collection tarball to be installed.\n :param b_collection_path: Collection dirs layout path.\n :param b_temp_path: Temporary dir path.\n :param signatures: frozenset of signatures to verify the MANIFEST.json\n :param keyring: The keyring used during GPG verification\n ",
"language": "en",
"n_whitespaces": 62,
"n_words": 44,
"vocab_size": 34
} | https://github.com/ansible/ansible.git |
|
1 | test_create_version | def test_create_version(self) -> None:
version = self.get_success(
self.handler.create_version(
self.local_user,
{
"algorithm": "m.megolm_backup.v1",
"auth_data": "first_version_auth_data",
},
)
)
self.assertEqual(version, "1")
# check we can retrieve it as the current version
res = self.get_success(self.handler.get_version_info(self.local_user))
version_etag = res["etag"]
self.assertIsInstance(version_etag, str)
del res["etag"]
self.assertDictEqual(
res,
{
"version": "1",
"algorithm": "m.megolm_backup.v1",
"auth_data": "first_version_auth_data",
"count": 0,
},
)
# check we can retrieve it as a specific version
res = self.get_success(self.handler.get_version_info(self.local_user, "1"))
self.assertEqual(res["etag"], version_etag)
del res["etag"]
self.assertDictEqual(
res,
{
"version": "1",
"algorithm": "m.megolm_backup.v1",
"auth_data": "first_version_auth_data",
"count": 0,
},
)
# upload a new one...
version = self.get_success(
self.handler.create_version(
self.local_user,
{
"algorithm": "m.megolm_backup.v1",
"auth_data": "second_version_auth_data",
},
)
)
self.assertEqual(version, "2")
# check we can retrieve it as the current version
res = self.get_success(self.handler.get_version_info(self.local_user))
del res["etag"]
self.assertDictEqual(
res,
{
"version": "2",
"algorithm": "m.megolm_backup.v1",
"auth_data": "second_version_auth_data",
"count": 0,
},
)
| 652d1669c5a103b1c20478770c4aaf18849c09a3 | 13 | test_e2e_room_keys.py | 448 | Add missing type hints to tests.handlers. (#14680)
And do not allow untyped defs in tests.handlers. | 73,370 | 0 | 802 | 253 | 52 | 250,292 | 131 | synapse | 14 | tests/handlers/test_e2e_room_keys.py | Python | 58 | {
"docstring": "Check that we can create and then retrieve versions.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/matrix-org/synapse.git |
|
4 | is_parametric_dtype | def is_parametric_dtype(dtype):
if dtype.itemsize == 0:
return True
if issubclass(dtype.type, (np.datetime64, np.timedelta64)):
if dtype.name.endswith("64"):
# Generic time units
return True
return False
| 7332a698c8194c6e680510da086678fe07d9cf9d | 10 | test_array_coercion.py | 78 | Fix some typos. | 38,598 | 0 | 70 | 47 | 17 | 160,332 | 22 | numpy | 10 | numpy/core/tests/test_array_coercion.py | Python | 7 | {
"docstring": "Returns True if the dtype is a parametric legacy dtype (itemsize\n is 0, or a datetime without units)\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 18,
"vocab_size": 15
} | https://github.com/numpy/numpy.git |
|
1 | _handle_default_message | def _handle_default_message(self, type, data):
logger.debug(f"Received message from Leader of type {type}: {data}")
| 2b5f0678772bea0abaf4abe93efc55de43ea3e0e | 9 | rpc.py | 37 | Refactoring, minor improvements, data provider improvements | 34,858 | 0 | 26 | 17 | 12 | 150,868 | 12 | freqtrade | 6 | freqtrade/rpc/rpc.py | Python | 2 | {
"docstring": "\n Default leader message handler, just logs it. We should never have to\n run this unless the leader sends us some weird message.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 22,
"vocab_size": 21
} | https://github.com/freqtrade/freqtrade.git |
|
3 | borrowed | def borrowed(self) -> float:
if self.has_no_leverage:
return 0.0
elif not self.is_short:
return (self.amount * self.open_rate) * ((self.leverage - 1) / self.leverage)
else:
return self.amount
| baefda80d19e3eaf14e2ec6a600dcb8c456a3ccf | 14 | models.py | 83 | Enable flake8 E226 rule | 34,448 | 0 | 85 | 53 | 21 | 149,544 | 24 | freqtrade | 8 | freqtrade/persistence/models.py | Python | 12 | {
"docstring": "\n The amount of currency borrowed from the exchange for leverage trades\n If a long trade, the amount is in base currency\n If a short trade, the amount is in the other currency being traded\n ",
"language": "en",
"n_whitespaces": 75,
"n_words": 34,
"vocab_size": 22
} | https://github.com/freqtrade/freqtrade.git |
|
5 | fit_transform | def fit_transform(self, y):
if self.classes is not None:
return self.fit(y).transform(y)
self._validate_params()
self._cached_dict = None
# Automatically increment on new class
class_mapping = defaultdict(int)
class_mapping.default_factory = class_mapping.__len__
yt = self._transform(y, class_mapping)
# sort classes and reorder columns
tmp = sorted(class_mapping, key=class_mapping.get)
# (make safe for tuples)
dtype = int if all(isinstance(c, int) for c in tmp) else object
class_mapping = np.empty(len(tmp), dtype=dtype)
class_mapping[:] = tmp
self.classes_, inverse = np.unique(class_mapping, return_inverse=True)
# ensure yt.indices keeps its current dtype
yt.indices = np.array(inverse[yt.indices], dtype=yt.indices.dtype, copy=False)
if not self.sparse_output:
yt = yt.toarray()
return yt
| fd60379f95f5c0d3791b2f54c4d070c0aa2ac576 | 11 | _label.py | 269 | MAINT parameter validation for Multilabel Binarizer (#23802)
Co-authored-by: Jérémie du Boisberranger <[email protected]> | 76,245 | 0 | 244 | 171 | 65 | 260,432 | 89 | scikit-learn | 36 | sklearn/preprocessing/_label.py | Python | 17 | {
"docstring": "Fit the label sets binarizer and transform the given label sets.\n\n Parameters\n ----------\n y : iterable of iterables\n A set of labels (any orderable and hashable object) for each\n sample. If the `classes` parameter is set, `y` will not be\n iterated.\n\n Returns\n -------\n y_indicator : {ndarray, sparse matrix} of shape (n_samples, n_classes)\n A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]`\n is in `y[i]`, and 0 otherwise. Sparse matrix will be of CSR\n format.\n ",
"language": "en",
"n_whitespaces": 190,
"n_words": 75,
"vocab_size": 61
} | https://github.com/scikit-learn/scikit-learn.git |
|
1 | select_query | def select_query(self, query):
renderer = SqlalchemyRender('mysql')
query_str = renderer.get_string(query, with_failback=True)
return self.native_query(query_str)
| e00014335bbda3623ee1ccac02c9427ae324141d | 9 | mysql_handler.py | 56 | Update mysql handler | 25,255 | 0 | 42 | 33 | 11 | 114,701 | 12 | mindsdb | 9 | mindsdb/integrations/mysql_handler/mysql_handler.py | Python | 4 | {
"docstring": "\n Retrieve the data from the SQL statement.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 6
} | https://github.com/mindsdb/mindsdb.git |
|
1 | test_multigraph_add_edges_from_four_tuple_misordered | def test_multigraph_add_edges_from_four_tuple_misordered(self):
G = nx.MultiGraph()
with pytest.raises(TypeError):
# key/data values flipped in 4-tuple
G.add_edges_from([(0, 1, {"color": "red"}, 0)])
| 15614a4e2736752b0200d3a770d83f2be2d130b9 | 14 | test_multigraph.py | 73 | Update tests in base class and simple rename in convert.py (#5848) | 42,237 | 0 | 61 | 41 | 18 | 177,027 | 18 | networkx | 9 | networkx/classes/tests/test_multigraph.py | Python | 4 | {
"docstring": "add_edges_from expects 4-tuples of the format (u, v, key, data_dict).\n\n Ensure 4-tuples of form (u, v, data_dict, key) raise exception.\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 20,
"vocab_size": 16
} | https://github.com/networkx/networkx.git |
|
1 | test_get_previous_start_date_none | def test_get_previous_start_date_none(self, dag_maker):
with dag_maker("test_get_previous_start_date_none", schedule_interval=None) as dag:
task = EmptyOperator(task_id="op")
day_1 = DEFAULT_DATE
day_2 = DEFAULT_DATE + datetime.timedelta(days=1)
# Create a DagRun for day_1 and day_2. Calling ti_2.get_previous_start_date()
# should return the start_date of ti_1 (which is None because ti_1 was not run).
# It should not raise an error.
dagrun_1 = dag_maker.create_dagrun(
execution_date=day_1,
state=State.RUNNING,
run_type=DagRunType.MANUAL,
)
dagrun_2 = dag.create_dagrun(
execution_date=day_2,
state=State.RUNNING,
run_type=DagRunType.MANUAL,
)
ti_1 = dagrun_1.get_task_instance(task.task_id)
ti_2 = dagrun_2.get_task_instance(task.task_id)
ti_1.task = task
ti_2.task = task
assert ti_2.get_previous_start_date() == ti_1.start_date
assert ti_1.start_date is None
| 49e336ae0302b386a2f47269a6d13988382d975f | 12 | test_taskinstance.py | 219 | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | 9,179 | 0 | 281 | 135 | 59 | 47,625 | 85 | airflow | 29 | tests/models/test_taskinstance.py | Python | 21 | {
"docstring": "\n Test that get_previous_start_date() can handle TaskInstance with no start_date.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 9
} | https://github.com/apache/airflow.git |
|
2 | print_mathml | def print_mathml(expr, printer='content', **settings):
if printer == 'presentation':
s = MathMLPresentationPrinter(settings)
else:
s = MathMLContentPrinter(settings)
xml = s._print(sympify(expr))
s.apply_patch()
pretty_xml = xml.toprettyxml()
s.restore_patch()
print(pretty_xml)
# For backward compatibility
MathMLPrinter = MathMLContentPrinter
| 59d22b6bb7287613d598611027f640d068ca5748 | 11 | mathml.py | 119 | Moved imports to higher level | 47,914 | 0 | 67 | 64 | 26 | 196,414 | 31 | sympy | 16 | sympy/printing/mathml.py | Python | 10 | {
"docstring": "\n Prints a pretty representation of the MathML code for expr. If printer is\n presentation then prints Presentation MathML else prints content MathML.\n\n Examples\n ========\n\n >>> ##\n >>> from sympy import print_mathml\n >>> from sympy.abc import x\n >>> print_mathml(x+1) #doctest: +NORMALIZE_WHITESPACE\n <apply>\n <plus/>\n <ci>x</ci>\n <cn>1</cn>\n </apply>\n >>> print_mathml(x+1, printer='presentation')\n <mrow>\n <mi>x</mi>\n <mo>+</mo>\n <mn>1</mn>\n </mrow>\n\n ",
"language": "en",
"n_whitespaces": 138,
"n_words": 53,
"vocab_size": 45
} | https://github.com/sympy/sympy.git |
|
2 | sync_to_numpy_or_python_type | def sync_to_numpy_or_python_type(tensors):
if isinstance(tensors, tf.distribute.experimental.coordinator.RemoteValue):
tensors = tensors.fetch()
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 11 | tf_utils.py | 49 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 81,872 | 0 | 21 | 42 | 8 | 277,101 | 8 | keras | 9 | keras/utils/tf_utils.py | Python | 5 | {
"docstring": "Syncs and converts a structure of `Tensor`s to `NumPy` arrays or Python scalar types.\n\n For each tensor, it calls `tensor.numpy()`. If the result is a scalar value,\n it converts it to a Python type, such as a float or int, by calling\n `result.item()`.\n\n Numpy scalars are converted, as Python types are often more convenient to deal\n with. This is especially useful for bfloat16 Numpy scalars, which don't\n support as many operations as other Numpy values.\n\n Async strategies (such as `TPUStrategy` and `ParameterServerStrategy`) are\n forced to\n sync during this process.\n\n Args:\n tensors: A structure of tensors.\n\n Returns:\n `tensors`, but scalar tensors are converted to Python types and non-scalar\n tensors are converted to Numpy arrays.\n ",
"language": "en",
"n_whitespaces": 164,
"n_words": 113,
"vocab_size": 77
} | https://github.com/keras-team/keras.git |
|
2 | test_setup_permanent_error | async def test_setup_permanent_error(hass, aioclient_mock):
fake_async_add_entities = MagicMock()
errors = [HTTPStatus.BAD_REQUEST, HTTPStatus.UNAUTHORIZED, HTTPStatus.FORBIDDEN]
for error in errors:
aioclient_mock.get(re.compile("api.foobot.io/v2/owner/.*"), status=error)
result = await foobot.async_setup_platform(
hass, VALID_CONFIG, fake_async_add_entities
)
assert result is None
| 8896229ea641a558161d8caed796895e9a78f457 | 12 | test_sensor.py | 103 | Improve type hint in foobot sensor entity (#77164) | 103,613 | 0 | 81 | 65 | 26 | 304,818 | 30 | core | 19 | tests/components/foobot/test_sensor.py | Python | 9 | {
"docstring": "Expected failures caused by permanent errors in API response.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/home-assistant/core.git |
|
9 | integrate | def integrate(self, comet_ml=None, wandb=None, mlflow=None) -> None:
analytics_integration = ""
if comet_ml is not None:
analytics_integration = "CometML"
comet_ml.log_other("Created from", "Gradio")
if self.share_url is not None:
comet_ml.log_text("gradio: " + self.share_url)
comet_ml.end()
else:
comet_ml.log_text("gradio: " + self.local_url)
comet_ml.end()
if wandb is not None:
analytics_integration = "WandB"
if self.share_url is not None:
wandb.log(
{
"Gradio panel": wandb.Html(
'<iframe src="'
+ self.share_url
+ '" width="'
+ str(self.width)
+ '" height="'
+ str(self.height)
+ '" frameBorder="0"></iframe>'
)
}
)
else:
print(
"The WandB integration requires you to "
"`launch(share=True)` first."
)
if mlflow is not None:
analytics_integration = "MLFlow"
if self.share_url is not None:
mlflow.log_param("Gradio Interface Share Link", self.share_url)
else:
mlflow.log_param("Gradio Interface Local Link", self.local_url)
if self.analytics_enabled and analytics_integration:
data = {"integration": analytics_integration}
utils.integration_analytics(data)
| cc0cff893f9d7d472788adc2510c123967b384fe | 22 | interface.py | 356 | Format The Codebase
- black formatting
- isort formatting | 42,929 | 0 | 755 | 202 | 66 | 179,258 | 120 | gradio | 22 | gradio/interface.py | Python | 52 | {
"docstring": "\n A catch-all method for integrating with other libraries.\n Should be run after launch()\n Parameters:\n comet_ml (Experiment): If a comet_ml Experiment object is provided,\n will integrate with the experiment and appear on Comet dashboard\n wandb (module): If the wandb module is provided, will integrate\n with it and appear on WandB dashboard\n mlflow (module): If the mlflow module is provided, will integrate\n with the experiment and appear on ML Flow dashboard\n ",
"language": "en",
"n_whitespaces": 165,
"n_words": 69,
"vocab_size": 39
} | https://github.com/gradio-app/gradio.git |
|
2 | require_huggingface_suite | def require_huggingface_suite(test_case):
return unittest.skipUnless(
is_transformers_available() and is_datasets_available(), "test requires the Hugging Face suite"
)(test_case)
| 8944975a3c8db55f57774e9e566e7a791be766cb | 11 | testing.py | 44 | Reenable Gather for Metrics (#590)
* Clean and finish
Co-authored-by: Sylvain Gugger <[email protected]> | 121,157 | 0 | 30 | 24 | 14 | 338,046 | 14 | accelerate | 6 | src/accelerate/test_utils/testing.py | Python | 4 | {
"docstring": "\n Decorator marking a test that requires transformers and datasets. These tests are skipped when they are not.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 17,
"vocab_size": 16
} | https://github.com/huggingface/accelerate.git |
|
14 | _type | def _type(string, has_invisible=True, numparse=True):
if has_invisible and (
isinstance(string, _text_type) or isinstance(string, _binary_type)
):
string = _strip_invisible(string)
if string is None:
return _none_type
elif hasattr(string, "isoformat"): # datetime.datetime, date, and time
return _text_type
elif _isbool(string):
return _bool_type
elif _isint(string) and numparse:
return int
elif _isint(string, _long_type) and numparse:
return int
elif _isnumber(string) and numparse:
return float
elif isinstance(string, _binary_type):
return _binary_type
else:
return _text_type
| adf24bfa9723b0621183bb27f0c889b813c06e8a | 10 | tabulate.py | 179 | [State Observability] Use a table format by default (#26159)
NOTE: tabulate is copied/pasted to the codebase for table formatting.
This PR changes the default layout to be the table format for both summary and list APIs. | 27,792 | 0 | 168 | 110 | 40 | 125,175 | 64 | ray | 17 | python/ray/_private/thirdparty/tabulate/tabulate.py | Python | 21 | {
"docstring": "The least generic type (type(None), int, float, str, unicode).\n\n >>> _type(None) is type(None)\n True\n >>> _type(\"foo\") is type(\"\")\n True\n >>> _type(\"1\") is type(1)\n True\n >>> _type('\\x1b[31m42\\x1b[0m') is type(42)\n True\n >>> _type('\\x1b[31m42\\x1b[0m') is type(42)\n True\n\n ",
"language": "en",
"n_whitespaces": 67,
"n_words": 34,
"vocab_size": 20
} | https://github.com/ray-project/ray.git |
|
9 | multi_gpu_train_one_step | def multi_gpu_train_one_step(algorithm, train_batch) -> Dict:
config = algorithm.config
workers = algorithm.workers
local_worker = workers.local_worker()
num_sgd_iter = config.get("num_sgd_iter", 1)
sgd_minibatch_size = config.get("sgd_minibatch_size", config["train_batch_size"])
# Determine the number of devices (GPUs or 1 CPU) we use.
num_devices = int(math.ceil(config["num_gpus"] or 1))
# Make sure total batch size is dividable by the number of devices.
# Batch size per tower.
per_device_batch_size = sgd_minibatch_size // num_devices
# Total batch size.
batch_size = per_device_batch_size * num_devices
assert batch_size % num_devices == 0
assert batch_size >= num_devices, "Batch size too small!"
# Handle everything as if multi-agent.
train_batch = train_batch.as_multi_agent()
# Load data into GPUs.
load_timer = algorithm._timers[LOAD_BATCH_TIMER]
with load_timer:
num_loaded_samples = {}
for policy_id, batch in train_batch.policy_batches.items():
# Not a policy-to-train.
if not local_worker.is_policy_to_train(policy_id, train_batch):
continue
# Decompress SampleBatch, in case some columns are compressed.
batch.decompress_if_needed()
# Load the entire train batch into the Policy's only buffer
# (idx=0). Policies only have >1 buffers, if we are training
# asynchronously.
num_loaded_samples[policy_id] = local_worker.policy_map[
policy_id
].load_batch_into_buffer(batch, buffer_index=0)
# Execute minibatch SGD on loaded data.
learn_timer = algorithm._timers[LEARN_ON_BATCH_TIMER]
with learn_timer:
# Use LearnerInfoBuilder as a unified way to build the final
# results dict from `learn_on_loaded_batch` call(s).
# This makes sure results dicts always have the same structure
# no matter the setup (multi-GPU, multi-agent, minibatch SGD,
# tf vs torch).
learner_info_builder = LearnerInfoBuilder(num_devices=num_devices)
for policy_id, samples_per_device in num_loaded_samples.items():
policy = local_worker.policy_map[policy_id]
num_batches = max(1, int(samples_per_device) // int(per_device_batch_size))
logger.debug("== sgd epochs for {} ==".format(policy_id))
for _ in range(num_sgd_iter):
permutation = np.random.permutation(num_batches)
for batch_index in range(num_batches):
# Learn on the pre-loaded data in the buffer.
# Note: For minibatch SGD, the data is an offset into
# the pre-loaded entire train batch.
results = policy.learn_on_loaded_batch(
permutation[batch_index] * per_device_batch_size, buffer_index=0
)
learner_info_builder.add_learn_on_batch_results(results, policy_id)
# Tower reduce and finalize results.
learner_info = learner_info_builder.finalize()
load_timer.push_units_processed(train_batch.count)
learn_timer.push_units_processed(train_batch.count)
# TODO: Move this into Trainer's `training_iteration` method for
# better transparency.
algorithm._counters[NUM_ENV_STEPS_TRAINED] += train_batch.count
algorithm._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps()
if algorithm.reward_estimators:
learner_info[DEFAULT_POLICY_ID]["off_policy_estimation"] = {}
for name, estimator in algorithm.reward_estimators.items():
learner_info[DEFAULT_POLICY_ID]["off_policy_estimation"][
name
] = estimator.train(train_batch)
return learner_info
| 38c9e1d52aeada536a0460fba01b2633973bc989 | 18 | train_ops.py | 616 | [RLlib]: Fix OPE trainables (#26279)
Co-authored-by: Kourosh Hakhamaneshi <[email protected]> | 27,750 | 0 | 892 | 362 | 216 | 125,012 | 330 | ray | 63 | rllib/execution/train_ops.py | Python | 66 | {
"docstring": "Multi-GPU version of train_one_step.\n\n Uses the policies' `load_batch_into_buffer` and `learn_on_loaded_batch` methods\n to be more efficient wrt CPU/GPU data transfers. For example, when doing multiple\n passes through a train batch (e.g. for PPO) using `config.num_sgd_iter`, the\n actual train batch is only split once and loaded once into the GPU(s).\n\n Examples:\n >>> from ray.rllib.execution.rollout_ops import synchronous_parallel_sample\n >>> algo = [...] # doctest: +SKIP\n >>> train_batch = synchronous_parallel_sample(algo.workers) # doctest: +SKIP\n >>> # This trains the policy on one batch.\n >>> results = multi_gpu_train_one_step(algo, train_batch)) # doctest: +SKIP\n {\"default_policy\": ...}\n\n Updates the NUM_ENV_STEPS_TRAINED and NUM_AGENT_STEPS_TRAINED counters as well as\n the LOAD_BATCH_TIMER and LEARN_ON_BATCH_TIMER timers of the Algorithm instance.\n ",
"language": "en",
"n_whitespaces": 171,
"n_words": 105,
"vocab_size": 78
} | https://github.com/ray-project/ray.git |
|
11 | get_keys | def get_keys(self, subpath, method, view):
if hasattr(view, 'action'):
# Viewsets have explicitly named actions.
action = view.action
else:
# Views have no associated action, so we determine one from the method.
if is_list_view(subpath, method, view):
action = 'list'
else:
action = self.default_mapping[method.lower()]
named_path_components = [
component for component
in subpath.strip('/').split('/')
if '{' not in component
]
if is_custom_action(action):
# Custom action, eg "/users/{pk}/activate/", "/users/active/"
mapped_methods = {
# Don't count head mapping, e.g. not part of the schema
method for method in view.action_map if method != 'head'
}
if len(mapped_methods) > 1:
action = self.default_mapping[method.lower()]
if action in self.coerce_method_names:
action = self.coerce_method_names[action]
return named_path_components + [action]
else:
return named_path_components[:-1] + [action]
if action in self.coerce_method_names:
action = self.coerce_method_names[action]
# Default action, eg "/users/", "/users/{pk}/"
return named_path_components + [action]
| df584350b4f77143d84615f05000f71408aec9c0 | 15 | coreapi.py | 287 | Prevent head method mapping to coerce action name (#7729) | 9,553 | 0 | 484 | 174 | 73 | 48,644 | 128 | django-rest-framework | 19 | rest_framework/schemas/coreapi.py | Python | 27 | {
"docstring": "\n Return a list of keys that should be used to layout a link within\n the schema document.\n\n /users/ (\"users\", \"list\"), (\"users\", \"create\")\n /users/{pk}/ (\"users\", \"read\"), (\"users\", \"update\"), (\"users\", \"delete\")\n /users/enabled/ (\"users\", \"enabled\") # custom viewset list action\n /users/{pk}/star/ (\"users\", \"star\") # custom viewset detail action\n /users/{pk}/groups/ (\"users\", \"groups\", \"list\"), (\"users\", \"groups\", \"create\")\n /users/{pk}/groups/{pk}/ (\"users\", \"groups\", \"read\"), (\"users\", \"groups\", \"update\"), (\"users\", \"groups\", \"delete\")\n ",
"language": "en",
"n_whitespaces": 187,
"n_words": 62,
"vocab_size": 36
} | https://github.com/encode/django-rest-framework.git |
|
3 | set_pickradius | def set_pickradius(self, pickradius):
if not isinstance(pickradius, Number) or pickradius < 0:
raise ValueError("pick radius should be a distance")
self._pickradius = pickradius
pickradius = property(get_pickradius, set_pickradius)
| 91f47d6eff63187f582c395c007d0152980be6b3 | 10 | lines.py | 65 | Unify set_pickradius argument | 23,452 | 0 | 60 | 31 | 22 | 109,136 | 25 | matplotlib | 9 | lib/matplotlib/lines.py | Python | 4 | {
"docstring": "\n Set the pick radius used for containment tests.\n\n See `.contains` for more details.\n\n Parameters\n ----------\n pickradius : float\n Pick radius, in points.\n ",
"language": "en",
"n_whitespaces": 76,
"n_words": 22,
"vocab_size": 21
} | https://github.com/matplotlib/matplotlib.git |
|
1 | test_missing_config | def test_missing_config(self):
with patch("streamlit.config.os.path.exists") as path_exists:
path_exists.return_value = False
config.get_config_options()
self.assertEqual(True, config.get_option("client.caching"))
self.assertIsNone(config.get_option("theme.font"))
| dd9084523e365e637443ea351eaaaa25f52d8412 | 12 | config_test.py | 90 | Report sharing removal (#4260)
The report sharing feature is a substantial but completely unused portion of the code in Streamlit's underlying machinery. The feature was created early on, used by just a few groups, and has not been used by anyone for a while, as indicated by no activity in the associated S3 buckets. This commit removes that code to make the remaining code easier to navigate and understand. | 26,360 | 0 | 71 | 48 | 13 | 118,685 | 13 | streamlit | 10 | lib/tests/streamlit/config_test.py | Python | 6 | {
"docstring": "Test that we can initialize our config even if the file is missing.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | https://github.com/streamlit/streamlit.git |
|
3 | get_last_runtime | def get_last_runtime(self, file_path):
stat = self._file_stats.get(file_path)
return stat.last_duration.total_seconds() if stat and stat.last_duration else None
| 1507ca48d7c211799129ce7956c11f4c45fee5bc | 9 | manager.py | 55 | Fix StatD timing metric units (#21106)
Co-authored-by: Tzu-ping Chung <[email protected]>
Co-authored-by: Tzu-ping Chung <[email protected]> | 7,740 | 0 | 35 | 34 | 13 | 42,816 | 14 | airflow | 8 | airflow/dag_processing/manager.py | Python | 3 | {
"docstring": "\n :param file_path: the path to the file that was processed\n :return: the runtime (in seconds) of the process of the last run, or\n None if the file was never processed.\n :rtype: float\n ",
"language": "en",
"n_whitespaces": 72,
"n_words": 32,
"vocab_size": 24
} | https://github.com/apache/airflow.git |
|
4 | turn_on | def turn_on(self) -> None:
if DPCode.SWITCH in self.device.function:
self._send_command([{"code": DPCode.SWITCH, "value": True}])
return
# Fake turn on
for mode in (HVACMode.HEAT_COOL, HVACMode.HEAT, HVACMode.COOL):
if mode not in self.hvac_modes:
continue
self.set_hvac_mode(mode)
break
| e688f6b315a2edcab6c7e6bd1dfe8a77bf715fed | 13 | climate.py | 116 | Use climate enums in tuya (#70747) | 97,746 | 0 | 129 | 71 | 27 | 298,805 | 31 | core | 14 | homeassistant/components/tuya/climate.py | Python | 10 | {
"docstring": "Turn the device on, retaining current HVAC (if supported).",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/home-assistant/core.git |
|
13 | train | def train(self):
if self._warmup_time is None:
self._warmup_time = time.time() - self._start_time
start = time.time()
try:
result = self.step()
except Exception as e:
raise skip_exceptions(e) from None
assert isinstance(result, dict), "step() needs to return a dict."
# We do not modify internal state nor update this result if duplicate.
if RESULT_DUPLICATE in result:
return result
result = result.copy()
self._iteration += 1
self._iterations_since_restore += 1
if result.get(TIME_THIS_ITER_S) is not None:
time_this_iter = result[TIME_THIS_ITER_S]
else:
time_this_iter = time.time() - start
self._time_total += time_this_iter
self._time_since_restore += time_this_iter
result.setdefault(DONE, False)
# self._timesteps_total should only be tracked if increments provided
if result.get(TIMESTEPS_THIS_ITER) is not None:
if self._timesteps_total is None:
self._timesteps_total = 0
self._timesteps_total += result[TIMESTEPS_THIS_ITER]
self._timesteps_since_restore += result[TIMESTEPS_THIS_ITER]
# self._episodes_total should only be tracked if increments provided
if result.get(EPISODES_THIS_ITER) is not None:
if self._episodes_total is None:
self._episodes_total = 0
self._episodes_total += result[EPISODES_THIS_ITER]
# self._timesteps_total should not override user-provided total
result.setdefault(TIMESTEPS_TOTAL, self._timesteps_total)
result.setdefault(EPISODES_TOTAL, self._episodes_total)
result.setdefault(TRAINING_ITERATION, self._iteration)
# Provides auto-filled neg_mean_loss for avoiding regressions
if result.get("mean_loss"):
result.setdefault("neg_mean_loss", -result["mean_loss"])
now = datetime.today()
result.update(self.get_auto_filled_metrics(now, time_this_iter))
monitor_data = self._monitor.get_data()
if monitor_data:
result.update(monitor_data)
self.log_result(result)
if self._stdout_context:
self._stdout_stream.flush()
if self._stderr_context:
self._stderr_stream.flush()
self._last_result = result
return result
| d0678b80edf7ab50ffde93c13f4a8cdbd7dbba99 | 12 | trainable.py | 536 | [rfc] [air/tune/train] Improve trial/training failure error printing (#27946)
When training fails, the console output is currently cluttered with tracebacks which are hard to digest. This problem is exacerbated when running multiple trials in a tuning run.
The main problems here are:
1. Tracebacks are printed multiple times: In the remote worker and on the driver
2. Tracebacks include many internal wrappers
The proposed solution for 1 is to only print tracebacks once (on the driver) or never (if configured).
The proposed solution for 2 is to shorten the tracebacks to include mostly user-provided code.
### Deduplicating traceback printing
The solution here is to use `logger.error` instead of `logger.exception` in the `function_trainable.py` to avoid printing a traceback in the trainable.
Additionally, we introduce an environment variable `TUNE_PRINT_ALL_TRIAL_ERRORS` which defaults to 1. If set to 0, trial errors will not be printed at all in the console (only the error.txt files will exist).
To be discussed: We could also default this to 0, but I think the expectation is to see at least some failure output in the console logs per default.
### Removing internal wrappers from tracebacks
The solution here is to introcude a magic local variable `_ray_start_tb`. In two places, we use this magic local variable to reduce the stacktrace. A utility `shorten_tb` looks for the last occurence of `_ray_start_tb` in the stacktrace and starts the traceback from there. This takes only linear time. If the magic variable is not present, the full traceback is returned - this means that if the error does not come up in user code, the full traceback is returned, giving visibility in possible internal bugs. Additionally there is an env variable `RAY_AIR_FULL_TRACEBACKS` which disables traceback shortening.
Signed-off-by: Kai Fricke <[email protected]> | 28,398 | 0 | 623 | 328 | 104 | 127,235 | 183 | ray | 47 | python/ray/tune/trainable/trainable.py | Python | 47 | {
"docstring": "Runs one logical iteration of training.\n\n Calls ``step()`` internally. Subclasses should override ``step()``\n instead to return results.\n This method automatically fills the following fields in the result:\n\n `done` (bool): training is terminated. Filled only if not provided.\n\n `time_this_iter_s` (float): Time in seconds this iteration\n took to run. This may be overridden in order to override the\n system-computed time difference.\n\n `time_total_s` (float): Accumulated time in seconds for this\n entire experiment.\n\n `experiment_id` (str): Unique string identifier\n for this experiment. This id is preserved\n across checkpoint / restore calls.\n\n `training_iteration` (int): The index of this\n training iteration, e.g. call to train(). This is incremented\n after `step()` is called.\n\n `pid` (str): The pid of the training process.\n\n `date` (str): A formatted date of when the result was processed.\n\n `timestamp` (str): A UNIX timestamp of when the result\n was processed.\n\n `hostname` (str): Hostname of the machine hosting the training\n process.\n\n `node_ip` (str): Node ip of the machine hosting the training\n process.\n\n Returns:\n A dict that describes training progress.\n ",
"language": "en",
"n_whitespaces": 429,
"n_words": 163,
"vocab_size": 104
} | https://github.com/ray-project/ray.git |
|
3 | legacy_plugin_dir_to_plugin_type | def legacy_plugin_dir_to_plugin_type(legacy_plugin_dir_name):
legacy_plugin_dir_name = to_text(legacy_plugin_dir_name)
plugin_type = legacy_plugin_dir_name.removesuffix(u'_plugins')
if plugin_type == u'library':
plugin_type = u'modules'
if plugin_type not in AnsibleCollectionRef.VALID_REF_TYPES:
raise ValueError('{0} cannot be mapped to a valid collection ref type'.format(to_native(legacy_plugin_dir_name)))
return plugin_type
| 884244f1b2da3c3f367e064ef4ac0123fcb12675 | 13 | _collection_finder.py | 93 | Python 3.9 min for controller (#77566) | 79,008 | 0 | 97 | 54 | 26 | 267,700 | 33 | ansible | 10 | lib/ansible/utils/collection_loader/_collection_finder.py | Python | 8 | {
"docstring": "\n Utility method to convert from a PluginLoader dir name to a plugin ref_type\n :param legacy_plugin_dir_name: PluginLoader dir name (eg, 'action_plugins', 'library')\n :return: the corresponding plugin ref_type (eg, 'action', 'role')\n ",
"language": "en",
"n_whitespaces": 58,
"n_words": 29,
"vocab_size": 21
} | https://github.com/ansible/ansible.git |
|
1 | test_fs_checkpoint_dict | def test_fs_checkpoint_dict(self):
checkpoint = self._prepare_fs_checkpoint()
# Convert into dict checkpoint
data_dict = checkpoint.to_dict()
self.assertIsInstance(data_dict, dict)
# Create from dict
checkpoint = Checkpoint.from_dict(data_dict)
self.assertTrue(checkpoint._data_dict)
self._assert_fs_checkpoint(checkpoint)
| b267be475863a66e9feedb2be5f0a30a2ed8c493 | 8 | test_checkpoints.py | 87 | [ml] Add Ray ML / AIR checkpoint implementation (#22691)
This PR splits up the changes in #22393 and introduces an implementation of the ML Checkpoint interface used by Ray Tune.
This means, the TuneCheckpoint class implements the to/from_[bytes|dict|directory|object_ref|uri] conversion functions, as well as more high-level functions to transition between the different TuneCheckpoint classes. It also includes test cases for Tune's main conversion modes, i.e. dict - intermediate - dict and fs - intermediate - fs.
These changes will be the basis for refactoring the tune interface to use TuneCheckpoint objects instead of TrialCheckpoints (externally) and instead of paths/objects (internally). | 33,588 | 0 | 87 | 50 | 18 | 146,015 | 24 | ray | 13 | python/ray/ml/tests/test_checkpoints.py | Python | 7 | {
"docstring": "Test conversion from fs to dict checkpoint and back.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/ray-project/ray.git |
|
6 | _validate_index_level | def _validate_index_level(self, level) -> None:
if isinstance(level, int):
if level < 0 and level != -1:
raise IndexError(
"Too many levels: Index has only 1 level, "
f"{level} is not a valid level number"
)
if level > 0:
raise IndexError(
f"Too many levels: Index has only 1 level, not {level + 1}"
)
elif level != self.name:
raise KeyError(
f"Requested level ({level}) does not match index name ({self.name})"
)
| d13c9e034ce8a1d738766c4b1cec80c76f5523be | 14 | base.py | 121 | STYLE: fix pylint: no-else-raise (#49520)
* fix pylint: no-else-raise
* fix possible imbalanced tuple unpacking warning
Co-authored-by: carlotta <[email protected]> | 40,622 | 0 | 267 | 59 | 48 | 170,932 | 70 | pandas | 8 | pandas/core/indexes/base.py | Python | 22 | {
"docstring": "\n Validate index level.\n\n For single-level Index getting level number is a no-op, but some\n verification must be done like in MultiIndex.\n\n ",
"language": "en",
"n_whitespaces": 50,
"n_words": 21,
"vocab_size": 21
} | https://github.com/pandas-dev/pandas.git |
|
15 | make_sl_entries | def make_sl_entries(sl_entries, allow_negative_stock=False, via_landed_cost_voucher=False):
from erpnext.controllers.stock_controller import future_sle_exists
if sl_entries:
cancel = sl_entries[0].get("is_cancelled")
if cancel:
validate_cancellation(sl_entries)
set_as_cancel(sl_entries[0].get("voucher_type"), sl_entries[0].get("voucher_no"))
args = get_args_for_future_sle(sl_entries[0])
future_sle_exists(args, sl_entries)
for sle in sl_entries:
if sle.serial_no and not via_landed_cost_voucher:
validate_serial_no(sle)
if cancel:
sle["actual_qty"] = -flt(sle.get("actual_qty"))
if sle["actual_qty"] < 0 and not sle.get("outgoing_rate"):
sle["outgoing_rate"] = get_incoming_outgoing_rate_for_cancel(
sle.item_code, sle.voucher_type, sle.voucher_no, sle.voucher_detail_no
)
sle["incoming_rate"] = 0.0
if sle["actual_qty"] > 0 and not sle.get("incoming_rate"):
sle["incoming_rate"] = get_incoming_outgoing_rate_for_cancel(
sle.item_code, sle.voucher_type, sle.voucher_no, sle.voucher_detail_no
)
sle["outgoing_rate"] = 0.0
if sle.get("actual_qty") or sle.get("voucher_type") == "Stock Reconciliation":
sle_doc = make_entry(sle, allow_negative_stock, via_landed_cost_voucher)
args = sle_doc.as_dict()
if sle.get("voucher_type") == "Stock Reconciliation":
# preserve previous_qty_after_transaction for qty reposting
args.previous_qty_after_transaction = sle.get("previous_qty_after_transaction")
is_stock_item = frappe.get_cached_value("Item", args.get("item_code"), "is_stock_item")
if is_stock_item:
bin_name = get_or_make_bin(args.get("item_code"), args.get("warehouse"))
repost_current_voucher(args, allow_negative_stock, via_landed_cost_voucher)
update_bin_qty(bin_name, args)
else:
frappe.msgprint(
_("Item {0} ignored since it is not a stock item").format(args.get("item_code"))
)
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 19 | stock_ledger.py | 563 | style: format code with black | 14,689 | 0 | 93 | 336 | 85 | 67,967 | 132 | erpnext | 37 | erpnext/stock/stock_ledger.py | Python | 38 | {
"docstring": "Create SL entries from SL entry dicts\n\n\targs:\n\t - allow_negative_stock: disable negative stock valiations if true\n\t - via_landed_cost_voucher: landed cost voucher cancels and reposts\n\t entries of purchase document. This flag is used to identify if\n\t cancellation and repost is happening via landed cost voucher, in\n\t such cases certain validations need to be ignored (like negative\n\t stock)\n\t",
"language": "en",
"n_whitespaces": 112,
"n_words": 56,
"vocab_size": 46
} | https://github.com/frappe/erpnext.git |
|
1 | get_feature_names_out | def get_feature_names_out(self, input_features=None):
class_name = self.__class__.__name__.lower()
return np.asarray([f"{class_name}0"], dtype=object)
| 8991c3d7870df692fe01510e0fe6de62ea550cad | 10 | isotonic.py | 61 | ENH Adds get_feature_names to isotonic module (#22249) | 75,349 | 0 | 30 | 35 | 9 | 258,647 | 9 | scikit-learn | 11 | sklearn/isotonic.py | Python | 3 | {
"docstring": "Get output feature names for transformation.\n\n Parameters\n ----------\n input_features : array-like of str or None, default=None\n Ignored.\n\n Returns\n -------\n feature_names_out : ndarray of str objects\n An ndarray with one string i.e. [\"isotonicregression0\"].\n ",
"language": "en",
"n_whitespaces": 103,
"n_words": 32,
"vocab_size": 28
} | https://github.com/scikit-learn/scikit-learn.git |
|
2 | config_test | def config_test(self) -> None:
try:
util.run_script([self.conf('ctl'), "-c", self.nginx_conf, "-t"])
except errors.SubprocessError as err:
raise errors.MisconfigurationError(str(err))
| 16aad35d31a887dab157f9d4f5e0fe9218d06064 | 13 | configurator.py | 84 | Fully type certbot-nginx module (#9124)
* Work in progress
* Fix type
* Work in progress
* Work in progress
* Work in progress
* Work in progress
* Work in progress
* Oups.
* Fix typing in UnspacedList
* Fix logic
* Finish typing
* List certbot-nginx as fully typed in tox
* Fix lint
* Fix checks
* Organize imports
* Fix typing for Python 3.6
* Fix checks
* Fix lint
* Update certbot-nginx/certbot_nginx/_internal/configurator.py
Co-authored-by: alexzorin <[email protected]>
* Update certbot-nginx/certbot_nginx/_internal/configurator.py
Co-authored-by: alexzorin <[email protected]>
* Fix signature of deploy_cert regarding the installer interface
* Update certbot-nginx/certbot_nginx/_internal/obj.py
Co-authored-by: alexzorin <[email protected]>
* Fix types
* Update certbot-nginx/certbot_nginx/_internal/parser.py
Co-authored-by: alexzorin <[email protected]>
* Precise type
* Precise _coerce possible inputs/outputs
* Fix type
* Update certbot-nginx/certbot_nginx/_internal/http_01.py
Co-authored-by: ohemorange <[email protected]>
* Fix type
* Remove an undesirable implementation.
* Fix type
Co-authored-by: alexzorin <[email protected]>
Co-authored-by: ohemorange <[email protected]> | 45,498 | 0 | 58 | 48 | 15 | 186,582 | 15 | certbot | 11 | certbot-nginx/certbot_nginx/_internal/configurator.py | Python | 10 | {
"docstring": "Check the configuration of Nginx for errors.\n\n :raises .errors.MisconfigurationError: If config_test fails\n\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 12,
"vocab_size": 12
} | https://github.com/certbot/certbot.git |
|
2 | _is_arraylike_not_scalar | def _is_arraylike_not_scalar(array):
return _is_arraylike(array) and not np.isscalar(array)
| 5d1571db5c1f5b65b1dfc47c21d08050238a60f7 | 9 | validation.py | 34 | FIX Supports numpy strings and array-like in KMean.init (#22154)
Co-authored-by: Jérémie du Boisberranger <[email protected]> | 75,501 | 0 | 13 | 19 | 7 | 258,972 | 7 | scikit-learn | 5 | sklearn/utils/validation.py | Python | 2 | {
"docstring": "Return True if array is array-like and not a scalar",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/scikit-learn/scikit-learn.git |
|
2 | get_result_type | def get_result_type(self) -> str:
if "result_type_string" in self._bound_other_args_to_resolve:
return self._bound_other_args_to_resolve["result_type_string"]
@DeveloperAPI | 203253321d34543aa25483803ebc21e3903679b6 | @DeveloperAPI | 9 | input_node.py | 45 | [serve] Add additional features to DAG visualization with Gradio (#28246) | 28,431 | 1 | 35 | 22 | 11 | 127,398 | 11 | ray | 5 | python/ray/dag/input_node.py | Python | 7 | {
"docstring": "Get type of the output of this DAGNode.\n\n Generated by ray.experimental.gradio_utils.type_to_string().\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 11,
"vocab_size": 10
} | https://github.com/ray-project/ray.git |
1 | test_album_from_url | def test_album_from_url():
album = Album.from_url("https://open.spotify.com/album/4MQnUDGXmHOvnsWCpzeqWT")
assert album.name == "NCS: The Best of 2017"
assert album.url == "https://open.spotify.com/album/4MQnUDGXmHOvnsWCpzeqWT"
assert album.artist["name"] == "Various Artists"
assert len(album.tracks) == 16
@pytest.mark.vcr() | fa2ad657482aca9dc628e6d7062b8badf2706bb6 | @pytest.mark.vcr() | 9 | test_album.py | 94 | v4 init | 5,332 | 1 | 44 | 43 | 21 | 30,131 | 27 | spotify-downloader | 12 | tests/types/test_album.py | Python | 6 | {
"docstring": "\n Test if Album class can be initialized from url.\n ",
"language": "en",
"n_whitespaces": 16,
"n_words": 9,
"vocab_size": 9
} | https://github.com/spotDL/spotify-downloader.git |
7 | link_pyqt | def link_pyqt(executable, venv_path, *, version='5'):
if version not in ["5", "6"]:
raise ValueError(f"Invalid version {version}")
try:
get_lib_path(executable, f'PyQt{version}.sip')
except Error:
# There is no PyQt5.sip, so we need to copy the toplevel sip.
sip_file = get_lib_path(executable, 'sip')
else:
# There is a PyQt5.sip, it'll get copied with the PyQt5 dir.
sip_file = None
sipconfig_file = get_lib_path(executable, 'sipconfig', required=False)
pyqt_dir = os.path.dirname(get_lib_path(executable, f'PyQt{version}.QtCore'))
for path in [sip_file, sipconfig_file, pyqt_dir]:
if path is None:
continue
fn = os.path.basename(path)
dest = os.path.join(venv_path, fn)
if os.path.exists(dest):
if needs_update(path, dest):
remove(dest)
else:
continue
copy_or_link(path, dest)
| 9212ba94d651e4bed5724dea28e90ca2e190b007 | 13 | link_pyqt.py | 262 | scripts: Allow linking/installing pyqt-5 or pyqt-6 | 117,879 | 0 | 254 | 153 | 68 | 321,744 | 90 | qutebrowser | 22 | scripts/link_pyqt.py | Python | 22 | {
"docstring": "Symlink the systemwide PyQt/sip into the venv.\n\n Args:\n executable: The python executable where the source files are present.\n venv_path: The path to the virtualenv site-packages.\n version: The PyQt version to use.\n ",
"language": "en",
"n_whitespaces": 58,
"n_words": 31,
"vocab_size": 25
} | https://github.com/qutebrowser/qutebrowser.git |
|
3 | get_next_unordered | def get_next_unordered(self, timeout=None):
if not self.has_next():
raise StopIteration("No more results to get")
# TODO(ekl) bulk wait for performance
res, _ = ray.wait(list(self._future_to_actor), num_returns=1, timeout=timeout)
if res:
[future] = res
else:
raise TimeoutError("Timed out waiting for result")
i, a = self._future_to_actor.pop(future)
self._return_actor(a)
del self._index_to_future[i]
self._next_return_index = max(self._next_return_index, i + 1)
return ray.get(future)
| 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | 11 | actor_pool.py | 175 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 29,857 | 0 | 161 | 107 | 45 | 132,898 | 51 | ray | 22 | python/ray/util/actor_pool.py | Python | 13 | {
"docstring": "Returns any of the next pending results.\n\n This returns some result produced by submit(), blocking for up to\n the specified timeout until it is available. Unlike get_next(), the\n results are not always returned in same order as submitted, which can\n improve performance.\n\n Returns:\n The next result.\n\n Raises:\n TimeoutError if the timeout is reached.\n\n Examples:\n >>> pool = ActorPool(...)\n >>> pool.submit(lambda a, v: a.double.remote(v), 1)\n >>> pool.submit(lambda a, v: a.double.remote(v), 2)\n >>> print(pool.get_next_unordered())\n 4\n >>> print(pool.get_next_unordered())\n 2\n ",
"language": "en",
"n_whitespaces": 231,
"n_words": 76,
"vocab_size": 61
} | https://github.com/ray-project/ray.git |
|
3 | normalize_eols | def normalize_eols(raw_contents):
lines_list = raw_contents.splitlines()
# Ensure last line has its EOL
if lines_list and lines_list[-1]:
lines_list.append("")
return "\n".join(lines_list)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 10 | makemessages.py | 67 | Refs #33476 -- Reformatted code with Black. | 50,833 | 0 | 41 | 35 | 18 | 204,660 | 19 | django | 6 | django/core/management/commands/makemessages.py | Python | 5 | {
"docstring": "\n Take a block of raw text that will be passed through str.splitlines() to\n get universal newlines treatment.\n\n Return the resulting block of text with normalized `\\n` EOL sequences ready\n to be written to disk using current platform's native EOLs.\n ",
"language": "en",
"n_whitespaces": 55,
"n_words": 39,
"vocab_size": 33
} | https://github.com/django/django.git |
|
5 | tree_data | def tree_data(G, root, ident="id", children="children"):
if G.number_of_nodes() != G.number_of_edges() + 1:
raise TypeError("G is not a tree.")
if not G.is_directed():
raise TypeError("G is not directed.")
if not nx.is_weakly_connected(G):
raise TypeError("G is not weakly connected.")
if ident == children:
raise nx.NetworkXError("The values for `id` and `children` must be different.")
| 5b7d549bf1369440151708fa6b988eb36e1a1bde | 10 | tree.py | 134 | remove old attr keyword from json_graph/tree (#5785) | 42,160 | 0 | 91 | 116 | 34 | 176,891 | 48 | networkx | 12 | networkx/readwrite/json_graph/tree.py | Python | 13 | {
"docstring": "Returns data in tree format that is suitable for JSON serialization\n and use in Javascript documents.\n\n Parameters\n ----------\n G : NetworkX graph\n G must be an oriented tree\n\n root : node\n The root of the tree\n\n ident : string\n Attribute name for storing NetworkX-internal graph data. `ident` must\n have a different value than `children`. The default is 'id'.\n\n children : string\n Attribute name for storing NetworkX-internal graph data. `children`\n must have a different value than `ident`. The default is 'children'.\n\n Returns\n -------\n data : dict\n A dictionary with node-link formatted data.\n\n Raises\n ------\n NetworkXError\n If `children` and `ident` attributes are identical.\n\n Examples\n --------\n >>> from networkx.readwrite import json_graph\n >>> G = nx.DiGraph([(1, 2)])\n >>> data = json_graph.tree_data(G, root=1)\n\n To serialize with json\n\n >>> import json\n >>> s = json.dumps(data)\n\n Notes\n -----\n Node attributes are stored in this format but keys\n for attributes must be strings if you want to serialize with JSON.\n\n Graph and edge attributes are not stored.\n\n See Also\n --------\n tree_graph, node_link_data, adjacency_data\n ",
"language": "en",
"n_whitespaces": 308,
"n_words": 165,
"vocab_size": 103
} | https://github.com/networkx/networkx.git |
|
15 | _check_cg_simp | def _check_cg_simp(expr, simp, sign, lt, term_list, variables, dep_variables, build_index_expr, index_expr):
other_part = 0
i = 0
while i < len(term_list):
sub_1 = _check_cg(term_list[i], expr, len(variables))
if sub_1 is None:
i += 1
continue
if not build_index_expr.subs(sub_1).is_number:
i += 1
continue
sub_dep = [(x, sub_1[x]) for x in dep_variables]
cg_index = [None]*build_index_expr.subs(sub_1)
for j in range(i, len(term_list)):
sub_2 = _check_cg(term_list[j], expr.subs(sub_dep), len(variables) - len(dep_variables), sign=(sign.subs(sub_1), sign.subs(sub_dep)))
if sub_2 is None:
continue
if not index_expr.subs(sub_dep).subs(sub_2).is_number:
continue
cg_index[index_expr.subs(sub_dep).subs(sub_2)] = j, expr.subs(lt, 1).subs(sub_dep).subs(sub_2), lt.subs(sub_2), sign.subs(sub_dep).subs(sub_2)
if not any(i is None for i in cg_index):
min_lt = min(*[ abs(term[2]) for term in cg_index ])
indices = [ term[0] for term in cg_index]
indices.sort()
indices.reverse()
[ term_list.pop(j) for j in indices ]
for term in cg_index:
if abs(term[2]) > min_lt:
term_list.append( (term[2] - min_lt*term[3])*term[1] )
other_part += min_lt*(sign*simp).subs(sub_1)
else:
i += 1
return term_list, other_part
| 2a1afca9477eb781f16d5d6b63fa37abed7740a3 | 20 | cg.py | 557 | Use sympify less | 48,858 | 0 | 450 | 366 | 82 | 198,289 | 139 | sympy | 33 | sympy/physics/quantum/cg.py | Python | 33 | {
"docstring": " Checks for simplifications that can be made, returning a tuple of the\n simplified list of terms and any terms generated by simplification.\n\n Parameters\n ==========\n\n expr: expression\n The expression with Wild terms that will be matched to the terms in\n the sum\n\n simp: expression\n The expression with Wild terms that is substituted in place of the CG\n terms in the case of simplification\n\n sign: expression\n The expression with Wild terms denoting the sign that is on expr that\n must match\n\n lt: expression\n The expression with Wild terms that gives the leading term of the\n matched expr\n\n term_list: list\n A list of all of the terms is the sum to be simplified\n\n variables: list\n A list of all the variables that appears in expr\n\n dep_variables: list\n A list of the variables that must match for all the terms in the sum,\n i.e. the dependent variables\n\n build_index_expr: expression\n Expression with Wild terms giving the number of elements in cg_index\n\n index_expr: expression\n Expression with Wild terms giving the index terms have when storing\n them to cg_index\n\n ",
"language": "en",
"n_whitespaces": 317,
"n_words": 172,
"vocab_size": 72
} | https://github.com/sympy/sympy.git |
|
1 | test_filtering | def test_filtering(self):
self.assertQuerysetEqual(
Book.published_objects.all(),
[
"How to program",
],
lambda b: b.title,
)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 10 | tests.py | 52 | Refs #33476 -- Reformatted code with Black. | 50,134 | 0 | 93 | 31 | 13 | 202,491 | 13 | django | 8 | tests/custom_managers/tests.py | Python | 8 | {
"docstring": "\n Custom managers respond to usual filtering methods\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | https://github.com/django/django.git |
|
1 | test_process_datetime_to_timestamp_freeze_time | def test_process_datetime_to_timestamp_freeze_time(time_zone, hass):
hass.config.set_time_zone(time_zone)
utc_now = dt_util.utcnow()
with freeze_time(utc_now):
epoch = utc_now.timestamp()
assert process_datetime_to_timestamp(dt_util.utcnow()) == epoch
now = dt_util.now()
assert process_datetime_to_timestamp(now) == epoch
@pytest.mark.parametrize(
"time_zone", ["Europe/Berlin", "America/Chicago", "US/Hawaii", "UTC"]
) | 1d9fb4bca871f97109684419f0f9526a0c151f2d | @pytest.mark.parametrize(
"time_zone", ["Europe/Berlin", "America/Chicago", "US/Hawaii", "UTC"]
) | 12 | test_models.py | 141 | Fix process_datetime_to_timestamp and add test coverage (#71755) | 99,327 | 1 | 71 | 61 | 24 | 300,467 | 30 | core | 16 | tests/components/recorder/test_models.py | Python | 8 | {
"docstring": "Test we can handle processing database datatimes to timestamps.\n\n This test freezes time to make sure everything matches.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 18,
"vocab_size": 17
} | https://github.com/home-assistant/core.git |
1 | test_null_localpart | def test_null_localpart(self) -> None:
userinfo = {
"sub": "tester",
"username": None,
}
self.get_success(_make_callback_with_userinfo(self.hs, userinfo))
self.assertRenderedError("mapping_error", "localpart is invalid: ")
| 5dd949bee6158a8b651db9f2ae417a62c8184bfd | 10 | test_oidc.py | 75 | Add type hints to some tests/handlers files. (#12224) | 71,794 | 0 | 76 | 41 | 19 | 247,630 | 19 | synapse | 7 | tests/handlers/test_oidc.py | Python | 8 | {
"docstring": "Mapping onto a null localpart via an empty OIDC attribute should be rejected",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | https://github.com/matrix-org/synapse.git |
|
3 | points2polygon | def points2polygon(points):
if isinstance(points, list):
points = np.array(points)
assert isinstance(points, np.ndarray)
assert (points.size % 2 == 0) and (points.size >= 8)
point_mat = points.reshape([-1, 2])
return plg.Polygon(point_mat)
| 9f62b610dea6161627200ed85d92e19b1923279a | 10 | fce_postprocess.py | 109 | add fcenet | 4,551 | 0 | 52 | 69 | 23 | 23,234 | 27 | PaddleOCR | 12 | ppocr/postprocess/fce_postprocess.py | Python | 7 | {
"docstring": "Convert k points to 1 polygon.\n\n Args:\n points (ndarray or list): A ndarray or a list of shape (2k)\n that indicates k points.\n\n Returns:\n polygon (Polygon): A polygon object.\n ",
"language": "en",
"n_whitespaces": 63,
"n_words": 29,
"vocab_size": 24
} | https://github.com/PaddlePaddle/PaddleOCR.git |
|
1 | import_pandas_dataframe | def import_pandas_dataframe(cls, df, name=None):
return cls.import_arrow_table(pa.Table.from_pandas(df))
| 1c0935c1bc0856d43f69c1e32498636ee24ebc85 | 10 | base_worker.py | 42 | FEAT-#4913: Enabling pyhdk (#4900)
Co-authored-by: ienkovich <[email protected]>
Signed-off-by: izamyati <[email protected]> | 35,959 | 0 | 20 | 26 | 6 | 154,398 | 6 | modin | 8 | modin/experimental/core/execution/native/implementations/omnisci_on_native/base_worker.py | Python | 2 | {
"docstring": "\n Import ``pandas.DataFrame`` to the worker.\n\n Parameters\n ----------\n df : pandas.DataFrame\n A frame to import.\n name : str, optional\n A table name to use. None to generate a unique name.\n\n Returns\n -------\n str\n Imported table name.\n ",
"language": "en",
"n_whitespaces": 132,
"n_words": 35,
"vocab_size": 27
} | https://github.com/modin-project/modin.git |
|
9 | get_operation_id_base | def get_operation_id_base(self, path, method, action):
model = getattr(getattr(self.view, 'queryset', None), 'model', None)
if self.operation_id_base is not None:
name = self.operation_id_base
# Try to deduce the ID from the view's model
elif model is not None:
name = model.__name__
# Try with the serializer class name
elif self.get_serializer(path, method) is not None:
name = self.get_serializer(path, method).__class__.__name__
if name.endswith('Serializer'):
name = name[:-10]
# Fallback to the view name
else:
name = self.view.__class__.__name__
if name.endswith('APIView'):
name = name[:-7]
elif name.endswith('View'):
name = name[:-4]
# Due to camel-casing of classes and `action` being lowercase, apply title in order to find if action truly
# comes at the end of the name
if name.endswith(action.title()): # ListView, UpdateAPIView, ThingDelete ...
name = name[:-len(action)]
if action == 'list':
name = pluralize(name)
return name
| 9e328a9549b8cc5fbeff7b70cf2523fa3d97518f | 16 | openapi.py | 295 | Fix OpenAPI operation name plural appropriately (#8017) | 9,574 | 0 | 385 | 177 | 72 | 48,712 | 126 | django-rest-framework | 17 | rest_framework/schemas/openapi.py | Python | 21 | {
"docstring": "\n Compute the base part for operation ID from the model, serializer or view name.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 14,
"vocab_size": 13
} | https://github.com/encode/django-rest-framework.git |
|
2 | workflow_logging_context | def workflow_logging_context(job_id) -> None:
node = ray.worker._global_node
original_out_file, original_err_file = node.get_log_file_handles(
get_worker_log_file_name("WORKER")
)
out_file, err_file = node.get_log_file_handles(
get_worker_log_file_name("WORKER", job_id)
)
try:
configure_log_file(out_file, err_file)
yield
finally:
configure_log_file(original_out_file, original_err_file)
| e8fc66af348f2afd2b578fe1c6776cc88ea82499 | 11 | workflow_context.py | 104 | [Workflow]Make workflow logs publish to the correct driver. (#24089)
All workflow tasks are executed as remote functions that submitted from WorkflowManagmentActor. WorkflowManagmentActor is a detached long-running actor whose owner is the first driver in the cluster that runs the very first workflow execution. Therefore, for new drivers that run workflows, the loggings won't be properly published back to the driver because loggings are saved and published based on job_id and the job_id is always the first driver's job_id as the ownership goes like: first_driver -> WorkflowManagmentActor -> workflow executions using remote functions.
To solve this, during workflow execution, we pass the actual driver's job_id along with execution, and re-configure the logging files on each worker that runs the remote functions. Notice that we need to do this in multiple places as a workflow task is executed with more than one remote functions that are running in different workers. | 31,619 | 0 | 86 | 60 | 23 | 139,165 | 27 | ray | 13 | python/ray/workflow/workflow_context.py | Python | 27 | {
"docstring": "Initialize the workflow logging context.\n\n Workflow executions are running as remote functions from\n WorkflowManagementActor. Without logging redirection, workflow\n inner execution logs will be pushed to the driver that initially\n created WorkflowManagementActor rather than the driver that\n actually submits the current workflow execution.\n We use this conext manager to re-configure the log files to send\n the logs to the correct driver, and to restore the log files once\n the execution is done.\n\n Args:\n job_id: The ID of the job that submits the workflow execution.\n ",
"language": "en",
"n_whitespaces": 120,
"n_words": 83,
"vocab_size": 56
} | https://github.com/ray-project/ray.git |
|
4 | load_model_from_db | def load_model_from_db(cls, instance_or_id, allow_cache=True):
if isinstance(instance_or_id, int):
if hasattr(cls.objects, "get_from_cache") and allow_cache:
return cls.objects.get_from_cache(pk=instance_or_id)
return cls.objects.get(pk=instance_or_id)
return instance_or_id
| 8c83731694d352c5832ef6dd26ec57333bc8d5b5 | 12 | base.py | 89 | feat(celery): Disallow complex pickle for new tasks (#35811) | 18,819 | 0 | 52 | 56 | 15 | 91,851 | 18 | sentry | 11 | src/sentry/tasks/base.py | Python | 6 | {
"docstring": "Utility function to allow a task to transition to passing ids rather than model instances.",
"language": "en",
"n_whitespaces": 14,
"n_words": 15,
"vocab_size": 13
} | https://github.com/getsentry/sentry.git |
|
2 | ndcg_score | def ndcg_score(y_true, y_score, *, k=None, sample_weight=None, ignore_ties=False):
y_true = check_array(y_true, ensure_2d=False)
y_score = check_array(y_score, ensure_2d=False)
check_consistent_length(y_true, y_score, sample_weight)
if y_true.min() < 0:
# TODO(1.4): Replace warning w/ ValueError
warnings.warn(
"ndcg_score should not be used on negative y_true values. ndcg_score will"
" raise a ValueError on negative y_true values starting from version 1.4.",
FutureWarning,
)
_check_dcg_target_type(y_true)
gain = _ndcg_sample_scores(y_true, y_score, k=k, ignore_ties=ignore_ties)
return np.average(gain, weights=sample_weight)
| 9bb209817a978d6da0fec392ed212685efe08694 | 11 | _ranking.py | 154 | ENH Added warning for `ndcg_score` when used w/ negative `y_true` values (#23461)
Co-authored-by: trinhcon <[email protected]>
Co-authored-by: Victor Ko <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]> | 76,130 | 0 | 142 | 100 | 54 | 260,239 | 64 | scikit-learn | 19 | sklearn/metrics/_ranking.py | Python | 13 | {
"docstring": "Compute Normalized Discounted Cumulative Gain.\n\n Sum the true scores ranked in the order induced by the predicted scores,\n after applying a logarithmic discount. Then divide by the best possible\n score (Ideal DCG, obtained for a perfect ranking) to obtain a score between\n 0 and 1.\n\n This ranking metric returns a high value if true labels are ranked high by\n ``y_score``.\n\n Parameters\n ----------\n y_true : ndarray of shape (n_samples, n_labels)\n True targets of multilabel classification, or true scores of entities\n to be ranked. Negative values in `y_true` may result in an output\n that is not between 0 and 1.\n\n .. versionchanged:: 1.2\n These negative values are deprecated, and will raise an error in v1.4.\n\n y_score : ndarray of shape (n_samples, n_labels)\n Target scores, can either be probability estimates, confidence values,\n or non-thresholded measure of decisions (as returned by\n \"decision_function\" on some classifiers).\n\n k : int, default=None\n Only consider the highest k scores in the ranking. If `None`, use all\n outputs.\n\n sample_weight : ndarray of shape (n_samples,), default=None\n Sample weights. If `None`, all samples are given the same weight.\n\n ignore_ties : bool, default=False\n Assume that there are no ties in y_score (which is likely to be the\n case if y_score is continuous) for efficiency gains.\n\n Returns\n -------\n normalized_discounted_cumulative_gain : float in [0., 1.]\n The averaged NDCG scores for all samples.\n\n See Also\n --------\n dcg_score : Discounted Cumulative Gain (not normalized).\n\n References\n ----------\n `Wikipedia entry for Discounted Cumulative Gain\n <https://en.wikipedia.org/wiki/Discounted_cumulative_gain>`_\n\n Jarvelin, K., & Kekalainen, J. (2002).\n Cumulated gain-based evaluation of IR techniques. ACM Transactions on\n Information Systems (TOIS), 20(4), 422-446.\n\n Wang, Y., Wang, L., Li, Y., He, D., Chen, W., & Liu, T. Y. (2013, May).\n A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th\n Annual Conference on Learning Theory (COLT 2013)\n\n McSherry, F., & Najork, M. (2008, March). Computing information retrieval\n performance measures efficiently in the presence of tied scores. In\n European conference on information retrieval (pp. 414-421). Springer,\n Berlin, Heidelberg.\n\n Examples\n --------\n >>> import numpy as np\n >>> from sklearn.metrics import ndcg_score\n >>> # we have groud-truth relevance of some answers to a query:\n >>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])\n >>> # we predict some scores (relevance) for the answers\n >>> scores = np.asarray([[.1, .2, .3, 4, 70]])\n >>> ndcg_score(true_relevance, scores)\n 0.69...\n >>> scores = np.asarray([[.05, 1.1, 1., .5, .0]])\n >>> ndcg_score(true_relevance, scores)\n 0.49...\n >>> # we can set k to truncate the sum; only top k answers contribute.\n >>> ndcg_score(true_relevance, scores, k=4)\n 0.35...\n >>> # the normalization takes k into account so a perfect answer\n >>> # would still get 1.0\n >>> ndcg_score(true_relevance, true_relevance, k=4)\n 1.0\n >>> # now we have some ties in our prediction\n >>> scores = np.asarray([[1, 0, 0, 0, 1]])\n >>> # by default ties are averaged, so here we get the average (normalized)\n >>> # true relevance of our top predictions: (10 / 10 + 5 / 10) / 2 = .75\n >>> ndcg_score(true_relevance, scores, k=1)\n 0.75\n >>> # we can choose to ignore ties for faster results, but only\n >>> # if we know there aren't ties in our scores, otherwise we get\n >>> # wrong results:\n >>> ndcg_score(true_relevance,\n ... scores, k=1, ignore_ties=True)\n 0.5\n ",
"language": "en",
"n_whitespaces": 835,
"n_words": 525,
"vocab_size": 314
} | https://github.com/scikit-learn/scikit-learn.git |
|
1 | get_supplier_block_status | def get_supplier_block_status(party_name):
supplier = frappe.get_doc("Supplier", party_name)
info = {
"on_hold": supplier.on_hold,
"release_date": supplier.release_date,
"hold_type": supplier.hold_type,
}
return info
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 9 | accounts_controller.py | 70 | style: format code with black | 13,959 | 0 | 10 | 40 | 16 | 65,623 | 18 | erpnext | 9 | erpnext/controllers/accounts_controller.py | Python | 8 | {
"docstring": "\n\tReturns a dict containing the values of `on_hold`, `release_date` and `hold_type` of\n\ta `Supplier`\n\t",
"language": "en",
"n_whitespaces": 12,
"n_words": 14,
"vocab_size": 12
} | https://github.com/frappe/erpnext.git |
|
1 | compare_total | def compare_total(self, a, b):
a = _convert_other(a, raiseit=True)
return a.compare_total(b)
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 9 | _pydecimal.py | 43 | add python 3.10.4 for windows | 55,795 | 0 | 31 | 27 | 10 | 219,780 | 10 | XX-Net | 6 | python3.10.4/Lib/_pydecimal.py | Python | 3 | {
"docstring": "Compares two operands using their abstract representation.\n\n This is not like the standard compare, which use their numerical\n value. Note that a total ordering is defined for all possible abstract\n representations.\n\n >>> ExtendedContext.compare_total(Decimal('12.73'), Decimal('127.9'))\n Decimal('-1')\n >>> ExtendedContext.compare_total(Decimal('-127'), Decimal('12'))\n Decimal('-1')\n >>> ExtendedContext.compare_total(Decimal('12.30'), Decimal('12.3'))\n Decimal('-1')\n >>> ExtendedContext.compare_total(Decimal('12.30'), Decimal('12.30'))\n Decimal('0')\n >>> ExtendedContext.compare_total(Decimal('12.3'), Decimal('12.300'))\n Decimal('1')\n >>> ExtendedContext.compare_total(Decimal('12.3'), Decimal('NaN'))\n Decimal('-1')\n >>> ExtendedContext.compare_total(1, 2)\n Decimal('-1')\n >>> ExtendedContext.compare_total(Decimal(1), 2)\n Decimal('-1')\n >>> ExtendedContext.compare_total(1, Decimal(2))\n Decimal('-1')\n ",
"language": "en",
"n_whitespaces": 224,
"n_words": 67,
"vocab_size": 46
} | https://github.com/XX-net/XX-Net.git |
|
1 | step_pred | def step_pred(self, score, x, t):
# TODO(Patrick) better comments + non-PyTorch
t = self.repeat_scalar(t, x.shape[0])
timesteps = self.long((t * (len(self.timesteps) - 1)))
sigma = self.discrete_sigmas[timesteps]
adjacent_sigma = self.get_adjacent_sigma(timesteps, t)
drift = self.zeros_like(x)
diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the score modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
drift = drift - diffusion[:, None, None, None] ** 2 * score
# equation 6: sample noise for the diffusion term of
noise = self.randn_like(x)
x_mean = x - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
x = x_mean + diffusion[:, None, None, None] * noise # add impact of diffusion field g
return x, x_mean
| 63c68d979a8e4a9c9fea306bdf63e73812843453 | 15 | scheduling_sde_ve.py | 215 | VE/VP SDE updates (#90)
* improve comments for sde_ve scheduler, init tests
* more comments, tweaking pipelines
* timesteps --> num_training_timesteps, some comments
* merge cpu test, add m1 data
* fix scheduler tests with num_train_timesteps
* make np compatible, add tests for sde ve
* minor default variable fixes
* make style and fix-copies
Co-authored-by: Patrick von Platen <[email protected]> | 120,850 | 0 | 263 | 140 | 87 | 336,027 | 141 | diffusers | 20 | src/diffusers/schedulers/scheduling_sde_ve.py | Python | 12 | {
"docstring": "\n Predict the sample at the previous timestep by reversing the SDE.\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 9
} | https://github.com/huggingface/diffusers.git |
|
1 | test_get_bad_image | def test_get_bad_image(self):
# Get
response = self.client.get(
reverse(
"wagtailimages:generate_url", args=(self.image.id + 1, "fill-800x600")
)
)
# Check response
self.assertEqual(response.status_code, 404)
self.assertEqual(response["Content-Type"], "application/json")
# Check JSON
self.assertJSONEqual(
response.content.decode(),
json.dumps(
{
"error": "Cannot find image.",
}
),
)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 15 | test_admin_views.py | 137 | Reformat with black | 16,373 | 0 | 225 | 79 | 30 | 75,173 | 36 | wagtail | 16 | wagtail/images/tests/test_admin_views.py | Python | 16 | {
"docstring": "\n This tests that the view gives a 404 response if a user attempts to use it with an image which doesn't exist\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 22,
"vocab_size": 21
} | https://github.com/wagtail/wagtail.git |
|
3 | get | def get(self, url, cache=True, **kwargs):
if not url.isValid():
urlutils.invalid_url_error(url, "start download")
return None
req = QNetworkRequest(url)
user_agent = websettings.user_agent(url)
req.setHeader(QNetworkRequest.KnownHeaders.UserAgentHeader, user_agent)
if not cache:
req.setAttribute(QNetworkRequest.Attribute.CacheSaveControlAttribute, False)
return self.get_request(req, **kwargs)
| 0877fb0d78635692e481c8bde224fac5ad0dd430 | 11 | qtnetworkdownloads.py | 136 | Run scripts/dev/rewrite_enums.py | 117,539 | 0 | 111 | 85 | 25 | 321,111 | 29 | qutebrowser | 19 | qutebrowser/browser/qtnetworkdownloads.py | Python | 10 | {
"docstring": "Start a download with a link URL.\n\n Args:\n url: The URL to get, as QUrl\n cache: If set to False, don't cache the response.\n **kwargs: passed to get_request().\n\n Return:\n The created DownloadItem.\n ",
"language": "en",
"n_whitespaces": 97,
"n_words": 32,
"vocab_size": 28
} | https://github.com/qutebrowser/qutebrowser.git |
|
23 | getcomments | def getcomments(object):
try:
lines, lnum = findsource(object)
except (OSError, TypeError):
return None
if ismodule(object):
# Look for a comment block at the top of the file.
start = 0
if lines and lines[0][:2] == '#!': start = 1
while start < len(lines) and lines[start].strip() in ('', '#'):
start = start + 1
if start < len(lines) and lines[start][:1] == '#':
comments = []
end = start
while end < len(lines) and lines[end][:1] == '#':
comments.append(lines[end].expandtabs())
end = end + 1
return ''.join(comments)
# Look for a preceding block of comments at the same indentation.
elif lnum > 0:
indent = indentsize(lines[lnum])
end = lnum - 1
if end >= 0 and lines[end].lstrip()[:1] == '#' and \
indentsize(lines[end]) == indent:
comments = [lines[end].expandtabs().lstrip()]
if end > 0:
end = end - 1
comment = lines[end].expandtabs().lstrip()
while comment[:1] == '#' and indentsize(lines[end]) == indent:
comments[:0] = [comment]
end = end - 1
if end < 0: break
comment = lines[end].expandtabs().lstrip()
while comments and comments[0].strip() == '#':
comments[:1] = []
while comments and comments[-1].strip() == '#':
comments[-1:] = []
return ''.join(comments)
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 20 | inspect.py | 571 | add python 3.10.4 for windows | 55,330 | 0 | 568 | 345 | 74 | 218,471 | 178 | XX-Net | 20 | python3.10.4/Lib/inspect.py | Python | 36 | {
"docstring": "Get lines of comments immediately preceding an object's source code.\n\n Returns None when source can't be found.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 17,
"vocab_size": 16
} | https://github.com/XX-net/XX-Net.git |
|
7 | convert_to_strings | def convert_to_strings(cls, obj):
if isinstance(obj, dict):
return {
cls.convert_to_strings(key): cls.convert_to_strings(value)
for key, value in obj.items()
}
elif isinstance(obj, list) or isinstance(obj, tuple):
return [cls.convert_to_strings(element) for element in obj]
elif isinstance(obj, bytes):
return str(obj)[2:-1]
return obj
| 8c2428c9d355ca5fbc3dd90e9820ceb1cc795837 | 11 | jsondump.py | 140 | [autofix.ci] apply automated fixes | 74,068 | 0 | 144 | 91 | 26 | 253,331 | 35 | mitmproxy | 13 | examples/contrib/jsondump.py | Python | 11 | {
"docstring": "\n Recursively convert all list/dict elements of type `bytes` into strings.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 10,
"vocab_size": 10
} | https://github.com/mitmproxy/mitmproxy.git |
|
1 | test_makemigrations_interactive_reject | def test_makemigrations_interactive_reject(self):
# Monkeypatch interactive questioner to auto reject
with mock.patch('builtins.input', mock.Mock(return_value='N')):
with self.temporary_migration_module(module="migrations.test_migrations_conflict") as migration_dir:
with captured_stdout():
call_command(
'makemigrations',
'migrations',
name='merge',
merge=True,
interactive=True,
verbosity=0,
)
merge_file = os.path.join(migration_dir, '0003_merge.py')
self.assertFalse(os.path.exists(merge_file))
| 0ab58c120939093fea90822f376e1866fc714d1f | 15 | test_commands.py | 158 | Refs #29026 -- Allowed customizing InteractiveMigrationQuestioner's prompt destination.
Previously, the questioner did not obey the value of stdout provided
to the command. | 50,165 | 0 | 284 | 88 | 29 | 202,906 | 31 | django | 21 | tests/migrations/test_commands.py | Python | 14 | {
"docstring": "\n makemigrations enters and exits interactive mode properly.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | https://github.com/django/django.git |
|
16 | customize_compiler | def customize_compiler(compiler):
if compiler.compiler_type == "unix":
if sys.platform == "darwin":
# Perform first-time customization of compiler-related
# config vars on OS X now that we know we need a compiler.
# This is primarily to support Pythons from binary
# installers. The kind and paths to build tools on
# the user system may vary significantly from the system
# that Python itself was built on. Also the user OS
# version and build tools may not support the same set
# of CPU architectures for universal builds.
if not _config_vars.get('CUSTOMIZED_OSX_COMPILER'):
import _osx_support
_osx_support.customize_compiler(_config_vars)
_config_vars['CUSTOMIZED_OSX_COMPILER'] = 'True'
(cc, cxx, cflags, ccshared, ldshared, shlib_suffix, ar, ar_flags) = \
get_config_vars('CC', 'CXX', 'CFLAGS',
'CCSHARED', 'LDSHARED', 'SHLIB_SUFFIX', 'AR', 'ARFLAGS')
if 'CC' in os.environ:
newcc = os.environ['CC']
if (sys.platform == 'darwin'
and 'LDSHARED' not in os.environ
and ldshared.startswith(cc)):
# On OS X, if CC is overridden, use that as the default
# command for LDSHARED as well
ldshared = newcc + ldshared[len(cc):]
cc = newcc
if 'CXX' in os.environ:
cxx = os.environ['CXX']
if 'LDSHARED' in os.environ:
ldshared = os.environ['LDSHARED']
if 'CPP' in os.environ:
cpp = os.environ['CPP']
else:
cpp = cc + " -E" # not always
if 'LDFLAGS' in os.environ:
ldshared = ldshared + ' ' + os.environ['LDFLAGS']
if 'CFLAGS' in os.environ:
cflags = cflags + ' ' + os.environ['CFLAGS']
ldshared = ldshared + ' ' + os.environ['CFLAGS']
if 'CPPFLAGS' in os.environ:
cpp = cpp + ' ' + os.environ['CPPFLAGS']
cflags = cflags + ' ' + os.environ['CPPFLAGS']
ldshared = ldshared + ' ' + os.environ['CPPFLAGS']
if 'AR' in os.environ:
ar = os.environ['AR']
if 'ARFLAGS' in os.environ:
archiver = ar + ' ' + os.environ['ARFLAGS']
else:
archiver = ar + ' ' + ar_flags
cc_cmd = cc + ' ' + cflags
compiler.set_executables(
preprocessor=cpp,
compiler=cc_cmd,
compiler_so=cc_cmd + ' ' + ccshared,
compiler_cxx=cxx,
linker_so=ldshared,
linker_exe=cc,
archiver=archiver)
compiler.shared_lib_extension = shlib_suffix
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 17 | sysconfig.py | 658 | add python 3.10.4 for windows | 56,846 | 0 | 957 | 369 | 147 | 223,004 | 303 | XX-Net | 32 | python3.10.4/Lib/distutils/sysconfig.py | Python | 50 | {
"docstring": "Do any platform-specific customization of a CCompiler instance.\n\n Mainly needed on Unix, so we can plug in the information that\n varies across Unices and is stored in Python's Makefile.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 29,
"vocab_size": 28
} | https://github.com/XX-net/XX-Net.git |
|
8 | _check_list_filter_item | def _check_list_filter_item(self, obj, item, label):
from django.contrib.admin import FieldListFilter, ListFilter
if callable(item) and not isinstance(item, models.Field):
# If item is option 3, it should be a ListFilter...
if not _issubclass(item, ListFilter):
return must_inherit_from(
parent="ListFilter", option=label, obj=obj, id="admin.E113"
)
# ... but not a FieldListFilter.
elif issubclass(item, FieldListFilter):
return [
checks.Error(
"The value of '%s' must not inherit from 'FieldListFilter'."
% label,
obj=obj.__class__,
id="admin.E114",
)
]
else:
return []
elif isinstance(item, (tuple, list)):
# item is option #2
field, list_filter_class = item
if not _issubclass(list_filter_class, FieldListFilter):
return must_inherit_from(
parent="FieldListFilter",
option="%s[1]" % label,
obj=obj,
id="admin.E115",
)
else:
return []
else:
# item is option #1
field = item
# Validate the field string
try:
get_fields_from_path(obj.model, field)
except (NotRelationField, FieldDoesNotExist):
return [
checks.Error(
"The value of '%s' refers to '%s', which does not refer to a Field."
% (label, field),
obj=obj.__class__,
id="admin.E116",
)
]
else:
return []
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 16 | checks.py | 331 | Refs #33476 -- Reformatted code with Black. | 50,332 | 0 | 880 | 207 | 89 | 203,361 | 144 | django | 31 | django/contrib/admin/checks.py | Python | 44 | {
"docstring": "\n Check one item of `list_filter`, i.e. check if it is one of three options:\n 1. 'field' -- a basic field filter, possibly w/ relationships (e.g.\n 'field__rel')\n 2. ('field', SomeFieldListFilter) - a field-based list filter class\n 3. SomeListFilter - a non-field list filter class\n ",
"language": "en",
"n_whitespaces": 89,
"n_words": 43,
"vocab_size": 35
} | https://github.com/django/django.git |
|
1 | get_redemption_details | def get_redemption_details(customer, loyalty_program, company):
return frappe._dict(
frappe.db.sql(
,
(customer, loyalty_program, company),
)
)
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 10 | loyalty_point_entry.py | 47 | style: format code with black | 13,746 | 0 | 6 | 32 | 11 | 64,880 | 13 | erpnext | 8 | erpnext/accounts/doctype/loyalty_point_entry/loyalty_point_entry.py | Python | 12 | {
"docstring": "\n\t\tselect redeem_against, sum(loyalty_points)\n\t\tfrom `tabLoyalty Point Entry`\n\t\twhere customer=%s and loyalty_program=%s and loyalty_points<0 and company=%s\n\t\tgroup by redeem_against\n\t",
"language": "en",
"n_whitespaces": 14,
"n_words": 18,
"vocab_size": 16
} | https://github.com/frappe/erpnext.git |
|
4 | get_all_tests | def get_all_tests():
test_root_dir = os.path.join(PATH_TO_TRANFORMERS, "tests")
# test folders/files directly under `tests` folder
tests = os.listdir(test_root_dir)
tests = sorted(
list(filter(lambda x: os.path.isdir(x) or x.startswith("tests/test_"), [f"tests/{x}" for x in tests]))
)
# model specific test folders
model_tests_folders = os.listdir(os.path.join(test_root_dir, "models"))
model_test_folders = sorted(list(filter(os.path.isdir, [f"tests/models/{x}" for x in model_tests_folders])))
tests.remove("tests/models")
tests = model_test_folders + tests
return tests
| 38043d8453b82a9c712f8d5c98323150fbee7503 | 17 | tests_fetcher.py | 205 | Update self-push workflow (#17177)
* update push ci
* install git-python
* update comment
* update deepspeed jobs
* fix report
* skip 2 more tests that require fairscale
* Fix changes in test_fetcher.py (to deal with `setup.py` is changed)
* set RUN_PT_TF_CROSS_TESTS=1 and final clean-up
* remove SIGOPT_API_TOKEN
* remove echo "$matrix_folders"
Co-authored-by: ydshieh <[email protected]> | 6,971 | 0 | 98 | 118 | 40 | 38,418 | 55 | transformers | 17 | utils/tests_fetcher.py | Python | 11 | {
"docstring": "\n Return a list of paths to all test folders and files under `tests`. All paths are rooted at `tests`.\n\n - folders under `tests`: `tokenization`, `pipelines`, etc. The folder `models` is excluded.\n - folders under `tests/models`: `bert`, `gpt2`, etc.\n - test files under `tests`: `test_modeling_common.py`, `test_tokenization_common.py`, etc.\n ",
"language": "en",
"n_whitespaces": 62,
"n_words": 46,
"vocab_size": 32
} | https://github.com/huggingface/transformers.git |
|
4 | promote_snaps | def promote_snaps(version):
assert_logged_into_snapcraft()
for snap in SNAPS:
revisions = get_snap_revisions(snap, version)
# The loop below is kind of slow, so let's print some output about what
# it is doing.
print('Releasing', snap, 'snaps to the stable channel')
for revision in revisions:
cmd = ['snapcraft', 'release', snap, revision, 'stable']
try:
subprocess.run(cmd, check=True, stdout=subprocess.PIPE, universal_newlines=True)
except subprocess.CalledProcessError as e:
print("The command", f"'{' '.join(cmd)}'", "failed.")
print("The output printed to stdout was:")
print(e.stdout)
raise
| 39e8d14e1b221bf37526cc05ecc83beee30a3c57 | 19 | finish_release.py | 180 | Set up 2.0 pre-releases (#9400)
* update credential info
* update release tooling to use candidate channel
* split deploy jobs
* pass parameter through
* add 2.0 pipeline prerelease
* add comments
* quote file path | 45,647 | 0 | 222 | 97 | 61 | 186,894 | 70 | certbot | 19 | tools/finish_release.py | Python | 14 | {
"docstring": "Promotes all Certbot snaps from the candidate to stable channel.\n\n If the snaps have already been released to the stable channel, this\n function will try to release them again which has no effect.\n\n :param str version: the version number that should be found in the\n candidate channel, e.g. 1.7.0\n\n :raises SystemExit: if the command snapcraft is unavailable or it\n isn't logged into an account\n\n :raises subprocess.CalledProcessError: if a snapcraft command fails\n for another reason\n\n ",
"language": "en",
"n_whitespaces": 113,
"n_words": 74,
"vocab_size": 59
} | https://github.com/certbot/certbot.git |
|
5 | get_ss_earning_map | def get_ss_earning_map(salary_slips, currency, company_currency):
ss_earnings = frappe.db.sql(
% (", ".join(["%s"] * len(salary_slips))),
tuple([d.name for d in salary_slips]),
as_dict=1,
)
ss_earning_map = {}
for d in ss_earnings:
ss_earning_map.setdefault(d.parent, frappe._dict()).setdefault(d.salary_component, 0.0)
if currency == company_currency:
ss_earning_map[d.parent][d.salary_component] += flt(d.amount) * flt(
d.exchange_rate if d.exchange_rate else 1
)
else:
ss_earning_map[d.parent][d.salary_component] += flt(d.amount)
return ss_earning_map
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 15 | salary_register.py | 221 | style: format code with black | 14,391 | 0 | 34 | 145 | 38 | 66,963 | 50 | erpnext | 22 | erpnext/payroll/report/salary_register/salary_register.py | Python | 18 | {
"docstring": "select sd.parent, sd.salary_component, sd.amount, ss.exchange_rate, ss.name\n\t\tfrom `tabSalary Detail` sd, `tabSalary Slip` ss where sd.parent=ss.name and sd.parent in (%s)",
"language": "en",
"n_whitespaces": 17,
"n_words": 19,
"vocab_size": 18
} | https://github.com/frappe/erpnext.git |
|
5 | _get_save_kwargs | def _get_save_kwargs(self) -> Dict[str, Union[bool, int, str]]:
filetype = self.config["format"]
kwargs = {}
if filetype in ("gif", "jpg", "png"):
kwargs["optimize"] = self.config["optimize"]
if filetype == "gif":
kwargs["interlace"] = self.config["gif_interlace"]
if filetype == "png":
kwargs["compress_level"] = self.config["png_compress_level"]
if filetype == "tif":
kwargs["compression"] = self.config["tif_compression"]
logger.debug(kwargs)
return kwargs
| 049314429f71a21e6595e9d27e9e36f6a3479c42 | 10 | pillow.py | 195 | Convert: Add option to output mask separately for draw-transparent | 20,507 | 0 | 153 | 110 | 31 | 101,070 | 46 | faceswap | 12 | plugins/convert/writer/pillow.py | Python | 20 | {
"docstring": " Return the save parameters for the file format\n\n Returns\n -------\n dict\n The specific keyword arguments for the selected file format\n ",
"language": "en",
"n_whitespaces": 60,
"n_words": 20,
"vocab_size": 15
} | https://github.com/deepfakes/faceswap.git |
|
1 | test_multi_conv | def test_multi_conv(self):
n = coord_net_spec()
# multi bottom/top
n.conv_data, n.conv_aux = L.Convolution(
n.data, n.aux, ntop=2, num_output=10, kernel_size=5, stride=2,
pad=0)
ax1, a1, b1 = coord_map_from_to(n.conv_data, n.data)
ax2, a2, b2 = coord_map_from_to(n.conv_aux, n.aux)
self.assertEquals(ax1, ax2)
self.assertEquals(a1, a2)
self.assertEquals(b1, b2)
| cc4d0564756ca067516f71718a3d135996525909 | 9 | test_coord_map.py | 160 | Balanced joint maximum mean discrepancy for deep transfer learning | 12,080 | 0 | 122 | 107 | 34 | 60,302 | 37 | transferlearning | 23 | code/deep/BJMMD/caffe/python/caffe/test/test_coord_map.py | Python | 10 | {
"docstring": "\n Multiple bottoms/tops of a layer are identically mapped.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | https://github.com/jindongwang/transferlearning.git |
|
7 | get_submatrix | def get_submatrix(self, matrix):
r
reduced, non_reduced = self.get_reduced_nonreduced()
# if reduced == [], then det(matrix) should be 1
if reduced == []:
return diag([1])
# reduced != []
reduction_set = [v ** self.degrees[i] for i, v
in enumerate(self.variables)]
ais = [self.polynomials[i].coeff(reduction_set[i])
for i in range(self.n)]
reduced_matrix = matrix[:, reduced]
keep = []
for row in range(reduced_matrix.rows):
check = [ai in reduced_matrix[row, :] for ai in ais]
if True not in check:
keep.append(row)
return matrix[keep, non_reduced]
| 9d58006fc0a23afcba38f641c9472917c436428a | 12 | multivariate_resultants.py | 212 | Code cleanup | 48,966 | 0 | 237 | 138 | 54 | 198,503 | 75 | sympy | 25 | sympy/polys/multivariate_resultants.py | Python | 24 | {
"docstring": "\n Returns\n =======\n\n macaulay_submatrix: Matrix\n The Macaulay denominator matrix. Columns that are non reduced are kept.\n The row which contains one of the a_{i}s is dropped. a_{i}s\n are the coefficients of x_i ^ {d_i}.\n ",
"language": "en",
"n_whitespaces": 95,
"n_words": 33,
"vocab_size": 27
} | https://github.com/sympy/sympy.git |
|
3 | check_components_alive | def check_components_alive(cluster, component_type, check_component_alive):
worker_nodes = get_other_nodes(cluster)
assert len(worker_nodes) > 0
for node in worker_nodes:
process = node.all_processes[component_type][0].process
if check_component_alive:
assert process.poll() is None
else:
print(
"waiting for "
+ component_type
+ " with PID "
+ str(process.pid)
+ "to terminate"
)
process.wait()
print(
"done waiting for "
+ component_type
+ " with PID "
+ str(process.pid)
+ "to terminate"
)
assert not process.poll() is None
@pytest.mark.parametrize(
"ray_start_cluster",
[
{
"num_cpus": 8,
"num_nodes": 4,
"_system_config": {"num_heartbeats_timeout": 10},
}
],
indirect=True,
) | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | @pytest.mark.parametrize(
"ray_start_cluster",
[
{
"num_cpus": 8,
"num_nodes": 4,
"_system_config": {"num_heartbeats_timeout": 10},
}
],
indirect=True,
) | 17 | test_component_failures_2.py | 235 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 29,534 | 1 | 398 | 102 | 52 | 131,456 | 81 | ray | 19 | python/ray/tests/test_component_failures_2.py | Python | 24 | {
"docstring": "Check that a given component type is alive on all worker nodes.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | https://github.com/ray-project/ray.git |
3 | compute_current_divisions | def compute_current_divisions(self, col=None):
if col is None and self.known_divisions:
return self.divisions
from dask.dataframe.shuffle import compute_divisions
return compute_divisions(self, col=col)
| cccb9d8d8e33a891396b1275c2448c352ef40c27 | 8 | core.py | 62 | absolufy-imports - No relative - PEP8 (#8796)
Conversation in https://github.com/dask/distributed/issues/5889 | 36,561 | 0 | 57 | 40 | 17 | 156,105 | 18 | dask | 9 | dask/dataframe/core.py | Python | 5 | {
"docstring": "Compute the current divisions of the DataFrame.\n\n This method triggers immediate computation. If you find yourself running this command\n repeatedly for the same dataframe, we recommend storing the result\n so you don't have to rerun it.\n\n If the column or index values overlap between partitions, raises ``ValueError``.\n To prevent this, make sure the data are sorted by the column or index.\n\n Parameters\n ----------\n col : string, optional\n Calculate the divisions for a non-index column by passing in the name of the column.\n If col is not specified, the index will be used to calculate divisions.\n In this case, if the divisions are already known, they will be returned\n immediately without computing.\n\n Examples\n --------\n >>> import dask\n >>> ddf = dask.datasets.timeseries(start=\"2021-01-01\", end=\"2021-01-07\", freq=\"1H\").clear_divisions()\n >>> divisions = ddf.compute_current_divisions()\n >>> print(divisions) # doctest: +NORMALIZE_WHITESPACE\n (Timestamp('2021-01-01 00:00:00'),\n Timestamp('2021-01-02 00:00:00'),\n Timestamp('2021-01-03 00:00:00'),\n Timestamp('2021-01-04 00:00:00'),\n Timestamp('2021-01-05 00:00:00'),\n Timestamp('2021-01-06 00:00:00'),\n Timestamp('2021-01-06 23:00:00'))\n\n >>> ddf.divisions = divisions\n >>> ddf.known_divisions\n True\n\n >>> ddf = ddf.reset_index().clear_divisions()\n >>> divisions = ddf.compute_current_divisions(\"timestamp\")\n >>> print(divisions) # doctest: +NORMALIZE_WHITESPACE\n (Timestamp('2021-01-01 00:00:00'),\n Timestamp('2021-01-02 00:00:00'),\n Timestamp('2021-01-03 00:00:00'),\n Timestamp('2021-01-04 00:00:00'),\n Timestamp('2021-01-05 00:00:00'),\n Timestamp('2021-01-06 00:00:00'),\n Timestamp('2021-01-06 23:00:00'))\n\n >>> ddf = ddf.set_index(\"timestamp\", divisions=divisions, sorted=True)\n ",
"language": "en",
"n_whitespaces": 495,
"n_words": 185,
"vocab_size": 113
} | https://github.com/dask/dask.git |
|
2 | parse_wheel | def parse_wheel(wheel_zip, name):
# type: (ZipFile, str) -> Tuple[str, Message]
try:
info_dir = wheel_dist_info_dir(wheel_zip, name)
metadata = wheel_metadata(wheel_zip, info_dir)
version = wheel_version(metadata)
except UnsupportedWheel as e:
raise UnsupportedWheel("{} has an invalid wheel, {}".format(name, str(e)))
check_compatibility(version, name)
return info_dir, metadata
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 14 | wheel.py | 103 | upd; format | 12,522 | 0 | 85 | 62 | 35 | 61,340 | 39 | transferlearning | 14 | .venv/lib/python3.8/site-packages/pip/_internal/utils/wheel.py | Python | 9 | {
"docstring": "Extract information from the provided wheel, ensuring it meets basic\n standards.\n\n Returns the name of the .dist-info directory and the parsed WHEEL metadata.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 23,
"vocab_size": 20
} | https://github.com/jindongwang/transferlearning.git |
|
1 | require_torch_bf16_cpu | def require_torch_bf16_cpu(test_case):
return unittest.skipUnless(
is_torch_bf16_cpu_available(),
"test requires torch>=1.10, using CPU",
)(test_case)
| a2d34b7c040723b92823310e3b8fd66874c9d667 | 10 | testing_utils.py | 38 | deprecate is_torch_bf16_available (#17738)
* deprecate is_torch_bf16_available
* address suggestions | 5,726 | 0 | 34 | 21 | 11 | 31,378 | 11 | transformers | 5 | src/transformers/testing_utils.py | Python | 5 | {
"docstring": "Decorator marking a test that requires torch>=1.10, using CPU.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/huggingface/transformers.git |
|
3 | is_show | def is_show(self, value, header=""):
# @TODO: possible optimisation: create a re.compile list
return any(j for j in [re.match(i, value) for i in self.get_conf_value('show', header=header)])
| 4d50e8cb5756755cf019cef1f45cd569bef708fc | 13 | glances_plugin.py | 69 | Create a Show option in the configuration file to only show some stats #2052 | 15,210 | 0 | 45 | 43 | 22 | 69,969 | 24 | glances | 10 | glances/plugins/glances_plugin.py | Python | 2 | {
"docstring": "Return True if the value is in the show configuration list.\n\n If the show value is empty, return True (show by default)\n\n The show configuration list is defined in the glances.conf file.\n It is a comma separated list of regexp.\n Example for diskio:\n show=sda.*\n ",
"language": "en",
"n_whitespaces": 86,
"n_words": 44,
"vocab_size": 31
} | https://github.com/nicolargo/glances.git |
|
2 | test_estimator_does_not_support_feature_names | def test_estimator_does_not_support_feature_names():
pytest.importorskip("pandas")
X, y = datasets.load_iris(as_frame=True, return_X_y=True)
all_feature_names = set(X.columns)
| 9f85c9d44965b764f40169ef2917e5f7a798684f | 9 | test_from_model.py | 61 | TST Better info when checking for no warnings in tests (#22362) | 75,364 | 0 | 23 | 114 | 10 | 258,673 | 11 | scikit-learn | 12 | sklearn/feature_selection/tests/test_from_model.py | Python | 14 | {
"docstring": "SelectFromModel works with estimators that do not support feature_names_in_.\n\n Non-regression test for #21949.\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 13
} | https://github.com/scikit-learn/scikit-learn.git |
|
1 | test_get_member_list_no_permission_former_member | def test_get_member_list_no_permission_former_member(self):
# create a room, invite the user and the user joins
room_id = self.helper.create_room_as("@alice:red")
self.helper.invite(room_id, "@alice:red", self.user_id)
self.helper.join(room_id, self.user_id)
# check that the user can see the member list to start with
channel = self.make_request("GET", "/rooms/%s/members" % room_id)
self.assertEqual(200, channel.code, msg=channel.result["body"])
# ban the user
self.helper.change_membership(room_id, "@alice:red", self.user_id, "ban")
# check the user can no longer see the member list
channel = self.make_request("GET", "/rooms/%s/members" % room_id)
self.assertEqual(403, channel.code, msg=channel.result["body"])
| 02d708568b476f2f7716000b35c0adfa4cbd31b3 | 10 | test_rooms.py | 203 | Replace assertEquals and friends with non-deprecated versions. (#12092) | 71,395 | 0 | 162 | 120 | 42 | 246,901 | 71 | synapse | 15 | tests/rest/client/test_rooms.py | Python | 9 | {
"docstring": "\n Tests that a former member of the room can not get the member list.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 14,
"vocab_size": 12
} | https://github.com/matrix-org/synapse.git |
|
6 | get_dated_queryset | def get_dated_queryset(self, **lookup):
qs = self.get_queryset().filter(**lookup)
date_field = self.get_date_field()
allow_future = self.get_allow_future()
allow_empty = self.get_allow_empty()
paginate_by = self.get_paginate_by(qs)
if not allow_future:
now = timezone.now() if self.uses_datetime_field else timezone_today()
qs = qs.filter(**{"%s__lte" % date_field: now})
if not allow_empty:
# When pagination is enabled, it's better to do a cheap query
# than to load the unpaginated queryset in memory.
is_empty = not qs if paginate_by is None else not qs.exists()
if is_empty:
raise Http404(
_("No %(verbose_name_plural)s available")
% {
"verbose_name_plural": qs.model._meta.verbose_name_plural,
}
)
return qs
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 17 | dates.py | 222 | Refs #33476 -- Reformatted code with Black. | 51,756 | 0 | 323 | 130 | 61 | 206,855 | 84 | django | 25 | django/views/generic/dates.py | Python | 19 | {
"docstring": "\n Get a queryset properly filtered according to `allow_future` and any\n extra lookup kwargs.\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 13,
"vocab_size": 13
} | https://github.com/django/django.git |
|
1 | default_params | def default_params(self) -> dict:
return {"order": "asc", "sort": self.sort_key, "limit": self.limit}
| 63af98e3b999d4b223237b51472a819915c5a558 | 8 | streams.py | 49 | 🎉 Recurly Schema Revamp (#9866)
* Cleanup Recurly connector schemas
* Add more Recurly schemas to the connector
- `billing_infos`
- `shipping_addresses`
- `shipping_methods`
- `subscription_changes`
* Add Recurly `add-on` resouce
* Add Recurly's account notes resource schema
* Add unique coupons to Recurly source
* Add credit payments to Recurly connector
* Add Recurly resources to integration tests configurations
* Bump Recurly source version to `0.4.0`
* Add `line_items` Recurly resource
* Add `line_items` to Recurly documentation
* Add missing `line_items` JSON schema
* Replace Subscription Change Recurly API call with Subscription `pending_changes` field
* Replace Recurly unique coupon codes API call with coupons `unique_coupon` field
To avoid the extra API call to import unique coupon calls
* Revert "Replace Recurly unique coupon codes API call with coupons `unique_coupon` field"
This reverts commit 1c4592d82da3c5e5e0026dda8eb2ed7a896ac5b8.
* Add `end_time` parameter to Recurly connector
* Order Recurly specs
* Set the Recurly `begin_time` and `end_time` to be optional
* Add `order` to Recurly `source_spec.yaml`
* Add `maxLength` to Recurly source schemas
* Set `maxLength` for Recurly Subscription and Transaction `uuid`
* Fix Recurly `export_dates` acceptance tests | 628 | 0 | 25 | 26 | 11 | 4,171 | 11 | airbyte | 5 | airbyte-integrations/connectors/source-recurly/source_recurly/streams.py | Python | 5 | {
"docstring": "\n Returns the parameters to be sent together with the API call to Recurly\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 13,
"vocab_size": 11
} | https://github.com/airbytehq/airbyte.git |
|
3 | filter_on_submodules | def filter_on_submodules(all_modules, submodule):
filtered_modules = [
mod for mod in all_modules if PACKAGE + submodule in mod.__name__
]
return filtered_modules
| a449efe29b092e658a29cd847e0494979a47d252 | 10 | keras_doctest.py | 43 | Add a keras doctest modeled on tensorflow doctest
PiperOrigin-RevId: 424672415 | 79,737 | 0 | 29 | 27 | 17 | 268,868 | 20 | keras | 7 | keras/tests/keras_doctest.py | Python | 5 | {
"docstring": "Filters all the modules based on the module flag.\n\n The module flag has to be relative to the core package imported.\n For example, if `submodule=keras.layers` then, this function will return\n all the modules in the submodule.\n\n Args:\n all_modules: All the modules in the core package.\n submodule: Submodule to filter from all the modules.\n\n Returns:\n All the modules in the submodule.\n ",
"language": "en",
"n_whitespaces": 75,
"n_words": 60,
"vocab_size": 38
} | https://github.com/keras-team/keras.git |
|
1 | is_stale | def is_stale(self, resource, path):
# Cache invalidation is a hard problem :-)
return True
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 6 | resources.py | 21 | upd; format | 12,869 | 0 | 35 | 12 | 14 | 62,100 | 14 | transferlearning | 4 | .venv/lib/python3.8/site-packages/pip/_vendor/distlib/resources.py | Python | 2 | {
"docstring": "\n Is the cache stale for the given resource?\n\n :param resource: The :class:`Resource` being cached.\n :param path: The path of the resource in the cache.\n :return: True if the cache is stale.\n ",
"language": "en",
"n_whitespaces": 67,
"n_words": 31,
"vocab_size": 24
} | https://github.com/jindongwang/transferlearning.git |
|
3 | set_result | def set_result(self, result):
with self._condition:
if self._state in {CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED}:
raise InvalidStateError('{}: {!r}'.format(self._state, self))
self._result = result
self._state = FINISHED
for waiter in self._waiters:
waiter.add_result(self)
self._condition.notify_all()
self._invoke_callbacks()
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 14 | _base.py | 123 | add python 3.10.4 for windows | 56,431 | 0 | 134 | 75 | 25 | 221,566 | 28 | XX-Net | 16 | python3.10.4/Lib/concurrent/futures/_base.py | Python | 10 | {
"docstring": "Sets the return value of work associated with the future.\n\n Should only be used by Executor implementations and unit tests.\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 20,
"vocab_size": 19
} | https://github.com/XX-net/XX-Net.git |
|
9 | get_data | def get_data(self, session_id, metric):
if session_id is None:
raw = self._data
else:
data = self._data.get(session_id)
if not data:
return None
raw = {session_id: data}
dtype = "float32" if metric == "loss" else "float64"
retval = {}
for idx, data in raw.items():
val = {metric: np.frombuffer(zlib.decompress(data[metric]),
dtype=dtype).reshape(data[f"{metric}_shape"])}
if metric == "loss":
val["labels"] = data["labels"]
retval[idx] = val
logger.debug("Obtained cached data: %s",
{session_id: {k: v.shape if isinstance(v, np.ndarray) else v
for k, v in data.items()}
for session_id, data in retval.items()})
return retval
| c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf | 17 | event_reader.py | 284 | Update code to support Tensorflow versions up to 2.8 (#1213)
* Update maximum tf version in setup + requirements
* - bump max version of tf version in launcher
- standardise tf version check
* update keras get_custom_objects for tf>2.6
* bugfix: force black text in GUI file dialogs (linux)
* dssim loss - Move to stock tf.ssim function
* Update optimizer imports for compatibility
* fix logging for tf2.8
* Fix GUI graphing for TF2.8
* update tests
* bump requirements.txt versions
* Remove limit on nvidia-ml-py
* Graphing bugfixes
- Prevent live graph from displaying if data not yet available
* bugfix: Live graph. Collect loss labels correctly
* fix: live graph - swallow inconsistent loss errors
* Bugfix: Prevent live graph from clearing during training
* Fix graphing for AMD | 19,804 | 0 | 358 | 173 | 52 | 100,307 | 80 | faceswap | 25 | lib/gui/analysis/event_reader.py | Python | 21 | {
"docstring": " Retrieve the decompressed cached data from the cache for the given session id.\n\n Parameters\n ----------\n session_id: int or ``None``\n If session_id is provided, then the cached data for that session is returned. If\n session_id is ``None`` then the cached data for all sessions is returned\n metric: ['loss', 'timestamps']\n The metric to return the data for.\n\n Returns\n -------\n dict\n The `session_id` (s) as key, the values are a dictionary containing the requested\n metric information for each session returned\n ",
"language": "en",
"n_whitespaces": 189,
"n_words": 77,
"vocab_size": 50
} | https://github.com/deepfakes/faceswap.git |
|
2 | find_all_matches | def find_all_matches(self, sources=None, finder=None):
# type: (Optional[List[Dict[S, Union[S, bool]]]], Optional[PackageFinder]) -> List[InstallationCandidate]
from .dependencies import find_all_matches, get_finder
if not finder:
_, finder = get_finder(sources=sources)
return find_all_matches(finder, self.as_ireq())
| cd5a9683be69c86c8f3adcd13385a9bc5db198ec | 11 | requirements.py | 77 | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | 4,277 | 0 | 73 | 46 | 27 | 22,233 | 27 | pipenv | 8 | pipenv/vendor/requirementslib/models/requirements.py | Python | 5 | {
"docstring": "Find all matching candidates for the current requirement.\n\n Consults a finder to find all matching candidates.\n\n :param sources: Pipfile-formatted sources, defaults to None\n :param sources: list[dict], optional\n :param PackageFinder finder: A **PackageFinder** instance from pip's repository implementation\n :return: A list of Installation Candidates\n :rtype: list[ :class:`~pipenv.patched.pip._internal.index.InstallationCandidate` ]\n ",
"language": "en",
"n_whitespaces": 96,
"n_words": 47,
"vocab_size": 40
} | https://github.com/pypa/pipenv.git |
|
4 | test_ddpg_compilation | def test_ddpg_compilation(self):
config = (
ddpg.DDPGConfig()
.training(num_steps_sampled_before_learning_starts=0)
.rollouts(num_rollout_workers=0, num_envs_per_worker=2)
)
explore = config.exploration_config.update({"random_timesteps": 100})
config.exploration(exploration_config=explore)
num_iterations = 1
# Test against all frameworks.
for _ in framework_iterator(config, with_eager_tracing=True):
algo = config.build(env="Pendulum-v1")
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)
check_compute_single_action(algo)
# Ensure apply_gradient_fn is being called and updating global_step
pol = algo.get_policy()
if config.framework_str == "tf":
a = pol.get_session().run(pol.global_step)
else:
a = pol.global_step
check(a, 500)
algo.stop()
| 0dceddb912ed92286032b5563dd2e541a8a7031f | 14 | test_ddpg.py | 257 | [RLlib] Move learning_starts logic from buffers into `training_step()`. (#26032) | 28,309 | 0 | 330 | 153 | 56 | 126,954 | 67 | ray | 37 | rllib/algorithms/ddpg/tests/test_ddpg.py | Python | 23 | {
"docstring": "Test whether DDPG can be built with both frameworks.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/ray-project/ray.git |
|
1 | increment_iterations | def increment_iterations(self) -> None:
self._iterations += 1
self._sessions[self._session_id]["iterations"] += 1
| ff6b0209dd5ad57b81b0aca570df7f39a7119bfb | 9 | model.py | 45 | Refactoring and TravisCI to Github Actions (#1239)
* refactor training
* travis to actions | 20,292 | 0 | 31 | 26 | 8 | 100,841 | 10 | faceswap | 5 | plugins/train/model/_base/model.py | Python | 4 | {
"docstring": " Increment :attr:`iterations` and session iterations by 1. ",
"language": "en",
"n_whitespaces": 8,
"n_words": 7,
"vocab_size": 7
} | https://github.com/deepfakes/faceswap.git |
|
1 | export_docker_compose | def export_docker_compose(args):
Flow.load_config(args.flowpath).to_docker_compose_yaml(
output_path=args.outpath, network_name=args.network_name
)
| 16b16b07a66cd5a8fc7cca1d3f1c378a9c63d38c | 10 | exporter.py | 48 | refactor: rename cli to jina_cli (#4890)
* chore: fix readme
* chore: fix readme
* chore: fix dockerignore
* fix: #4845
* style: fix overload and cli autocomplete
* fix: cicd export cli
Co-authored-by: Jina Dev Bot <[email protected]> | 2,301 | 0 | 22 | 29 | 6 | 12,474 | 6 | jina | 9 | jina/exporter.py | Python | 4 | {
"docstring": "Export to Docker compose yaml files\n\n :param args: args from CLI\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 11,
"vocab_size": 11
} | https://github.com/jina-ai/jina.git |
|
7 | test_search_based_stream_should_not_attempt_to_get_more_than_10k_records | def test_search_based_stream_should_not_attempt_to_get_more_than_10k_records(requests_mock, common_params, fake_properties_list):
responses = [
{
"json": {
"results": [{"id": f"{y}", "updatedAt": "2022-02-25T16:43:11Z"} for y in range(100)],
"paging": {"next": {"after": f"{x*100}",}}
},
"status_code": 200,
}
for x in range(1, 101)
]
# After reaching 10K records, it performs a new search query.
responses.extend([
{
"json": {
"results": [{"id": f"{y}", "updatedAt": "2022-03-01T00:00:00Z"} for y in range(100)],
"paging": {"next": {"after": f"{x*100}",}}
},
"status_code": 200,
}
for x in range(1, 10)
])
# Last page... it does not have paging->next->after
responses.append({
"json": {
"results": [{"id": f"{y}", "updatedAt": "2022-03-01T00:00:00Z"} for y in range(100)],
"paging": {}
},
"status_code": 200,
})
properties_response = [{
"json": [
{"name": property_name, "type": "string", "updatedAt": 1571085954360, "createdAt": 1565059306048}
for property_name in fake_properties_list
],
"status_code": 200
}]
# Create test_stream instance with some state
test_stream = Companies(**common_params)
test_stream.state = {"updatedAt": "2022-02-24T16:43:11Z"}
# Mocking Request
requests_mock.register_uri("POST", test_stream.url, responses)
requests_mock.register_uri("GET", "/properties/v2/company/properties", properties_response)
records = list(test_stream.read_records(sync_mode=SyncMode.incremental))
# The stream should not attempt to get more than 10K records.
# Instead, it should use the new state to start a new search query.
assert len(records) == 11000
assert test_stream.state['updatedAt'] == '2022-03-01T00:00:00+00:00'
| 710543a9abacc7578238cb5edaa47f43ed7c0431 | 19 | test_source.py | 514 | 🐛 Source Hubspot: Handled 10K+ search-endpoint queries (#10700)
* Handled search queries that would output more than 10K records
* Getting CRM search objects in ascending chronological ortder
* Fixed stream
* Fixed rebase
* Fixed condition
* Added unit test
* Removed unused import
* Started a new query when reached 10K records
* Moved comment | 652 | 0 | 524 | 277 | 107 | 4,306 | 178 | airbyte | 24 | airbyte-integrations/connectors/source-hubspot/unit_tests/test_source.py | Python | 42 | {
"docstring": "\n If there are more than 10,000 records that would be returned by the Hubspot search endpoint,\n the CRMSearchStream instance should stop at the 10Kth record\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 25,
"vocab_size": 23
} | https://github.com/airbytehq/airbyte.git |
|
4 | get_mode_of_payments | def get_mode_of_payments(filters):
mode_of_payments = {}
invoice_list = get_invoices(filters)
invoice_list_names = ",".join("'" + invoice["name"] + "'" for invoice in invoice_list)
if invoice_list:
inv_mop = frappe.db.sql(
.format(
invoice_list_names=invoice_list_names
),
as_dict=1,
)
for d in inv_mop:
mode_of_payments.setdefault(d["owner"] + cstr(d["posting_date"]), []).append(d.mode_of_payment)
return mode_of_payments
| 74a782d81d8f8c4a4d9214a9c06377e5e6e464dd | 18 | sales_payment_summary.py | 157 | refactor: DB independent quoting and truthy/falsy values (#31358)
* refactor: DB independent quoting and truthy/falsy values
* style: reformat to black spec
* fix: ifnull -> coalesce
* fix: coalesce -> Coalesce
* fix: revert pypika comparison
* refactor: convert queries to QB
* fix: incorrect value types for query
`=` query makes no sense with list of values
* fix: remove warehouse docstatus condition
* fix: keep using base rate as rate
Co-authored-by: Ankush Menat <[email protected]> | 14,908 | 0 | 25 | 93 | 31 | 68,837 | 39 | erpnext | 19 | erpnext/accounts/report/sales_payment_summary/sales_payment_summary.py | Python | 34 | {
"docstring": "select a.owner,a.posting_date, ifnull(b.mode_of_payment, '') as mode_of_payment\n\t\t\tfrom `tabSales Invoice` a, `tabSales Invoice Payment` b\n\t\t\twhere a.name = b.parent\n\t\t\tand a.docstatus = 1\n\t\t\tand a.name in ({invoice_list_names})\n\t\t\tunion\n\t\t\tselect a.owner,a.posting_date, ifnull(b.mode_of_payment, '') as mode_of_payment\n\t\t\tfrom `tabSales Invoice` a, `tabPayment Entry` b,`tabPayment Entry Reference` c\n\t\t\twhere a.name = c.reference_name\n\t\t\tand b.name = c.parent\n\t\t\tand b.docstatus = 1\n\t\t\tand a.name in ({invoice_list_names})\n\t\t\tunion\n\t\t\tselect a.owner, a.posting_date,\n\t\t\tifnull(a.voucher_type,'') as mode_of_payment\n\t\t\tfrom `tabJournal Entry` a, `tabJournal Entry Account` b\n\t\t\twhere a.name = b.parent\n\t\t\tand a.docstatus = 1\n\t\t\tand b.reference_type = 'Sales Invoice'\n\t\t\tand b.reference_name in ({invoice_list_names})\n\t\t\t",
"language": "en",
"n_whitespaces": 71,
"n_words": 91,
"vocab_size": 42
} | https://github.com/frappe/erpnext.git |
|
1 | test_alter_value | def test_alter_value(self):
altered_raw_data = apply_changes_to_raw_data(
raw_data=self.raw_data,
block_path_str="char1",
operation=AlterBlockValueOperation(new_value="foo"),
streamfield=models.SampleModel.content,
)
self.assertEqual(altered_raw_data[0]["value"], "foo")
self.assertEqual(altered_raw_data[1]["value"], self.raw_data[1]["value"])
self.assertEqual(altered_raw_data[2]["value"], "foo")
self.assertEqual(altered_raw_data[3]["value"], self.raw_data[3]["value"])
| ad65741b94f36fbe793cf15f0ab002482070cdb6 | 13 | test_simple_structures.py | 181 | Add tests for streamfield migration helpers
Currently failing due to wagtail-factories being broken on Wagtail 4.1: https://github.com/wagtail/wagtail-factories/issues/65 | 17,024 | 0 | 111 | 110 | 17 | 80,146 | 18 | wagtail | 14 | wagtail/tests/streamfield_migrations/test_simple_structures.py | Python | 11 | {
"docstring": "Change the value of each `char1` block to `foo`\n\n Check whether the value of each `char1` block has changed to `foo`.\n Check whether the values of other blocks are intact.\n ",
"language": "en",
"n_whitespaces": 51,
"n_words": 30,
"vocab_size": 19
} | https://github.com/wagtail/wagtail.git |
|
2 | get_popularity_based_topk | def get_popularity_based_topk(self, top_k=10, items=True):
# TODO: get most frequent users
if not items:
raise ValueError("Not implemented")
return self.item_frequencies.orderBy("frequency", ascending=False).limit(top_k)
| 99b46f65e08a67f3b11cbaf80edeffe6dd5d6b2e | 10 | SARPlus.py | 70 | Implemented get_popularity_based_topk | 7,242 | 0 | 58 | 41 | 19 | 39,476 | 19 | recommenders | 9 | contrib/sarplus/python/pysarplus/SARPlus.py | Python | 4 | {
"docstring": "Get top K most frequently occurring items across all users.\n\n Args:\n top_k (int): number of top items to recommend.\n items (bool): if false, return most frequent users instead.\n\n Returns:\n pyspark.sql.DataFrame: Spark dataframe with top k most popular items\n and their frequencies in descending order.\n ",
"language": "en",
"n_whitespaces": 109,
"n_words": 44,
"vocab_size": 37
} | https://github.com/microsoft/recommenders.git |
|
2 | format_string_to_json | def format_string_to_json(balance_info):
Working Account|KES|481000.00|481000.00|0.00|0.00
balance_dict = frappe._dict()
for account_info in balance_info.split("&"):
account_info = account_info.split("|")
balance_dict[account_info[0]] = dict(
current_balance=fmt_money(account_info[2], currency="KES"),
available_balance=fmt_money(account_info[3], currency="KES"),
reserved_balance=fmt_money(account_info[4], currency="KES"),
uncleared_balance=fmt_money(account_info[5], currency="KES"),
)
return dumps(balance_dict)
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 15 | mpesa_settings.py | 166 | style: format code with black | 14,080 | 0 | 16 | 103 | 22 | 65,988 | 28 | erpnext | 15 | erpnext/erpnext_integrations/doctype/mpesa_settings/mpesa_settings.py | Python | 11 | {
"docstring": "\n\tFormat string to json.\n\n\te.g: \n\t=> {'Working Account': {'current_balance': '481000.00',\n\t 'available_balance': '481000.00',\n\t 'reserved_balance': '0.00',\n\t 'uncleared_balance': '0.00'}}\n\t",
"language": "en",
"n_whitespaces": 35,
"n_words": 16,
"vocab_size": 15
} | https://github.com/frappe/erpnext.git |
|
8 | setimage | def setimage(self, im, extents=None):
# following c code
self.im = im
if extents:
(x0, y0, x1, y1) = extents
else:
(x0, y0, x1, y1) = (0, 0, 0, 0)
if x0 == 0 and x1 == 0:
self.state.xsize, self.state.ysize = self.im.size
else:
self.state.xoff = x0
self.state.yoff = y0
self.state.xsize = x1 - x0
self.state.ysize = y1 - y0
if self.state.xsize <= 0 or self.state.ysize <= 0:
raise ValueError("Size cannot be negative")
if (
self.state.xsize + self.state.xoff > self.im.size[0]
or self.state.ysize + self.state.yoff > self.im.size[1]
):
raise ValueError("Tile cannot extend outside image")
| a0e1fde1eddf45f26653e2ff6080d31e177adbec | 11 | ImageFile.py | 282 | Added PyEncoder | 69,856 | 0 | 282 | 184 | 54 | 242,434 | 91 | Pillow | 15 | src/PIL/ImageFile.py | Python | 20 | {
"docstring": "\n Called from ImageFile to set the core output image for the codec\n\n :param im: A core image object\n :param extents: a 4 tuple of (x0, y0, x1, y1) defining the rectangle\n for this tile\n :returns: None\n ",
"language": "en",
"n_whitespaces": 83,
"n_words": 36,
"vocab_size": 30
} | https://github.com/python-pillow/Pillow.git |
|
1 | get_cache_attr_name | def get_cache_attr_name(cls):
return "_{}.{}".format(cls._meta.app_label, cls._meta.model_name).lower()
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 11 | models.py | 47 | Reformat with black | 16,023 | 0 | 19 | 27 | 5 | 73,478 | 5 | wagtail | 7 | wagtail/contrib/settings/models.py | Python | 2 | {
"docstring": "\n Returns the name of the attribute that should be used to store\n a reference to the fetched/created object on a request.\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 21,
"vocab_size": 17
} | https://github.com/wagtail/wagtail.git |
|
5 | serialize | def serialize(self):
data = dict()
for attr in self.fattributes:
if attr not in ('block', 'rescue', 'always'):
data[attr] = getattr(self, attr)
data['dep_chain'] = self.get_dep_chain()
if self._role is not None:
data['role'] = self._role.serialize()
if self._parent is not None:
data['parent'] = self._parent.copy(exclude_tasks=True).serialize()
data['parent_type'] = self._parent.__class__.__name__
return data
| 43153c58310d02223f2cb0964f4255ba1ac4ed53 | 13 | block.py | 188 | `FieldAttribute`s as descriptors (#73908) | 78,962 | 0 | 152 | 112 | 30 | 267,581 | 44 | ansible | 14 | lib/ansible/playbook/block.py | Python | 12 | {
"docstring": "\n Override of the default serialize method, since when we're serializing\n a task we don't want to include the attribute list of tasks.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 22,
"vocab_size": 20
} | https://github.com/ansible/ansible.git |
|
4 | _apply_scores | def _apply_scores(self, scores, value, scores_mask=None, training=None):
if scores_mask is not None:
padding_mask = tf.logical_not(scores_mask)
# Bias so padding positions do not contribute to attention
# distribution. Note 65504. is the max float16 value.
if scores.dtype is tf.float16:
scores -= 65504.0 * tf.cast(padding_mask, dtype=scores.dtype)
else:
scores -= 1.0e9 * tf.cast(padding_mask, dtype=scores.dtype)
if training is None:
training = backend.learning_phase()
weights = tf.nn.softmax(scores)
| 8401e08334d4b1f102a6ee9479738bacfee0600c | 16 | base_dense_attention.py | 153 | reduce layers line-too-long | 82,075 | 0 | 185 | 133 | 44 | 277,507 | 60 | keras | 17 | keras/layers/attention/base_dense_attention.py | Python | 15 | {
"docstring": "Applies attention scores to the given value tensor.\n\n To use this method in your attention layer, follow the steps:\n\n * Use `query` tensor of shape `[batch_size, Tq]` and `key` tensor of\n shape `[batch_size, Tv]` to calculate the attention `scores`.\n * Pass `scores` and `value` tensors to this method. The method applies\n `scores_mask`, calculates `attention_distribution = softmax(scores)`,\n then returns `matmul(attention_distribution, value).\n * Apply `query_mask` and return the result.\n\n Args:\n scores: Scores float tensor of shape `[batch_size, Tq, Tv]`.\n value: Value tensor of shape `[batch_size, Tv, dim]`.\n scores_mask: A boolean mask `Tensor` of shape `[batch_size, 1, Tv]` or\n `[batch_size, Tq, Tv]`. If given, scores at positions where\n `scores_mask==False` do not contribute to the result. It must\n contain at least one `True` value in each line along the last\n dimension.\n training: Python boolean indicating whether the layer should behave in\n training mode (adding dropout) or in inference mode (no dropout).\n\n Returns:\n Tensor of shape `[batch_size, Tq, dim]`.\n Attention scores after masking and softmax with shape\n `[batch_size, Tq, Tv]`.\n ",
"language": "en",
"n_whitespaces": 361,
"n_words": 165,
"vocab_size": 108
} | https://github.com/keras-team/keras.git |
|
3 | _build_sub_tabs | def _build_sub_tabs(self) -> None:
for section, plugins in self.config_tools.plugins_dict.items():
for plugin in plugins:
config_key = ".".join((section, plugin))
config_dict = self.config_tools.config_dicts[config_key]
tab = ConfigFrame(self, config_key, config_dict)
self._tabs[section][plugin] = tab
text = plugin.replace("_", " ").title()
cast(ttk.Notebook, self._tabs[section]["tab"]).add(tab, text=text)
| 1022651eb8a7741014f5d2ec7cbfe882120dfa5f | 16 | preview.py | 175 | Bugfix: convert - Gif Writer
- Fix non-launch error on Gif Writer
- convert plugins - linting
- convert/fs_media/preview/queue_manager - typing
- Change convert items from dict to Dataclass | 20,846 | 0 | 151 | 110 | 29 | 101,433 | 36 | faceswap | 22 | tools/preview/preview.py | Python | 10 | {
"docstring": " Build the notebook sub tabs for each convert section's plugin. ",
"language": "en",
"n_whitespaces": 11,
"n_words": 10,
"vocab_size": 10
} | https://github.com/deepfakes/faceswap.git |
|
1 | test_realm_quota | def test_realm_quota(self) -> None:
self.login("hamlet")
d1 = StringIO("zulip!")
d1.name = "dummy_1.txt"
result = self.client_post("/json/user_uploads", {"file": d1})
response_dict = self.assert_json_success(result)
d1_path_id = re.sub("/user_uploads/", "", response_dict["uri"])
d1_attachment = Attachment.objects.get(path_id=d1_path_id)
realm = get_realm("zulip")
realm.upload_quota_gb = 1
realm.save(update_fields=["upload_quota_gb"])
# The size of StringIO("zulip!") is 6 bytes. Setting the size of
# d1_attachment to realm.upload_quota_bytes() - 11 should allow
# us to upload only one more attachment.
quota = realm.upload_quota_bytes()
assert quota is not None
d1_attachment.size = quota - 11
d1_attachment.save(update_fields=["size"])
d2 = StringIO("zulip!")
d2.name = "dummy_2.txt"
result = self.client_post("/json/user_uploads", {"file": d2})
self.assert_json_success(result)
d3 = StringIO("zulip!")
d3.name = "dummy_3.txt"
result = self.client_post("/json/user_uploads", {"file": d3})
self.assert_json_error(result, "Upload would exceed your organization's upload quota.")
realm.upload_quota_gb = None
realm.save(update_fields=["upload_quota_gb"])
result = self.client_post("/json/user_uploads", {"file": d3})
self.assert_json_success(result)
| a142fbff85302c5e3acb2e204eca2e9c75dbc74b | 11 | test_upload.py | 402 | tests: Refactor away result.json() calls with helpers.
Signed-off-by: Zixuan James Li <[email protected]> | 17,789 | 0 | 327 | 223 | 69 | 84,150 | 117 | zulip | 29 | zerver/tests/test_upload.py | Python | 30 | {
"docstring": "\n Realm quota for uploading should not be exceeded.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | https://github.com/zulip/zulip.git |
|
1 | forward_dummy | def forward_dummy(self, img, img_metas):
super(SingleStageDetector, self).forward_train(img, img_metas)
x = self.extract_feat(img)
outs = self.panoptic_head(x, img_metas)
return outs
| cac356380d505bf15587f07c0529218cc36b9652 | 9 | maskformer.py | 67 | [Feature] Add Maskformer to mmdet (#7212)
* first commit
* add README
* move model description from config to readme
add description for binary_input
add description for dice loss
add a independent panoptic gt processing function
add a independent panoptic gt processing function
remove compatibility of pretrain in maskformer
* update comments in maskformer_head
* update docs format | 70,214 | 0 | 51 | 43 | 13 | 244,047 | 16 | mmdetection | 11 | mmdet/models/detectors/maskformer.py | Python | 5 | {
"docstring": "Used for computing network flops. See\n `mmdetection/tools/analysis_tools/get_flops.py`\n\n Args:\n img (Tensor): of shape (N, C, H, W) encoding input images.\n Typically these should be mean centered and std scaled.\n img_metas (list[Dict]): list of image info dict where each dict\n has: 'img_shape', 'scale_factor', 'flip', and may also contain\n 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.\n For details on the values of these keys see\n `mmdet/datasets/pipelines/formatting.py:Collect`.\n ",
"language": "en",
"n_whitespaces": 179,
"n_words": 61,
"vocab_size": 55
} | https://github.com/open-mmlab/mmdetection.git |