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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
47,513 | 97 | 13 | 28 | 347 | 40 | 0 | 136 | 402 | test_timeout_triggers | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | https://github.com/apache/airflow.git | def test_timeout_triggers(self, dag_maker):
session = settings.Session()
# Create the test DAG and task
with dag_maker(
dag_id='test_timeout_triggers',
start_date=DEFAULT_DATE,
schedule_interval='@once',
max_active_runs=1,
session=session,
):
EmptyOperator(task_id='dummy1')
# Create a Task Instance for the task that is allegedly deferred
# but past its timeout, and one that is still good.
# We don't actually need a linked trigger here; the code doesn't check.
dr1 = dag_maker.create_dagrun()
dr2 = dag_maker.create_dagrun(
run_id="test2", execution_date=DEFAULT_DATE + datetime.timedelta(seconds=1)
)
ti1 = dr1.get_task_instance('dummy1', session)
ti2 = dr2.get_task_instance('dummy1', session)
ti1.state = State.DEFERRED
ti1.trigger_timeout = timezone.utcnow() - datetime.timedelta(seconds=60)
ti2.state = State.DEFERRED
ti2.trigger_timeout = timezone.utcnow() + datetime.timedelta(seconds=60)
session.flush()
# Boot up the scheduler and make it check timeouts
self.scheduler_job = SchedulerJob(subdir=os.devnull)
self.scheduler_job.check_trigger_timeouts(session=session)
# Make sure that TI1 is now scheduled to fail, and 2 wasn't touched
session.refresh(ti1)
session.refresh(ti2)
assert ti1.state == State.SCHEDULED
assert ti1.next_method == "__fail__"
assert ti2.state == State.DEFERRED
| 207 | test_scheduler_job.py | Python | tests/jobs/test_scheduler_job.py | 49e336ae0302b386a2f47269a6d13988382d975f | airflow | 1 |
|
275,522 | 23 | 10 | 6 | 69 | 7 | 0 | 27 | 59 | _var_key | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def _var_key(var):
# pylint: disable=protected-access
# Get the distributed variable if it exists.
if hasattr(var, "_distributed_container"):
var = var._distributed_container()
if getattr(var, "_in_graph_mode", False):
return var._shared_name
return var._unique_id
| 39 | optimizer_v2.py | Python | keras/optimizers/optimizer_v2/optimizer_v2.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 3 |
|
153,567 | 19 | 11 | 7 | 75 | 9 | 0 | 20 | 86 | diff | DOCS-#3099: Fix `BasePandasDataSet` docstrings warnings (#4333)
Co-authored-by: Yaroslav Igoshev <[email protected]>
Signed-off-by: Alexander Myskov <[email protected]> | https://github.com/modin-project/modin.git | def diff(self, periods=1, axis=0): # noqa: PR01, RT01, D200
axis = self._get_axis_number(axis)
return self.__constructor__(
query_compiler=self._query_compiler.diff(
fold_axis=axis, axis=axis, periods=periods
)
)
| 48 | base.py | Python | modin/pandas/base.py | 605efa618e7994681f57b11d04d417f353ef8d50 | modin | 1 |
|
243,968 | 66 | 13 | 21 | 325 | 38 | 0 | 91 | 218 | draw_masks | [Feature] Support visualization for Panoptic Segmentation (#7041)
* First commit of v2
* split the functions
* Support to show panoptic result
* temp
* Support to show gt
* support show gt
* fix lint
* Support to browse datasets
* Fix unit tests
* Fix findContours
* fix comments
* Fix pre-commit
* fix lint
* Add the type of an argument | https://github.com/open-mmlab/mmdetection.git | def draw_masks(ax, img, masks, color=None, with_edge=True, alpha=0.8):
taken_colors = set([0, 0, 0])
if color is None:
random_colors = np.random.randint(0, 255, (masks.size(0), 3))
color = [tuple(c) for c in random_colors]
color = np.array(color, dtype=np.uint8)
polygons = []
for i, mask in enumerate(masks):
if with_edge:
contours, _ = bitmap_to_polygon(mask)
polygons += [Polygon(c) for c in contours]
color_mask = color[i]
while tuple(color_mask) in taken_colors:
color_mask = _get_bias_color(color_mask)
taken_colors.add(tuple(color_mask))
mask = mask.astype(bool)
img[mask] = img[mask] * (1 - alpha) + color_mask * alpha
p = PatchCollection(
polygons, facecolor='none', edgecolors='w', linewidths=1, alpha=0.8)
ax.add_collection(p)
return ax, img
| 217 | image.py | Python | mmdet/core/visualization/image.py | 301d4a2d4cfe1cdb62608e2892924be3e67e3098 | mmdetection | 7 |
|
277,143 | 3 | 6 | 3 | 18 | 3 | 0 | 3 | 6 | inject_argument_info_in_traceback | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def inject_argument_info_in_traceback(fn, object_name=None):
| 25 | traceback_utils.py | Python | keras/utils/traceback_utils.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
|
215,557 | 140 | 17 | 50 | 563 | 48 | 0 | 211 | 790 | fire_master | Use salt.channel.client instead of salt.transport.client | https://github.com/saltstack/salt.git | def fire_master(data, tag, preload=None):
if (
__opts__.get("local", None) or __opts__.get("file_client", None) == "local"
) and not __opts__.get("use_master_when_local", False):
# We can't send an event if we're in masterless mode
log.warning("Local mode detected. Event with tag %s will NOT be sent.", tag)
return False
if preload or __opts__.get("__cli") == "salt-call":
# If preload is specified, we must send a raw event (this is
# slower because it has to independently authenticate)
if "master_uri" not in __opts__:
__opts__["master_uri"] = "tcp://{ip}:{port}".format(
ip=salt.utils.zeromq.ip_bracket(__opts__["interface"]),
port=__opts__.get("ret_port", "4506"), # TODO, no fallback
)
masters = list()
ret = True
if "master_uri_list" in __opts__:
for master_uri in __opts__["master_uri_list"]:
masters.append(master_uri)
else:
masters.append(__opts__["master_uri"])
auth = salt.crypt.SAuth(__opts__)
load = {
"id": __opts__["id"],
"tag": tag,
"data": data,
"tok": auth.gen_token(b"salt"),
"cmd": "_minion_event",
}
if isinstance(preload, dict):
load.update(preload)
for master in masters:
with salt.channel.client.ReqChannel.factory(
__opts__, master_uri=master
) as channel:
try:
channel.send(load)
# channel.send was successful.
# Ensure ret is True.
ret = True
except Exception: # pylint: disable=broad-except
ret = False
return ret
else:
# Usually, we can send the event via the minion, which is faster
# because it is already authenticated
try:
return salt.utils.event.MinionEvent(__opts__, listen=False).fire_event(
{"data": data, "tag": tag, "events": None, "pretag": None},
"fire_master",
)
except Exception: # pylint: disable=broad-except
exc_type, exc_value, exc_traceback = sys.exc_info()
lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
log.debug(lines)
return False
| 326 | event.py | Python | salt/modules/event.py | 70972c8016ff5d6fbdd7f83776077b0936f60dea | salt | 13 |
|
257,683 | 23 | 13 | 6 | 115 | 16 | 0 | 26 | 72 | test__create_document_index_wrong_mapping_raises | Use opensearch-py in OpenSearchDocumentStore (#2691)
* add Opensearch extras
* let OpenSearchDocumentStore use opensearch-py
* Update Documentation & Code Style
* fix a bug found after adding tests
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Sara Zan <[email protected]> | https://github.com/deepset-ai/haystack.git | def test__create_document_index_wrong_mapping_raises(self, mocked_document_store, index):
mocked_document_store.search_fields = ["age"]
mocked_document_store.client.indices.exists.return_value = True
mocked_document_store.client.indices.get.return_value = {self.index_name: index}
with pytest.raises(Exception, match=f"The search_field 'age' of index '{self.index_name}' with type 'integer'"):
mocked_document_store._create_document_index(self.index_name)
| 66 | test_opensearch.py | Python | test/document_stores/test_opensearch.py | e7627c3f8b241654b61f8523479c81f855102f0a | haystack | 1 |
|
119,982 | 12 | 11 | 5 | 64 | 10 | 0 | 12 | 99 | bcoo_broadcast_in_dim | [sparse] Update docstrings for bcoo primitives.
PiperOrigin-RevId: 438685829 | https://github.com/google/jax.git | def bcoo_broadcast_in_dim(mat, *, shape, broadcast_dimensions):
return BCOO(_bcoo_broadcast_in_dim(mat.data, mat.indices, spinfo=mat._info,
shape=shape,
broadcast_dimensions=broadcast_dimensions),
shape=shape)
| 44 | bcoo.py | Python | jax/experimental/sparse/bcoo.py | 3184dd65a222354bffa2466d9a375162f5649132 | jax | 1 |
|
70,190 | 30 | 13 | 7 | 67 | 10 | 0 | 34 | 106 | connect_docker | First version. Container list and CPU ok, need others stats | https://github.com/nicolargo/glances.git | def connect_docker(self):
try:
# Do not use the timeout option (see issue #1878)
ret = docker.from_env()
except Exception as e:
logger.error("docker plugin - Can not connect to Docker ({})".format(e))
ret = None
return ret
| 36 | glances_docker.py | Python | glances/plugins/glances_docker.py | 0e098407e15bc1dc341c571cec8fd34ecb4b0943 | glances | 2 |
|
309,563 | 37 | 15 | 26 | 185 | 21 | 0 | 41 | 267 | favorites_folder_payload | Allow browsing favorites in Sonos media browser (#64082)
* Allow browsing favorites in Sonos media browser
* Group favorites by type, add thumbnails
* Update homeassistant/components/sonos/media_player.py
* Apply suggestions from code review
Co-authored-by: Martin Hjelmare <[email protected]>
* Keep favorite groups ordering consistent
* Skip root folder if only one child available
Co-authored-by: Jason Lawrence <[email protected]>
Co-authored-by: Martin Hjelmare <[email protected]> | https://github.com/home-assistant/core.git | def favorites_folder_payload(favorites, media_content_id):
children = []
content_type = SONOS_TYPES_MAPPING[media_content_id]
for favorite in favorites:
if favorite.reference.item_class != media_content_id:
continue
children.append(
BrowseMedia(
title=favorite.title,
media_class=SONOS_TO_MEDIA_CLASSES[favorite.reference.item_class],
media_content_id=favorite.item_id,
media_content_type="favorite_item_id",
can_play=True,
can_expand=False,
thumbnail=getattr(favorite, "album_art_uri", None),
)
)
return BrowseMedia(
title=content_type.title(),
media_class=MEDIA_CLASS_DIRECTORY,
media_content_id="",
media_content_type="favorites",
can_play=False,
can_expand=True,
children=children,
)
| 123 | media_browser.py | Python | homeassistant/components/sonos/media_browser.py | 2f18058fe739cabc0495a14e00df3ced60009ba2 | core | 3 |
|
82,431 | 38 | 17 | 21 | 308 | 30 | 0 | 56 | 279 | test_sitemap_unpublished_titles | ci: Added codespell (#7355)
Co-authored-by: Christian Clauss <[email protected]>
* ci: codespell config taken from #7292 | https://github.com/django-cms/django-cms.git | def test_sitemap_unpublished_titles(self):
sitemap = CMSSitemap()
locations = []
urlset = sitemap.get_urls()
unpublished_titles = set()
for item in urlset:
locations.append(item['location'])
for page in Page.objects.drafts():
if page.get_public_object():
set1 = set(page.get_public_object().title_set.values_list('path', flat=True))
set2 = set(page.title_set.values_list('path', flat=True))
unpublished_titles.update(set2.difference(set1))
else:
unpublished_titles.update(page.title_set.values_list('path', flat=True))
for path in unpublished_titles:
title = Title.objects.get(path=path)
if title.path:
url = f'http://example.com/{title.language}/{title.path}/'
else:
url = f'http://example.com/{title.language}/{title.path}'
self.assertFalse(url in locations)
| 167 | test_sitemap.py | Python | cms/tests/test_sitemap.py | c1290c9ff89cb00caa5469129fd527e9d82cd820 | django-cms | 6 |
|
104,073 | 16 | 13 | 7 | 111 | 13 | 0 | 20 | 53 | xsplitext | Add SVHN dataset (#3535)
* Add SVHN
* Add support for streaming
* README fix
* Don't specify labels twice | https://github.com/huggingface/datasets.git | def xsplitext(a):
a, *b = a.split("::")
if is_local_path(a):
return os.path.splitext(Path(a).as_posix())
else:
a, ext = posixpath.splitext(a)
return "::".join([a] + b), ext
| 64 | streaming_download_manager.py | Python | src/datasets/utils/streaming_download_manager.py | a6ff8d6fb5ba2770737d2d922976c50f480d23a2 | datasets | 2 |
|
249,435 | 26 | 12 | 17 | 133 | 13 | 0 | 27 | 162 | test_400_missing_param_without_id_access_token | Drop support for calling `/_matrix/client/v3/rooms/{roomId}/invite` without an `id_access_token` (#13241)
Fixes #13206
Signed-off-by: Jacek Kusnierz [email protected] | https://github.com/matrix-org/synapse.git | def test_400_missing_param_without_id_access_token(self) -> None:
channel = self.make_request(
method="POST",
path="/rooms/" + self.room_id + "/invite",
content={
"id_server": "example.com",
"medium": "email",
"address": "[email protected]",
},
access_token=self.tok,
)
self.assertEqual(channel.code, 400)
self.assertEqual(channel.json_body["errcode"], "M_MISSING_PARAM")
| 75 | test_rooms.py | Python | tests/rest/client/test_rooms.py | 84ddcd7bbfe4100101741a408a91f283a8f742c7 | synapse | 1 |
|
14,004 | 8 | 9 | 27 | 39 | 6 | 0 | 9 | 38 | __anext__ | refactor: avoid run in executor creating threads (#5518) | https://github.com/jina-ai/jina.git | async def __anext__(self):
if isinstance(self.iterator, Iterator):
if not self._iterate_sync_in_thread: | 108 | helper.py | Python | jina/serve/stream/helper.py | 5a0830cfb6bfa33dcffb38681f86efe5f6f0f97c | jina | 7 |
|
153,106 | 20 | 10 | 4 | 44 | 6 | 0 | 21 | 64 | _check_index_name | FIX-#3197: do not pass lambdas to the backend in GroupBy (#3373)
Signed-off-by: Dmitry Chigarev <[email protected]> | https://github.com/modin-project/modin.git | def _check_index_name(self, result):
if self._by is not None:
# pandas does not name the index for this case
result._query_compiler.set_index_name(None)
return result
| 26 | groupby.py | Python | modin/pandas/groupby.py | 1e65a4afd191cf61ba05b80545d23f9b88962f41 | modin | 2 |
|
64,242 | 120 | 19 | 48 | 708 | 45 | 0 | 214 | 162 | update_billed_amount_based_on_so | refactor: use frappe.qb instead of sql
(cherry picked from commit 0a9ec9f591f8b4d0e630a3c902b69c9996f080dd) | https://github.com/frappe/erpnext.git | def update_billed_amount_based_on_so(so_detail, update_modified=True):
from frappe.query_builder.functions import Sum
# Billed against Sales Order directly
si = frappe.qb.DocType("Sales Invoice").as_("si")
si_item = frappe.qb.DocType("Sales Invoice Item").as_("si_item")
sum_amount = Sum(si_item.amount).as_("amount")
billed_against_so = frappe.qb.from_(si).from_(si_item).select(sum_amount).where(
(si_item.parent == si.name) &
(si_item.so_detail == so_detail) &
((si_item.dn_detail.isnull()) | (si_item.dn_detail == '')) &
(si_item.docstatus == 1) &
(si.update_stock == 0)
).run()
billed_against_so = billed_against_so and billed_against_so[0][0] or 0
# Get all Delivery Note Item rows against the Sales Order Item row
dn = frappe.qb.DocType("Delivery Note").as_("dn")
dn_item = frappe.qb.DocType("Delivery Note Item").as_("dn_item")
dn_details = frappe.qb.from_(dn).from_(dn_item).select(dn_item.name, dn_item.amount, dn_item.si_detail, dn_item.parent, dn_item.stock_qty, dn_item.returned_qty).where(
(dn.name == dn_item.parent) &
(dn_item.so_detail == so_detail) &
(dn.docstatus == 1) &
(dn.is_return == 0)
).orderby(
dn.posting_date, dn.posting_time, dn.name
).run(as_dict=True)
updated_dn = []
for dnd in dn_details:
billed_amt_agianst_dn = 0
# If delivered against Sales Invoice
if dnd.si_detail:
billed_amt_agianst_dn = flt(dnd.amount)
billed_against_so -= billed_amt_agianst_dn
else:
# Get billed amount directly against Delivery Note
billed_amt_agianst_dn = frappe.db.sql(, dnd.name)
billed_amt_agianst_dn = billed_amt_agianst_dn and billed_amt_agianst_dn[0][0] or 0
# Distribute billed amount directly against SO between DNs based on FIFO
if billed_against_so and billed_amt_agianst_dn < dnd.amount:
if dnd.returned_qty:
pending_to_bill = flt(dnd.amount) * (dnd.stock_qty - dnd.returned_qty) / dnd.stock_qty
else:
pending_to_bill = flt(dnd.amount)
pending_to_bill -= billed_amt_agianst_dn
if pending_to_bill <= billed_against_so:
billed_amt_agianst_dn += pending_to_bill
billed_against_so -= pending_to_bill
else:
billed_amt_agianst_dn += billed_against_so
billed_against_so = 0
frappe.db.set_value("Delivery Note Item", dnd.name, "billed_amt", billed_amt_agianst_dn, update_modified=update_modified)
updated_dn.append(dnd.parent)
return updated_dn
| 440 | delivery_note.py | Python | erpnext/stock/doctype/delivery_note/delivery_note.py | ce0b84f54d495fc78a6792a9b05d0eb1dc799ed2 | erpnext | 11 |
|
257,136 | 68 | 11 | 13 | 206 | 30 | 1 | 82 | 148 | mock_json_schema | Change YAML version exception into a warning (#2385)
* Change exception into warning, add strict_version param, and remove compatibility between schemas
* Simplify update_json_schema
* Rename unstable into master
* Prevent validate_config from changing the config to validate
* Fix version validation and add tests
* Rename master into ignore
* Complete parameter rename
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | https://github.com/deepset-ai/haystack.git | def mock_json_schema(request, monkeypatch, tmp_path):
# Do not patch integration tests
if "integration" in request.keywords:
return
# Mock the subclasses list to make it very small, containing only mock nodes
monkeypatch.setattr(
haystack.nodes._json_schema,
"find_subclasses_in_modules",
lambda *a, **k: [(conftest, MockDocumentStore), (conftest, MockReader), (conftest, MockRetriever)],
)
# Point the JSON schema path to tmp_path
monkeypatch.setattr(haystack.pipelines.config, "JSON_SCHEMAS_PATH", tmp_path)
# Generate mock schema in tmp_path
filename = f"haystack-pipeline-master.schema.json"
test_schema = _json_schema.get_json_schema(filename=filename, version="ignore")
with open(tmp_path / filename, "w") as schema_file:
json.dump(test_schema, schema_file, indent=4)
#
# Integration
#
@pytest.mark.integration
@pytest.mark.elasticsearch | @pytest.mark.integration
@pytest.mark.elasticsearch | 114 | test_pipeline_yaml.py | Python | test/test_pipeline_yaml.py | 4eec2dc45ee60e8b8780aa4f956aea8ad3624da3 | haystack | 2 |
176,591 | 6 | 7 | 2 | 25 | 4 | 0 | 6 | 12 | node_connected_component | Added examples in connected and strongly connected functions (#5559)
* added examples
* Update networkx/algorithms/components/connected.py
Co-authored-by: Ross Barnowski <[email protected]>
Co-authored-by: Ross Barnowski <[email protected]> | https://github.com/networkx/networkx.git | def node_connected_component(G, n):
return _plain_bfs(G, n)
| 15 | connected.py | Python | networkx/algorithms/components/connected.py | 7cad29b3542ad867f1eb5b7b6a9087495f252749 | networkx | 1 |
|
199,702 | 33 | 13 | 9 | 132 | 13 | 1 | 42 | 84 | dup_hermite | Run orthopolys and appellseqs through a common interface
Including unifying the two Chebyshev generators into one function.
There are also two kinds of Hermite polynomials, and they too share the
same recurrence, but the second type He_n(x) (aka the probabilist,
reduced or small polynomials) will not be added here. | https://github.com/sympy/sympy.git | def dup_hermite(n, K):
if n < 1:
return [1]
m2, m1 = [1], [2, 0]
for i in range(2, n+1):
a = dup_lshift(m1, 1, K)
b = dup_mul_ground(m2, i-1, K)
m2, m1 = m1, dup_mul_ground(dup_sub(a, b, K), 2, K)
return m1
@public | @public | 87 | orthopolys.py | Python | sympy/polys/orthopolys.py | d1d46df73ebaad94089847558d00a8b7269f554d | sympy | 3 |
155,341 | 84 | 22 | 43 | 473 | 23 | 0 | 173 | 786 | _build_repr_df | REFACTOR-#5310: Remove some hasattr('columns') checks. (#5311)
Signed-off-by: mvashishtha <[email protected]> | https://github.com/modin-project/modin.git | def _build_repr_df(self, num_rows, num_cols):
# Fast track for empty dataframe.
if len(self.index) == 0 or (self._is_dataframe and len(self.columns) == 0):
return pandas.DataFrame(
index=self.index,
columns=self.columns if self._is_dataframe else None,
)
if len(self.index) <= num_rows:
row_indexer = slice(None)
else:
# Add one here so that pandas automatically adds the dots
# It turns out to be faster to extract 2 extra rows and columns than to
# build the dots ourselves.
num_rows_for_head = num_rows // 2 + 1
num_rows_for_tail = (
num_rows_for_head
if len(self.index) > num_rows
else len(self.index) - num_rows_for_head
if len(self.index) - num_rows_for_head >= 0
else None
)
row_indexer = list(range(len(self.index))[:num_rows_for_head]) + (
list(range(len(self.index))[-num_rows_for_tail:])
if num_rows_for_tail is not None
else []
)
if self._is_dataframe:
if len(self.columns) <= num_cols:
col_indexer = slice(None)
else:
num_cols_for_front = num_cols // 2 + 1
num_cols_for_back = (
num_cols_for_front
if len(self.columns) > num_cols
else len(self.columns) - num_cols_for_front
if len(self.columns) - num_cols_for_front >= 0
else None
)
col_indexer = list(range(len(self.columns))[:num_cols_for_front]) + (
list(range(len(self.columns))[-num_cols_for_back:])
if num_cols_for_back is not None
else []
)
indexer = row_indexer, col_indexer
else:
indexer = row_indexer
return self.iloc[indexer]._query_compiler.to_pandas()
| 295 | base.py | Python | modin/pandas/base.py | 2ebc9cf51bfc773e3d4c898f5a33c0f60ad7ebc5 | modin | 14 |
|
131,824 | 65 | 13 | 19 | 335 | 24 | 1 | 117 | 269 | test_get_conda_env_dir | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | https://github.com/ray-project/ray.git | def test_get_conda_env_dir(tmp_path):
# Simulate starting in an env named tf1.
d = tmp_path / "envs" / "tf1"
Path.mkdir(d, parents=True)
with mock.patch.dict(
os.environ, {"CONDA_PREFIX": str(d), "CONDA_DEFAULT_ENV": "tf1"}
):
with pytest.raises(ValueError):
# Env tf2 should not exist.
env_dir = get_conda_env_dir("tf2")
tf2_dir = tmp_path / "envs" / "tf2"
Path.mkdir(tf2_dir, parents=True)
env_dir = get_conda_env_dir("tf2")
assert env_dir == str(tmp_path / "envs" / "tf2")
# Simulate starting in (base) conda env.
with mock.patch.dict(
os.environ, {"CONDA_PREFIX": str(tmp_path), "CONDA_DEFAULT_ENV": "base"}
):
with pytest.raises(ValueError):
# Env tf3 should not exist.
env_dir = get_conda_env_dir("tf3")
# Env tf2 still should exist.
env_dir = get_conda_env_dir("tf2")
assert env_dir == str(tmp_path / "envs" / "tf2")
@pytest.mark.skipif(
os.environ.get("CI") and sys.platform != "linux",
reason="This test is only run on linux CI machines.",
) | @pytest.mark.skipif(
os.environ.get("CI") and sys.platform != "linux",
reason="This test is only run on linux CI machines.",
) | 152 | test_runtime_env_complicated.py | Python | python/ray/tests/test_runtime_env_complicated.py | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | 1 |
166,973 | 14 | 9 | 2 | 45 | 3 | 0 | 14 | 35 | reso | DOC: Added docstrings to fixtures defined in array module (#47211) | https://github.com/pandas-dev/pandas.git | def reso(self, unit):
# TODO: avoid hard-coding
return {"s": 7, "ms": 8, "us": 9}[unit]
| 25 | test_datetimes.py | Python | pandas/tests/arrays/test_datetimes.py | 89be1f053b695c4ce1c0569f737caf3f03c12128 | pandas | 1 |
|
267,468 | 18 | 10 | 4 | 48 | 7 | 0 | 18 | 50 | set_queue | Forked display via queue (#77056)
* Forked Display via queue
* Docs and simple code cleanup
* Only proxy Display.display
* Remove unused import
* comment
* Update deadlock comment, remove py3 check
* Don't flush display, and don't lock from forks
* clog frag
* ci_complete ci_coverage
* Add units for queue proxying
* Cleanup flush
* ci_complete
* Only lock the write, switch to RLock
* Remove unused import | https://github.com/ansible/ansible.git | def set_queue(self, queue):
if multiprocessing_context.parent_process() is None:
raise RuntimeError('queue cannot be set in parent process')
self._final_q = queue
| 27 | display.py | Python | lib/ansible/utils/display.py | 5e369604e1930b1a2e071fecd7ec5276ebd12cb1 | ansible | 2 |
|
124,651 | 32 | 13 | 32 | 220 | 24 | 0 | 44 | 421 | _get_tune_run_arguments | [AIR] Fix `ResourceChangingScheduler` not working with AIR (#26307)
This PR ensures that the new trial resources set by `ResourceChangingScheduler` are respected by the train loop logic by modifying the scaling config to match. Previously, even though trials had their resources updated, the scaling config was not modified which lead to eg. new workers not being spawned in the `DataParallelTrainer` even though resources were available.
In order to accomplish this, `ScalingConfigDataClass` is updated to allow equality comparisons with other `ScalingConfigDataClass`es (using the underlying PGF) and to create a `ScalingConfigDataClass` from a PGF.
Please note that this is an internal only change intended to actually make `ResourceChangingScheduler` work. In the future, `ResourceChangingScheduler` should be updated to operate on `ScalingConfigDataClass`es instead of PGFs as it is now. That will require a deprecation cycle. | https://github.com/ray-project/ray.git | def _get_tune_run_arguments(self) -> Dict[str, Any]:
return dict(
mode=self._tune_config.mode,
metric=self._tune_config.metric,
callbacks=self._run_config.callbacks,
sync_config=self._run_config.sync_config,
stop=self._run_config.stop,
max_failures=(
self._run_config.failure_config.max_failures
if self._run_config.failure_config
else 0
),
keep_checkpoints_num=(
self._run_config.checkpoint_config.num_to_keep
if self._run_config.checkpoint_config
else None
),
checkpoint_score_attr=(
self._run_config.checkpoint_config._tune_legacy_checkpoint_score_attr
if self._run_config.checkpoint_config
else None
),
_experiment_checkpoint_dir=self._experiment_checkpoint_dir,
raise_on_failed_trial=False,
fail_fast=(
self._run_config.failure_config.fail_fast
if self._run_config.failure_config
else False
),
verbose=self._run_config.verbose,
)
| 155 | tuner_internal.py | Python | python/ray/tune/impl/tuner_internal.py | b3878e26d765e28dd7c69abadbd856181037db97 | ray | 5 |
|
274,552 | 31 | 10 | 13 | 134 | 15 | 0 | 43 | 110 | get | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def get(identifier):
if identifier is None:
return None
if isinstance(identifier, str):
identifier = str(identifier)
return deserialize(identifier)
if isinstance(identifier, dict):
return deserialize(identifier)
if callable(identifier):
return identifier
raise ValueError(
f"Could not interpret loss function identifier: {identifier}"
)
LABEL_DTYPES_FOR_LOSSES = {
tf.compat.v1.losses.sparse_softmax_cross_entropy: "int32",
sparse_categorical_crossentropy: "int32",
}
| 59 | losses.py | Python | keras/losses.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 5 |
|
288,074 | 33 | 9 | 12 | 126 | 15 | 1 | 37 | 84 | test_discover_device_tracker | Move MQTT discovery hass.data globals to dataclass (#78706)
* Add MQTT discovery hass.data globals to dataclass
* isort
* Additional rework
* Add hass.data["mqtt_tags"] to dataclass
* Follow-up comment
* Corrections | https://github.com/home-assistant/core.git | async def test_discover_device_tracker(hass, mqtt_mock_entry_no_yaml_config, caplog):
await mqtt_mock_entry_no_yaml_config()
async_fire_mqtt_message(
hass,
"homeassistant/device_tracker/bla/config",
'{ "name": "test", "state_topic": "test_topic" }',
)
await hass.async_block_till_done()
state = hass.states.get("device_tracker.test")
assert state is not None
assert state.name == "test"
assert ("device_tracker", "bla") in hass.data["mqtt"].discovery_already_discovered
@pytest.mark.no_fail_on_log_exception | @pytest.mark.no_fail_on_log_exception | 65 | test_device_tracker_discovery.py | Python | tests/components/mqtt/test_device_tracker_discovery.py | 84b2c74746b694d217fe6d448a8dfff4bc2d7a9e | core | 1 |
263,853 | 106 | 13 | 19 | 216 | 21 | 0 | 165 | 283 | _get_module_collection_mode | building: more module collection modes, consolidate noarchive codepath
Map the module collection mode strings into (combinations of)
integer flags that control try basic collection modes:
- collect a pyc into PYZ archive
- collect a pyc as a data file
- collect a py as a data file
Consolidate the `noarchive=True` codepath into module collection
mode, where "collect a pyc into PYZ archive" flag is swapped
for a "collect a pyc as a data file".
The new collection mode also implicitly fixes couple of minor
annoyances of the `noarchive=True` mode:
- the user-writable paths containing python source code are not
littered with pyc/pyo files anymore; all pycs are now gathered
in build directory
- the name of pycs in local build directory are not mangled anymore
(was previously the case for pycs that could not be written to
their original locations due to lack of permissions)
- the pycs have code paths stripped from them, same as in
noarchive=False mode | https://github.com/pyinstaller/pyinstaller.git | def _get_module_collection_mode(mode_dict, name, noarchive=False):
# Default mode: collect into PYZ, unless noarchive is enabled. In that case, collect as pyc.
mode_flags = _ModuleCollectionMode.PYC if noarchive else _ModuleCollectionMode.PYZ
# If we have no collection mode settings, end here and now.
if not mode_dict:
return mode_flags
# Search the parent modules/packages in top-down fashion, and take the last given setting. This ensures that
# a setting given for the top-level package is recursively propagated to all its subpackages and submodules,
# but also allows individual sub-modules to override the setting again.
mode = 'pyz'
name_parts = name.split('.')
for i in range(len(name_parts)):
modlevel = ".".join(name_parts[:i + 1])
modlevel_mode = mode_dict.get(modlevel, None)
if modlevel_mode is not None:
mode = modlevel_mode
# Convert mode string to _ModuleCollectionMode flags
try:
mode_flags = _MODULE_COLLECTION_MODES[mode]
except KeyError:
raise ValueError(f"Unknown module collection mode for {name!r}: {mode!r}!")
# noarchive flag being set means that we need to change _ModuleCollectionMode.PYZ into _ModuleCollectionMode.PYC
if noarchive and _ModuleCollectionMode.PYZ in mode_flags:
mode_flags ^= _ModuleCollectionMode.PYZ
mode_flags |= _ModuleCollectionMode.PYC
return mode_flags
| 122 | build_main.py | Python | PyInstaller/building/build_main.py | 6e1bfa2de254d8ae302f54dcea0cfefae4dd3585 | pyinstaller | 8 |
|
160,904 | 39 | 11 | 7 | 143 | 18 | 0 | 50 | 145 | test_error_message_unsigned | TST: Add a failing test case to demonstrate the bug gh2176 | https://github.com/numpy/numpy.git | def test_error_message_unsigned(self):
# Ensure to test for potential overflow in the case of:
# x - y
# and
# y - x
x = np.asarray([0, 1, 8], dtype='uint8')
y = np.asarray([4, 4, 4], dtype='uint8')
with pytest.raises(AssertionError) as exc_info:
assert_allclose(x, y, atol=3)
msgs = str(exc_info.value).split('\n')
assert_equal(msgs[4], 'Max absolute difference: 4')
| 84 | test_utils.py | Python | numpy/testing/tests/test_utils.py | 57d04d883e874c611091933c4c36e1cd43ea0e04 | numpy | 1 |
|
211,439 | 48 | 12 | 26 | 272 | 33 | 0 | 73 | 343 | forward | Update box head (#6804)
* add flag loss_normalize_pos
* add faster vitdet
* update faster vitdet | https://github.com/PaddlePaddle/PaddleDetection.git | def forward(self, body_feats=None, rois=None, rois_num=None, inputs=None):
if self.training:
rois, rois_num, targets = self.bbox_assigner(rois, rois_num, inputs)
self.assigned_rois = (rois, rois_num)
self.assigned_targets = targets
rois_feat = self.roi_extractor(body_feats, rois, rois_num)
bbox_feat = self.head(rois_feat)
if self.with_pool:
feat = F.adaptive_avg_pool2d(bbox_feat, output_size=1)
feat = paddle.squeeze(feat, axis=[2, 3])
else:
feat = bbox_feat
scores = self.bbox_score(feat)
deltas = self.bbox_delta(feat)
if self.training:
loss = self.get_loss(
scores,
deltas,
targets,
rois,
self.bbox_weight,
loss_normalize_pos=self.loss_normalize_pos)
return loss, bbox_feat
else:
pred = self.get_prediction(scores, deltas)
return pred, self.head
| 182 | bbox_head.py | Python | ppdet/modeling/heads/bbox_head.py | 0b8165cf0029495d9b51007863d73c7db414313f | PaddleDetection | 4 |
|
107,161 | 14 | 11 | 8 | 129 | 17 | 0 | 18 | 42 | test_constrained_layout22 | ENH: implement and use base layout_engine for more flexible layout. | https://github.com/matplotlib/matplotlib.git | def test_constrained_layout22():
fig, ax = plt.subplots(layout="constrained")
fig.draw_without_rendering()
extents0 = np.copy(ax.get_position().extents)
fig.suptitle("Suptitle", y=0.5)
fig.draw_without_rendering()
extents1 = np.copy(ax.get_position().extents)
np.testing.assert_allclose(extents0, extents1)
| 77 | test_constrainedlayout.py | Python | lib/matplotlib/tests/test_constrainedlayout.py | ec4dfbc3c83866f487ff0bc9c87b0d43a1c02b22 | matplotlib | 1 |
|
107,536 | 70 | 17 | 31 | 380 | 26 | 0 | 98 | 376 | encode_multipart_formdata | Properly capitalize "Unicode".
See e.g. https://en.wikipedia.org/wiki/Unicode,
https://docs.python.org/3/howto/unicode.html.
Also associated minor doc cleanups. | https://github.com/matplotlib/matplotlib.git | def encode_multipart_formdata(fields, boundary=None):
# copy requests imports in here:
from io import BytesIO
from requests.packages.urllib3.filepost import (
choose_boundary, writer, b, get_content_type
)
body = BytesIO()
if boundary is None:
boundary = choose_boundary()
for fieldname, value in iter_fields(fields):
body.write(b('--%s\r\n' % (boundary)))
if isinstance(value, tuple):
filename, data = value
writer(body).write('Content-Disposition: form-data; name="%s"; '
'filename="%s"\r\n' % (fieldname, filename))
body.write(b('Content-Type: %s\r\n\r\n' %
(get_content_type(filename))))
else:
data = value
writer(body).write('Content-Disposition: form-data; name="%s"\r\n'
% (fieldname))
body.write(b'Content-Type: text/plain\r\n\r\n')
if isinstance(data, int):
data = str(data) # Backwards compatibility
if isinstance(data, str):
writer(body).write(data)
else:
body.write(data)
body.write(b'\r\n')
body.write(b('--%s--\r\n' % (boundary)))
content_type = b('multipart/form-data; boundary=%s' % boundary)
return body.getvalue(), content_type
| 215 | gh_api.py | Python | tools/gh_api.py | 88cb4c9d0aa1e790fc4689ca7e68725bf851bf63 | matplotlib | 6 |
|
155,520 | 181 | 15 | 66 | 824 | 71 | 0 | 299 | 798 | take | DOC: normalize whitespace in doctests in slicing.py (#8512) | https://github.com/dask/dask.git | def take(outname, inname, chunks, index, itemsize, axis=0):
from .core import PerformanceWarning
plan = slicing_plan(chunks[axis], index)
if len(plan) >= len(chunks[axis]) * 10:
factor = math.ceil(len(plan) / len(chunks[axis]))
warnings.warn(
"Slicing with an out-of-order index is generating %d "
"times more chunks" % factor,
PerformanceWarning,
stacklevel=6,
)
if not is_arraylike(index):
index = np.asarray(index)
# Check for chunks from the plan that would violate the user's
# configured chunk size.
nbytes = utils.parse_bytes(config.get("array.chunk-size"))
other_chunks = [chunks[i] for i in range(len(chunks)) if i != axis]
other_numel = np.prod([sum(x) for x in other_chunks])
if math.isnan(other_numel):
warnsize = maxsize = math.inf
else:
maxsize = math.ceil(nbytes / (other_numel * itemsize))
warnsize = maxsize * 5
split = config.get("array.slicing.split-large-chunks", None)
# Warn only when the default is not specified.
warned = split is not None
for _, index_list in plan:
if not warned and len(index_list) > warnsize:
msg = (
"Slicing is producing a large chunk. To accept the large\n"
"chunk and silence this warning, set the option\n"
" >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n"
" ... array[indexer]\n\n"
"To avoid creating the large chunks, set the option\n"
" >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n"
" ... array[indexer]"
)
warnings.warn(msg, PerformanceWarning, stacklevel=6)
warned = True
where_index = []
index_lists = []
for where_idx, index_list in plan:
index_length = len(index_list)
if split and index_length > maxsize:
index_sublist = np.array_split(
index_list, math.ceil(index_length / maxsize)
)
index_lists.extend(index_sublist)
where_index.extend([where_idx] * len(index_sublist))
else:
if not is_arraylike(index_list):
index_list = np.array(index_list)
index_lists.append(index_list)
where_index.append(where_idx)
dims = [range(len(bd)) for bd in chunks]
indims = list(dims)
indims[axis] = list(range(len(where_index)))
keys = list(product([outname], *indims))
outdims = list(dims)
outdims[axis] = where_index
slices = [[colon] * len(bd) for bd in chunks]
slices[axis] = index_lists
slices = list(product(*slices))
inkeys = list(product([inname], *outdims))
values = [(getitem, inkey, slc) for inkey, slc in zip(inkeys, slices)]
chunks2 = list(chunks)
chunks2[axis] = tuple(map(len, index_lists))
dsk = dict(zip(keys, values))
return tuple(chunks2), dsk
| 509 | slicing.py | Python | dask/array/slicing.py | fa8dfede71677a2301d4cd602cf4b27af41cbc4f | dask | 17 |
|
292,711 | 33 | 14 | 12 | 211 | 20 | 0 | 49 | 157 | _fetch_status | Correctly handle missing mpd albumart (#66771)
Co-authored-by: Martin Hjelmare <[email protected]> | https://github.com/home-assistant/core.git | async def _fetch_status(self):
self._status = await self._client.status()
self._currentsong = await self._client.currentsong()
await self._async_update_media_image_hash()
if (position := self._status.get("elapsed")) is None:
position = self._status.get("time")
if isinstance(position, str) and ":" in position:
position = position.split(":")[0]
if position is not None and self._media_position != position:
self._media_position_updated_at = dt_util.utcnow()
self._media_position = int(float(position))
await self._update_playlists()
| 123 | media_player.py | Python | homeassistant/components/mpd/media_player.py | facf22c2ddccbf9b205a2d8b26da457330b53ba6 | core | 6 |
|
9,833 | 26 | 10 | 17 | 105 | 9 | 0 | 39 | 126 | mixin_head_parser | feat: star routing (#3900)
* feat(proto): adjust proto for star routing (#3844)
* feat(proto): adjust proto for star routing
* feat(proto): generate proto files
* feat(grpc): refactor grpclet interface (#3846)
* feat: refactor connection pool for star routing (#3872)
* feat(k8s): add more labels to k8s deployments
* feat(network): refactor connection pool
* feat(network): refactor k8s pool
* feat: star routing graph gateway (#3877)
* feat: star routing - refactor grpc data runtime (#3887)
* feat(runtimes): refactor grpc dataruntime
* fix(tests): adapt worker runtime tests
* fix(import): fix import
* feat(proto): enable sending multiple lists (#3891)
* feat: star routing gateway (#3893)
* feat: star routing gateway all protocols (#3897)
* test: add streaming and prefetch tests (#3901)
* feat(head): new head runtime for star routing (#3899)
* feat(head): new head runtime
* feat(head): new head runtime
* style: fix overload and cli autocomplete
* feat(network): improve proto comments
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(worker): merge docs in worker runtime (#3905)
* feat(worker): merge docs in worker runtime
* feat(tests): assert after clean up
* feat(tests): star routing runtime integration tests (#3908)
* fix(tests): fix integration tests
* test: test runtimes fast slow request (#3910)
* feat(zmq): purge zmq, zed, routing_table (#3915)
* feat(zmq): purge zmq, zed, routing_table
* style: fix overload and cli autocomplete
* feat(zmq): adapt comment in dependency list
* style: fix overload and cli autocomplete
* fix(tests): fix type tests
Co-authored-by: Jina Dev Bot <[email protected]>
* test: add test gateway to worker connection (#3921)
* feat(pea): adapt peas for star routing (#3918)
* feat(pea): adapt peas for star routing
* style: fix overload and cli autocomplete
* feat(pea): add tests
* feat(tests): add failing head pea test
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(tests): integration tests for peas (#3923)
* feat(tests): integration tests for peas
* feat(pea): remove _inner_pea function
* feat: star routing container pea (#3922)
* test: rescue tests (#3942)
* fix: fix streaming tests (#3945)
* refactor: move docker run to run (#3948)
* feat: star routing pods (#3940)
* feat(pod): adapt pods for star routing
* feat(pods): adapt basepod to star routing
* feat(pod): merge pod and compound pod
* feat(tests): fix tests
* style: fix overload and cli autocomplete
* feat(test): add container pea int test
* feat(ci): remove more unnecessary tests
* fix(tests): remove jinad runtime
* feat(ci): remove latency tracking
* fix(ci): fix ci def
* fix(runtime): enable runtime to be exited
* fix(tests): wrap runtime test in process
* fix(runtimes): remove unused runtimes
* feat(runtimes): improve cancel wait
* fix(ci): build test pip again in ci
* fix(tests): fix a test
* fix(test): run async in its own process
* feat(pod): include shard in activate msg
* fix(pea): dont join
* feat(pod): more debug out
* feat(grpc): manage channels properly
* feat(pods): remove exitfifo
* feat(network): add simple send retry mechanism
* fix(network): await pool close
* fix(test): always close grpc server in worker
* fix(tests): remove container pea from tests
* fix(tests): reorder tests
* fix(ci): split tests
* fix(ci): allow alias setting
* fix(test): skip a test
* feat(pods): address comments
Co-authored-by: Jina Dev Bot <[email protected]>
* test: unblock skipped test (#3957)
* feat: jinad pea (#3949)
* feat: jinad pea
* feat: jinad pea
* test: remote peas
* test: toplogy tests with jinad
* ci: parallel jobs
* feat(tests): add pod integration tests (#3958)
* feat(tests): add pod integration tests
* fix(tests): make tests less flaky
* fix(test): fix test
* test(pea): remote pea topologies (#3961)
* test(pea): remote pea simple topology
* test: remote pea topologies
* refactor: refactor streamer result handling (#3960)
* feat(k8s): adapt K8s Pod for StarRouting (#3964)
* test: optimize k8s test
* test: increase timeout and use different namespace
* test: optimize k8s test
* test: build and load image when needed
* test: refactor k8s test
* test: fix image name error
* test: fix k8s image load
* test: fix typoe port expose
* test: update tests in connection pool and handling
* test: remove unused fixture
* test: parameterize docker images
* test: parameterize docker images
* test: parameterize docker images
* feat(k8s): adapt k8s pod for star routing
* fix(k8s): dont overwrite add/remove function in pool
* fix(k8s): some fixes
* fix(k8s): some more fixes
* fix(k8s): linting
* fix(tests): fix tests
* fix(tests): fix k8s unit tests
* feat(k8s): complete k8s integration test
* feat(k8s): finish k8s tests
* feat(k8s): fix test
* fix(tests): fix test with no name
* feat(k8s): unify create/replace interface
* feat(k8s): extract k8s port constants
* fix(tests): fix tests
* fix(tests): wait for runtime being ready in tests
* feat(k8s): address comments
Co-authored-by: bwanglzu <[email protected]>
* feat(flow): adapt Flow for StarRouting (#3986)
* feat(flow): add routes
* feat(flow): adapt flow to star routing
* style: fix overload and cli autocomplete
* feat(flow): handle empty topologies
* feat(k8s): allow k8s pool disabling
* style: fix overload and cli autocomplete
* fix(test): fix test with mock
* fix(tests): fix more tests
* feat(flow): clean up tests
* style: fix overload and cli autocomplete
* fix(tests): fix more tests
* feat: add plot function (#3994)
* fix(tests): avoid hanging tests
* feat(flow): add type hinting
* fix(test): fix duplicate exec name in test
* fix(tests): fix more tests
* fix(tests): enable jinad test again
* fix(tests): random port fixture
* fix(style): replace quotes
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* feat(ci): bring back ci (#3997)
* feat(ci): enable ci again
* style: fix overload and cli autocomplete
* feat(ci): add latency tracking
* feat(ci): bring back some tests
* fix(tests): remove invalid port test
* feat(ci): disable daemon and distributed tests
* fix(tests): fix entrypoint in hub test
* fix(tests): wait for gateway to be ready
* fix(test): fix more tests
* feat(flow): do rolling update and scale sequentially
* fix(tests): fix more tests
* style: fix overload and cli autocomplete
* feat: star routing hanging pods (#4011)
* fix: try to handle hanging pods better
* test: hanging pods test work
* fix: fix topology graph problem
* test: add unit test to graph
* fix(tests): fix k8s tests
* fix(test): fix k8s test
* fix(test): fix k8s pool test
* fix(test): fix k8s test
* fix(test): fix k8s connection pool setting
* fix(tests): make runtime test more reliable
* fix(test): fix routes test
* fix(tests): make rolling update test less flaky
* feat(network): gurantee unique ports
* feat(network): do round robin for shards
* fix(ci): increase pytest timeout to 10 min
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* fix(ci): fix ci file
* feat(daemon): jinad pod for star routing
* Revert "feat(daemon): jinad pod for star routing"
This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92.
* feat(daemon): remote jinad pod support (#4042)
* feat(daemon): add pod tests for star routing
* feat(daemon): add remote pod test
* test(daemon): add remote pod arguments test
* test(daemon): add async scale test
* test(daemon): add rolling update test
* test(daemon): fix host
* feat(proto): remove message proto (#4051)
* feat(proto): remove message proto
* fix(tests): fix tests
* fix(tests): fix some more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* feat(proto): put docs back in data
* fix(proto): clean up
* feat(proto): clean up
* fix(tests): skip latency tracking
* fix(test): fix hub test
* fix(tests): fix k8s test
* fix(test): some test clean up
* fix(style): clean up style issues
* feat(proto): adjust for rebase
* fix(tests): bring back latency tracking
* fix(tests): fix merge accident
* feat(proto): skip request serialization (#4074)
* feat: add reduce to star routing (#4070)
* feat: add reduce on shards to head runtime
* test: add reduce integration tests with fixed order
* feat: add reduce on needs
* chore: get_docs_matrix_from_request becomes public
* style: fix overload and cli autocomplete
* docs: remove undeterministic results warning
* fix: fix uses_after
* test: assert correct num docs after reducing in test_external_pod
* test: correct asserts after reduce in test_rolling_update
* fix: no reduce if uses_after_address is set
* fix: get_docs_from_request only if needed
* fix: fix tests after merge
* refactor: move reduce from data_request_handler to head
* style: fix overload and cli autocomplete
* chore: apply suggestions
* fix: fix asserts
* chore: minor test fix
* chore: apply suggestions
* test: remove flow tests with external executor (pea)
* fix: fix test_expected_messages_routing
* fix: fix test_func_joiner
* test: adapt k8s test
Co-authored-by: Jina Dev Bot <[email protected]>
* fix(k8s): fix static pool config
* fix: use custom protoc doc generator image (#4088)
* fix: use custom protoc doc generator image
* fix(docs): minor doc improvement
* fix(docs): use custom image
* fix(docs): copy docarray
* fix: doc building local only
* fix: timeout doc building
* fix: use updated args when building ContainerPea
* test: add container PeaFactory test
* fix: force pea close on windows (#4098)
* fix: dont reduce if uses exist (#4099)
* fix: dont use reduce if uses exist
* fix: adjust reduce tests
* fix: adjust more reduce tests
* fix: fix more tests
* fix: adjust more tests
* fix: ignore non jina resources (#4101)
* feat(executor): enable async executors (#4102)
* feat(daemon): daemon flow on star routing (#4096)
* test(daemon): add remote flow test
* feat(daemon): call scale in daemon
* feat(daemon): remove tail args and identity
* test(daemon): rename scalable executor
* test(daemon): add a small delay in async test
* feat(daemon): scale partial flow only
* feat(daemon): call scale directly in partial flow store
* test(daemon): use asyncio sleep
* feat(daemon): enable flow level distributed tests
* test(daemon): fix jinad env workspace config
* test(daemon): fix pod test use new port rolling update
* feat(daemon): enable distribuetd tests
* test(daemon): remove duplicate tests and zed runtime test
* test(daemon): fix stores unit test
* feat(daemon): enable part of distributed tests
* feat(daemon): enable part of distributed tests
* test: correct test paths
* test(daemon): add client test for remote flows
* test(daemon): send a request with jina client
* test(daemon): assert async generator
* test(daemon): small interval between tests
* test(daemon): add flow test for container runtime
* test(daemon): add flow test for container runtime
* test(daemon): fix executor name
* test(daemon): fix executor name
* test(daemon): use async client fetch result
* test(daemon): finish container flow test
* test(daemon): enable distributed in ci
* test(daemon): enable distributed in ci
* test(daemon): decare flows and pods
* test(daemon): debug ci if else
* test(daemon): debug ci if else
* test(daemon): decare flows and pods
* test(daemon): correct test paths
* test(daemon): add small delay for async tests
* fix: star routing fixes (#4100)
* docs: update docs
* fix: fix Request.__repr__
* docs: update flow remarks
* docs: fix typo
* test: add non_empty_fields test
* chore: remove non_empty_fields test
* feat: polling per endpoint (#4111)
* feat(polling): polling per endpoint configurable
* fix: adjust tests
* feat(polling): extend documentation
* style: fix overload and cli autocomplete
* fix: clean up
* fix: adjust more tests
* fix: remove repeat from flaky test
* fix: k8s test
* feat(polling): address pr feedback
* feat: improve docs
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(grpc): support connect grpc server via ssl tunnel (#4092)
* feat(grpc): support ssl grpc connect if port is 443
* fix(grpc): use https option instead of detect port automatically
* chore: fix typo
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* test(networking): add test for peapods networking
* fix: address comments
Co-authored-by: Joan Fontanals <[email protected]>
* feat(polling): unify polling args (#4113)
* fix: several issues for jinad pods (#4119)
* fix: activate for jinad pods
* fix: dont expose worker pod in partial daemon
* fix: workspace setting
* fix: containerized flows
* fix: hub test
* feat(daemon): remote peas on star routing (#4112)
* test(daemon): fix request in peas
* test(daemon): fix request in peas
* test(daemon): fix sync async client test
* test(daemon): enable remote peas test
* test(daemon): replace send message to send request
* test(daemon): declare pea tests in ci
* test(daemon): use pea args fixture
* test(daemon): head pea use default host
* test(daemon): fix peas topologies
* test(daemon): fix pseudo naming
* test(daemon): use default host as host
* test(daemon): fix executor path
* test(daemon): add remote worker back
* test(daemon): skip local remote remote topology
* fix: jinad pea test setup
* fix: jinad pea tests
* fix: remove invalid assertion
Co-authored-by: jacobowitz <[email protected]>
* feat: enable daemon tests again (#4132)
* feat: enable daemon tests again
* fix: remove bogy empty script file
* fix: more jinad test fixes
* style: fix overload and cli autocomplete
* fix: scale and ru in jinad
* fix: fix more jinad tests
Co-authored-by: Jina Dev Bot <[email protected]>
* fix: fix flow test
* fix: improve pea tests reliability (#4136)
Co-authored-by: Joan Fontanals <[email protected]>
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Deepankar Mahapatro <[email protected]>
Co-authored-by: bwanglzu <[email protected]>
Co-authored-by: AlaeddineAbdessalem <[email protected]>
Co-authored-by: Zhaofeng Miao <[email protected]> | https://github.com/jina-ai/jina.git | def mixin_head_parser(parser):
gp = add_arg_group(parser, title='Head')
gp.add_argument(
'--uses-before-address',
type=str,
help='The address of the uses-before runtime',
)
gp.add_argument(
'--uses-after-address',
type=str,
help='The address of the uses-before runtime',
)
gp.add_argument(
'--connection-list',
type=str,
help='dictionary JSON with a list of connections to configure',
)
| 61 | head.py | Python | jina/parsers/peapods/runtimes/head.py | 933415bfa1f9eb89f935037014dfed816eb9815d | jina | 1 |
|
266,160 | 22 | 11 | 7 | 85 | 7 | 0 | 28 | 122 | _get_color | Closes #10904: Added Colors to SVG for Front and Rear Ports (#10905)
* Added Colors to SVG for Front and Reaer Ports
Fix for feature request 10904 thanks to @TheZackCodec
* Simplify termination color resolution
Co-authored-by: jeremystretch <[email protected]> | https://github.com/netbox-community/netbox.git | def _get_color(cls, instance):
if hasattr(instance, 'parent_object'):
# Termination
return getattr(instance, 'color', 'f0f0f0') or 'f0f0f0'
if hasattr(instance, 'device_role'):
# Device
return instance.device_role.color
else:
# Other parent object
return 'e0e0e0'
| 45 | cables.py | Python | netbox/dcim/svg/cables.py | a2007a4728faafeee5c296afabc116707db42344 | netbox | 4 |
|
181,559 | 14 | 9 | 10 | 61 | 7 | 0 | 17 | 33 | mask_color | format(black): formatted files to latest version (#1735) | https://github.com/Zulko/moviepy.git | def mask_color(clip, color=None, threshold=0, stiffness=1):
if color is None:
color = [0, 0, 0]
color = np.array(color)
| 67 | mask_color.py | Python | moviepy/video/fx/mask_color.py | 0b63f472ee0b67bf000412179bfb8ee30c52ce27 | moviepy | 2 |
|
269,377 | 70 | 17 | 34 | 380 | 17 | 0 | 119 | 390 | _preprocess_symbolic_input | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def _preprocess_symbolic_input(x, data_format, mode):
if mode == "tf":
x /= 127.5
x -= 1.0
return x
elif mode == "torch":
x /= 255.0
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
else:
if data_format == "channels_first":
# 'RGB'->'BGR'
if backend.ndim(x) == 3:
x = x[::-1, ...]
else:
x = x[:, ::-1, ...]
else:
# 'RGB'->'BGR'
x = x[..., ::-1]
mean = [103.939, 116.779, 123.68]
std = None
mean_tensor = backend.constant(-np.array(mean))
# Zero-center by mean pixel
if backend.dtype(x) != backend.dtype(mean_tensor):
x = backend.bias_add(
x,
backend.cast(mean_tensor, backend.dtype(x)),
data_format=data_format,
)
else:
x = backend.bias_add(x, mean_tensor, data_format)
if std is not None:
std_tensor = backend.constant(np.array(std), dtype=backend.dtype(x))
if data_format == "channels_first":
std_tensor = backend.reshape(std_tensor, (-1, 1, 1))
x /= std_tensor
return x
| 266 | imagenet_utils.py | Python | keras/applications/imagenet_utils.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 8 |
|
261,290 | 42 | 11 | 14 | 164 | 26 | 0 | 51 | 109 | test_lassolarsic_noise_variance | MAINT Clean deprecation for 1.2: normalize in linear models (#24391) | https://github.com/scikit-learn/scikit-learn.git | def test_lassolarsic_noise_variance(fit_intercept):
rng = np.random.RandomState(0)
X, y = datasets.make_regression(
n_samples=10, n_features=11 - fit_intercept, random_state=rng
)
model = make_pipeline(StandardScaler(), LassoLarsIC(fit_intercept=fit_intercept))
err_msg = (
"You are using LassoLarsIC in the case where the number of samples is smaller"
" than the number of features"
)
with pytest.raises(ValueError, match=err_msg):
model.fit(X, y)
model.set_params(lassolarsic__noise_variance=1.0)
model.fit(X, y).predict(X)
| 102 | test_least_angle.py | Python | sklearn/linear_model/tests/test_least_angle.py | e41753ebd57c44ae91b389f190c43ddc0b384a75 | scikit-learn | 1 |
|
249,737 | 75 | 13 | 34 | 406 | 13 | 0 | 101 | 523 | test_tokenize_query | Unified search query syntax using the full-text search capabilities of the underlying DB. (#11635)
Support a unified search query syntax which leverages more of the full-text
search of each database supported by Synapse.
Supports, with the same syntax across Postgresql 11+ and Sqlite:
- quoted "search terms"
- `AND`, `OR`, `-` (negation) operators
- Matching words based on their stem, e.g. searches for "dog" matches
documents containing "dogs".
This is achieved by
- If on postgresql 11+, pass the user input to `websearch_to_tsquery`
- If on sqlite, manually parse the query and transform it into the sqlite-specific
query syntax.
Note that postgresql 10, which is close to end-of-life, falls back to using
`phraseto_tsquery`, which only supports a subset of the features.
Multiple terms separated by a space are implicitly ANDed.
Note that:
1. There is no escaping of full-text syntax that might be supported by the database;
e.g. `NOT`, `NEAR`, `*` in sqlite. This runs the risk that people might discover this
as accidental functionality and depend on something we don't guarantee.
2. English text is assumed for stemming. To support other languages, either the target
language needs to be known at the time of indexing the message (via room metadata,
or otherwise), or a separate index for each language supported could be created.
Sqlite docs: https://www.sqlite.org/fts3.html#full_text_index_queries
Postgres docs: https://www.postgresql.org/docs/11/textsearch-controls.html | https://github.com/matrix-org/synapse.git | def test_tokenize_query(self) -> None:
cases = (
("brown", ["brown"]),
("quick brown", ["quick", SearchToken.And, "brown"]),
("quick \t brown", ["quick", SearchToken.And, "brown"]),
('"brown quick"', [Phrase(["brown", "quick"])]),
("furphy OR fox", ["furphy", SearchToken.Or, "fox"]),
("fox -brown", ["fox", SearchToken.Not, "brown"]),
("- fox", [SearchToken.Not, "fox"]),
('"fox" quick', [Phrase(["fox"]), SearchToken.And, "quick"]),
# No trailing double quoe.
('"fox quick', ["fox", SearchToken.And, "quick"]),
('"-fox quick', [SearchToken.Not, "fox", SearchToken.And, "quick"]),
('" quick "', [Phrase(["quick"])]),
(
'q"uick brow"n',
[
"q",
SearchToken.And,
Phrase(["uick", "brow"]),
SearchToken.And,
"n",
],
),
(
'-"quick brown"',
[SearchToken.Not, Phrase(["quick", "brown"])],
),
)
for query, expected in cases:
tokenized = _tokenize_query(query)
self.assertEqual(
tokenized, expected, f"{tokenized} != {expected} for {query}"
)
| 240 | test_room_search.py | Python | tests/storage/test_room_search.py | d902181de98399d90c46c4e4e2cf631064757941 | synapse | 2 |
|
43,076 | 25 | 11 | 6 | 119 | 15 | 0 | 26 | 72 | test_test_with_existing_dag_run | Mask secrets in stdout for 'airflow tasks test' (#24362)
A stdout redirector is implemented to mask all values to stdout and
redact any secrets in it with the secrets masker. This redirector is
applied to the 'airflow.task' logger.
Co-authored-by: Alex Kennedy <[email protected]> | https://github.com/apache/airflow.git | def test_test_with_existing_dag_run(self, caplog):
task_id = 'print_the_context'
args = self.parser.parse_args(["tasks", "test", self.dag_id, task_id, DEFAULT_DATE.isoformat()])
with caplog.at_level("INFO", logger="airflow.task"):
task_command.task_test(args)
assert f"Marking task as SUCCESS. dag_id={self.dag_id}, task_id={task_id}" in caplog.text
| 62 | test_task_command.py | Python | tests/cli/commands/test_task_command.py | 3007159c2468f8e74476cc17573e03655ab168fa | airflow | 1 |
|
40,644 | 69 | 15 | 16 | 175 | 12 | 0 | 91 | 364 | _determine_axis_sharing | Refactor figure setup and subplot metadata tracking into Subplots class
Squashed commit of the following:
commit e6f99078d46947eab678b9dd0303657a3129f9fc
Author: Michael Waskom <[email protected]>
Date: Sun Aug 1 17:56:49 2021 -0400
Address a couple TODOs
commit c48ba3af8095973b7dca9554934a695751f58726
Author: Michael Waskom <[email protected]>
Date: Mon Jul 26 06:42:29 2021 -0400
Add docstrings in Subplots
commit 97e6465b0f998f541b445b189682fbf134869391
Author: Michael Waskom <[email protected]>
Date: Sun Jul 25 17:53:22 2021 -0400
Fix unshared label visibility test
commit e2d93a28313c2cb9170e56b2e4b373987993be7c
Author: Michael Waskom <[email protected]>
Date: Sun Jul 25 17:16:41 2021 -0400
Add more label visibility tests
commit 698ee72b5d5f9f3939c50cde9e2baacdf5487807
Author: Michael Waskom <[email protected]>
Date: Sat Jul 24 11:08:32 2021 -0400
Begin adding label visibility tests
commit 97167b4701532eeccadaa899520d57e38c26dd43
Author: Michael Waskom <[email protected]>
Date: Mon Jul 19 06:55:48 2021 -0400
Fix interior tick labels with unshared axes
commit 9331d5d91a7861aebfe03fa86ee122902c0d1d8a
Author: Michael Waskom <[email protected]>
Date: Sat Jul 17 17:03:48 2021 -0400
Fix interior labels for wrapped plots
commit 38f2efa7e732958430c006f24827c6ac69640ef3
Author: Michael Waskom <[email protected]>
Date: Sat Jul 17 16:03:34 2021 -0400
Fix non-cartesian interior labels
commit 3c07f981110890d38aee19b38c43080863132122
Author: Michael Waskom <[email protected]>
Date: Sat Jul 17 15:44:48 2021 -0400
Integrate Subplots into Plot
commit 841a3c998eae8f8cc85fd65af7ea8e6f32fc5510
Author: Michael Waskom <[email protected]>
Date: Sat Jul 17 13:00:09 2021 -0400
Complete subplots tests
commit 8ceb7e6c35ea0cbcd014067035d7ea219204f464
Author: Michael Waskom <[email protected]>
Date: Fri Jul 16 19:45:29 2021 -0400
Continue building out subplot tests
commit b0ce0e7a9e3534fdad04ef9e287e4c6bb19fe684
Author: Michael Waskom <[email protected]>
Date: Thu Jul 15 21:35:21 2021 -0400
Continue building out subplots tests
commit 5f4b67d4d90cde7d0d899527b1fd8607348a5f5b
Author: Michael Waskom <[email protected]>
Date: Wed Jul 14 20:57:35 2021 -0400
Add some tests for Subplots functionality
commit 58fbf8e3f349174f4d1d29f71fa867ad4b49d264
Author: Michael Waskom <[email protected]>
Date: Sun Jul 11 20:49:29 2021 -0400
Begin refactoring figure setup into Subplots class
commit 6bb853e20ad3b42b2728d212a51ed8de2ff47bde
Author: Michael Waskom <[email protected]>
Date: Sun Jul 11 16:02:26 2021 -0400
Fix overlooked lint and test | https://github.com/mwaskom/seaborn.git | def _determine_axis_sharing(self) -> None:
axis_to_dim = {"x": "col", "y": "row"}
key: str
val: str | bool
for axis in "xy":
key = f"share{axis}"
# Always use user-specified value, if present
if key not in self.subplot_spec:
if axis in self.pair_spec:
# Paired axes are shared along one dimension by default
if self.wrap in [None, 1] and self.pair_spec.get("cartesian", True):
val = axis_to_dim[axis]
else:
val = False
else:
# This will pick up faceted plots, as well as single subplot
# figures, where the value doesn't really matter
val = True
self.subplot_spec[key] = val
| 97 | subplots.py | Python | seaborn/_core/subplots.py | c16180493bd44fd76092fdd9ea0060bac91e47fe | seaborn | 6 |
|
69,330 | 72 | 20 | 35 | 544 | 39 | 0 | 118 | 83 | get_item_map | refactor: rewrite `Stock Projected Qty Report` queries in `QB` | https://github.com/frappe/erpnext.git | def get_item_map(item_code, include_uom):
bin = frappe.qb.DocType("Bin")
item = frappe.qb.DocType("Item")
query = (
frappe.qb.from_(item)
.select(item.name, item.item_name, item.description, item.item_group, item.brand, item.stock_uom)
.where(
(item.is_stock_item == 1)
& (item.disabled == 0)
& (
(item.end_of_life > today()) | (item.end_of_life.isnull()) | (item.end_of_life == "0000-00-00")
)
& (ExistsCriterion(frappe.qb.from_(bin).select(bin.name).where(bin.item_code == item.name)))
)
)
if item_code:
query = query.where(item.item_code == item_code)
if include_uom:
ucd = frappe.qb.DocType("UOM Conversion Detail")
query = query.left_join(ucd).on((ucd.parent == item.name) & (ucd.uom == include_uom))
items = query.run(as_dict=True)
ir = frappe.qb.DocType("Item Reorder")
query = frappe.qb.from_(ir).select("*")
if item_code:
query = query.where(ir.parent == item_code)
reorder_levels = frappe._dict()
for d in query.run(as_dict=True):
if d.parent not in reorder_levels:
reorder_levels[d.parent] = []
reorder_levels[d.parent].append(d)
item_map = frappe._dict()
for item in items:
item["reorder_levels"] = reorder_levels.get(item.name) or []
item_map[item.name] = item
return item_map
| 340 | stock_projected_qty.py | Python | erpnext/stock/report/stock_projected_qty/stock_projected_qty.py | c18f13a45ba6498245ba0429e61205a0df2434a6 | erpnext | 8 |
|
259,435 | 110 | 15 | 32 | 446 | 43 | 1 | 159 | 438 | test_loss_of_perfect_prediction | ENH migrate GLMs / TweedieRegressor to linear loss (#22548)
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def test_loss_of_perfect_prediction(loss, sample_weight):
if not loss.is_multiclass:
# Use small values such that exp(value) is not nan.
raw_prediction = np.array([-10, -0.1, 0, 0.1, 3, 10])
# If link is identity, we must respect the interval of y_pred:
if isinstance(loss.link, IdentityLink):
eps = 1e-10
low = loss.interval_y_pred.low
if not loss.interval_y_pred.low_inclusive:
low = low + eps
high = loss.interval_y_pred.high
if not loss.interval_y_pred.high_inclusive:
high = high - eps
raw_prediction = np.clip(raw_prediction, low, high)
y_true = loss.link.inverse(raw_prediction)
else:
# HalfMultinomialLoss
y_true = np.arange(loss.n_classes).astype(float)
# raw_prediction with entries -exp(10), but +exp(10) on the diagonal
# this is close enough to np.inf which would produce nan
raw_prediction = np.full(
shape=(loss.n_classes, loss.n_classes),
fill_value=-np.exp(10),
dtype=float,
)
raw_prediction.flat[:: loss.n_classes + 1] = np.exp(10)
if sample_weight == "range":
sample_weight = np.linspace(1, y_true.shape[0], num=y_true.shape[0])
loss_value = loss.loss(
y_true=y_true,
raw_prediction=raw_prediction,
sample_weight=sample_weight,
)
constant_term = loss.constant_to_optimal_zero(
y_true=y_true, sample_weight=sample_weight
)
# Comparing loss_value + constant_term to zero would result in large
# round-off errors.
assert_allclose(loss_value, -constant_term, atol=1e-14, rtol=1e-15)
@pytest.mark.parametrize("loss", LOSS_INSTANCES, ids=loss_instance_name)
@pytest.mark.parametrize("sample_weight", [None, "range"]) | @pytest.mark.parametrize("loss", LOSS_INSTANCES, ids=loss_instance_name)
@pytest.mark.parametrize("sample_weight", [None, "range"]) | 266 | test_loss.py | Python | sklearn/_loss/tests/test_loss.py | 75a94f518f7bd7d0bf581ffb67d9f961e3c4efbc | scikit-learn | 6 |
259,424 | 22 | 12 | 8 | 110 | 16 | 0 | 24 | 88 | transform | ENH Preserving dtypes for ICA (#22806)
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]>
Co-authored-by: Julien Jerphanion <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def transform(self, X, copy=True):
check_is_fitted(self)
X = self._validate_data(
X, copy=(copy and self._whiten), dtype=[np.float64, np.float32], reset=False
)
if self._whiten:
X -= self.mean_
return np.dot(X, self.components_.T)
| 73 | _fastica.py | Python | sklearn/decomposition/_fastica.py | d14fd82cf423c21ab6d01f7d0430083f9d7026be | scikit-learn | 3 |
|
120,507 | 103 | 17 | 81 | 439 | 27 | 0 | 181 | 334 | polar | Change implementation of jax.scipy.linalg.polar() and jax._src.scipy.eigh to use the QDWH decomposition from jax._src.lax.qdwh.
Remove jax._src.lax.polar.
PiperOrigin-RevId: 448241206 | https://github.com/google/jax.git | def polar(a, side='right', *, method='qdwh', eps=None, max_iterations=None):
r
a = jnp.asarray(a)
if a.ndim != 2:
raise ValueError("The input `a` must be a 2-D array.")
if side not in ["right", "left"]:
raise ValueError("The argument `side` must be either 'right' or 'left'.")
m, n = a.shape
if method == "qdwh":
# TODO(phawkins): return info also if the user opts in?
if m >= n and side == "right":
unitary, posdef, _, _ = qdwh.qdwh(a, is_hermitian=False, eps=eps)
elif m < n and side == "left":
a = a.T.conj()
unitary, posdef, _, _ = qdwh.qdwh(a, is_hermitian=False, eps=eps)
posdef = posdef.T.conj()
unitary = unitary.T.conj()
else:
raise NotImplementedError("method='qdwh' only supports mxn matrices "
"where m < n where side='right' and m >= n "
f"side='left', got {a.shape} with side={side}")
elif method == "svd":
u_svd, s_svd, vh_svd = lax_linalg.svd(a, full_matrices=False)
unitary = u_svd @ vh_svd
if side == "right":
# a = u * p
posdef = (vh_svd.T.conj() * s_svd[None, :]) @ vh_svd
else:
# a = p * u
posdef = (u_svd * s_svd[None, :]) @ (u_svd.T.conj())
else:
raise ValueError(f"Unknown polar decomposition method {method}.")
return unitary, posdef
| 258 | linalg.py | Python | jax/_src/scipy/linalg.py | 7ba36fc1784a7a286aa13ab7c098f84ff64336f1 | jax | 10 |
|
167,666 | 9 | 9 | 4 | 55 | 6 | 1 | 9 | 24 | data_missing_for_sorting | ENH/TST: Add BaseMethodsTests tests for ArrowExtensionArray (#47552)
* ENH/TST: Add BaseMethodsTests tests for ArrowExtensionArray
* Passing test now
* add xfails for arraymanager
* Fix typo
* Trigger CI
* Add xfails for min version and datamanger
* Adjust more tests | https://github.com/pandas-dev/pandas.git | def data_missing_for_sorting(data_for_grouping):
return type(data_for_grouping)._from_sequence(
[data_for_grouping[0], data_for_grouping[2], data_for_grouping[4]]
)
@pytest.fixture | @pytest.fixture | 31 | test_arrow.py | Python | pandas/tests/extension/test_arrow.py | 700ef33b4dd5d5e76220ee50d1f07775dff15428 | pandas | 1 |
190,658 | 4 | 6 | 11 | 16 | 2 | 0 | 4 | 11 | test_class_or_function_idval | Refactor idmaker functions into class IdMaker
This commit only refactors, it does not change or add functionality yet. Public
API is retained. Reason or refactoring:
User provided parameter IDs (e.g. Metafunc.parametrize(ids=...)) had so far
only been used to calculate a unique test ID for each test invocation. That
test ID was a joined string where each parameter contributed some partial ID.
We're soon going to reuse functionality to generate parameter keys for
reorder_items and FixtureDef cache. We will be interested in the partial
IDs, and only if they originate from explicit user information. Refactoring
makes logic and data accessible for reuse, and increases cohesion in general. | https://github.com/pytest-dev/pytest.git | def test_class_or_function_idval(self) -> None:
| 66 | metafunc.py | Python | testing/python/metafunc.py | b21b008118fc8cf65b4bcd9b059f1cd704e05c68 | pytest | 2 |
|
257,715 | 56 | 16 | 10 | 152 | 17 | 0 | 85 | 214 | load_search_paths | Add enhanced pydoc-markdown pre-hook (#2979)
* add pydoc-markdown pre-hook
* add more comments, remove debug prints | https://github.com/deepset-ai/haystack.git | def load_search_paths():
paths = dict()
for fname in glob.glob("docs/_src/api/pydoc/*.yml"):
with open(fname) as f:
config = yaml.safe_load(f.read())
# we always have only one loader in Haystack
loader = config["loaders"][0]
# `search_path` is a list but we always have only one item in Haystack
search_path = loader["search_path"][0]
# we only need the relative path from the root, let's call `resolve` to
# get rid of the `../../` prefix
search_path = str(pathlib.Path(search_path).resolve())
# `resolve` will prepend a `/` to the path, remove it
paths[search_path[1:]] = fname
return paths
| 85 | pydoc-markdown.py | Python | .github/utils/pydoc-markdown.py | 0e8efdafa9e1b3e1a7096308b13760e5163f1439 | haystack | 2 |
|
195,007 | 41 | 11 | 10 | 155 | 14 | 0 | 49 | 125 | contains_offensive_language | Fix `detect_offensive_language` script (#4054)
* add fix
* add test
* make utils/safety robust to empty strings | https://github.com/facebookresearch/ParlAI.git | def contains_offensive_language(self, text):
if not text:
return False, 1.0
act = {'text': text, 'episode_done': True}
self.model.observe(act)
response = self.model.act()['text']
pred_class, prob = [x.split(': ')[-1] for x in response.split('\n')]
pred_not_ok = self.classes[pred_class] # check whether classified as NOT OK
prob = float(prob) # cast string to float
return pred_not_ok, prob
| 92 | safety.py | Python | parlai/utils/safety.py | e98b8603d83ab538ec6715f913a5ca980421ad6a | ParlAI | 3 |
|
211,971 | 76 | 12 | 35 | 273 | 34 | 0 | 110 | 327 | recompute | Redesign serialization protocol (#11960)
* Redesign serialization in bokeh
* Redesign deserialization in bokehjs
* Resolve type issues and test failures
* Make 'bytes' serialization work in bokeh
* Partially update bokeh's serialization tests
* Resolve issues with cyclic references
* Don't limit StaticGraphProvider to tuples
* Make FixedTicker.ticks' type more flexible
* Use np.array instead of np.ndarray
* Remove references to BokehJSONEncoder
* Resolve sphinx warnings related to JSON
* Implement hybrid serialization for map/dict
* Use === or !== with unset symbol
* Finalize deserialization of refs
* Remove 'old' attribute from ModelChangedEvent
* Make ButtonClick.__init__ less restrictive
* Use Map<number, ...> in StaticLayoutProvider.graph_layout
* Start using Map<K, V> for non-string keys
* Fix plotting/file/line_on_off example
* Don't denormalize specs in bokehjs
* Hack around issues with resolving figure model
* Remove special cases from defaults' tests
* Temporarily update unit/bokeh/test_objects
* Promote streaming/patching events and remove hints
* Allow to stream/patch any property in bokehjs
* Drop unneeded Property.serializable_value()
* Set callback_invoker on hinted events
* Simplify unit/bokeh/test_objects.py
* Always preserve ndarrays even for dtype="object"
* Refine and normalize naming conventions
* Remove unused functions
* Move Model.to_json() to sphinxext.bokeh_model
* Include references in serialized values
* Actually encode data when streaming/patching
* Robustify differential serialization
* Allow bokehjs to send binary buffers
* Add dtype=object code path to ColorSpec
* Simplify definitions of data specs
* Remove meaningless code comments
* Introduce Bytes and replace Base64String
* Add support for serialization of slices
* Remove obsolete comment from property/dataspec.py
* Add a comment regarding ndarray.tobytes()
* Try serializing pandas' types last
* Standardize error reporting
* Resturucture bokehjs serialization code
* Redesign default model resolution
* Refactor 'kind' in document events
* Don't depend on Document in Deserializer
* Make Deserializer.encode() re-entrant
* Move *Buffer to serialization/buffer
* Finalize differential serialization
* Serialize vectorized values as structures
* Rename Event.{decode_json->from_serializable}
* Don't use has_ref() in Model.to_serializable()
* Handle circular object references in bokehjs
* Reorganize serialization unit tests
* Redesign model registry and qualified names
* Remove the need for StaticSerializer
* Make 'attributes' optional in type reps
* Allow to serialize typed arrays as binary
* Finalize handling of binary buffers
* Use memoryview to further defer encoding
* Test dict serialization and ordering
* Downcast ndarrays {u}int{64->32} if possible
* Add preliminary release/migration notes
* Robustify encoding of objects and object refs
* Remove support for serialization of relativedelta
* Import pandas only if really necessary
* Statically type bokeh.core.serialization
* Add preliminary serialization's documentation
* Add Deserializer.deserialize() for symmetric APIs
* Handle streaming/patching/data events in io.notebook
* Update handling of buffers in io.notebook
* Properly serialize MessageSent event
* Add a regression test for issue #11694
* Preserve order of inherited properties
* Add support for serialization of symbols
* Update defaults' tests to use type="object"
* Move DocJson.version to the first entry
* Add a preliminary regression test for #11930
* Fix integration/glyphs/rect_log_axis.py
* Fix value detection in dataspecs involving String
* Remove an unnecessary type assertion | https://github.com/bokeh/bokeh.git | def recompute(self) -> None:
document = self._document()
if document is None:
return
new_models: Set[Model] = set()
for mr in document.roots:
new_models |= mr.references()
old_models = set(self._models.values())
to_detach = old_models - new_models
to_attach = new_models - old_models
recomputed: Dict[ID, Model] = {}
recomputed_by_name: MultiValuedDict[str, Model] = MultiValuedDict()
for mn in new_models:
recomputed[mn.id] = mn
if mn.name is not None:
recomputed_by_name.add_value(mn.name, mn)
for md in to_detach:
self._seen_model_ids.add(md.id)
md._detach_document()
for ma in to_attach:
ma._attach_document(document)
self._new_models.add(ma)
self._models = recomputed
self._models_by_name = recomputed_by_name
# XXX (bev) In theory, this is a potential issue for long-running apps that
# update the model graph continuously, since this set of "seen" model ids can
# grow without bound. | 170 | models.py | Python | bokeh/document/models.py | fca16442ae90afcd2ac61f4e554e538776730831 | bokeh | 7 |
|
84,404 | 14 | 9 | 26 | 75 | 10 | 0 | 16 | 51 | test_receive_stream_email_forwarded_success | email_error: Improve tests for inbound email edge case.
Tests to assist with clarifying #22585. | https://github.com/zulip/zulip.git | def test_receive_stream_email_forwarded_success(self) -> None:
msgtext =
user_profile = self.example_user("hamlet")
self.login_user(user_profile)
self.subscribe(user_profile, "Denmark")
stream = get_stream("Denmark", user_profile.realm)
| 73 | test_email_mirror.py | Python | zerver/tests/test_email_mirror.py | c6931434e994aee5fa15696be4d8db2c99c6c891 | zulip | 1 |
|
30,315 | 9 | 8 | 6 | 33 | 4 | 0 | 9 | 39 | clean_ansi_sequence | remove ansi sequences from output in tests
fixing tests
Update test_entry_point.py | https://github.com/spotDL/spotify-downloader.git | def clean_ansi_sequence(text):
return re.sub(
r'(?:\x1B[@-Z\\-_]|[\x80-\x9A\x9C-\x9F]|(?:\x1B\[|\x9B)[0-?]*[ -/]*[@-~])',
'',
text,
)
| 19 | conftest.py | Python | tests/conftest.py | e2155b300889d657a29f4db0471643b25a8af982 | spotify-downloader | 1 |
|
101,386 | 7 | 11 | 3 | 42 | 6 | 0 | 7 | 21 | has_predicted_mask | 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 | https://github.com/deepfakes/faceswap.git | def has_predicted_mask(self) -> bool:
return bool(self._model.config.get("learn_mask", False))
| 24 | convert.py | Python | scripts/convert.py | 1022651eb8a7741014f5d2ec7cbfe882120dfa5f | faceswap | 1 |
|
12,806 | 13 | 12 | 6 | 54 | 7 | 0 | 14 | 68 | collect_all_results | fix: fix specific params problem with branches (#5038) | https://github.com/jina-ai/jina.git | def collect_all_results(self):
res = {}
for node in self.all_nodes:
if node.result_in_params_returned:
res.update(node.result_in_params_returned)
return res
| 32 | topology_graph.py | Python | jina/serve/runtimes/gateway/graph/topology_graph.py | 032bd5e662dd16d4cfb394857074779970ae444a | jina | 3 |
|
19,465 | 21 | 11 | 11 | 64 | 8 | 0 | 23 | 53 | _looks_like_bpo_44860 | Vendor in pip 21.2.4 release (from pip 21.2.2 prior). (#5009)
* Vendor in pip 21.2.4 release (from pip 21.2.2 prior).
* Add news fragment for pip 21.2.4 vendor update.
* Add potentially missing LICENSE files | https://github.com/pypa/pipenv.git | def _looks_like_bpo_44860() -> bool:
from distutils.command.install import INSTALL_SCHEMES # type: ignore
try:
unix_user_platlib = INSTALL_SCHEMES["unix_user"]["platlib"]
except KeyError:
return False
return unix_user_platlib == "$usersite"
| 35 | __init__.py | Python | pipenv/patched/notpip/_internal/locations/__init__.py | 7e33fcae4384563b4c927fd44318c29dd524a097 | pipenv | 2 |
|
247,938 | 9 | 8 | 14 | 32 | 5 | 0 | 10 | 31 | test_lots_of_queued_things | Convert `Linearizer` tests from `inlineCallbacks` to async (#12353)
Signed-off-by: Sean Quah <[email protected]> | https://github.com/matrix-org/synapse.git | def test_lots_of_queued_things(self) -> None:
linearizer = Linearizer()
key = ""
| 72 | test_linearizer.py | Python | tests/util/test_linearizer.py | 41b5f72677ea9763f3cf920d4f6df507653222f2 | synapse | 2 |
|
243,932 | 116 | 19 | 42 | 642 | 63 | 0 | 178 | 828 | forward | Fix None grad problem during training TOOD by adding SigmoidGeometricMean (#7090) | https://github.com/open-mmlab/mmdetection.git | def forward(self, feats):
cls_scores = []
bbox_preds = []
for idx, (x, scale, stride) in enumerate(
zip(feats, self.scales, self.prior_generator.strides)):
b, c, h, w = x.shape
anchor = self.prior_generator.single_level_grid_priors(
(h, w), idx, device=x.device)
anchor = torch.cat([anchor for _ in range(b)])
# extract task interactive features
inter_feats = []
for inter_conv in self.inter_convs:
x = inter_conv(x)
inter_feats.append(x)
feat = torch.cat(inter_feats, 1)
# task decomposition
avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
cls_feat = self.cls_decomp(feat, avg_feat)
reg_feat = self.reg_decomp(feat, avg_feat)
# cls prediction and alignment
cls_logits = self.tood_cls(cls_feat)
cls_prob = self.cls_prob_module(feat)
cls_score = sigmoid_geometric_mean(cls_logits, cls_prob)
# reg prediction and alignment
if self.anchor_type == 'anchor_free':
reg_dist = scale(self.tood_reg(reg_feat).exp()).float()
reg_dist = reg_dist.permute(0, 2, 3, 1).reshape(-1, 4)
reg_bbox = distance2bbox(
self.anchor_center(anchor) / stride[0],
reg_dist).reshape(b, h, w, 4).permute(0, 3, 1,
2) # (b, c, h, w)
elif self.anchor_type == 'anchor_based':
reg_dist = scale(self.tood_reg(reg_feat)).float()
reg_dist = reg_dist.permute(0, 2, 3, 1).reshape(-1, 4)
reg_bbox = self.bbox_coder.decode(anchor, reg_dist).reshape(
b, h, w, 4).permute(0, 3, 1, 2) / stride[0]
else:
raise NotImplementedError(
f'Unknown anchor type: {self.anchor_type}.'
f'Please use `anchor_free` or `anchor_based`.')
reg_offset = self.reg_offset_module(feat)
bbox_pred = self.deform_sampling(reg_bbox.contiguous(),
reg_offset.contiguous())
cls_scores.append(cls_score)
bbox_preds.append(bbox_pred)
return tuple(cls_scores), tuple(bbox_preds)
| 415 | tood_head.py | Python | mmdet/models/dense_heads/tood_head.py | 08bc3d7a8b08d9452d0734e9ff29c2e9b98890e5 | mmdetection | 6 |
|
68,836 | 50 | 18 | 71 | 304 | 22 | 0 | 80 | 49 | get_mode_of_payment_details | 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]> | https://github.com/frappe/erpnext.git | def get_mode_of_payment_details(filters):
mode_of_payment_details = {}
invoice_list = get_invoices(filters)
invoice_list_names = ",".join("'" + invoice["name"] + "'" for invoice in invoice_list)
if invoice_list:
inv_mop_detail = frappe.db.sql(
.format(
invoice_list_names=invoice_list_names
),
as_dict=1,
)
inv_change_amount = frappe.db.sql(
.format(
invoice_list_names=invoice_list_names
),
as_dict=1,
)
for d in inv_change_amount:
for det in inv_mop_detail:
if (
det["owner"] == d["owner"]
and det["posting_date"] == d["posting_date"]
and det["mode_of_payment"] == d["mode_of_payment"]
):
paid_amount = det["paid_amount"] - d["change_amount"]
det["paid_amount"] = paid_amount
for d in inv_mop_detail:
mode_of_payment_details.setdefault(d["owner"] + cstr(d["posting_date"]), []).append(
(d.mode_of_payment, d.paid_amount)
)
return mode_of_payment_details
| 181 | sales_payment_summary.py | Python | erpnext/accounts/report/sales_payment_summary/sales_payment_summary.py | 74a782d81d8f8c4a4d9214a9c06377e5e6e464dd | erpnext | 9 |
|
278,956 | 6 | 7 | 22 | 22 | 4 | 0 | 6 | 12 | _replace_child_layer_functions | Remove pylint comments.
PiperOrigin-RevId: 452353044 | https://github.com/keras-team/keras.git | def _replace_child_layer_functions(layer, serialization_cache):
original_fns = {}
| 106 | save_impl.py | Python | keras/saving/saved_model/save_impl.py | 3613c3defc39c236fb1592c4f7ba1a9cc887343a | keras | 6 |
|
130,340 | 64 | 14 | 24 | 267 | 18 | 0 | 92 | 296 | fillout_resources_kubernetes | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | https://github.com/ray-project/ray.git | def fillout_resources_kubernetes(config):
if "available_node_types" not in config:
return config
node_types = copy.deepcopy(config["available_node_types"])
head_node_type = config["head_node_type"]
for node_type in node_types:
node_config = node_types[node_type]["node_config"]
# The next line is for compatibility with configs like
# kubernetes/example-ingress.yaml,
# cf. KubernetesNodeProvider.create_node().
pod = node_config.get("pod", node_config)
container_data = pod["spec"]["containers"][0]
autodetected_resources = get_autodetected_resources(container_data)
if node_types == head_node_type:
# we only autodetect worker type node memory resource
autodetected_resources.pop("memory")
if "resources" not in config["available_node_types"][node_type]:
config["available_node_types"][node_type]["resources"] = {}
autodetected_resources.update(
config["available_node_types"][node_type]["resources"]
)
config["available_node_types"][node_type]["resources"] = autodetected_resources
logger.debug(
"Updating the resources of node type {} to include {}.".format(
node_type, autodetected_resources
)
)
return config
| 151 | config.py | Python | python/ray/autoscaler/_private/_kubernetes/config.py | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | 5 |
|
132,909 | 20 | 9 | 3 | 47 | 10 | 0 | 20 | 48 | nodes | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | https://github.com/ray-project/ray.git | def nodes(self):
# This should be imported here, otherwise, it will error doc build.
import ray.core.generated.ray_client_pb2 as ray_client_pb2
return self.worker.get_cluster_info(ray_client_pb2.ClusterInfoType.NODES)
| 29 | api.py | Python | python/ray/util/client/api.py | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | 1 |
|
260,539 | 20 | 10 | 13 | 124 | 16 | 0 | 21 | 128 | fit | MAINT parameter validation for CountVectorizer & TfidfVectorizer (#23853)
Co-authored-by: Meekail Zain <[email protected]>
Co-authored-by: jeremiedbb <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def fit(self, raw_documents, y=None):
self._validate_params()
self._check_params()
self._warn_for_unused_params()
self._tfidf = TfidfTransformer(
norm=self.norm,
use_idf=self.use_idf,
smooth_idf=self.smooth_idf,
sublinear_tf=self.sublinear_tf,
)
X = super().fit_transform(raw_documents)
self._tfidf.fit(X)
return self
| 78 | text.py | Python | sklearn/feature_extraction/text.py | c300a8f2178fcae847f82ad548fe9452f2ba8bbb | scikit-learn | 1 |
|
296,854 | 30 | 9 | 10 | 123 | 7 | 0 | 48 | 126 | pretty_duration | Refactor history_stats to minimize database access (part 2) (#70255) | https://github.com/home-assistant/core.git | def pretty_duration(hours):
seconds = int(3600 * hours)
days, seconds = divmod(seconds, 86400)
hours, seconds = divmod(seconds, 3600)
minutes, seconds = divmod(seconds, 60)
if days > 0:
return "%dd %dh %dm" % (days, hours, minutes)
if hours > 0:
return "%dh %dm" % (hours, minutes)
return "%dm" % minutes
| 76 | helpers.py | Python | homeassistant/components/history_stats/helpers.py | 73a368c24246b081cdb98923ca3180937d436c3b | core | 3 |
|
249,151 | 36 | 10 | 36 | 177 | 13 | 0 | 47 | 219 | test_search_term | Use literals in place of `HTTPStatus` constants in tests (#13469) | https://github.com/matrix-org/synapse.git | def test_search_term(self) -> None:
# Create two test rooms
room_id_1 = self.helper.create_room_as(self.admin_user, tok=self.admin_user_tok)
room_id_2 = self.helper.create_room_as(self.admin_user, tok=self.admin_user_tok)
room_name_1 = "something"
room_name_2 = "LoremIpsum"
# Set the name for each room
self.helper.send_state(
room_id_1,
"m.room.name",
{"name": room_name_1},
tok=self.admin_user_tok,
)
self.helper.send_state(
room_id_2,
"m.room.name",
{"name": room_name_2},
tok=self.admin_user_tok,
)
self._set_canonical_alias(room_id_1, "#Room_Alias1:test", self.admin_user_tok)
| 221 | test_room.py | Python | tests/rest/admin/test_room.py | c97042f7eef3748e17c90e48a4122389a89c4735 | synapse | 1 |
|
273,184 | 70 | 12 | 19 | 248 | 25 | 0 | 99 | 294 | get_vocabulary | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def get_vocabulary(self, include_special_tokens=True):
# The lookup table data will not be sorted, so we will create a inverted
# lookup here, and use that to lookup a range of indices [0, vocab_size).
if self.lookup_table.size() == 0:
vocab, indices = [], []
else:
keys, values = self.lookup_table.export()
vocab, indices = (values, keys) if self.invert else (keys, values)
vocab, indices = (
self._tensor_vocab_to_numpy(vocab),
indices.numpy(),
)
lookup = collections.defaultdict(
lambda: self.oov_token, zip(indices, vocab)
)
vocab = [lookup[x] for x in range(self.vocabulary_size())]
if self.mask_token is not None and self.output_mode == INT:
vocab[0] = self.mask_token
if not include_special_tokens:
vocab = vocab[self._token_start_index() :]
return vocab
| 158 | index_lookup.py | Python | keras/layers/preprocessing/index_lookup.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 7 |
|
26,497 | 9 | 8 | 3 | 31 | 5 | 0 | 11 | 16 | test_validate_invalid_subscription_and_query | Add Webhook payload via graphql subscriptions (#9394)
* Add PoC of webhook subscriptions
* add async webhooks subscription payloads feature
* remove unneeded file
* add translations subscription handling, fixes after review
* remove todo
* add descriptions
* add descriptions, move subsrciption_payloads.py
* refactor
* fix imports, add changelog
* check_document_is_single_subscription refactor
Co-authored-by: Maciej Korycinski <[email protected]>
Co-authored-by: Marcin Gębala <[email protected]> | https://github.com/saleor/saleor.git | def test_validate_invalid_subscription_and_query():
result = validate_subscription_query(TEST_INVALID_MULTIPLE_SUBSCRIPTION_AND_QUERY)
assert result is False
TEST_INVALID_MULTIPLE_SUBSCRIPTION =
| 14 | test_create_deliveries_for_subscription.py | Python | saleor/plugins/webhook/tests/subscription_webhooks/test_create_deliveries_for_subscription.py | aca6418d6c36956bc1ab530e6ef7e146ec9df90c | saleor | 1 |
|
176,825 | 37 | 15 | 23 | 209 | 19 | 0 | 62 | 319 | analyze_symmetry | Update ISMAGS.analyze_symmetry docstring. (#5696)
* Update ISMAGS.analyze_symmetry docstring.
Co-authored-by: Peter C Kroon <[email protected]>
* Fix formatting.
Co-authored-by: Peter C Kroon <[email protected]> | https://github.com/networkx/networkx.git | def analyze_symmetry(self, graph, node_partitions, edge_colors):
if self._symmetry_cache is not None:
key = hash(
(
tuple(graph.nodes),
tuple(graph.edges),
tuple(map(tuple, node_partitions)),
tuple(edge_colors.items()),
)
)
if key in self._symmetry_cache:
return self._symmetry_cache[key]
node_partitions = list(
self._refine_node_partitions(graph, node_partitions, edge_colors)
)
assert len(node_partitions) == 1
node_partitions = node_partitions[0]
permutations, cosets = self._process_ordered_pair_partitions(
graph, node_partitions, node_partitions, edge_colors
)
if self._symmetry_cache is not None:
self._symmetry_cache[key] = permutations, cosets
return permutations, cosets
| 140 | ismags.py | Python | networkx/algorithms/isomorphism/ismags.py | 52fe06608904ded6d3a0d49a64129e043351b308 | networkx | 4 |
|
261,313 | 31 | 11 | 9 | 95 | 8 | 0 | 34 | 125 | set_output | ENH Introduces set_output API for pandas output (#23734)
* Introduces set_output API for all transformers
* TransformerMixin inherits from _SetOutputMixin
* Adds tests
* Adds whatsnew
* Adds example on using set_output API
* Adds developer docs for set_output | https://github.com/scikit-learn/scikit-learn.git | def set_output(self, *, transform=None):
if hasattr(super(), "set_output"):
return super().set_output(transform=transform)
if transform == "pandas" and self.feature_names_out is None:
warnings.warn(
'With transform="pandas", `func` should return a DataFrame to follow'
" the set_output API."
)
return self
| 53 | _function_transformer.py | Python | sklearn/preprocessing/_function_transformer.py | 2a6703d9e8d1e54d22dd07f2bfff3c92adecd758 | scikit-learn | 4 |
|
167,798 | 40 | 11 | 21 | 138 | 11 | 0 | 45 | 116 | add_flex_arithmetic_methods | TYP: more return annotations in core/ (#47618)
* TYP: more return annotations in core/
* from __future__ import annotations
* more __future__ | https://github.com/pandas-dev/pandas.git | def add_flex_arithmetic_methods(cls) -> None:
flex_arith_method, flex_comp_method = _get_method_wrappers(cls)
new_methods = _create_methods(cls, flex_arith_method, flex_comp_method)
new_methods.update(
{
"multiply": new_methods["mul"],
"subtract": new_methods["sub"],
"divide": new_methods["div"],
}
)
# opt out of bool flex methods for now
assert not any(kname in new_methods for kname in ("ror_", "rxor", "rand_"))
_add_methods(cls, new_methods=new_methods)
| 80 | methods.py | Python | pandas/core/ops/methods.py | f65417656ba8c59438d832b6e2a431f78d40c21c | pandas | 2 |
|
19,668 | 68 | 15 | 30 | 349 | 35 | 0 | 88 | 425 | get_paths | Issue 4993 Add standard pre commit hooks and apply linting. (#4994)
* Add .pre-commit-config.yaml to the project and exclude tests (for now). This does not include the MyPy linting that pip does but does include everything else. | https://github.com/pypa/pipenv.git | def get_paths(self):
# type: () -> Optional[Dict[str, str]]
tmpfile = vistir.path.create_tracked_tempfile(suffix=".json")
tmpfile.close()
tmpfile_path = make_posix(tmpfile.name)
py_command = self.build_command(
python_lib=True, python_inc=True, scripts=True, py_version=True
)
command = [self.python, "-c", py_command.format(tmpfile_path)]
c = subprocess_run(command)
if c.returncode == 0:
paths = {}
with open(tmpfile_path, "r", encoding="utf-8") as fh:
paths = json.load(fh)
if "purelib" in paths:
paths["libdir"] = paths["purelib"] = make_posix(paths["purelib"])
for key in (
"platlib",
"scripts",
"platstdlib",
"stdlib",
"include",
"platinclude",
):
if key in paths:
paths[key] = make_posix(paths[key])
return paths
else:
vistir.misc.echo(f"Failed to load paths: {c.stderr}", fg="yellow")
vistir.misc.echo(f"Output: {c.stdout}", fg="yellow")
return None
| 197 | environment.py | Python | pipenv/environment.py | 9a3b3ce70621af6f9adaa9eeac9cf83fa149319c | pipenv | 5 |
|
153,789 | 8 | 9 | 43 | 38 | 6 | 0 | 8 | 22 | test_postprocessing_with_all_metadata | FEAT-#4412: Add Batch Pipeline API to Modin (#4452)
Co-authored-by: Yaroslav Igoshev <[email protected]>
Co-authored-by: Mahesh Vashishtha <[email protected]>
Signed-off-by: Rehan Durrani <[email protected]> | https://github.com/modin-project/modin.git | def test_postprocessing_with_all_metadata(self):
arr = np.random.randint(0, 1000, (1000, 1000))
| 335 | test_pipeline.py | Python | modin/experimental/batch/test/test_pipeline.py | 3d4404e9d9a9b2a3327f8aee664a8e71ac1f18b8 | modin | 9 |
|
19,989 | 6 | 8 | 10 | 30 | 4 | 0 | 6 | 12 | captured_stdout | check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4 | https://github.com/pypa/pipenv.git | def captured_stdout() -> ContextManager[StreamWrapper]:
return captured_output("stdout")
| 15 | misc.py | Python | pipenv/patched/notpip/_internal/utils/misc.py | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | 1 |
|
215,964 | 104 | 19 | 40 | 451 | 33 | 0 | 135 | 470 | list_sites | Update to latest ``pyupgrade`` hook. Stop skipping it on CI.
Signed-off-by: Pedro Algarvio <[email protected]> | https://github.com/saltstack/salt.git | def list_sites():
ret = dict()
ps_cmd = [
"Get-ChildItem",
"-Path",
r"'IIS:\Sites'",
"|",
"Select-Object applicationPool, Bindings, ID, Name, PhysicalPath, State",
]
keep_keys = ("certificateHash", "certificateStoreName", "protocol", "sslFlags")
cmd_ret = _srvmgr(cmd=ps_cmd, return_json=True)
try:
items = salt.utils.json.loads(cmd_ret["stdout"], strict=False)
except ValueError:
raise CommandExecutionError("Unable to parse return data as Json.")
for item in items:
bindings = dict()
for binding in item["bindings"]["Collection"]:
# Ignore bindings which do not have host names
if binding["protocol"] not in ["http", "https"]:
continue
filtered_binding = dict()
for key in binding:
if key in keep_keys:
filtered_binding.update({key.lower(): binding[key]})
binding_info = binding["bindingInformation"].split(":", 2)
ipaddress, port, hostheader = (element.strip() for element in binding_info)
filtered_binding.update(
{"hostheader": hostheader, "ipaddress": ipaddress, "port": port}
)
bindings[binding["bindingInformation"]] = filtered_binding
ret[item["name"]] = {
"apppool": item["applicationPool"],
"bindings": bindings,
"id": item["id"],
"state": item["state"],
"sourcepath": item["physicalPath"],
}
if not ret:
log.warning("No sites found in output: %s", cmd_ret["stdout"])
return ret
| 260 | win_iis.py | Python | salt/modules/win_iis.py | f2a783643de61cac1ff3288b40241e5ce6e1ddc8 | salt | 9 |
|
200,378 | 33 | 14 | 8 | 82 | 10 | 0 | 41 | 133 | _construct | Fix various typos
Found via `codespell -q 3 -L aboves,aline,ans,aother,arithmetics,assum,atleast,braket,clen,declar,declars,dorder,dum,enew,fo,fro,inout,iself,ist,ket,lamda,lightyear,lightyears,nd,numer,numers,orderd,ot,pring,rcall,rever,ro,ser,siz,splitted,sring,supercedes,te,tht,unequality,upto,vas,versin,whet` | https://github.com/sympy/sympy.git | def _construct(cls, attr, arg):
# arg may be ``NoneToken()``, so comparison is done using == instead of ``is`` operator
if arg == None:
return cls.defaults.get(attr, none)
else:
if isinstance(arg, Dummy): # SymPy's replace uses Dummy instances
return arg
else:
return cls._get_constructor(attr)(arg)
| 50 | ast.py | Python | sympy/codegen/ast.py | 24f1e7730119fe958cc8e28411f790c9a5ec04eb | sympy | 3 |
|
101,232 | 10 | 10 | 4 | 66 | 9 | 0 | 10 | 43 | _kwarg_requirements | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | https://github.com/deepfakes/faceswap.git | def _kwarg_requirements(self) -> Dict[Literal["gaussian", "normalized"], List[str]]:
return dict(gaussian=["ksize", "sigmaX"],
normalized=["ksize"])
| 38 | detected_face.py | Python | lib/align/detected_face.py | 5e73437be47f2410439a3c6716de96354e6a0c94 | faceswap | 1 |
|
259,041 | 56 | 14 | 18 | 207 | 15 | 0 | 74 | 194 | _set_order | MNT Drops Python 3.7 in CI, wheel building, and docs (#22617)
* MNT Drops Python 3.7
* MNT Bump NumPy and SciPy
* FIX Fix build
* FIX Bump versions improved
* DOC Fixes numpy version [pypy]
* BLD [pypy] [icc-build]
* Update docs
* MAINT use scipy.optimize.LinearConstraint in test
* MAINT scipy 1.1.0 related code clean-up
* scipy>=1.3.2 in pyproject.toml's build deps
* [cd build]
* DOC Adds comment about pypy
* MAINT remove _astype_copy_false
* FIX Update check for python version in setup.py
Co-authored-by: Olivier Grisel <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def _set_order(X, y, order="C"):
if order not in [None, "C", "F"]:
raise ValueError(
"Unknown value for order. Got {} instead of None, 'C' or 'F'.".format(order)
)
sparse_X = sparse.issparse(X)
sparse_y = sparse.issparse(y)
if order is not None:
sparse_format = "csc" if order == "F" else "csr"
if sparse_X:
X = X.asformat(sparse_format, copy=False)
else:
X = np.asarray(X, order=order)
if sparse_y:
y = y.asformat(sparse_format)
else:
y = np.asarray(y, order=order)
return X, y
###############################################################################
# Paths functions
| 123 | _coordinate_descent.py | Python | sklearn/linear_model/_coordinate_descent.py | f1d3417b086550be670cbfbb5b3c1760ac99203f | scikit-learn | 6 |
|
47,487 | 48 | 13 | 22 | 314 | 38 | 0 | 68 | 238 | test_find_executable_task_instances_order_execution_date | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | https://github.com/apache/airflow.git | def test_find_executable_task_instances_order_execution_date(self, dag_maker):
dag_id_1 = 'SchedulerJobTest.test_find_executable_task_instances_order_execution_date-a'
dag_id_2 = 'SchedulerJobTest.test_find_executable_task_instances_order_execution_date-b'
task_id = 'task-a'
session = settings.Session()
with dag_maker(dag_id=dag_id_1, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id)
dr1 = dag_maker.create_dagrun(execution_date=DEFAULT_DATE + timedelta(hours=1))
with dag_maker(dag_id=dag_id_2, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id)
dr2 = dag_maker.create_dagrun()
dr1 = session.merge(dr1, load=False)
self.scheduler_job = SchedulerJob(subdir=os.devnull)
tis = dr1.task_instances + dr2.task_instances
for ti in tis:
ti.state = State.SCHEDULED
session.merge(ti)
session.flush()
res = self.scheduler_job._executable_task_instances_to_queued(max_tis=1, session=session)
session.flush()
assert [ti.key for ti in res] == [tis[1].key]
session.rollback()
| 193 | test_scheduler_job.py | Python | tests/jobs/test_scheduler_job.py | 49e336ae0302b386a2f47269a6d13988382d975f | airflow | 3 |
|
166,202 | 6 | 10 | 5 | 36 | 6 | 0 | 6 | 20 | null_count | ENH: Implement DataFrame interchange protocol (#46141) | https://github.com/pandas-dev/pandas.git | def null_count(self) -> int:
return self._col.isna().sum()
| 20 | column.py | Python | pandas/core/exchange/column.py | 90140f055892a46f473bd26affab88a7f171e394 | pandas | 1 |
|
305,039 | 5 | 6 | 2 | 20 | 3 | 1 | 5 | 11 | async_remove | Add a callback for data flow handler removal (#77394)
* Add a callback for when data flows are removed
* Call `async_remove` at the very end
* Handle and log exceptions caught during flow removal
* Log the error as an exception, with a traceback
* Adjust test's expected logging output to match updated format specifier | https://github.com/home-assistant/core.git | def async_remove(self) -> None:
@callback | @callback | 8 | data_entry_flow.py | Python | homeassistant/data_entry_flow.py | 2224d0f43a048052cfc4572df95c7afcccdf3a57 | core | 1 |
185,711 | 28 | 12 | 18 | 122 | 12 | 0 | 36 | 167 | _binding_chain | Don't include self in DOMNode.ancestors any more
As well as dropping `self` from the list that DOMNode.ancestors provides,
this commit also adds DOMNode.ancestors_with_self, which maintains the
previous behaviour of DOMNode.ancestors. | https://github.com/Textualize/textual.git | def _binding_chain(self) -> list[tuple[DOMNode, Bindings]]:
focused = self.focused
namespace_bindings: list[tuple[DOMNode, Bindings]]
if focused is None:
namespace_bindings = [
(self.screen, self.screen._bindings),
(self, self._bindings),
]
else:
namespace_bindings = [
(node, node._bindings) for node in focused.ancestors_with_self
]
return namespace_bindings
| 82 | app.py | Python | src/textual/app.py | e3130f95c69648916f121e779a325b6f6f87e6ba | textual | 3 |
|
177,497 | 12 | 9 | 3 | 55 | 8 | 0 | 14 | 23 | multi_source_dijkstra_path | Hide edges with a weight of None in A*. (#5945)
* Hide edges with a weight of None in A*.
This matches the Dijkstra's weight interface.
* Update Dijkstra's and A* docs for weights of None.
* Add tests for A* with weight of None.
* Add another test for A* with a weight function.
* Document that None indicates a hidden edge. | https://github.com/networkx/networkx.git | def multi_source_dijkstra_path(G, sources, cutoff=None, weight="weight"):
length, path = multi_source_dijkstra(G, sources, cutoff=cutoff, weight=weight)
return path
| 36 | weighted.py | Python | networkx/algorithms/shortest_paths/weighted.py | d82815dba6c8ddce19cd49f700298dc82a58f066 | networkx | 1 |
|
224,010 | 29 | 12 | 7 | 69 | 11 | 1 | 29 | 108 | load_file | Remove spaces at the ends of docstrings, normalize quotes | https://github.com/mkdocs/mkdocs.git | def load_file(self, config_file):
try:
return self.load_dict(utils.yaml_load(config_file))
except YAMLError as e:
# MkDocs knows and understands ConfigurationErrors
raise exceptions.ConfigurationError(
f"MkDocs encountered an error parsing the configuration file: {e}"
)
@contextmanager | @contextmanager | 35 | base.py | Python | mkdocs/config/base.py | e7f07cc82ab2be920ab426ba07456d8b2592714d | mkdocs | 2 |
101,270 | 64 | 19 | 29 | 480 | 34 | 0 | 135 | 430 | reload_images | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | https://github.com/deepfakes/faceswap.git | def reload_images(self, group_method, img_list):
logger.info("Preparing to group...")
if group_method == 'group_blur':
filename_list, image_list = self._get_images()
blurs = [self.estimate_blur(img) for img in image_list]
temp_list = list(zip(filename_list, blurs))
elif group_method == 'group_blur_fft':
filename_list, image_list = self._get_images()
fft_blurs = [self.estimate_blur_fft(img) for img in image_list]
temp_list = list(zip(filename_list, fft_blurs))
elif group_method == 'group_face_cnn':
filename_list, image_list, landmarks = self._get_landmarks()
temp_list = list(zip(filename_list, landmarks))
elif group_method == 'group_face_yaw':
filename_list, image_list, landmarks = self._get_landmarks()
yaws = [self.calc_landmarks_face_yaw(mark) for mark in landmarks]
temp_list = list(zip(filename_list, yaws))
elif group_method == 'group_hist':
filename_list, image_list = self._get_images()
histograms = [cv2.calcHist([img], [0], None, [256], [0, 256]) for img in image_list]
temp_list = list(zip(filename_list, histograms))
elif group_method == 'group_black_pixels':
filename_list, image_list = self._get_images()
black_pixels = [np.ndarray.all(img == [0, 0, 0], axis=2).sum()/img.size*100*3
for img in image_list]
temp_list = list(zip(filename_list, black_pixels))
else:
raise ValueError(f"{group_method} group_method not found.")
return self.splice_lists(img_list, temp_list)
| 301 | sort.py | Python | tools/sort/sort.py | 5e73437be47f2410439a3c6716de96354e6a0c94 | faceswap | 12 |
|
149,760 | 21 | 10 | 8 | 153 | 13 | 0 | 26 | 82 | append_predictions | add freqao backend machinery, user interface, documentation | https://github.com/freqtrade/freqtrade.git | def append_predictions(self, predictions, do_predict, len_dataframe):
ones = np.ones(len_dataframe)
s_mean, s_std = ones*self.data['s_mean'], ones*self.data['s_std']
self.predictions = np.append(self.predictions,predictions)
self.do_predict = np.append(self.do_predict,do_predict)
self.target_mean = np.append(self.target_mean,s_mean)
self.target_std = np.append(self.target_std,s_std)
return
| 98 | data_handler.py | Python | freqtrade/freqai/data_handler.py | fc837c4daa27a18ff0e86128f4d52089b88fa5fb | freqtrade | 1 |
|
154,264 | 50 | 12 | 23 | 230 | 29 | 0 | 64 | 265 | build_query_compiler | FEAT-#4733: Support fastparquet as engine for `read_parquet` (#4807)
Signed-off-by: Karthik Velayutham <[email protected]> | https://github.com/modin-project/modin.git | def build_query_compiler(cls, dataset, columns, index_columns, **kwargs):
storage_options = kwargs.pop("storage_options", {}) or {}
col_partitions, column_widths = cls.build_columns(columns)
partition_ids = cls.call_deploy(
dataset, col_partitions, storage_options, **kwargs
)
index, sync_index = cls.build_index(dataset, partition_ids, index_columns)
remote_parts = cls.build_partition(partition_ids, column_widths)
if len(partition_ids) > 0:
row_lengths = [part.length() for part in remote_parts.T[0]]
else:
row_lengths = None
frame = cls.frame_cls(
remote_parts,
index,
columns,
row_lengths=row_lengths,
column_widths=column_widths,
dtypes=None,
)
if sync_index:
frame.synchronize_labels(axis=0)
return cls.query_compiler_cls(frame)
| 152 | parquet_dispatcher.py | Python | modin/core/io/column_stores/parquet_dispatcher.py | b240370bf83c88589d293b76b4a2409294e06f90 | modin | 5 |
|
83,239 | 64 | 12 | 34 | 414 | 13 | 0 | 121 | 389 | test_collapse_event | docs: Fix many spelling mistakes.
Signed-off-by: Anders Kaseorg <[email protected]> | https://github.com/zulip/zulip.git | def test_collapse_event(self) -> None:
client = self.get_client_descriptor()
queue = client.event_queue
queue.push({"type": "restart", "server_generation": 1, "timestamp": "1"})
# Verify the server_generation event is stored as a virtual event
self.assertEqual(
queue.virtual_events,
{"restart": {"id": 0, "type": "restart", "server_generation": 1, "timestamp": "1"}},
)
# And we can reconstruct newest_pruned_id etc.
self.verify_to_dict_end_to_end(client)
queue.push({"type": "unknown", "timestamp": "1"})
self.assertEqual(list(queue.queue), [{"id": 1, "type": "unknown", "timestamp": "1"}])
self.assertEqual(
queue.virtual_events,
{"restart": {"id": 0, "type": "restart", "server_generation": 1, "timestamp": "1"}},
)
# And we can still reconstruct newest_pruned_id etc. correctly
self.verify_to_dict_end_to_end(client)
# Verify virtual events are converted to real events by .contents()
self.assertEqual(
queue.contents(),
[
{"id": 0, "type": "restart", "server_generation": 1, "timestamp": "1"},
{"id": 1, "type": "unknown", "timestamp": "1"},
],
)
# And now verify to_dict after pruning
queue.prune(0)
self.verify_to_dict_end_to_end(client)
queue.prune(1)
self.verify_to_dict_end_to_end(client)
| 223 | test_event_queue.py | Python | zerver/tests/test_event_queue.py | b0ce4f1bce8031881addecb1e86073483517f392 | zulip | 1 |
|
100,362 | 89 | 17 | 26 | 366 | 33 | 0 | 109 | 595 | _download_model | 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 | https://github.com/deepfakes/faceswap.git | def _download_model(self):
self.logger.info("Downloading model: '%s' from: %s", self._model_name, self._url_download)
for attempt in range(self._retries):
try:
downloaded_size = self._url_partial_size
req = urllib.request.Request(self._url_download)
if downloaded_size != 0:
req.add_header("Range", f"bytes={downloaded_size}-")
with urllib.request.urlopen(req, timeout=10) as response:
self.logger.debug("header info: {%s}", response.info())
self.logger.debug("Return Code: %s", response.getcode())
self._write_zipfile(response, downloaded_size)
break
except (socket_error, socket_timeout,
urllib.error.HTTPError, urllib.error.URLError) as err:
if attempt + 1 < self._retries:
self.logger.warning("Error downloading model (%s). Retrying %s of %s...",
str(err), attempt + 2, self._retries)
else:
self.logger.error("Failed to download model. Exiting. (Error: '%s', URL: "
"'%s')", str(err), self._url_download)
self.logger.info("You can try running again to resume the download.")
self.logger.info("Alternatively, you can manually download the model from: %s "
"and unzip the contents to: %s",
self._url_download, self._cache_dir)
sys.exit(1)
| 220 | utils.py | Python | lib/utils.py | c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf | faceswap | 5 |
|
275,791 | 5 | 9 | 2 | 31 | 5 | 0 | 5 | 19 | texts_to_sequences | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def texts_to_sequences(self, texts):
return list(self.texts_to_sequences_generator(texts))
| 18 | text.py | Python | keras/preprocessing/text.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
|
76,882 | 6 | 9 | 2 | 39 | 7 | 1 | 6 | 11 | notification_static | Rename BASE_URL to WAGTAILADMIN_BASE_URL & add docs
- add documentation for WAGTAILADMIN_BASE_URL
- ensure that WAGTAILADMIN_BASE_URL is used via a util so that it can fallback from request
- resolves #3248
- remove settings URL from pagination template - not required for query string URLs | https://github.com/wagtail/wagtail.git | def notification_static(path):
return urljoin(base_url_setting(), static(path))
@register.simple_tag | @register.simple_tag | 18 | wagtailadmin_tags.py | Python | wagtail/admin/templatetags/wagtailadmin_tags.py | cb7fc4cb1403436c9e7803c341278c20d3b06158 | wagtail | 1 |
270,330 | 3 | 6 | 22 | 15 | 3 | 0 | 3 | 6 | _make_eager_execution_function | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def _make_eager_execution_function(model, mode):
| 119 | distributed_training_utils_v1.py | Python | keras/distribute/distributed_training_utils_v1.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
|
270,738 | 141 | 10 | 8 | 118 | 15 | 0 | 212 | 380 | add_variable | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def add_variable(self, *args, **kwargs):
warnings.warn(
"`layer.add_variable` is deprecated and "
"will be removed in a future version. "
"Please use the `layer.add_weight()` method instead.",
stacklevel=2,
)
return self.add_weight(*args, **kwargs)
##############################################################################
# Methods & attributes below are all private and only used by the framework. #
##############################################################################
# See tf.Module for the usage of this property.
# The key for _obj_reference_counts_dict is a Trackable, which could be a
# variable or layer etc. tf.Module._flatten will fail to flatten the key
# since it is trying to convert Trackable to a string. This attribute can be
# ignored even after the fix of nest lib, since the trackable object should
# already been available as individual attributes. _obj_reference_counts_dict
# just contains a copy of them.
_TF_MODULE_IGNORED_PROPERTIES = frozenset(
itertools.chain(
("_obj_reference_counts_dict",),
tf.Module._TF_MODULE_IGNORED_PROPERTIES,
)
)
# When loading from a SavedModel, Layers typically can be revived into a
# generic Layer wrapper. Sometimes, however, layers may implement methods
# that go beyond this wrapper, as in the case of PreprocessingLayers'
# `adapt` method. When this is the case, layer implementers can override
# must_restore_from_config to return True; layers with this property must
# be restored into their actual objects (and will fail if the object is
# not available to the restoration code).
_must_restore_from_config = False
| 36 | base_layer.py | Python | keras/engine/base_layer.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
|
22,670 | 4 | 6 | 2 | 19 | 3 | 0 | 4 | 18 | width | refactor: clean code
Signed-off-by: slowy07 <[email protected]> | https://github.com/geekcomputers/Python.git | def width(self):
return self.__width
| 10 | lib.py | Python | linear-algebra-python/src/lib.py | f0af0c43340763724f139fa68aa1e5a9ffe458b4 | Python | 1 |
|
52,364 | 33 | 13 | 14 | 163 | 16 | 0 | 47 | 129 | get_in_turn_repetition | update plato2_en_base (#2113)
* update plato2_en_base
* update README | https://github.com/PaddlePaddle/PaddleHub.git | def get_in_turn_repetition(pred, is_cn=False):
if len(pred) == 0:
return 1.0
if isinstance(pred[0], str):
pred = [tok.lower() for tok in pred]
if is_cn:
pred = "".join(pred)
tri_grams = set()
for i in range(len(pred) - 2):
tri_gram = tuple(pred[i:i + 3])
if tri_gram in tri_grams:
return 1.0
tri_grams.add(tri_gram)
return 0.0
| 107 | model.py | Python | modules/text/text_generation/plato2_en_base/model.py | 36ce4789c748040238a87598b5d6b2a5c817a73e | PaddleHub | 7 |
|
247,331 | 7 | 9 | 5 | 45 | 7 | 0 | 7 | 21 | test_legacy_on_create_room | Add type hints to `tests/rest/client` (#12108)
* Add type hints to `tests/rest/client`
* newsfile
* fix imports
* add `test_account.py`
* Remove one type hint in `test_report_event.py`
* change `on_create_room` to `async`
* update new functions in `test_third_party_rules.py`
* Add `test_filter.py`
* add `test_rooms.py`
* change to `assertEquals` to `assertEqual`
* lint | https://github.com/matrix-org/synapse.git | def test_legacy_on_create_room(self) -> None:
self.helper.create_room_as(self.user_id, tok=self.tok, expect_code=403)
| 28 | test_third_party_rules.py | Python | tests/rest/client/test_third_party_rules.py | 2ffaf30803f93273a4d8a65c9e6c3110c8433488 | synapse | 1 |
|
47,589 | 42 | 13 | 18 | 281 | 35 | 0 | 61 | 203 | test_dag_removed_if_serialized_dag_is_removed | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | https://github.com/apache/airflow.git | def test_dag_removed_if_serialized_dag_is_removed(self, dag_maker):
from airflow.operators.empty import EmptyOperator
with dag_maker(
dag_id="test_dag_removed_if_serialized_dag_is_removed",
schedule_interval=None,
start_date=tz.datetime(2021, 10, 12),
) as dag:
EmptyOperator(task_id="task_1")
dag_maker.create_dagrun()
dagbag = DagBag(dag_folder=self.empty_dir, include_examples=False, read_dags_from_db=True)
dagbag.dags = {dag.dag_id: SerializedDAG.from_dict(SerializedDAG.to_dict(dag))}
dagbag.dags_last_fetched = {dag.dag_id: (tz.utcnow() - timedelta(minutes=2))}
dagbag.dags_hash = {dag.dag_id: mock.ANY}
assert SerializedDagModel.has_dag(dag.dag_id) is False
assert dagbag.get_dag(dag.dag_id) is None
assert dag.dag_id not in dagbag.dags
assert dag.dag_id not in dagbag.dags_last_fetched
assert dag.dag_id not in dagbag.dags_hash
| 180 | test_dagbag.py | Python | tests/models/test_dagbag.py | 49e336ae0302b386a2f47269a6d13988382d975f | airflow | 1 |
|
256,315 | 45 | 10 | 20 | 191 | 14 | 0 | 71 | 171 | reduce_annotations | Apply black formatting (#2115)
* Testing black on ui/
* Applying black on docstores
* Add latest docstring and tutorial changes
* Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too
* Remove comments
* Relax constraints on pydoc-markdown
* Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade
* Fix a couple of bugs
* Add a type: ignore that was missing somehow
* Give path to black
* Apply Black
* Apply Black
* Relocate a couple of type: ignore
* Update documentation
* Make Linux CI run after applying Black
* Triggering Black
* Apply Black
* Remove dependency, does not work well
* Remove manually double trailing commas
* Update documentation
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | https://github.com/deepset-ai/haystack.git | def reduce_annotations(anno_types, answers):
for at in set(anno_types):
assert at in ("no_answer", "short_answer")
if anno_types.count("short_answer") >= anno_types.count("no_answer"):
majority = "short_answer"
is_impossible = False
else:
majority = "no_answer"
is_impossible = True
answers = [a for at, a in zip(anno_types, answers) if at == majority]
reduction = len(anno_types) - len(answers)
assert reduction < 3
if not is_impossible:
global n_no_ans
n_no_ans += reduction
else:
global n_short
n_short += reduction
answers = []
return answers, is_impossible
| 112 | nq_to_squad.py | Python | test/benchmarks/nq_to_squad.py | a59bca366174d9c692fa19750c24d65f47660ef7 | haystack | 6 |
|
681 | 8 | 8 | 4 | 27 | 2 | 0 | 9 | 18 | reference_scalar_manager | Reverting changes to REPT and moving new code to NDEPT
- Added tests and methods to lazyrepeatarray
- Started fleshing out NDEPT
- Added serde to NDEPT
- Added test for NDEPT serde | https://github.com/OpenMined/PySyft.git | def reference_scalar_manager() -> VirtualMachinePrivateScalarManager:
reference_scalar_manager = VirtualMachinePrivateScalarManager()
return reference_scalar_manager
| 14 | ndim_entity_phi_test.py | Python | packages/syft/tests/syft/core/tensor/adp/ndim_entity_phi_test.py | d8bf644d529b155467539a707cf5f8538ae41283 | PySyft | 1 |
|
247,933 | 59 | 8 | 21 | 221 | 23 | 0 | 88 | 264 | test_cancellation | Convert `Linearizer` tests from `inlineCallbacks` to async (#12353)
Signed-off-by: Sean Quah <[email protected]> | https://github.com/matrix-org/synapse.git | def test_cancellation(self) -> None:
linearizer = Linearizer()
key = object()
d1, acquired_d1, unblock1 = self._start_task(linearizer, key)
self.assertTrue(acquired_d1.called)
# Create a second task, waiting for the first task.
d2, acquired_d2, _ = self._start_task(linearizer, key)
self.assertFalse(acquired_d2.called)
# Create a third task, waiting for the second task.
d3, acquired_d3, unblock3 = self._start_task(linearizer, key)
self.assertFalse(acquired_d3.called)
# Cancel the waiting second task.
d2.cancel()
unblock1()
self.successResultOf(d1)
self.assertTrue(d2.called)
self.failureResultOf(d2, CancelledError)
# The third task should continue running.
self.assertTrue(
acquired_d3.called,
"Third task did not get the lock after the second task was cancelled",
)
unblock3()
self.successResultOf(d3)
| 134 | test_linearizer.py | Python | tests/util/test_linearizer.py | 41b5f72677ea9763f3cf920d4f6df507653222f2 | synapse | 1 |
|
264,000 | 85 | 13 | 11 | 170 | 14 | 0 | 130 | 216 | add_suffix_to_extension | building: move filename processing of EXTENSION entries to analysis stage
Move filename processing of EXTENSION TOC entries (i.e., converting
the module name to file path and adding the suffix) from the
build stage (i.e., `assemble` in `PKG`, `COLLECT`, and `BUNDLE`)
into analysis stage.
This ensures that during the build stage, the EXTENSION entries
in the TOC are already full filenames, same as other file-based
entries (DATA, BINARY, etc.).
This in turn means that the `add_suffix_to_extension` helper does
not need to worry about DEPENDENCY entries anymore, and can
process only EXTENSION ones, as implied by its name.
Early conversion of EXTENSION module names to file names also
prevents duplication when the same file is collected as both
an EXTENSION and some other type, for example DATA:
```
('torch._C',
'...\\site-packages\\torch\\_C.cp39-win_amd64.pyd',
'EXTENSION'),
('torch\\_C.cp39-win_amd64.pyd',
'...\\site-pakages\\torch\\_C.cp39-win_amd64.pyd',
'DATA'),
```
Prior to this commit, the entries were considered different
from the `TOC` perspective, but led to duplication in onefile
build's PKG once extension's name was changed to the file name
(whereas in onedir build, the first entry was overwritten by
the second). | https://github.com/pyinstaller/pyinstaller.git | def add_suffix_to_extension(inm, fnm, typ):
# No-op for non-extension
if typ != 'EXTENSION':
return inm, fnm, typ
# If inm completely fits into end of the fnm, it has already been processed.
if fnm.endswith(inm):
return inm, fnm, typ
# Change the dotted name into a relative path. This places C extensions in the Python-standard location.
inm = inm.replace('.', os.sep)
# In some rare cases extension might already contain a suffix. Skip it in this case.
if os.path.splitext(inm)[1] not in EXTENSION_SUFFIXES:
# Determine the base name of the file.
base_name = os.path.basename(inm)
assert '.' not in base_name
# Use this file's existing extension. For extensions such as ``libzmq.cp36-win_amd64.pyd``, we cannot use
# ``os.path.splitext``, which would give only the ```.pyd`` part of the extension.
inm = inm + os.path.basename(fnm)[len(base_name):]
return inm, fnm, typ
| 103 | utils.py | Python | PyInstaller/building/utils.py | 04984a040c2396127f234518f783cbed088408bb | pyinstaller | 4 |
|
86,877 | 9 | 9 | 5 | 34 | 3 | 0 | 10 | 33 | clear_region_to_control_producer | chore(hybrid-cloud): AuditLogEntry is a control silo model now (#39890)
In the control silo, creating an audit log entry writes to the db
directly, whilst in region silo mode creating an audit log entry will
instead push to a new kafka producer that consumes into the control silo
asynchronously. | https://github.com/getsentry/sentry.git | def clear_region_to_control_producer():
global _publisher
if _publisher:
_publisher.close()
_publisher = None
| 18 | producer.py | Python | src/sentry/region_to_control/producer.py | 941184cd24186324fd9f7f304b7f713041834726 | sentry | 2 |
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