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int64 20
338k
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int64 2
671
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int64 4
32
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int64 1
451
| n_ast_nodes
int64 12
5.6k
| n_identifiers
int64 1
186
| n_ast_errors
int64 0
10
| n_words
int64 2
2.17k
| n_whitespaces
int64 2
13.8k
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stringlengths 2
73
| commit_message
stringlengths 51
15.3k
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stringlengths 31
59
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stringlengths 51
31k
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stringlengths 0
1.46k
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int64 6
3.32k
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stringlengths 5
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stringclasses 1
value | path
stringlengths 7
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stringlengths 40
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stringlengths 3
28
| complexity
int64 1
153
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
223,960 | 50 | 13 | 16 | 249 | 19 | 0 | 70 | 141 |
load_config
|
Fix imports to get tests passing (#2751)
* Fix broken localization import in theme_tests.py
* Fix tests by importing directly from config submodule
* Fix test runner top level directory
|
https://github.com/mkdocs/mkdocs.git
|
def load_config(**cfg):
path_base = os.path.join(
os.path.abspath(os.path.dirname(__file__)), 'integration', 'minimal'
)
cfg = cfg or {}
if 'site_name' not in cfg:
cfg['site_name'] = 'Example'
if 'config_file_path' not in cfg:
cfg['config_file_path'] = os.path.join(path_base, 'mkdocs.yml')
if 'docs_dir' not in cfg:
# Point to an actual dir to avoid a 'does not exist' error on validation.
cfg['docs_dir'] = os.path.join(path_base, 'docs')
conf = config.Config(schema=config_defaults.get_schema(), config_file_path=cfg['config_file_path'])
conf.load_dict(cfg)
errors_warnings = conf.validate()
assert(errors_warnings == ([], [])), errors_warnings
return conf
| 145 |
base.py
|
Python
|
mkdocs/tests/base.py
|
c93fc91e4dc0ef33e2ea418aaa32b0584a8d354a
|
mkdocs
| 5 |
|
125,174 | 17 | 9 | 3 | 61 | 10 | 0 | 17 | 26 |
_column_type
|
[State Observability] Use a table format by default (#26159)
NOTE: tabulate is copied/pasted to the codebase for table formatting.
This PR changes the default layout to be the table format for both summary and list APIs.
|
https://github.com/ray-project/ray.git
|
def _column_type(strings, has_invisible=True, numparse=True):
types = [_type(s, has_invisible, numparse) for s in strings]
return reduce(_more_generic, types, _bool_type)
| 39 |
tabulate.py
|
Python
|
python/ray/_private/thirdparty/tabulate/tabulate.py
|
adf24bfa9723b0621183bb27f0c889b813c06e8a
|
ray
| 2 |
|
267,750 | 41 | 16 | 12 | 136 | 15 | 0 | 57 | 114 |
parse_python_requires
|
ansible-test - Parse content config only once. (#78418)
|
https://github.com/ansible/ansible.git
|
def parse_python_requires(value): # type: (t.Any) -> tuple[str, ...]
if not isinstance(value, str):
raise ValueError('python_requires must must be of type `str` not type `%s`' % type(value))
versions: tuple[str, ...]
if value == 'default':
versions = SUPPORTED_PYTHON_VERSIONS
elif value == 'controller':
versions = CONTROLLER_PYTHON_VERSIONS
else:
specifier_set = SpecifierSet(value)
versions = tuple(version for version in SUPPORTED_PYTHON_VERSIONS if specifier_set.contains(Version(version)))
return versions
| 79 |
content_config.py
|
Python
|
test/lib/ansible_test/_internal/content_config.py
|
f2abfc4b3d03a2baa078477d0ad2241263a00668
|
ansible
| 6 |
|
249,554 | 78 | 14 | 29 | 313 | 26 | 0 | 136 | 539 |
test_persisting_event_invalidates_cache
|
Fix `have_seen_event` cache not being invalidated (#13863)
Fix https://github.com/matrix-org/synapse/issues/13856
Fix https://github.com/matrix-org/synapse/issues/13865
> Discovered while trying to make Synapse fast enough for [this MSC2716 test for importing many batches](https://github.com/matrix-org/complement/pull/214#discussion_r741678240). As an example, disabling the `have_seen_event` cache saves 10 seconds for each `/messages` request in that MSC2716 Complement test because we're not making as many federation requests for `/state` (speeding up `have_seen_event` itself is related to https://github.com/matrix-org/synapse/issues/13625)
>
> But this will also make `/messages` faster in general so we can include it in the [faster `/messages` milestone](https://github.com/matrix-org/synapse/milestone/11).
>
> *-- https://github.com/matrix-org/synapse/issues/13856*
### The problem
`_invalidate_caches_for_event` doesn't run in monolith mode which means we never even tried to clear the `have_seen_event` and other caches. And even in worker mode, it only runs on the workers, not the master (AFAICT).
Additionally there was bug with the key being wrong so `_invalidate_caches_for_event` never invalidates the `have_seen_event` cache even when it does run.
Because we were using the `@cachedList` wrong, it was putting items in the cache under keys like `((room_id, event_id),)` with a `set` in a `set` (ex. `(('!TnCIJPKzdQdUlIyXdQ:test', '$Iu0eqEBN7qcyF1S9B3oNB3I91v2o5YOgRNPwi_78s-k'),)`) and we we're trying to invalidate with just `(room_id, event_id)` which did nothing.
|
https://github.com/matrix-org/synapse.git
|
def test_persisting_event_invalidates_cache(self):
event, event_context = self.get_success(
create_event(
self.hs,
room_id=self.room_id,
sender=self.user,
type="test_event_type",
content={"body": "garply"},
)
)
with LoggingContext(name="test") as ctx:
# First, check `have_seen_event` for an event we have not seen yet
# to prime the cache with a `false` value.
res = self.get_success(
self.store.have_seen_events(event.room_id, [event.event_id])
)
self.assertEqual(res, set())
# That should result in a single db query to lookup
self.assertEqual(ctx.get_resource_usage().db_txn_count, 1)
# Persist the event which should invalidate or prefill the
# `have_seen_event` cache so we don't return stale values.
persistence = self.hs.get_storage_controllers().persistence
self.get_success(
persistence.persist_event(
event,
event_context,
)
)
with LoggingContext(name="test") as ctx:
# Check `have_seen_event` again and we should see the updated fact
# that we have now seen the event after persisting it.
res = self.get_success(
self.store.have_seen_events(event.room_id, [event.event_id])
)
self.assertEqual(res, {event.event_id})
# That should result in a single db query to lookup
self.assertEqual(ctx.get_resource_usage().db_txn_count, 1)
| 187 |
test_events_worker.py
|
Python
|
tests/storage/databases/main/test_events_worker.py
|
29269d9d3f3419a3d92cdd80dae4a37e2d99a395
|
synapse
| 1 |
|
288,114 | 7 | 8 | 3 | 40 | 6 | 0 | 7 | 21 |
iot_standards
|
Add support for integrations v2 (#78801)
Co-authored-by: Martin Hjelmare <[email protected]>
|
https://github.com/home-assistant/core.git
|
def iot_standards(self) -> list[str]:
return self.brand.get("iot_standards", [])
| 23 |
model.py
|
Python
|
script/hassfest/model.py
|
b173ae7f444a330f92c25dfb5e3d581616a768cd
|
core
| 1 |
|
212,530 | 22 | 15 | 10 | 103 | 14 | 0 | 24 | 97 |
js_files
|
Normalize built-in types and remove `Unknown` (#12252)
* Use lower case names for built-in types
Also incidentally apply TypeAlias marker.
* Drop `Unknown` in favour of consistent usage of `Any`
* Enable lazy annotations in conftest.py
|
https://github.com/bokeh/bokeh.git
|
def js_files(self) -> list[str]:
js_files: list[str] = []
for root, _, files in os.walk(self.bokehjsdir()):
for fname in files:
if fname.endswith(".js"):
js_files.append(join(root, fname))
return js_files
| 64 |
settings.py
|
Python
|
bokeh/settings.py
|
528d85e642340ef30ec91f30b65c7c43370f648d
|
bokeh
| 4 |
|
304,814 | 41 | 10 | 15 | 115 | 20 | 0 | 53 | 224 |
_on_click
|
Improve type hint in flic binary sensor entity (#77161)
|
https://github.com/home-assistant/core.git
|
def _on_click(self, channel, click_type, was_queued, time_diff):
# Return if click event was queued beyond allowed timeout
if was_queued and self._queued_event_check(click_type, time_diff):
return
# Return if click event is in ignored click types
hass_click_type = self._hass_click_types[click_type]
if hass_click_type in self._ignored_click_types:
return
self._hass.bus.fire(
EVENT_NAME,
{
EVENT_DATA_NAME: self.name,
EVENT_DATA_ADDRESS: self._address,
EVENT_DATA_QUEUED_TIME: time_diff,
EVENT_DATA_TYPE: hass_click_type,
},
)
| 77 |
binary_sensor.py
|
Python
|
homeassistant/components/flic/binary_sensor.py
|
3031caafed9811e0b3da146c2ee5a8a7f0080b5e
|
core
| 4 |
|
9,834 | 39 | 10 | 22 | 145 | 15 | 0 | 48 | 162 |
mixin_gateway_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_gateway_parser(parser):
gp = add_arg_group(parser, title='Gateway')
_add_host(gp)
_add_proxy(gp)
gp.add_argument(
'--port-expose',
type=int,
default=helper.random_port(),
help='The port that the gateway exposes for clients for GRPC connections.',
)
parser.add_argument(
'--graph-description',
type=str,
help='Routing graph for the gateway',
default='{}',
)
parser.add_argument(
'--pods-addresses',
type=str,
help='dictionary JSON with the input addresses of each Pod',
default='{}',
)
| 85 |
remote.py
|
Python
|
jina/parsers/peapods/runtimes/remote.py
|
933415bfa1f9eb89f935037014dfed816eb9815d
|
jina
| 1 |
|
64,349 | 79 | 14 | 38 | 462 | 34 | 0 | 107 | 69 |
create_production_plan
|
test: Production Plan Pending Qty impact tests
- Two tests to check impact on pending qty: From SO and independent Prod Plan
- Added docstring to each test case for brief summary
- Changed helper function args to fallback to 0 instead of 1 if no arg is passed
- Removed unnecessary `get_doc()`
- Made helper function actions optional depending on args passed
|
https://github.com/frappe/erpnext.git
|
def create_production_plan(**args):
args = frappe._dict(args)
pln = frappe.get_doc({
'doctype': 'Production Plan',
'company': args.company or '_Test Company',
'customer': args.customer or '_Test Customer',
'posting_date': nowdate(),
'include_non_stock_items': args.include_non_stock_items or 0,
'include_subcontracted_items': args.include_subcontracted_items or 0,
'ignore_existing_ordered_qty': args.ignore_existing_ordered_qty or 0,
'get_items_from': 'Sales Order'
})
if not args.get("sales_order"):
pln.append('po_items', {
'use_multi_level_bom': args.use_multi_level_bom or 1,
'item_code': args.item_code,
'bom_no': frappe.db.get_value('Item', args.item_code, 'default_bom'),
'planned_qty': args.planned_qty or 1,
'planned_start_date': args.planned_start_date or now_datetime()
})
if args.get("get_items_from") == "Sales Order" and args.get("sales_order"):
so = args.get("sales_order")
pln.append('sales_orders', {
'sales_order': so.name,
'sales_order_date': so.transaction_date,
'customer': so.customer,
'grand_total': so.grand_total
})
pln.get_items()
if not args.get("skip_getting_mr_items"):
mr_items = get_items_for_material_requests(pln.as_dict())
for d in mr_items:
pln.append('mr_items', d)
if not args.do_not_save:
pln.insert()
if not args.do_not_submit:
pln.submit()
return pln
| 261 |
test_production_plan.py
|
Python
|
erpnext/manufacturing/doctype/production_plan/test_production_plan.py
|
86ca41b14af45f44ec63a27ed10580b161a33b4c
|
erpnext
| 16 |
|
12,002 | 20 | 13 | 8 | 93 | 9 | 0 | 25 | 65 |
in_docker
|
feat: remove head for non sharded deployments (#4517)
* refactor: remove rolling_update and scale
* feat: skip head creation
* feat: dont create head in k8s and docker
* feat: reduce needs at the gateway
* feat: adapt tests
* style: fix overload and cli autocomplete
* fix: more tests
* fix: more tests
* fix: more tests
* fix: k8s tests
* fix: file handler leaking
* fix: more tests
* fix: k8s tests
* fix: k8s tests
* refactor: move exception
* fix: merge accident
* fix: broken jinad test
* refactor: update docs
* feat: add ports property
* style: fix overload and cli autocomplete
Co-authored-by: Jina Dev Bot <[email protected]>
|
https://github.com/jina-ai/jina.git
|
def in_docker():
path = '/proc/self/cgroup'
if os.path.exists('/.dockerenv'):
return True
if os.path.isfile(path):
with open(path) as file:
return any('docker' in line for line in file)
return False
| 51 |
networking.py
|
Python
|
jina/serve/networking.py
|
12163af01009772035b2e87523663beb890a2549
|
jina
| 4 |
|
20,532 | 34 | 11 | 8 | 71 | 7 | 0 | 39 | 139 |
with_attribute
|
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 with_attribute(*args, **attr_dict):
<div>
Some text
<div type="grid">1 4 0 1 0</div>
<div type="graph">1,3 2,3 1,1</div>
<div>this has no type</div>
</div>
if args:
attrs = args[:]
else:
attrs = attr_dict.items()
attrs = [(k, v) for k, v in attrs]
| 47 |
actions.py
|
Python
|
pipenv/patched/notpip/_vendor/pyparsing/actions.py
|
f3166e673fe8d40277b804d35d77dcdb760fc3b3
|
pipenv
| 3 |
|
47,940 | 22 | 12 | 18 | 109 | 16 | 0 | 24 | 157 |
test_copy_with_target_credential
|
Update to the released version of DBSQL connector
Also added additional parameters for further customization of connection
if it's required
|
https://github.com/apache/airflow.git
|
def test_copy_with_target_credential(self):
expression = "col1, col2"
op = DatabricksCopyIntoOperator(
file_location=COPY_FILE_LOCATION,
file_format='CSV',
table_name='test',
task_id=TASK_ID,
expression_list=expression,
storage_credential='abc',
credential={'AZURE_SAS_TOKEN': 'abc'},
)
assert (
op._create_sql_query()
== f.strip()
)
| 60 |
test_databricks_sql.py
|
Python
|
tests/providers/databricks/operators/test_databricks_sql.py
|
6a3d6cc32b4e3922d259c889460fe82e0ebf3663
|
airflow
| 1 |
|
270,641 | 6 | 7 | 12 | 20 | 3 | 0 | 6 | 21 |
configTestMesh
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def configTestMesh(device_type_mesh_map): # pylint: disable=invalid-name
reset_context()
| 75 |
test_util.py
|
Python
|
keras/dtensor/test_util.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 2 |
|
202,989 | 22 | 13 | 8 | 126 | 15 | 0 | 31 | 67 |
get_commands
|
Refs #32355 -- Removed unnecessary list() calls before reversed() on dictviews.
Dict and dictviews are iterable in reversed insertion order using
reversed() in Python 3.8+.
|
https://github.com/django/django.git
|
def get_commands():
commands = {name: 'django.core' for name in find_commands(__path__[0])}
if not settings.configured:
return commands
for app_config in reversed(apps.get_app_configs()):
path = os.path.join(app_config.path, 'management')
commands.update({name: app_config.name for name in find_commands(path)})
return commands
| 77 |
__init__.py
|
Python
|
django/core/management/__init__.py
|
7346c288e307e1821e3ceb757d686c9bd879389c
|
django
| 5 |
|
171,245 | 4 | 12 | 2 | 41 | 6 | 0 | 4 | 18 |
count
|
DEPR: Remove df.reduction(level) (#49611)
* DEPR: Remove df.reduction(level)
* test_*_consistency
* Fix asv
* Add issue ref
|
https://github.com/pandas-dev/pandas.git
|
def count(self):
return notna(self._values).sum().astype("int64")
| 22 |
series.py
|
Python
|
pandas/core/series.py
|
dbb2adc1f353d9b0835901c274cbe0d2f5a5664f
|
pandas
| 1 |
|
125,678 | 39 | 12 | 12 | 124 | 18 | 0 | 44 | 149 |
get_assigned_resources
|
[core] runtime context resource ids getter (#26907)
|
https://github.com/ray-project/ray.git
|
def get_assigned_resources(self):
assert (
self.worker.mode == ray._private.worker.WORKER_MODE
), f"This method is only available when the process is a\
worker. Current mode: {self.worker.mode}"
self.worker.check_connected()
resource_id_map = self.worker.core_worker.resource_ids()
resource_map = {
res: sum(amt for _, amt in mapping)
for res, mapping in resource_id_map.items()
}
return resource_map
| 71 |
runtime_context.py
|
Python
|
python/ray/runtime_context.py
|
d01a80eb11d32e62e0a20ef8f84852b65be93892
|
ray
| 3 |
|
314,204 | 15 | 9 | 10 | 43 | 6 | 0 | 17 | 67 |
_temperature_unit
|
Weather unit conversion (#73441)
Co-authored-by: Erik <[email protected]>
|
https://github.com/home-assistant/core.git
|
def _temperature_unit(self) -> str:
if (
weather_option_temperature_unit := self._weather_option_temperature_unit
) is not None:
return weather_option_temperature_unit
return self._default_temperature_unit
| 26 |
__init__.py
|
Python
|
homeassistant/components/weather/__init__.py
|
90e1fb6ce2faadb9a35fdbe1774fce7b4456364f
|
core
| 2 |
|
299,644 | 20 | 12 | 11 | 116 | 13 | 0 | 36 | 133 |
referenced_devices
|
Fix missing device & entity references in automations (#71103)
|
https://github.com/home-assistant/core.git
|
def referenced_devices(self):
if self._referenced_devices is not None:
return self._referenced_devices
referenced = self.action_script.referenced_devices
if self._cond_func is not None:
for conf in self._cond_func.config:
referenced |= condition.async_extract_devices(conf)
for conf in self._trigger_config:
referenced |= set(_trigger_extract_device(conf))
self._referenced_devices = referenced
return referenced
| 73 |
__init__.py
|
Python
|
homeassistant/components/automation/__init__.py
|
63679d3d2927f9e6b1029bca994a3fe138480faa
|
core
| 5 |
|
186,082 | 5 | 6 | 5 | 16 | 1 | 0 | 5 | 8 |
test_pressing_alpha_on_app
|
Add a test for actions being fired from bound keys
Do this with a focus on detecting a bound alpha key, and a bound movement key
|
https://github.com/Textualize/textual.git
|
async def test_pressing_alpha_on_app() -> None:
| 40 |
test_binding_inheritance.py
|
Python
|
tests/test_binding_inheritance.py
|
751042f9d7c3a5ffeb026e025412db511e8a04ed
|
textual
| 1 |
|
269,622 | 72 | 18 | 19 | 288 | 37 | 1 | 100 | 366 |
set_value
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def set_value(x, value):
value = np.asarray(value, dtype=dtype_numpy(x))
if tf.compat.v1.executing_eagerly_outside_functions():
x.assign(value)
else:
with get_graph().as_default():
tf_dtype = tf.as_dtype(x.dtype.name.split("_")[0])
if hasattr(x, "_assign_placeholder"):
assign_placeholder = x._assign_placeholder
assign_op = x._assign_op
else:
# In order to support assigning weights to resizable variables in
# Keras, we make a placeholder with the correct number of dimensions
# but with None in each dimension. This way, we can assign weights
# of any size (as long as they have the correct dimensionality).
placeholder_shape = tf.TensorShape([None] * value.ndim)
assign_placeholder = tf.compat.v1.placeholder(
tf_dtype, shape=placeholder_shape
)
assign_op = x.assign(assign_placeholder)
x._assign_placeholder = assign_placeholder
x._assign_op = assign_op
get_session().run(assign_op, feed_dict={assign_placeholder: value})
@keras_export("keras.backend.batch_set_value")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs
|
@keras_export("keras.backend.batch_set_value")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs
| 155 |
backend.py
|
Python
|
keras/backend.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 3 |
45,150 | 43 | 15 | 17 | 183 | 17 | 0 | 64 | 237 |
upsert_document
|
(AzureCosmosDBHook) Update to latest Cosmos API (#21514)
* Bumping the ms azure cosmos providers to work with the 4.x azure python sdk api
Co-authored-by: gatewoodb <[email protected]>
|
https://github.com/apache/airflow.git
|
def upsert_document(self, document, database_name=None, collection_name=None, document_id=None):
# Assign unique ID if one isn't provided
if document_id is None:
document_id = str(uuid.uuid4())
if document is None:
raise AirflowBadRequest("You cannot insert a None document")
# Add document id if isn't found
if 'id' in document:
if document['id'] is None:
document['id'] = document_id
else:
document['id'] = document_id
created_document = (
self.get_conn()
.get_database_client(self.__get_database_name(database_name))
.get_container_client(self.__get_collection_name(collection_name))
.upsert_item(document)
)
return created_document
| 108 |
cosmos.py
|
Python
|
airflow/providers/microsoft/azure/hooks/cosmos.py
|
3c4524b4ec2b42a8af0a8c7b9d8f1d065b2bfc83
|
airflow
| 5 |
|
299,371 | 158 | 14 | 190 | 1,720 | 40 | 0 | 466 | 2,128 |
test_group_features
|
Migrate hue v1 light to color_mode (#69275)
* Migrate hue v1 light to color_mode
* Fix test
* Correct filter_supported_color_modes + add test
* Use ColorMode enum
|
https://github.com/home-assistant/core.git
|
async def test_group_features(hass, mock_bridge_v1):
color_temp_type = "Color temperature light"
extended_color_type = "Extended color light"
group_response = {
"1": {
"name": "Group 1",
"lights": ["1", "2"],
"type": "LightGroup",
"action": {
"on": True,
"bri": 254,
"hue": 10000,
"sat": 254,
"effect": "none",
"xy": [0.5, 0.5],
"ct": 250,
"alert": "select",
"colormode": "ct",
},
"state": {"any_on": True, "all_on": False},
},
"2": {
"name": "Living Room",
"lights": ["2", "3"],
"type": "Room",
"action": {
"on": True,
"bri": 153,
"hue": 4345,
"sat": 254,
"effect": "none",
"xy": [0.5, 0.5],
"ct": 250,
"alert": "select",
"colormode": "ct",
},
"state": {"any_on": True, "all_on": False},
},
"3": {
"name": "Dining Room",
"lights": ["4"],
"type": "Room",
"action": {
"on": True,
"bri": 153,
"hue": 4345,
"sat": 254,
"effect": "none",
"xy": [0.5, 0.5],
"ct": 250,
"alert": "select",
"colormode": "ct",
},
"state": {"any_on": True, "all_on": False},
},
}
light_1 = {
"state": {
"on": True,
"bri": 144,
"ct": 467,
"alert": "none",
"effect": "none",
"reachable": True,
},
"capabilities": {
"control": {
"colorgamuttype": "A",
"colorgamut": [[0.704, 0.296], [0.2151, 0.7106], [0.138, 0.08]],
}
},
"type": color_temp_type,
"name": "Hue Lamp 1",
"modelid": "LCT001",
"swversion": "66009461",
"manufacturername": "Philips",
"uniqueid": "456",
}
light_2 = {
"state": {
"on": False,
"bri": 0,
"ct": 0,
"alert": "none",
"effect": "none",
"colormode": "xy",
"reachable": True,
},
"capabilities": {
"control": {
"colorgamuttype": "A",
"colorgamut": [[0.704, 0.296], [0.2151, 0.7106], [0.138, 0.08]],
}
},
"type": color_temp_type,
"name": "Hue Lamp 2",
"modelid": "LCT001",
"swversion": "66009461",
"manufacturername": "Philips",
"uniqueid": "4567",
}
light_3 = {
"state": {
"on": False,
"bri": 0,
"hue": 0,
"sat": 0,
"xy": [0, 0],
"ct": 0,
"alert": "none",
"effect": "none",
"colormode": "hs",
"reachable": True,
},
"capabilities": {
"control": {
"colorgamuttype": "A",
"colorgamut": [[0.704, 0.296], [0.2151, 0.7106], [0.138, 0.08]],
}
},
"type": extended_color_type,
"name": "Hue Lamp 3",
"modelid": "LCT001",
"swversion": "66009461",
"manufacturername": "Philips",
"uniqueid": "123",
}
light_4 = {
"state": {
"on": True,
"bri": 100,
"hue": 13088,
"sat": 210,
"xy": [0.5, 0.4],
"ct": 420,
"alert": "none",
"effect": "none",
"colormode": "hs",
"reachable": True,
},
"capabilities": {
"control": {
"colorgamuttype": "A",
"colorgamut": [[0.704, 0.296], [0.2151, 0.7106], [0.138, 0.08]],
}
},
"type": extended_color_type,
"name": "Hue Lamp 4",
"modelid": "LCT001",
"swversion": "66009461",
"manufacturername": "Philips",
"uniqueid": "1234",
}
light_response = {
"1": light_1,
"2": light_2,
"3": light_3,
"4": light_4,
}
mock_bridge_v1.mock_light_responses.append(light_response)
mock_bridge_v1.mock_group_responses.append(group_response)
await setup_bridge(hass, mock_bridge_v1)
assert len(mock_bridge_v1.mock_requests) == 2
color_temp_feature = hue_light.SUPPORT_HUE["Color temperature light"]
color_temp_mode = sorted(hue_light.COLOR_MODES_HUE["Color temperature light"])
extended_color_feature = hue_light.SUPPORT_HUE["Extended color light"]
extended_color_mode = sorted(hue_light.COLOR_MODES_HUE["Extended color light"])
group_1 = hass.states.get("light.group_1")
assert group_1.attributes["supported_color_modes"] == color_temp_mode
assert group_1.attributes["supported_features"] == color_temp_feature
group_2 = hass.states.get("light.living_room")
assert group_2.attributes["supported_color_modes"] == extended_color_mode
assert group_2.attributes["supported_features"] == extended_color_feature
group_3 = hass.states.get("light.dining_room")
assert group_3.attributes["supported_color_modes"] == extended_color_mode
assert group_3.attributes["supported_features"] == extended_color_feature
entity_registry = er.async_get(hass)
device_registry = dr.async_get(hass)
entry = entity_registry.async_get("light.hue_lamp_1")
device_entry = device_registry.async_get(entry.device_id)
assert device_entry.suggested_area is None
entry = entity_registry.async_get("light.hue_lamp_2")
device_entry = device_registry.async_get(entry.device_id)
assert device_entry.suggested_area == "Living Room"
entry = entity_registry.async_get("light.hue_lamp_3")
device_entry = device_registry.async_get(entry.device_id)
assert device_entry.suggested_area == "Living Room"
entry = entity_registry.async_get("light.hue_lamp_4")
device_entry = device_registry.async_get(entry.device_id)
assert device_entry.suggested_area == "Dining Room"
| 1,012 |
test_light_v1.py
|
Python
|
tests/components/hue/test_light_v1.py
|
573e966d74221641b13e3530fcf60240da6596be
|
core
| 1 |
|
270,132 | 61 | 15 | 25 | 351 | 27 | 0 | 90 | 194 |
load_data
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def load_data(path="boston_housing.npz", test_split=0.2, seed=113):
assert 0 <= test_split < 1
origin_folder = (
"https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
)
path = get_file(
path,
origin=origin_folder + "boston_housing.npz",
file_hash="f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5",
)
with np.load(
path, allow_pickle=True
) as f: # pylint: disable=unexpected-keyword-arg
x = f["x"]
y = f["y"]
rng = np.random.RandomState(seed)
indices = np.arange(len(x))
rng.shuffle(indices)
x = x[indices]
y = y[indices]
x_train = np.array(x[: int(len(x) * (1 - test_split))])
y_train = np.array(y[: int(len(x) * (1 - test_split))])
x_test = np.array(x[int(len(x) * (1 - test_split)) :])
y_test = np.array(y[int(len(x) * (1 - test_split)) :])
return (x_train, y_train), (x_test, y_test)
| 219 |
boston_housing.py
|
Python
|
keras/datasets/boston_housing.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 1 |
|
119,699 | 91 | 17 | 27 | 451 | 20 | 0 | 200 | 384 |
mock_devices
|
Improve TPU v2 and v3 mesh_utils.create_device_mesh logic.
* Fixes a bug when a non-3D mesh was requested
* Adds new logic when requesting a single-host mesh
* Extends logic to v2 as well as v3
|
https://github.com/google/jax.git
|
def mock_devices(x, y, z, dev_kind, two_cores_per_chip):
devices = []
process_index = 0
for k in range(z):
for j in range(0, y, 2):
for i in range(0, x, 2):
# Local 2x2 subgrid of chips, with 2 cores per chip.
host_devices = [
MockTpuDevice(-1, 'tpu', dev_kind, process_index, (i, j, k), 0),
MockTpuDevice(-1, 'tpu', dev_kind, process_index, (i, j, k), 1),
MockTpuDevice(-1, 'tpu', dev_kind, process_index, (i + 1, j, k), 0),
MockTpuDevice(-1, 'tpu', dev_kind, process_index, (i + 1, j, k), 1),
MockTpuDevice(-1, 'tpu', dev_kind, process_index, (i, j + 1, k), 0),
MockTpuDevice(-1, 'tpu', dev_kind, process_index, (i, j + 1, k), 1),
MockTpuDevice(-1, 'tpu', dev_kind, process_index, (i + 1, j + 1, k), 0),
MockTpuDevice(-1, 'tpu', dev_kind, process_index, (i + 1, j + 1, k), 1),
]
if two_cores_per_chip:
# Only include core_on_chip = 0.
host_devices = host_devices[::2]
devices.extend(host_devices)
# Simulate one process per host (1 host = 2x2x1 slice)
process_index += 1
# id grows in (z, y, x) major order
for d in devices:
i, j, k = d.coords
d.id = k*x*y + j*x + i
if not two_cores_per_chip:
d.id = d.id * 2 + d.core_on_chip
_validate_mocked_process_indices(devices, two_cores_per_chip)
return devices
# If this function raises, it's a bug in the test code!
| 322 |
mesh_utils_test.py
|
Python
|
tests/mesh_utils_test.py
|
bcee442390e0dfbbe078493af0314b515fff97cc
|
jax
| 7 |
|
168,195 | 117 | 16 | 38 | 367 | 43 | 0 | 155 | 615 |
transform_dict_like
|
PERF cache find_stack_level (#48023)
cache stacklevel
|
https://github.com/pandas-dev/pandas.git
|
def transform_dict_like(self, func):
from pandas.core.reshape.concat import concat
obj = self.obj
args = self.args
kwargs = self.kwargs
# transform is currently only for Series/DataFrame
assert isinstance(obj, ABCNDFrame)
if len(func) == 0:
raise ValueError("No transform functions were provided")
func = self.normalize_dictlike_arg("transform", obj, func)
results: dict[Hashable, DataFrame | Series] = {}
failed_names = []
all_type_errors = True
for name, how in func.items():
colg = obj._gotitem(name, ndim=1)
try:
results[name] = colg.transform(how, 0, *args, **kwargs)
except Exception as err:
if str(err) in {
"Function did not transform",
"No transform functions were provided",
}:
raise err
else:
if not isinstance(err, TypeError):
all_type_errors = False
failed_names.append(name)
# combine results
if not results:
klass = TypeError if all_type_errors else ValueError
raise klass("Transform function failed")
if len(failed_names) > 0:
warnings.warn(
f"{failed_names} did not transform successfully. If any error is "
f"raised, this will raise in a future version of pandas. "
f"Drop these columns/ops to avoid this warning.",
FutureWarning,
stacklevel=find_stack_level(inspect.currentframe()),
)
return concat(results, axis=1)
| 227 |
apply.py
|
Python
|
pandas/core/apply.py
|
2f8d0a36703e81e4dca52ca9fe4f58c910c1b304
|
pandas
| 9 |
|
92,049 | 15 | 10 | 46 | 63 | 10 | 0 | 18 | 57 |
set_logged_in
|
feat(SU modal) : Improved superuser modal flow when user has an expired sso session (#35553)
|
https://github.com/getsentry/sentry.git
|
def set_logged_in(self, user, prefilled_su_modal=None, current_datetime=None):
request = self.request
if current_datetime is None:
current_datetime = timezone.now()
token = get_random_string(12)
| 244 |
superuser.py
|
Python
|
src/sentry/auth/superuser.py
|
05ffe4df7f0018cb0990fbd25fc838d0187ccca5
|
sentry
| 8 |
|
127,694 | 9 | 8 | 4 | 39 | 5 | 0 | 10 | 38 |
node_id
|
[core/docs] Update worker docstring (#28495)
Co-authored-by: Philipp Moritz <[email protected]>
|
https://github.com/ray-project/ray.git
|
def node_id(self):
node_id = self.worker.current_node_id
assert not node_id.is_nil()
return node_id
| 22 |
runtime_context.py
|
Python
|
python/ray/runtime_context.py
|
8ffe435173aee0f313f786e7301d05f608b6d5be
|
ray
| 1 |
|
291,293 | 9 | 9 | 2 | 31 | 3 | 0 | 9 | 15 |
test_get_rpc_channel_name
|
Add Shelly tests coverage (#82642)
* Add Shelly tests coverage
* Review comments
* Remove leftovers
|
https://github.com/home-assistant/core.git
|
async def test_get_rpc_channel_name(mock_rpc_device):
assert get_rpc_channel_name(mock_rpc_device, "input:0") == "test switch_0"
| 15 |
test_utils.py
|
Python
|
tests/components/shelly/test_utils.py
|
1e68e8c4b4836c9aabe5451426053428b2af905c
|
core
| 1 |
|
134,166 | 48 | 12 | 11 | 135 | 16 | 0 | 60 | 192 |
collect
|
Fix metrics exporter exporting metrics in incorrect format (#29488)
Signed-off-by: Alan Guo [email protected]
Ray was using prometheus client wrong a few ways:
We were registering the Collector to the RegistryCollector multiple times
The collector was exporting a new "metric" for each tag combination instead of using a single Metric with multiple samples.
We were creating a new RegistryCollector that was unused instead of re-using the "REGISTRY" singleton
|
https://github.com/ray-project/ray.git
|
def collect(self): # pragma: NO COVER
# Make a shallow copy of self._view_name_to_data_map, to avoid seeing
# concurrent modifications when iterating through the dictionary.
metrics_map = {}
for v_name, view_data in self._view_name_to_data_map.copy().items():
if v_name not in self.registered_views:
continue
desc = self.registered_views[v_name]
for tag_values in view_data.tag_value_aggregation_data_map:
agg_data = view_data.tag_value_aggregation_data_map[tag_values]
metric = self.to_metric(desc, tag_values, agg_data, metrics_map)
for metric in metrics_map.values():
yield metric
| 84 |
prometheus_exporter.py
|
Python
|
python/ray/_private/prometheus_exporter.py
|
05ea05d05659eb2bf89ab374f6df67c5573bd4d9
|
ray
| 5 |
|
246,084 | 51 | 12 | 7 | 100 | 13 | 0 | 65 | 178 |
_store_rejected_events_txn
|
Add `state_key` and `rejection_reason` to `events` (#11792)
... and start populating them for new events
|
https://github.com/matrix-org/synapse.git
|
def _store_rejected_events_txn(self, txn, events_and_contexts):
# Remove the rejected events from the list now that we've added them
# to the events table and the events_json table.
to_remove = set()
for event, context in events_and_contexts:
if context.rejected:
# Insert the event_id into the rejections table
# (events.rejection_reason has already been done)
self._store_rejections_txn(txn, event.event_id, context.rejected)
to_remove.add(event)
return [ec for ec in events_and_contexts if ec[0] not in to_remove]
| 63 |
events.py
|
Python
|
synapse/storage/databases/main/events.py
|
2aa37a4250675f6d9feb57ec0dce65b2a6a3cde6
|
synapse
| 5 |
|
210,789 | 28 | 10 | 10 | 186 | 16 | 0 | 45 | 119 |
resize_pos_embed
|
add vit, adamw_ld (#6059)
* add vit, adamw_ld
* update
|
https://github.com/PaddlePaddle/PaddleDetection.git
|
def resize_pos_embed(self, pos_embed, old_hw, new_hw):
cls_pos_embed = pos_embed[:, :1, :]
pos_embed = pos_embed[:, 1:, :]
pos_embed = pos_embed.transpose([0, 2, 1])
pos_embed = pos_embed.reshape([1, -1, old_hw[0], old_hw[1]])
pos_embed = F.interpolate(
pos_embed, new_hw, mode='bicubic', align_corners=False)
pos_embed = pos_embed.flatten(2).transpose([0, 2, 1])
pos_embed = paddle.concat([cls_pos_embed, pos_embed], axis=1)
return pos_embed
| 126 |
vision_transformer.py
|
Python
|
ppdet/modeling/backbones/vision_transformer.py
|
63e7cfa414f67fc7f7cc1117325a0026d7721aab
|
PaddleDetection
| 1 |
|
259,210 | 146 | 20 | 79 | 671 | 45 | 0 | 259 | 1,645 |
_compute_drop_idx
|
ENH Adds infrequent categories to OneHotEncoder (#16018)
* ENH Completely adds infrequent categories
* STY Linting
* STY Linting
* DOC Improves wording
* DOC Lint
* BUG Fixes
* CLN Address comments
* CLN Address comments
* DOC Uses math to description float min_frequency
* DOC Adds comment regarding drop
* BUG Fixes method name
* DOC Clearer docstring
* TST Adds more tests
* FIX Fixes mege
* CLN More pythonic
* CLN Address comments
* STY Flake8
* CLN Address comments
* DOC Fix
* MRG
* WIP
* ENH Address comments
* STY Fix
* ENH Use functiion call instead of property
* ENH Adds counts feature
* CLN Rename variables
* DOC More details
* CLN Remove unneeded line
* CLN Less lines is less complicated
* CLN Less diffs
* CLN Improves readiabilty
* BUG Fix
* CLN Address comments
* TST Fix
* CLN Address comments
* CLN Address comments
* CLN Move docstring to userguide
* DOC Better wrapping
* TST Adds test to handle_unknown='error'
* ENH Spelling error in docstring
* BUG Fixes counter with nan values
* BUG Removes unneeded test
* BUG Fixes issue
* ENH Sync with main
* DOC Correct settings
* DOC Adds docstring
* DOC Immprove user guide
* DOC Move to 1.0
* DOC Update docs
* TST Remove test
* DOC Update docstring
* STY Linting
* DOC Address comments
* ENH Neater code
* DOC Update explaination for auto
* Update sklearn/preprocessing/_encoders.py
Co-authored-by: Roman Yurchak <[email protected]>
* TST Uses docstring instead of comments
* TST Remove call to fit
* TST Spelling error
* ENH Adds support for drop + infrequent categories
* ENH Adds infrequent_if_exist option
* DOC Address comments for user guide
* DOC Address comments for whats_new
* DOC Update docstring based on comments
* CLN Update test with suggestions
* ENH Adds computed property infrequent_categories_
* DOC Adds where the infrequent column is located
* TST Adds more test for infrequent_categories_
* DOC Adds docstring for _compute_drop_idx
* CLN Moves _convert_to_infrequent_idx into its own method
* TST Increases test coverage
* TST Adds failing test
* CLN Careful consideration of dropped and inverse_transform
* STY Linting
* DOC Adds docstrinb about dropping infrequent
* DOC Uses only
* DOC Numpydoc
* TST Includes test for get_feature_names_out
* DOC Move whats new
* DOC Address docstring comments
* DOC Docstring changes
* TST Better comments
* TST Adds check for handle_unknown='ignore' for infrequent
* CLN Make _infrequent_indices private
* CLN Change min_frequency default to None
* DOC Adds comments
* ENH adds support for max_categories=1
* ENH Describe lexicon ordering for ties
* DOC Better docstring
* STY Fix
* CLN Error when explicity dropping an infrequent category
* STY Grammar
Co-authored-by: Joel Nothman <[email protected]>
Co-authored-by: Roman Yurchak <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
|
https://github.com/scikit-learn/scikit-learn.git
|
def _compute_drop_idx(self):
if self.drop is None:
return None
elif isinstance(self.drop, str):
if self.drop == "first":
return np.zeros(len(self.categories_), dtype=object)
elif self.drop == "if_binary":
n_features_out_no_drop = [len(cat) for cat in self.categories_]
if self._infrequent_enabled:
for i, infreq_idx in enumerate(self._infrequent_indices):
if infreq_idx is None:
continue
n_features_out_no_drop[i] -= infreq_idx.size - 1
return np.array(
[
0 if n_features_out == 2 else None
for n_features_out in n_features_out_no_drop
],
dtype=object,
)
else:
msg = (
"Wrong input for parameter `drop`. Expected "
"'first', 'if_binary', None or array of objects, got {}"
)
raise ValueError(msg.format(type(self.drop)))
else:
try:
drop_array = np.asarray(self.drop, dtype=object)
droplen = len(drop_array)
except (ValueError, TypeError):
msg = (
"Wrong input for parameter `drop`. Expected "
"'first', 'if_binary', None or array of objects, got {}"
)
raise ValueError(msg.format(type(drop_array)))
if droplen != len(self.categories_):
msg = (
"`drop` should have length equal to the number "
"of features ({}), got {}"
)
raise ValueError(msg.format(len(self.categories_), droplen))
missing_drops = []
drop_indices = []
for feature_idx, (drop_val, cat_list) in enumerate(
zip(drop_array, self.categories_)
):
if not is_scalar_nan(drop_val):
drop_idx = np.where(cat_list == drop_val)[0]
if drop_idx.size: # found drop idx
drop_indices.append(
self._map_drop_idx_to_infrequent(feature_idx, drop_idx[0])
)
else:
missing_drops.append((feature_idx, drop_val))
continue
# drop_val is nan, find nan in categories manually
for cat_idx, cat in enumerate(cat_list):
if is_scalar_nan(cat):
drop_indices.append(
self._map_drop_idx_to_infrequent(feature_idx, cat_idx)
)
break
else: # loop did not break thus drop is missing
missing_drops.append((feature_idx, drop_val))
if any(missing_drops):
msg = (
"The following categories were supposed to be "
"dropped, but were not found in the training "
"data.\n{}".format(
"\n".join(
[
"Category: {}, Feature: {}".format(c, v)
for c, v in missing_drops
]
)
)
)
raise ValueError(msg)
return np.array(drop_indices, dtype=object)
| 411 |
_encoders.py
|
Python
|
sklearn/preprocessing/_encoders.py
|
7f0006c8aad1a09621ad19c3db19c3ff0555a183
|
scikit-learn
| 20 |
|
181,812 | 6 | 6 | 3 | 24 | 6 | 0 | 6 | 13 |
_gen_grow_safe
|
Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068.
|
https://github.com/EpistasisLab/tpot.git
|
def _gen_grow_safe(self, pset, min_, max_, type_=None):
| 33 |
base.py
|
Python
|
tpot/base.py
|
388616b6247ca4ea8de4e2f340d6206aee523541
|
tpot
| 1 |
|
261,237 | 61 | 12 | 24 | 317 | 26 | 0 | 91 | 216 |
weighted_mode
|
DOC Ensures that sklearn.utils.extmath.weighted_mode passes numpydoc validation (#24571)
Co-authored-by: jeremie du boisberranger <[email protected]>
|
https://github.com/scikit-learn/scikit-learn.git
|
def weighted_mode(a, w, *, axis=0):
if axis is None:
a = np.ravel(a)
w = np.ravel(w)
axis = 0
else:
a = np.asarray(a)
w = np.asarray(w)
if a.shape != w.shape:
w = np.full(a.shape, w, dtype=w.dtype)
scores = np.unique(np.ravel(a)) # get ALL unique values
testshape = list(a.shape)
testshape[axis] = 1
oldmostfreq = np.zeros(testshape)
oldcounts = np.zeros(testshape)
for score in scores:
template = np.zeros(a.shape)
ind = a == score
template[ind] = w[ind]
counts = np.expand_dims(np.sum(template, axis), axis)
mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
oldcounts = np.maximum(counts, oldcounts)
oldmostfreq = mostfrequent
return mostfrequent, oldcounts
| 203 |
extmath.py
|
Python
|
sklearn/utils/extmath.py
|
c674e589f9aa19ebd1151c19413622f96c8ed368
|
scikit-learn
| 4 |
|
64,494 | 8 | 11 | 28 | 48 | 9 | 0 | 9 | 6 |
get_data
|
fix: Get MRs that are yet to be received but fully ordered in Report
- Remove incorrect query clause that only check if ordered qty < 100
- MR should be visible in report until fully received (cycle complete)
|
https://github.com/frappe/erpnext.git
|
def get_data(filters, conditions):
data = frappe.db.sql(.format(conditions=conditions), as_dict=1)
return data
| 30 |
requested_items_to_order_and_receive.py
|
Python
|
erpnext/buying/report/requested_items_to_order_and_receive/requested_items_to_order_and_receive.py
|
d3b0ca30c6ae0e979b7bdddbe67018941be8d59b
|
erpnext
| 1 |
|
260,537 | 38 | 12 | 13 | 147 | 17 | 0 | 44 | 159 |
transform
|
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 transform(self, X):
if isinstance(X, str):
raise ValueError(
"Iterable over raw text documents expected, string object received."
)
self._validate_ngram_range()
analyzer = self.build_analyzer()
X = self._get_hasher().transform(analyzer(doc) for doc in X)
if self.binary:
X.data.fill(1)
if self.norm is not None:
X = normalize(X, norm=self.norm, copy=False)
return X
| 91 |
text.py
|
Python
|
sklearn/feature_extraction/text.py
|
c300a8f2178fcae847f82ad548fe9452f2ba8bbb
|
scikit-learn
| 5 |
|
35,532 | 6 | 9 | 3 | 43 | 6 | 0 | 6 | 27 |
test_causal_lm_model_as_decoder
|
Fix tf.concatenate + test past_key_values for TF models (#15774)
* fix wrong method name tf.concatenate
* add tests related to causal LM / decoder
* make style and quality
* clean-up
* Fix TFBertModel's extended_attention_mask when past_key_values is provided
* Fix tests
* fix copies
* More tf.int8 -> tf.int32 in TF test template
* clean-up
* Update TF test template
* revert the previous commit + update the TF test template
* Fix TF template extended_attention_mask when past_key_values is provided
* Fix some styles manually
* clean-up
* Fix ValueError: too many values to unpack in the test
* Fix more: too many values to unpack in the test
* Add a comment for extended_attention_mask when there is past_key_values
* Fix TFElectra extended_attention_mask when past_key_values is provided
* Add tests to other TF models
* Fix for TF Electra test: add prepare_config_and_inputs_for_decoder
* Fix not passing training arg to lm_head in TFRobertaForCausalLM
* Fix tests (with past) for TF Roberta
* add testing for pask_key_values for TFElectra model
Co-authored-by: ydshieh <[email protected]>
|
https://github.com/huggingface/transformers.git
|
def test_causal_lm_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs)
| 24 |
test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py
|
Python
|
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py
|
8635407bc724c45142c1f91dbc9ef3ea681e1a56
|
transformers
| 1 |
|
144,511 | 11 | 9 | 7 | 86 | 12 | 0 | 11 | 60 |
testAsyncSave
|
[tune] Single wait refactor. (#21852)
This is a down scoped change. For the full overview picture of Tune control loop, see [`Tune control loop refactoring`](https://docs.google.com/document/d/1RDsW7SVzwMPZfA0WLOPA4YTqbRyXIHGYmBenJk33HaE/edit#heading=h.2za3bbxbs5gn)
1. Previously there are separate waits on pg ready and other events. As a result, there are quite a few timing tweaks that are inefficient, hard to understand and unit test. This PR consolidates into a single wait that is handled by TrialRunner in each step.
- A few event types are introduced, and their mapping into scenarios
* PG_READY --> Should place a trial onto it. If somehow there is no trial to be placed there, the pg will be put in _ready momentarily. This is due to historically resources is conceptualized as a pull based model.
* NO_RUNNING_TRIALS_TIME_OUT --> possibly not sufficient resources case
* TRAINING_RESULT
* SAVING_RESULT
* RESTORING_RESULT
* YIELD --> This just means that simply taking very long to train. We need to punt back to the main loop to print out status info etc.
2. Previously TrialCleanup is not very efficient and can be racing between Trainable.stop() and `return_placement_group`. This PR streamlines the Trial cleanup process by explicitly let Trainable.stop() to finish followed by `return_placement_group(pg)`. Note, graceful shutdown is needed in cases like `pause_trial` where checkpointing to memory needs to be given the time to happen before the actor is gone.
3. There are quite some env variables removed (timing tweaks), that I consider OK to proceed without deprecation cycle.
|
https://github.com/ray-project/ray.git
|
def testAsyncSave(self):
trial = Trial("__fake")
self._simulate_starting_trial(trial)
self._simulate_getting_result(trial)
self._simulate_saving(trial)
self.trial_executor.stop_trial(trial)
self.assertEqual(Trial.TERMINATED, trial.status)
| 50 |
test_ray_trial_executor.py
|
Python
|
python/ray/tune/tests/test_ray_trial_executor.py
|
323511b716416088859967686c71889ef8425204
|
ray
| 1 |
|
199,970 | 14 | 17 | 6 | 185 | 9 | 0 | 15 | 69 |
phase_retarder
|
removed backticks around variable names in docs according to PR review
|
https://github.com/sympy/sympy.git
|
def phase_retarder(theta=0, delta=0):
R = Matrix([[cos(theta)**2 + exp(I*delta)*sin(theta)**2,
(1-exp(I*delta))*cos(theta)*sin(theta)],
[(1-exp(I*delta))*cos(theta)*sin(theta),
sin(theta)**2 + exp(I*delta)*cos(theta)**2]])
return R*exp(-I*delta/2)
| 118 |
polarization.py
|
Python
|
sympy/physics/optics/polarization.py
|
ae2baaa0bbcd42792bb2e7887ca61b97abc40463
|
sympy
| 1 |
|
259,552 | 81 | 13 | 30 | 453 | 37 | 1 | 129 | 261 |
test_ridge_regression
|
TST tight and clean tests for Ridge (#22910)
* MNT replace pinvh by solve
* DOC more info for svd solver
* TST rewrite test_ridge
* MNT remove test_ridge_singular
* MNT restructure into several tests
* MNT remove test_toy_ridge_object
* MNT remove test_ridge_sparse_svd
This is tested in test_ridge_fit_intercept_sparse_error.
* TST exclude cholesky from singular problem
* CLN two fixes
* MNT parametrize test_ridge_sample_weights
* MNT restructure test_ridge_sample_weights
* CLN tighten tolerance for sag solver
* CLN try to fix saga tolerance
* CLN make test_ridge_sample_weights nicer
* MNT remove test_ridge_regression_sample_weights
* MNT rename to test_ridge_regression_sample_weights
* CLN make test_ridge_regression_unpenalized pass for all random seeds
* CLN make tests pass for all random seeds
* DOC fix typos
* TST skip cholesky for singular problems
* MNT move up test_ridge_regression_sample_weights
* CLN set skip reason as comment
|
https://github.com/scikit-learn/scikit-learn.git
|
def test_ridge_regression(solver, fit_intercept, ols_ridge_dataset, global_random_seed):
X, y, _, coef = ols_ridge_dataset
alpha = 1.0 # because ols_ridge_dataset uses this.
params = dict(
alpha=alpha,
fit_intercept=True,
solver=solver,
tol=1e-15 if solver in ("sag", "saga") else 1e-10,
random_state=global_random_seed,
)
# Calculate residuals and R2.
res_null = y - np.mean(y)
res_Ridge = y - X @ coef
R2_Ridge = 1 - np.sum(res_Ridge**2) / np.sum(res_null**2)
model = Ridge(**params)
X = X[:, :-1] # remove intercept
if fit_intercept:
intercept = coef[-1]
else:
X = X - X.mean(axis=0)
y = y - y.mean()
intercept = 0
model.fit(X, y)
coef = coef[:-1]
assert model.intercept_ == pytest.approx(intercept)
assert_allclose(model.coef_, coef)
assert model.score(X, y) == pytest.approx(R2_Ridge)
# Same with sample_weight.
model = Ridge(**params).fit(X, y, sample_weight=np.ones(X.shape[0]))
assert model.intercept_ == pytest.approx(intercept)
assert_allclose(model.coef_, coef)
assert model.score(X, y) == pytest.approx(R2_Ridge)
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("fit_intercept", [True, False])
|
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("fit_intercept", [True, False])
| 270 |
test_ridge.py
|
Python
|
sklearn/linear_model/tests/test_ridge.py
|
6528e14085d059f9d0c94f93378e7e3c0b967f27
|
scikit-learn
| 3 |
19,871 | 22 | 10 | 7 | 60 | 7 | 0 | 25 | 75 |
get_file_to_edit
|
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 get_file_to_edit(self) -> Optional[str]:
assert self.load_only is not None, "Need to be specified a file to be editing"
try:
return self._get_parser_to_modify()[0]
except IndexError:
return None
| 36 |
configuration.py
|
Python
|
pipenv/patched/notpip/_internal/configuration.py
|
f3166e673fe8d40277b804d35d77dcdb760fc3b3
|
pipenv
| 2 |
|
171,449 | 47 | 14 | 20 | 166 | 21 | 0 | 64 | 201 |
_set_group_selection
|
BUG: groupby.describe with as_index=False incorrect (#49643)
* BUG: groupby.describe with as_index=False incorrect
* Add test for two groupings
* Simplify logic
|
https://github.com/pandas-dev/pandas.git
|
def _set_group_selection(self) -> None:
# This is a no-op for SeriesGroupBy
grp = self.grouper
if not (
grp.groupings is not None
and self.obj.ndim > 1
and self._group_selection is None
):
return
groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis]
if len(groupers):
# GH12839 clear selected obj cache when group selection changes
ax = self.obj._info_axis
self._group_selection = ax.difference(Index(groupers), sort=False).tolist()
self._reset_cache("_selected_obj")
| 102 |
groupby.py
|
Python
|
pandas/core/groupby/groupby.py
|
68e2c2ae8b714bc9bcbdf9e98793bb681048273a
|
pandas
| 8 |
|
210,791 | 29 | 15 | 10 | 151 | 14 | 0 | 42 | 88 |
layerwise_lr_decay
|
add vit, adamw_ld (#6059)
* add vit, adamw_ld
* update
|
https://github.com/PaddlePaddle/PaddleDetection.git
|
def layerwise_lr_decay(decay_rate, name_dict, n_layers, param):
ratio = 1.0
static_name = name_dict[param.name]
if "blocks" in static_name:
idx = static_name.find("blocks.")
layer = int(static_name[idx:].split(".")[1])
ratio = decay_rate**(n_layers - layer)
elif "cls_token" in static_name or 'patch_embed' in static_name:
ratio = decay_rate**(n_layers + 1)
param.optimize_attr["learning_rate"] *= ratio
| 92 |
adamw.py
|
Python
|
ppdet/optimizer/adamw.py
|
63e7cfa414f67fc7f7cc1117325a0026d7721aab
|
PaddleDetection
| 4 |
|
186,686 | 10 | 10 | 8 | 53 | 6 | 0 | 10 | 46 |
ensure_augeas_state
|
Add typing to certbot.apache (#9071)
* Add typing to certbot.apache
Co-authored-by: Adrien Ferrand <[email protected]>
|
https://github.com/certbot/certbot.git
|
def ensure_augeas_state(self) -> None:
if self.unsaved_files():
self.configurator.save_notes += "(autosave)"
self.configurator.save()
| 29 |
parser.py
|
Python
|
certbot-apache/certbot_apache/_internal/parser.py
|
7d9e9a49005de7961e84d2a7c608db57dbab3046
|
certbot
| 2 |
|
35,049 | 19 | 11 | 6 | 82 | 14 | 0 | 22 | 65 |
overflow_fallback
|
Upgrade black to version ~=22.0 (#15565)
* Upgrade black to version ~=22.0
* Check copies
* Fix code
|
https://github.com/huggingface/transformers.git
|
def overflow_fallback(self, y_int):
self.set_shift(y_int) # adjusts `self.shift`
y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
y_sq_int = y_int_shifted**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
return var_int
| 51 |
quant_modules.py
|
Python
|
src/transformers/models/ibert/quant_modules.py
|
7732d0fe7a759c9844215920e9f1c5540eafb1a6
|
transformers
| 1 |
|
46,604 | 17 | 16 | 7 | 97 | 12 | 0 | 20 | 102 |
_discover_secrets_backends
|
Suppress import errors for providers from sources (#22579)
When we are running airflow locally with providers installed from sources, often many providers will be discovered which we haven't installed the deps for. This generally results in a very large amount of traceback logging, which has a very negative effect on usefulness of terminal output. Here we suppress this error logging for providers that are installed from sources.
|
https://github.com/apache/airflow.git
|
def _discover_secrets_backends(self) -> None:
for provider_package, provider in self._provider_dict.items():
if provider.data.get("secrets-backends"):
for secrets_backends_class_name in provider.data["secrets-backends"]:
if _sanity_check(provider_package, secrets_backends_class_name, provider):
self._secrets_backend_class_name_set.add(secrets_backends_class_name)
| 59 |
providers_manager.py
|
Python
|
airflow/providers_manager.py
|
b5a786b38148295c492da8ab731d5e2f6f86ccf7
|
airflow
| 5 |
|
209,829 | 15 | 10 | 3 | 47 | 5 | 0 | 17 | 53 |
availablemodes
|
[Hinty] Core typing: windows (#3684)
* Core typing: windows
Co-authored-by: Pierre <[email protected]>
|
https://github.com/secdev/scapy.git
|
def availablemodes(self):
# type: () -> List[str]
# According to https://nmap.org/npcap/guide/npcap-devguide.html#npcap-feature-dot11 # noqa: E501
self._check_npcap_requirement()
return self._npcap_get("modes").split(",")
| 23 |
__init__.py
|
Python
|
scapy/arch/windows/__init__.py
|
a2b7a28faff1db058dd22ce097a268e0ad5d1d33
|
scapy
| 1 |
|
261,639 | 33 | 10 | 9 | 120 | 8 | 0 | 51 | 92 |
_safe_assign
|
MAINT test globally setting output via context manager (#24932)
Co-authored-by: jeremie du boisberranger <[email protected]>
|
https://github.com/scikit-learn/scikit-learn.git
|
def _safe_assign(X, values, *, row_indexer=None, column_indexer=None):
row_indexer = slice(None, None, None) if row_indexer is None else row_indexer
column_indexer = (
slice(None, None, None) if column_indexer is None else column_indexer
)
if hasattr(X, "iloc"): # pandas dataframe
X.iloc[row_indexer, column_indexer] = values
else: # numpy array or sparse matrix
X[row_indexer, column_indexer] = values
| 80 |
__init__.py
|
Python
|
sklearn/utils/__init__.py
|
af16e5934ae269d05fd7df983b97def7c0ef0bd2
|
scikit-learn
| 4 |
|
5,829 | 44 | 14 | 18 | 149 | 10 | 0 | 71 | 185 |
verify_liking
|
Updated Instagram xpaths. Added (#6649)
Co-authored-by: RDC Projects <[email protected]>
|
https://github.com/InstaPy/InstaPy.git
|
def verify_liking(browser, maximum, minimum, logger):
post_page = get_additional_data(browser)
likes_count = post_page["items"][0]["like_count"]
if not likes_count:
likes_count = 0
if maximum is not None and likes_count > maximum:
logger.info(
"Not liked this post! ~more likes exist off maximum limit at "
"{}".format(likes_count)
)
return False
elif minimum is not None and likes_count < minimum:
logger.info(
"Not liked this post! ~less likes exist off minimum limit "
"at {}".format(likes_count)
)
return False
return True
| 87 |
like_util.py
|
Python
|
instapy/like_util.py
|
f0f568e5b89952d1609f69a7820d80f1d34b45ad
|
InstaPy
| 6 |
|
248,625 | 12 | 9 | 8 | 88 | 9 | 0 | 16 | 51 |
test_first_upgrade_does_not_block_second
|
Add more tests for room upgrades (#13074)
Signed-off-by: Sean Quah <[email protected]>
|
https://github.com/matrix-org/synapse.git
|
def test_first_upgrade_does_not_block_second(self) -> None:
channel = self._upgrade_room(self.other_token)
self.assertEqual(403, channel.code, channel.result)
channel = self._upgrade_room(expire_cache=False)
self.assertEqual(200, channel.code, channel.result)
| 56 |
test_upgrade_room.py
|
Python
|
tests/rest/client/test_upgrade_room.py
|
99d3931974e65865d1102ee79d7b7e2b017a3180
|
synapse
| 1 |
|
257,656 | 28 | 10 | 6 | 102 | 12 | 0 | 30 | 79 |
test_query_by_embedding_excluded_meta_data_return_embedding_true
|
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_query_by_embedding_excluded_meta_data_return_embedding_true(self, mocked_document_store):
mocked_document_store.return_embedding = True
mocked_document_store.excluded_meta_data = ["foo", "embedding"]
mocked_document_store.query_by_embedding(self.query_emb)
_, kwargs = mocked_document_store.client.search.call_args
# we expect "embedding" was removed from the final query
assert kwargs["body"]["_source"] == {"excludes": ["foo"]}
| 57 |
test_opensearch.py
|
Python
|
test/document_stores/test_opensearch.py
|
e7627c3f8b241654b61f8523479c81f855102f0a
|
haystack
| 1 |
|
166,353 | 62 | 13 | 30 | 325 | 34 | 0 | 106 | 262 |
write_to_compressed
|
ENH: add support for reading .tar archives (#44787)
* Add reproduction test for .tar.gz archives
co-authored-by: Margarete Dippel <[email protected]>
* add support for .tar archives
python's `tarfile` supports gzip, xz and bz2 encoding,
so we don't need to make any special cases for that.
co-authored-by: Margarete Dippel <[email protected]>
* update doc comments
* fix: pep8 errors
* refactor: flip _compression_to_extension around to support multiple extensions on same compression
co-authored-by: Margarete Dippel <[email protected]
y.github.com>
* refactor: detect tar files using existing extension mapping
co-authored-by: Margarete Dippel <[email protected]>
* feat: add support for writing tar files
co-authored-by: Margarete Dippel <[email protected]>
* feat: assure it respects .gz endings
* feat: add "tar" entry to compressionoptions
* chore: add whatsnew entry
* fix: test_compression_size_fh
* add tarfile to shared compression docs
* fix formatting
* pass through "mode" via compression args
* fix pickle test
* add class comment
* sort imports
* add _compression_to_extension back for backwards compatibility
* fix some type warnings
* fix: formatting
* fix: mypy complaints
* fix: more tests
* fix: some error with xml
* fix: interpreted text role
* move to v1.5 whatsnw
* add versionadded note
* don't leave blank lines
* add tests for zero files / multiple files
* move _compression_to_extension to tests
* revert added "mode" argument
* add test to ensure that `compression.mode` works
* compare strings, not bytes
* replace carriage returns
Co-authored-by: Margarete Dippel <[email protected]>
|
https://github.com/pandas-dev/pandas.git
|
def write_to_compressed(compression, path, data, dest="test"):
args: tuple[Any, ...] = (data,)
mode = "wb"
method = "write"
compress_method: Callable
if compression == "zip":
compress_method = zipfile.ZipFile
mode = "w"
args = (dest, data)
method = "writestr"
elif compression == "tar":
compress_method = tarfile.TarFile
mode = "w"
file = tarfile.TarInfo(name=dest)
bytes = io.BytesIO(data)
file.size = len(data)
args = (file, bytes)
method = "addfile"
elif compression == "gzip":
compress_method = gzip.GzipFile
elif compression == "bz2":
compress_method = bz2.BZ2File
elif compression == "zstd":
compress_method = import_optional_dependency("zstandard").open
elif compression == "xz":
compress_method = get_lzma_file()
else:
raise ValueError(f"Unrecognized compression type: {compression}")
with compress_method(path, mode=mode) as f:
getattr(f, method)(*args)
# ------------------------------------------------------------------
# Plotting
| 181 |
_io.py
|
Python
|
pandas/_testing/_io.py
|
864729813a0203af8bb0d30b6c883588ae2c96f8
|
pandas
| 7 |
|
310,960 | 41 | 13 | 16 | 212 | 21 | 0 | 55 | 191 |
extra_state_attributes
|
Add data update coordinator to Whois (#64846)
Co-authored-by: Joakim Sørensen <[email protected]>
|
https://github.com/home-assistant/core.git
|
def extra_state_attributes(self) -> dict[str, int | float | None] | None:
# Only add attributes to the original sensor
if self.entity_description.key != "days_until_expiration":
return None
if self.coordinator.data is None:
return None
attrs = {
ATTR_EXPIRES: self.coordinator.data.expiration_date.isoformat(),
}
if self.coordinator.data.name_servers:
attrs[ATTR_NAME_SERVERS] = " ".join(self.coordinator.data.name_servers)
if self.coordinator.data.last_updated:
attrs[ATTR_UPDATED] = self.coordinator.data.last_updated.isoformat()
if self.coordinator.data.registrar:
attrs[ATTR_REGISTRAR] = self.coordinator.data.registrar
return attrs
| 133 |
sensor.py
|
Python
|
homeassistant/components/whois/sensor.py
|
d15d081646c26d32f860d8f84b4f29d848dab148
|
core
| 6 |
|
43,244 | 9 | 6 | 11 | 23 | 4 | 0 | 9 | 30 |
get_conn
|
fix: RedshiftDataHook and RdsHook not use cached connection (#24387)
|
https://github.com/apache/airflow.git
|
def get_conn(self) -> BaseAwsConnection:
# Compat shim
return self.conn
| 12 |
base_aws.py
|
Python
|
airflow/providers/amazon/aws/hooks/base_aws.py
|
796e0a0b525def2f24d41fc0b5f4dfbe40b29e9e
|
airflow
| 1 |
|
119,831 | 155 | 17 | 54 | 700 | 50 | 1 | 293 | 463 |
polyfit
|
lax_numpy: move poly functions into numpy.polynomial
|
https://github.com/google/jax.git
|
def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
_check_arraylike("polyfit", x, y)
deg = core.concrete_or_error(int, deg, "deg must be int")
order = deg + 1
# check arguments
if deg < 0:
raise ValueError("expected deg >= 0")
if x.ndim != 1:
raise TypeError("expected 1D vector for x")
if x.size == 0:
raise TypeError("expected non-empty vector for x")
if y.ndim < 1 or y.ndim > 2:
raise TypeError("expected 1D or 2D array for y")
if x.shape[0] != y.shape[0]:
raise TypeError("expected x and y to have same length")
# set rcond
if rcond is None:
rcond = len(x) * finfo(x.dtype).eps
rcond = core.concrete_or_error(float, rcond, "rcond must be float")
# set up least squares equation for powers of x
lhs = vander(x, order)
rhs = y
# apply weighting
if w is not None:
_check_arraylike("polyfit", w)
w, = _promote_dtypes_inexact(w)
if w.ndim != 1:
raise TypeError("expected a 1-d array for weights")
if w.shape[0] != y.shape[0]:
raise TypeError("expected w and y to have the same length")
lhs *= w[:, np.newaxis]
if rhs.ndim == 2:
rhs *= w[:, np.newaxis]
else:
rhs *= w
# scale lhs to improve condition number and solve
scale = sqrt((lhs*lhs).sum(axis=0))
lhs /= scale[np.newaxis,:]
c, resids, rank, s = linalg.lstsq(lhs, rhs, rcond)
c = (c.T/scale).T # broadcast scale coefficients
if full:
return c, resids, rank, s, rcond
elif cov:
Vbase = linalg.inv(dot(lhs.T, lhs))
Vbase /= outer(scale, scale)
if cov == "unscaled":
fac = 1
else:
if len(x) <= order:
raise ValueError("the number of data points must exceed order "
"to scale the covariance matrix")
fac = resids / (len(x) - order)
fac = fac[0] #making np.array() of shape (1,) to int
if y.ndim == 1:
return c, Vbase * fac
else:
return c, Vbase[:, :, np.newaxis] * fac
else:
return c
_POLY_DOC =
@_wraps(np.poly, lax_description=_POLY_DOC)
@jit
|
@_wraps(np.poly, lax_description=_POLY_DOC)
@jit
| 424 |
polynomial.py
|
Python
|
jax/_src/numpy/polynomial.py
|
603bb3c5ca288674579211e64fa47c6b2b0fb7a6
|
jax
| 17 |
195,108 | 117 | 18 | 42 | 437 | 49 | 0 | 150 | 639 |
select_paths
|
Add CUDA Kernel for TreeSearch Ngram Blocking (#4633)
* add cuda and cpp code for ngram blocking
* add python wrapper
* modify agent to use cuda kernel for self-blocking
* add context blocking
* change load paths
* add ninja to requirement
* modify setup script to install kernel ahead of time
* change circleci test to use gpu to build website
* change back to JIT, switch directory when loadingcuda moddule
* add check for cuda
* get rid of ninja
* remove unused param
* move "hyps to cuda" into _block_ngrams()
* set gpu_beam_blocking as attribute for TreeSearch, modify block_list function to cast into list, set current ngram_size for context blocking, move path to cpu when needed
* fix lint formatting issues
* add init file to new folders
* add new line at end of file
* new lint errors
* add ninja
* set protobuf
* cast tensor to list in to pass gpu tests
* debug long gpu tests
* fix pointer bug in kernel code and change ngram_size param
* add gpu unit tests and fix torch warning
* skip gpu test unless cuda enabled
* use tolist() for conversion
* get rid of context's conversion to list, add check data before kernel code
* Revert "get rid of context's conversion to list, add check data before kernel code"
This reverts commit 9af834a435bcefb9bd2a049219fe078b7e62e9fd.
* replace tensor with list for cpu code to make faster
* remove unused import
* change botocore version
* change botocore again
* Revert "change botocore again"
This reverts commit a73241c06586015c7c38897fe7aea26e9bca7f16.
* Revert "change botocore version"
This reverts commit 38c8d97aabebc20b109995b1f0413baefe75fc26.
* modify pacer set_batch_context
* remove comments and outdated changes
* add comments and copyright headers
* format c++ and cu file
|
https://github.com/facebookresearch/ParlAI.git
|
def select_paths(self, logprobs, prior_scores, current_length) -> _PathSelection:
# if numel is 1, then this is the first time step, only one hyp is expanded
if prior_scores.numel() == 1:
logprobs = logprobs[0:1]
# beam search actually looks over all hypotheses together so we flatten
beam_scores = logprobs + prior_scores.unsqueeze(1).expand_as(logprobs)
flat_beam_scores = beam_scores.view(-1)
best_scores, best_idxs = torch.topk(flat_beam_scores, self.beam_size, dim=-1)
voc_size = logprobs.size(-1)
# get the backtracking hypothesis id as a multiple of full voc_sizes
hyp_ids = torch.div(best_idxs, voc_size, rounding_mode='trunc')
# get the actual word id from residual of the same division
tok_ids = best_idxs % voc_size
token_details: Optional[List[_PathSelectionTokenDetails]] = None
if self.verbose:
probs = torch.softmax(logprobs, dim=-1)
tok_probs = (
torch.index_select(probs, 0, hyp_ids)
.gather(1, tok_ids.unsqueeze(1))
.view(-1)
)
tok_ranks = (
probs.argsort(1, descending=True)
.argsort(1)
.view(-1)
.gather(0, best_idxs)
)
token_details = []
for tok_logprob, tok_rank in zip(
tok_probs.log().cpu().numpy(), tok_ranks.cpu().numpy()
):
token_details.append(
{
"token_logprob": tok_logprob.item(),
"token_rank": int(tok_rank.item()),
}
)
return _PathSelection(
hypothesis_ids=hyp_ids,
token_ids=tok_ids,
scores=best_scores,
token_details=token_details,
)
| 277 |
torch_generator_agent.py
|
Python
|
parlai/core/torch_generator_agent.py
|
dff9aabb5024c30c81e146cebffbc88bc6431b61
|
ParlAI
| 4 |
|
163,416 | 26 | 15 | 11 | 119 | 14 | 0 | 33 | 138 |
_format_attrs
|
REF: improve rendering of categories in CategoricalIndex (#45340)
|
https://github.com/pandas-dev/pandas.git
|
def _format_attrs(self):
attrs: list[tuple[str, str | int | bool | None]]
attrs = [
(
"categories",
"[" + ", ".join(self._data._repr_categories()) + "]",
),
("ordered", self.ordered),
]
extra = super()._format_attrs()
return attrs + extra
| 70 |
category.py
|
Python
|
pandas/core/indexes/category.py
|
a377f03b190d2802b0061669e8676450205bc479
|
pandas
| 1 |
|
262,880 | 42 | 11 | 11 | 100 | 11 | 0 | 49 | 102 |
get_package_paths
|
hookutils: support multiple package paths in collect_* helpers
Split the functionality of ``get_package_paths`` into two new helpers,
``get_all_package_paths`` and ``package_base_path``. The former obtains
all package paths, while the latter simplifies removal of
package-specific sub-path from the full package-path.
Implement the old, backwards-compatible ``get_package_paths`` using
these helpers; the function now supports namespace packages, but
always returns a single package path and its base path.
Have ``collect_submodules``, ``collect_dynamic_libs``, and
``collect_data_files`` helpers use the new ``get_all_package_paths``
and extend them to process all returned package paths. This enables
proper support for PEP420 namespace packages with multiple package
paths.
|
https://github.com/pyinstaller/pyinstaller.git
|
def get_package_paths(package):
pkg_paths = get_all_package_paths(package)
if not pkg_paths:
raise ValueError(f"Package '{package}' does not exist or is not a package!")
if len(pkg_paths) > 1:
logger.warning(
"get_package_paths - package %s has multiple paths (%r); returning only first one!", package, pkg_paths
)
pkg_dir = pkg_paths[0]
pkg_base = package_base_path(pkg_dir, package)
return pkg_base, pkg_dir
| 58 |
__init__.py
|
Python
|
PyInstaller/utils/hooks/__init__.py
|
e232aaf089d150b085502b97ce0fcf699b45e1b2
|
pyinstaller
| 3 |
|
35,794 | 13 | 10 | 22 | 57 | 9 | 0 | 13 | 38 |
_resize
|
Maskformer (#15682)
* maskformer
* conflicts
* conflicts
* minor fixes
* feature extractor test fix
refactor MaskFormerLoss following conversation
MaskFormer related types should not trigger a module time import error
missed one
removed all the types that are not used
update config mapping
minor updates in the doc
resolved conversation that doesn't need a discussion
minor changes
resolved conversations
fixed DetrDecoder
* minor changes
minor changes
fixed mdx file
test feature_extractor return types
functional losses -> classes
removed the return type test for the feature extractor
minor changes + style + quality
* conflicts?
* rebase master
* readme
* added missing files
* deleded poolformers test that where in the wrong palce
* CI
* minor changes
* Apply suggestions from code review
Co-authored-by: NielsRogge <[email protected]>
* resolved conversations
* minor changes
* conversations
[Unispeech] Fix slow tests (#15818)
* remove soundfile old way of loading audio
* Adapt slow test
[Barthez Tokenizer] Fix saving (#15815)
[TFXLNet] Correct tf xlnet generate (#15822)
* [TFXLNet] Correct tf xlnet
* adapt test comment
Fix the push run (#15807)
Fix semantic segmentation pipeline test (#15826)
Fix dummy_inputs() to dummy_inputs in symbolic_trace doc (#15776)
Add model specific output classes to PoolFormer model docs (#15746)
* Added model specific output classes to poolformer docs
* Fixed Segformer typo in Poolformer docs
Adding the option to return_timestamps on pure CTC ASR models. (#15792)
* Adding the option to return_timestamps on pure CTC ASR models.
* Remove `math.prod` which was introduced in Python 3.8
* int are not floats.
* Reworking the PR to support "char" vs "word" output.
* Fixup!
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <[email protected]>
* Quality.
Co-authored-by: Patrick von Platen <[email protected]>
HFTracer.trace should use/return self.graph to be compatible with torch.fx.Tracer (#15824)
Fix tf.concatenate + test past_key_values for TF models (#15774)
* fix wrong method name tf.concatenate
* add tests related to causal LM / decoder
* make style and quality
* clean-up
* Fix TFBertModel's extended_attention_mask when past_key_values is provided
* Fix tests
* fix copies
* More tf.int8 -> tf.int32 in TF test template
* clean-up
* Update TF test template
* revert the previous commit + update the TF test template
* Fix TF template extended_attention_mask when past_key_values is provided
* Fix some styles manually
* clean-up
* Fix ValueError: too many values to unpack in the test
* Fix more: too many values to unpack in the test
* Add a comment for extended_attention_mask when there is past_key_values
* Fix TFElectra extended_attention_mask when past_key_values is provided
* Add tests to other TF models
* Fix for TF Electra test: add prepare_config_and_inputs_for_decoder
* Fix not passing training arg to lm_head in TFRobertaForCausalLM
* Fix tests (with past) for TF Roberta
* add testing for pask_key_values for TFElectra model
Co-authored-by: ydshieh <[email protected]>
[examples/summarization and translation] fix readme (#15833)
Add ONNX Runtime quantization for text classification notebook (#15817)
Re-enable doctests for the quicktour (#15828)
* Re-enable doctests for the quicktour
* Re-enable doctests for task_summary (#15830)
* Remove &
Framework split model report (#15825)
Add TFConvNextModel (#15750)
* feat: initial implementation of convnext in tensorflow.
* fix: sample code for the classification model.
* chore: added checked for from the classification model.
* chore: set bias initializer in the classification head.
* chore: updated license terms.
* chore: removed ununsed imports
* feat: enabled argument during using drop_path.
* chore: replaced tf.identity with layers.Activation(linear).
* chore: edited default checkpoint.
* fix: minor bugs in the initializations.
* partial-fix: tf model errors for loading pretrained pt weights.
* partial-fix: call method updated
* partial-fix: cross loading of weights (4x3 variables to be matched)
* chore: removed unneeded comment.
* removed playground.py
* rebasing
* rebasing and removing playground.py.
* fix: renaming TFConvNextStage conv and layer norm layers
* chore: added initializers and other minor additions.
* chore: added initializers and other minor additions.
* add: tests for convnext.
* fix: integration tester class.
* fix: issues mentioned in pr feedback (round 1).
* fix: how output_hidden_states arg is propoagated inside the network.
* feat: handling of arg for pure cnn models.
* chore: added a note on equal contribution in model docs.
* rebasing
* rebasing and removing playground.py.
* feat: encapsulation for the convnext trunk.
* Fix variable naming; Test-related corrections; Run make fixup
* chore: added Joao as a contributor to convnext.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* chore: corrected copyright year and added comment on NHWC.
* chore: fixed the black version and ran formatting.
* chore: ran make style.
* chore: removed from_pt argument from test, ran make style.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* fix: tests in the convnext subclass, ran make style.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* chore: moved convnext test to the correct location
* fix: locations for the test file of convnext.
* fix: convnext tests.
* chore: applied sgugger's suggestion for dealing w/ output_attentions.
* chore: added comments.
* chore: applied updated quality enviornment style.
* chore: applied formatting with quality enviornment.
* chore: revert to the previous tests/test_modeling_common.py.
* chore: revert to the original test_modeling_common.py
* chore: revert to previous states for test_modeling_tf_common.py and modeling_tf_utils.py
* fix: tests for convnext.
* chore: removed output_attentions argument from convnext config.
* chore: revert to the earlier tf utils.
* fix: output shapes of the hidden states
* chore: removed unnecessary comment
* chore: reverting to the right test_modeling_tf_common.py.
* Styling nits
Co-authored-by: ariG23498 <[email protected]>
Co-authored-by: Joao Gante <[email protected]>
Co-authored-by: Sylvain Gugger <[email protected]>
* minor changes
* doc fix in feature extractor
* doc
* typose
* removed detr logic from config
* removed detr logic from config
* removed num_labels
* small fix in the config
* auxilary -> auxiliary
* make style
* some test is failing
* fix a weird char in config prevending doc-builder
* retry to fix the doc-builder issue
* make style
* new try to fix the doc builder
* CI
* change weights to facebook
Co-authored-by: NielsRogge <[email protected]>
Co-authored-by: ariG23498 <[email protected]>
Co-authored-by: Joao Gante <[email protected]>
Co-authored-by: Sylvain Gugger <[email protected]>
|
https://github.com/huggingface/transformers.git
|
def _resize(self, image, size, target=None, max_size=None):
if not isinstance(image, Image.Image):
image = self.to_pil_image(image)
| 219 |
feature_extraction_maskformer.py
|
Python
|
src/transformers/models/maskformer/feature_extraction_maskformer.py
|
d83d22f578276e9f201b0b3b0f8f9bd68e86c133
|
transformers
| 5 |
|
83,181 | 83 | 10 | 19 | 324 | 20 | 0 | 126 | 365 |
test_guest_user_subscribe
|
docs: Consistently hyphenate “web-public”.
In English, compound adjectives should essentially always be
hyphenated. This makes them easier to parse, especially for users who
might not recognize that the words “web public” go together as a
phrase.
Signed-off-by: Anders Kaseorg <[email protected]>
|
https://github.com/zulip/zulip.git
|
def test_guest_user_subscribe(self) -> None:
guest_user = self.example_user("polonius")
result = self.common_subscribe_to_streams(guest_user, ["Denmark"], allow_fail=True)
self.assert_json_error(result, "Not allowed for guest users")
# Verify the internal checks also block guest users.
stream = get_stream("Denmark", guest_user.realm)
self.assertEqual(filter_stream_authorization(guest_user, [stream]), ([], [stream]))
stream = self.make_stream("private_stream", invite_only=True)
result = self.common_subscribe_to_streams(guest_user, ["private_stream"], allow_fail=True)
self.assert_json_error(result, "Not allowed for guest users")
self.assertEqual(filter_stream_authorization(guest_user, [stream]), ([], [stream]))
web_public_stream = self.make_stream("web_public_stream", is_web_public=True)
public_stream = self.make_stream("public_stream", invite_only=False)
private_stream = self.make_stream("private_stream2", invite_only=True)
# This test should be added as soon as the subscription endpoint allows
# guest users to subscribe to web-public streams. Although they are already
# authorized, the decorator in "add_subscriptions_backend" still needs to be
# deleted.
#
# result = self.common_subscribe_to_streams(guest_user, ['web_public_stream'],
# is_web_public=True, allow_fail=True)
# self.assert_json_success(result)
streams_to_sub = [web_public_stream, public_stream, private_stream]
self.assertEqual(
filter_stream_authorization(guest_user, streams_to_sub),
([web_public_stream], [public_stream, private_stream]),
)
| 199 |
test_subs.py
|
Python
|
zerver/tests/test_subs.py
|
90e202cd38d00945c81da4730d39e3f5c5b1e8b1
|
zulip
| 1 |
|
243,994 | 29 | 12 | 11 | 147 | 16 | 0 | 37 | 146 |
get_classes_from_csv
|
[Feature] Support OpenImages Dataset (#6331)
* [Feature] support openimage group of eval
* [Feature] support openimage group of eval
* support openimage dataset
* support openimage challenge dataset
* fully support OpenImages-V6 and OpenImages Challenge 2019
* Fix some logic error
* update config file
* fix get data_infos error
* fully support OpenImages evaluation
* update OpenImages config files
* [Feature] support OpenImages datasets
* fix bug
* support load image metas from pipeline
* fix bug
* fix get classes logic error
* update code
* support get image metas
* support openimags
* support collect image metas
* support Open Images
* fix openimages logic
* minor fix
* add a new function to compute openimages tpfp
* minor fix
* fix ci error
* minor fix
* fix indication
* minor fix
* fix returns
* fix returns
* fix returns
* fix returns
* fix returns
* minor fix
* update readme
* support loading image level labels and fix some logic
* minor fix
* minor fix
* add class names
* minor fix
* minor fix
* minor fix
* add openimages test unit
* minor fix
* minor fix
* fix test unit
* minor fix
* fix logic error
* minor fix
* fully support openimages
* minor fix
* fix docstring
* fix docstrings in readthedocs
* update get image metas script
* label_description_file -> label_file
* update openimages readme
* fix test unit
* fix test unit
* minor fix
* update readme file
* Update get_image_metas.py
|
https://github.com/open-mmlab/mmdetection.git
|
def get_classes_from_csv(self, label_file):
index_list = []
classes_names = []
with open(label_file, 'r') as f:
reader = csv.reader(f)
for line in reader:
self.cat2label[line[0]] = line[1]
classes_names.append(line[1])
index_list.append(line[0])
self.index_dict = {index: i for i, index in enumerate(index_list)}
return classes_names
| 91 |
openimages.py
|
Python
|
mmdet/datasets/openimages.py
|
1516986a616fee8bb741d0ab2be40683045efccd
|
mmdetection
| 3 |
|
107,794 | 28 | 12 | 21 | 142 | 22 | 0 | 31 | 152 |
subprocess_run_helper
|
TST: re-arrange sub-process tests to be able to get coverage on them
By putting the implementation in top-level functions and then importing the
test module in the sub-process we are able to get accurate coverage on these
tests.
pytest-cov takes care of all of the coverage related magic implicitly.
Also get coverage information out of isolated tk tests.
Co-authored-by: Elliott Sales de Andrade <[email protected]>
|
https://github.com/matplotlib/matplotlib.git
|
def subprocess_run_helper(func, *args, timeout, **extra_env):
target = func.__name__
module = func.__module__
proc = subprocess.run(
[sys.executable,
"-c",
f,
*args],
env={
**os.environ,
"SOURCE_DATE_EPOCH": "0",
**extra_env
},
timeout=timeout, check=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True)
return proc
| 86 |
__init__.py
|
Python
|
lib/matplotlib/testing/__init__.py
|
efc7f81cf0ee0f9f2875bd1dc5eabf48b06ae14e
|
matplotlib
| 1 |
|
245,726 | 65 | 13 | 22 | 449 | 27 | 0 | 131 | 297 |
encode
|
[Refactor] Refactor anchor head and base head with boxlist (#8625)
* Refactor anchor head
* Update
* Update
* Update
* Add a series of boxes tools
* Fix box type to support n x box_dim boxes
* revert box type changes
* Add docstring
* refactor retina_head
* Update
* Update
* Fix comments
* modify docstring of coder and ioucalculator
* Replace with_boxlist with use_box_type
|
https://github.com/open-mmlab/mmdetection.git
|
def encode(self, bboxes, gt_bboxes, stride):
bboxes = get_box_tensor(bboxes)
gt_bboxes = get_box_tensor(gt_bboxes)
assert bboxes.size(0) == gt_bboxes.size(0)
assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
x_center_gt = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) * 0.5
y_center_gt = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) * 0.5
w_gt = gt_bboxes[..., 2] - gt_bboxes[..., 0]
h_gt = gt_bboxes[..., 3] - gt_bboxes[..., 1]
x_center = (bboxes[..., 0] + bboxes[..., 2]) * 0.5
y_center = (bboxes[..., 1] + bboxes[..., 3]) * 0.5
w = bboxes[..., 2] - bboxes[..., 0]
h = bboxes[..., 3] - bboxes[..., 1]
w_target = torch.log((w_gt / w).clamp(min=self.eps))
h_target = torch.log((h_gt / h).clamp(min=self.eps))
x_center_target = ((x_center_gt - x_center) / stride + 0.5).clamp(
self.eps, 1 - self.eps)
y_center_target = ((y_center_gt - y_center) / stride + 0.5).clamp(
self.eps, 1 - self.eps)
encoded_bboxes = torch.stack(
[x_center_target, y_center_target, w_target, h_target], dim=-1)
return encoded_bboxes
| 321 |
yolo_bbox_coder.py
|
Python
|
mmdet/models/task_modules/coders/yolo_bbox_coder.py
|
d915740fa8228cf57741b27d9e5d66e358456b8e
|
mmdetection
| 1 |
|
176,973 | 25 | 11 | 6 | 91 | 8 | 0 | 33 | 55 |
out_degree_centrality
|
added examples to degree_alg.py (#5644)
* added example on degree centrality
* added example on in degree centrality
* added example on out degree centrality
* added opening braces
|
https://github.com/networkx/networkx.git
|
def out_degree_centrality(G):
if len(G) <= 1:
return {n: 1 for n in G}
s = 1.0 / (len(G) - 1.0)
centrality = {n: d * s for n, d in G.out_degree()}
return centrality
| 61 |
degree_alg.py
|
Python
|
networkx/algorithms/centrality/degree_alg.py
|
b8d1438e4ea3d8190c650110b3b7d7c141224842
|
networkx
| 4 |
|
276,100 | 46 | 17 | 17 | 166 | 14 | 0 | 65 | 217 |
tracing_scope
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def tracing_scope():
# This enables the LayerCallCollection's tracing mechanism to trace all call
# functions in the collection.
previous_value = _thread_local_data.enable_call_tracing
previous_queue = _thread_local_data.trace_queue
try:
_thread_local_data.enable_call_tracing = True
_thread_local_data.trace_queue = []
yield
finally:
# Run traces from the queue.
while _thread_local_data.trace_queue:
fn, args, kwargs, training = _thread_local_data.trace_queue.pop()
if training is not None:
with backend.deprecated_internal_learning_phase_scope(training):
fn.get_concrete_function(*args, **kwargs)
else:
fn.get_concrete_function(*args, **kwargs)
_thread_local_data.trace_queue = previous_queue
_thread_local_data.enable_call_tracing = previous_value
| 97 |
save_impl.py
|
Python
|
keras/saving/saved_model/save_impl.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 4 |
|
8,267 | 38 | 13 | 11 | 139 | 8 | 0 | 49 | 92 |
Deprecated
|
Add API Annotations to Ludwig (#2596)
* Modified annotations.py from Ray for Ludwig
* minor cleanup
* address feedback
* Add reference to Ray in LICENSE
* remove args
* add annotation message
* address comments
|
https://github.com/ludwig-ai/ludwig.git
|
def Deprecated(*args, **kwargs):
if len(args) == 1 and len(kwargs) == 0 and callable(args[0]):
return Deprecated()(args[0])
message = "**DEPRECATED:** This API is deprecated and may be removed in a future Ludwig release."
if "message" in kwargs:
message += " " + kwargs["message"]
del kwargs["message"]
if kwargs:
raise ValueError(f"Unknown kwargs: {kwargs.keys()}")
| 77 |
api_annotations.py
|
Python
|
ludwig/api_annotations.py
|
0c30938d0eeb2383a141012800652e5d59d4aa18
|
ludwig
| 6 |
|
262,617 | 18 | 12 | 4 | 65 | 10 | 0 | 19 | 59 |
on_epoch_start
|
Minors bug fixes on VITS/YourTTS and inference (#2054)
* Set the right device to the speaker encoder
* Bug fix on inference list_language_idxs parameter
* Bug fix on speaker encoder resample audio transform
|
https://github.com/coqui-ai/TTS.git
|
def on_epoch_start(self, trainer): # pylint: disable=W0613
self._freeze_layers()
# set the device of speaker encoder
if self.args.use_speaker_encoder_as_loss:
self.speaker_manager.encoder = self.speaker_manager.encoder.to(self.device)
| 38 |
vits.py
|
Python
|
TTS/tts/models/vits.py
|
f3b947e7066083f97f34ff1bc40911389fd52154
|
TTS
| 2 |
|
320,361 | 11 | 9 | 4 | 59 | 8 | 0 | 12 | 40 |
test_multi_part_language
|
Fixes language code checks around two part languages
|
https://github.com/paperless-ngx/paperless-ngx.git
|
def test_multi_part_language(self, m):
m.return_value = ["chi_sim", "eng"]
msgs = check_default_language_available(None)
self.assertEqual(len(msgs), 0)
| 34 |
test_checks.py
|
Python
|
src/paperless_tesseract/tests/test_checks.py
|
55ef0d4a1b62c3abe8500cad97ddeecf9f746b84
|
paperless-ngx
| 1 |
|
167,736 | 6 | 7 | 5 | 25 | 4 | 0 | 6 | 20 |
kind
|
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 kind(self) -> str:
return self.subtype.kind
| 14 |
dtype.py
|
Python
|
pandas/core/arrays/sparse/dtype.py
|
f65417656ba8c59438d832b6e2a431f78d40c21c
|
pandas
| 1 |
|
208,148 | 39 | 14 | 12 | 222 | 17 | 0 | 55 | 171 |
test_chord_clone_kwargs
|
BLM-2: Adding unit tests to chord clone (#7668)
* Added .python-version and .vscode to .gitignore
* Added test_chord_clone_kwargs() to verify chord cloning treats kwargs correctly
* Happify linter
|
https://github.com/celery/celery.git
|
def test_chord_clone_kwargs(self, subtests):
with subtests.test(msg='Verify chord cloning clones kwargs correctly'):
c = chord([signature('g'), signature('h')], signature('i'), kwargs={'U': 6})
c2 = c.clone()
assert c2.kwargs == c.kwargs
with subtests.test(msg='Cloning the chord with overridden kwargs'):
override_kw = {'X': 2}
c3 = c.clone(args=(1,), kwargs=override_kw)
with subtests.test(msg='Verify the overridden kwargs were cloned correctly'):
new_kw = c.kwargs.copy()
new_kw.update(override_kw)
assert c3.kwargs == new_kw
| 127 |
test_canvas.py
|
Python
|
t/unit/tasks/test_canvas.py
|
c3c6594b4cdea898abba218f576a669700dba98d
|
celery
| 1 |
|
315,391 | 6 | 6 | 3 | 22 | 4 | 0 | 6 | 20 |
source
|
Add instance attributes to GeolocationEvent (#74389)
|
https://github.com/home-assistant/core.git
|
def source(self) -> str:
return self._attr_source
| 12 |
__init__.py
|
Python
|
homeassistant/components/geo_location/__init__.py
|
18840c8af59bfd12c262ca1c6bb68a4cb5f0445c
|
core
| 1 |
|
271,365 | 9 | 11 | 4 | 53 | 9 | 0 | 9 | 25 |
is_input_keras_tensor
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def is_input_keras_tensor(tensor):
if not node_module.is_keras_tensor(tensor):
raise ValueError(_KERAS_TENSOR_TYPE_CHECK_ERROR_MSG.format(tensor))
return tensor.node.is_input
| 31 |
functional_utils.py
|
Python
|
keras/engine/functional_utils.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 2 |
|
250,281 | 15 | 11 | 7 | 72 | 12 | 0 | 16 | 62 |
test_delete_missing_version
|
Add missing type hints to tests.handlers. (#14680)
And do not allow untyped defs in tests.handlers.
|
https://github.com/matrix-org/synapse.git
|
def test_delete_missing_version(self) -> None:
e = self.get_failure(
self.handler.delete_version(self.local_user, "1"), SynapseError
)
res = e.value.code
self.assertEqual(res, 404)
| 44 |
test_e2e_room_keys.py
|
Python
|
tests/handlers/test_e2e_room_keys.py
|
652d1669c5a103b1c20478770c4aaf18849c09a3
|
synapse
| 1 |
|
200,266 | 72 | 17 | 26 | 301 | 23 | 0 | 109 | 276 |
ldescent
|
replaced some broken reference links in doc with working ones
|
https://github.com/sympy/sympy.git
|
def ldescent(A, B):
if abs(A) > abs(B):
w, y, x = ldescent(B, A)
return w, x, y
if A == 1:
return (1, 1, 0)
if B == 1:
return (1, 0, 1)
if B == -1: # and A == -1
return
r = sqrt_mod(A, B)
Q = (r**2 - A) // B
if Q == 0:
B_0 = 1
d = 0
else:
div = divisors(Q)
B_0 = None
for i in div:
sQ, _exact = integer_nthroot(abs(Q) // i, 2)
if _exact:
B_0, d = sign(Q)*i, sQ
break
if B_0 is not None:
W, X, Y = ldescent(A, B_0)
return _remove_gcd((-A*X + r*W), (r*X - W), Y*(B_0*d))
| 190 |
diophantine.py
|
Python
|
sympy/solvers/diophantine/diophantine.py
|
af5e5abd15bb0e914c620a36c74a7555348cd37e
|
sympy
| 9 |
|
209,603 | 84 | 14 | 20 | 192 | 24 | 0 | 118 | 434 |
_send_get_slave_id
|
Add Automotive Logger for all debug outputs of the automotive layer
|
https://github.com/secdev/scapy.git
|
def _send_get_slave_id(self, identifier):
# type: (int) -> List[XCPScannerResult]
all_slaves = []
body = TransportLayerCmd() / TransportLayerCmdGetSlaveId()
xcp_req_and_res_list = \
self._scan(
identifier, body, 0xF2, TransportLayerCmdGetSlaveIdResponse)
for req_and_res in xcp_req_and_res_list:
response = req_and_res[1]
# The protocol will also mark other XCP messages that might be
# send as TransportLayerCmdGetSlaveIdResponse
# -> Payload must be checked. It must include XCP
if response.position_1 != 0x58 or response.position_2 != 0x43 or \
response.position_3 != 0x50:
continue
# Identifier that the master must use to send packets to the slave
# and the slave will answer with
request_id = \
response["TransportLayerCmdGetSlaveIdResponse"].can_identifier
result = XCPScannerResult(request_id=request_id,
response_id=response.identifier)
all_slaves.append(result)
log_automotive.info(
"Detected XCP slave for broadcast identifier: " + str(
identifier) + "\nResponse: " + str(result))
return all_slaves
| 117 |
scanner.py
|
Python
|
scapy/contrib/automotive/xcp/scanner.py
|
495b21f2867e48286767085c8cf2918e4092e9dc
|
scapy
| 5 |
|
104,422 | 4 | 7 | 2 | 22 | 3 | 0 | 4 | 18 |
num_rows
|
Update docs to new frontend/UI (#3690)
* WIP: update docs to new UI
* make style
* Rm unused
* inject_arrow_table_documentation __annotations__
* hasattr(arrow_table_method, "__annotations__")
* Update task_template.rst
* Codeblock PT-TF-SPLIT
* Convert loading scripts
* Convert docs to mdx
* Fix mdx
* Add <Tip>
* Convert mdx tables
* Fix codeblock
* Rm unneded hashlinks
* Update index.mdx
* Redo dev change
* Rm circle ci `build_doc` & `deploy_doc`
* Rm unneeded files
* Update docs reamde
* Standardize to `Example::`
* mdx logging levels doc
* Table properties inject_arrow_table_documentation
* ``` to ```py mdx
* Add Tips mdx
* important,None -> <Tip warning={true}>
* More misc
* Center imgs
* Update instllation page
* `setup.py` docs section
* Rm imgs since they are in hf.co
* Update docs/source/access.mdx
Co-authored-by: Steven Liu <[email protected]>
* Update index mdx
* Update docs/source/access.mdx
Co-authored-by: Steven Liu <[email protected]>
* just `Dataset` obj
* Addedversion just italics
* Update ReadInstruction doc example syntax
* Change docstring for `prepare_for_task`
* Chore
* Remove `code` syntax from headings
* Rm `code` syntax from headings
* Hashlink backward compatability
* S3FileSystem doc
* S3FileSystem doc updates
* index.mdx updates
* Add darkmode gifs
* Index logo img css classes
* Index mdx dataset logo img size
* Docs for DownloadMode class
* Doc DownloadMode table
* format docstrings
* style
* Add doc builder scripts (#3790)
* add doc builder scripts
* fix docker image
* Docs new UI actions no self hosted (#3793)
* No self hosted
* replace doc injection by actual docstrings
* Docstring formatted
Co-authored-by: Quentin Lhoest <[email protected]>
Co-authored-by: Mishig Davaadorj <[email protected]>
Co-authored-by: Lysandre Debut <[email protected]>
Co-authored-by: Mishig Davaadorj <[email protected]>
* Rm notebooks from docs actions since they dont exi
* Update tsting branch
* More docstring
* Chore
* bump up node version
* bump up node
* ``` -> ```py for audio_process.mdx
* Update .github/workflows/build_documentation.yml
Co-authored-by: Quentin Lhoest <[email protected]>
* Uodate dev doc build
* remove run on PR
* fix action
* Fix gh doc workflow
* forgot this change when merging master
* Update build doc
Co-authored-by: Steven Liu <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>
Co-authored-by: Lysandre Debut <[email protected]>
|
https://github.com/huggingface/datasets.git
|
def num_rows(self):
return self.table.num_rows
| 12 |
table.py
|
Python
|
src/datasets/table.py
|
e35be138148333078284b942ccc9ed7b1d826f97
|
datasets
| 1 |
|
244,031 | 28 | 11 | 7 | 146 | 13 | 0 | 37 | 86 |
binary_mask_dice_loss
|
[Feature] Add Maskformer to mmdet (#7212)
* first commit
* add README
* move model description from config to readme
add description for binary_input
add description for dice loss
add a independent panoptic gt processing function
add a independent panoptic gt processing function
remove compatibility of pretrain in maskformer
* update comments in maskformer_head
* update docs format
|
https://github.com/open-mmlab/mmdetection.git
|
def binary_mask_dice_loss(self, mask_preds, gt_masks):
mask_preds = mask_preds.flatten(1)
gt_masks = gt_masks.flatten(1).float()
numerator = 2 * torch.einsum('nc,mc->nm', mask_preds, gt_masks)
denominator = mask_preds.sum(-1)[:, None] + gt_masks.sum(-1)[None, :]
loss = 1 - (numerator + self.eps) / (denominator + self.eps)
return loss
| 92 |
match_cost.py
|
Python
|
mmdet/core/bbox/match_costs/match_cost.py
|
cac356380d505bf15587f07c0529218cc36b9652
|
mmdetection
| 1 |
|
108,043 | 22 | 8 | 59 | 37 | 8 | 0 | 23 | 32 |
_mark_every_path
|
FIX: allow float markevery with nans in line data in _mark_every_path()
- TST: new test_markevery_linear_scales_nans() test + baseline images
|
https://github.com/matplotlib/matplotlib.git
|
def _mark_every_path(markevery, tpath, affine, ax):
# pull out the two bits of data we want from the path
codes, verts = tpath.codes, tpath.vertices
| 485 |
lines.py
|
Python
|
lib/matplotlib/lines.py
|
99d9475dae7679cc99457f2f804665cfff972639
|
matplotlib
| 13 |
|
22,857 | 76 | 13 | 18 | 173 | 19 | 0 | 104 | 266 |
hear
|
VoiceAssistant
This is Voice Assistant coded using Python which can do the following: -
1. Speak Text entered by User.
2. Search anything on Google.
3. Search anything on Wikipedia.
4. Read an MS Word(docx) document.
5. Read a book(PDF).
6. Can be used as a Dictator.
|
https://github.com/geekcomputers/Python.git
|
def hear():
r = sr.Recognizer()
r.pause_threshold = 1 # a pause of more than 1 second will stop the microphone temporarily
r.energy_threshold = 300 # python by default sets it to 300. It is the minimum input energy to be considered.
r.dynamic_energy_threshold = True # pyhton now can dynamically change the threshold energy
with sr.Microphone() as source:
# read the audio data from the default microphone
print(Fore.RED + "\nListening...")
#time.sleep(0.5)
speech = r.record(source, duration = 9) # option
#speech = r.listen(source)
# convert speech to text
try:
#print("Recognizing...")
recognizing()
speech = r.recognize_google(speech)
print(speech + "\n")
except Exception as exception:
print(exception)
return "None"
return speech
| 89 |
speakListen.py
|
Python
|
VoiceAssistant/Project_Basic_struct/speakListen.py
|
39c49e07066b2a53e176d555af6a7bf8aabb8a9c
|
Python
| 2 |
|
126,016 | 15 | 10 | 6 | 68 | 5 | 0 | 18 | 40 |
force_on_current_node
|
[AIR] Remove ML code from `ray.util` (#27005)
Removes all ML related code from `ray.util`
Removes:
- `ray.util.xgboost`
- `ray.util.lightgbm`
- `ray.util.horovod`
- `ray.util.ray_lightning`
Moves `ray.util.ml_utils` to other locations
Closes #23900
Signed-off-by: Amog Kamsetty <[email protected]>
Signed-off-by: Kai Fricke <[email protected]>
Co-authored-by: Kai Fricke <[email protected]>
|
https://github.com/ray-project/ray.git
|
def force_on_current_node(task_or_actor=None):
node_resource_key = _get_current_node_resource_key()
options = {"resources": {node_resource_key: 0.01}}
if task_or_actor is None:
return options
return task_or_actor.options(**options)
| 41 |
node.py
|
Python
|
python/ray/tune/utils/node.py
|
862d10c162421706f77f73428429379a8b22fc38
|
ray
| 2 |
|
22,681 | 11 | 9 | 4 | 74 | 8 | 0 | 15 | 43 |
test_copy
|
refactor: clean code
Signed-off-by: slowy07 <[email protected]>
|
https://github.com/geekcomputers/Python.git
|
def test_copy(self):
x = Vector([1, 0, 0, 0, 0, 0])
y = x.copy()
self.assertEqual(x.__str__(), y.__str__())
| 47 |
tests.py
|
Python
|
linear-algebra-python/src/tests.py
|
f0af0c43340763724f139fa68aa1e5a9ffe458b4
|
Python
| 1 |
|
88,240 | 80 | 13 | 31 | 221 | 15 | 0 | 122 | 478 |
_get_context
|
test: Add missing tests to sentry/relay/config/__init__.py [TET-504] (#41058)
This PR increase code coverage from ~82% upto 98% in
sentry/relay/config/__init__.py.
codecov [report](https://app.codecov.io/gh/getsentry/sentry/pull/41058):
<img width="1060" alt="image"
src="https://user-images.githubusercontent.com/1374633/200516881-ed23da43-37df-4fc2-b291-310fc13f0ff5.png">
|
https://github.com/getsentry/sentry.git
|
def _get_context(self, key):
if not key:
return ({}, None, None)
sdk_version = get_browser_sdk_version(key)
# From JavaScript SDK version 7 onwards, the default bundle code is ES6, however, in the loader we
# want to provide the ES5 version. This is why we need to modify the requested bundle name here.
bundle_kind_modifier = ""
if sdk_version >= Version("7.0.0"):
bundle_kind_modifier = ".es5"
js_sdk_loader_default_sdk_url_template_slot_count = (
settings.JS_SDK_LOADER_DEFAULT_SDK_URL.count("%s")
)
try:
if js_sdk_loader_default_sdk_url_template_slot_count == 2:
sdk_url = settings.JS_SDK_LOADER_DEFAULT_SDK_URL % (
sdk_version,
bundle_kind_modifier,
)
elif js_sdk_loader_default_sdk_url_template_slot_count == 1:
sdk_url = settings.JS_SDK_LOADER_DEFAULT_SDK_URL % (sdk_version,)
else:
sdk_url = settings.JS_SDK_LOADER_DEFAULT_SDK_URL
except TypeError:
sdk_url = "" # It fails if it cannot inject the version in the string
return (
{
"config": {"dsn": key.dsn_public},
"jsSdkUrl": sdk_url,
"publicKey": key.public_key,
},
sdk_version,
sdk_url,
)
| 130 |
js_sdk_loader.py
|
Python
|
src/sentry/web/frontend/js_sdk_loader.py
|
4821e6846b007cce0092f43141e4b436beb2bedc
|
sentry
| 6 |
|
259,079 | 44 | 9 | 8 | 110 | 7 | 0 | 60 | 90 |
test_normalized_mutual_info_score_bounded
|
FIX better handle limit cases in normalized_mutual_info_score (#22635)
|
https://github.com/scikit-learn/scikit-learn.git
|
def test_normalized_mutual_info_score_bounded(average_method):
labels1 = [0] * 469
labels2 = [1] + labels1[1:]
labels3 = [0, 1] + labels1[2:]
# labels1 is constant. The mutual info between labels1 and any other labelling is 0.
nmi = normalized_mutual_info_score(labels1, labels2, average_method=average_method)
assert nmi == 0
# non constant, non perfect matching labels
nmi = normalized_mutual_info_score(labels2, labels3, average_method=average_method)
assert 0 <= nmi < 1
| 71 |
test_supervised.py
|
Python
|
sklearn/metrics/cluster/tests/test_supervised.py
|
020ee761c5c737e12a1e98897c7e4617271d0f66
|
scikit-learn
| 1 |
|
293,290 | 21 | 10 | 10 | 88 | 7 | 0 | 30 | 109 |
async_internal_added_to_hass
|
Prevent scene from restoring unavailable states (#67836)
|
https://github.com/home-assistant/core.git
|
async def async_internal_added_to_hass(self) -> None:
await super().async_internal_added_to_hass()
state = await self.async_get_last_state()
if (
state is not None
and state.state is not None
and state.state != STATE_UNAVAILABLE
):
self.__last_activated = state.state
| 52 |
__init__.py
|
Python
|
homeassistant/components/scene/__init__.py
|
c9ac0b49f6e0c566f97a053da6a242455ac40671
|
core
| 4 |
|
181,904 | 5 | 6 | 36 | 25 | 5 | 0 | 5 | 8 |
generate_import_code
|
Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068.
|
https://github.com/EpistasisLab/tpot.git
|
def generate_import_code(pipeline, operators, impute=False, random_state=None):
| 222 |
export_utils.py
|
Python
|
tpot/export_utils.py
|
388616b6247ca4ea8de4e2f340d6206aee523541
|
tpot
| 16 |
|
259,701 | 89 | 16 | 34 | 371 | 36 | 0 | 146 | 574 |
_minibatch_step
|
FEA Online implementation of non-negative matrix factorization (#16948)
Co-authored-by: Tom Dupré la Tour <[email protected]>
Co-authored-by: jeremie du boisberranger <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]>
|
https://github.com/scikit-learn/scikit-learn.git
|
def _minibatch_step(self, X, W, H, update_H):
batch_size = X.shape[0]
# get scaled regularization terms. Done for each minibatch to take into account
# variable sizes of minibatches.
l1_reg_W, l1_reg_H, l2_reg_W, l2_reg_H = self._scale_regularization(X)
# update W
if self.fresh_restarts or W is None:
W = self._solve_W(X, H, self.fresh_restarts_max_iter)
else:
W, *_ = _multiplicative_update_w(
X, W, H, self._beta_loss, l1_reg_W, l2_reg_W, self._gamma
)
# necessary for stability with beta_loss < 1
if self._beta_loss < 1:
W[W < np.finfo(np.float64).eps] = 0.0
batch_cost = (
_beta_divergence(X, W, H, self._beta_loss)
+ l1_reg_W * W.sum()
+ l1_reg_H * H.sum()
+ l2_reg_W * (W**2).sum()
+ l2_reg_H * (H**2).sum()
) / batch_size
# update H (only at fit or fit_transform)
if update_H:
H[:] = _multiplicative_update_h(
X,
W,
H,
beta_loss=self._beta_loss,
l1_reg_H=l1_reg_H,
l2_reg_H=l2_reg_H,
gamma=self._gamma,
A=self._components_numerator,
B=self._components_denominator,
rho=self._rho,
)
# necessary for stability with beta_loss < 1
if self._beta_loss <= 1:
H[H < np.finfo(np.float64).eps] = 0.0
return batch_cost
| 253 |
_nmf.py
|
Python
|
sklearn/decomposition/_nmf.py
|
69132ebbd39f070590ca01813340b5b12c0d02ab
|
scikit-learn
| 6 |
|
107,591 | 38 | 14 | 15 | 172 | 10 | 0 | 57 | 281 |
set_rlim
|
Simplify impl. of polar limits setting API.
AFAICT we can just inherit set_ylim. Also slightly improve the docs of
set_rlim.
|
https://github.com/matplotlib/matplotlib.git
|
def set_rlim(self, bottom=None, top=None, emit=True, auto=False, **kwargs):
if 'rmin' in kwargs:
if bottom is None:
bottom = kwargs.pop('rmin')
else:
raise ValueError('Cannot supply both positional "bottom"'
'argument and kwarg "rmin"')
if 'rmax' in kwargs:
if top is None:
top = kwargs.pop('rmax')
else:
raise ValueError('Cannot supply both positional "top"'
'argument and kwarg "rmax"')
return self.set_ylim(bottom=bottom, top=top, emit=emit, auto=auto,
**kwargs)
| 101 |
polar.py
|
Python
|
lib/matplotlib/projections/polar.py
|
4dd64cfe842dae6647ec0289a23fb3074272b0b6
|
matplotlib
| 5 |
|
189,212 | 68 | 16 | 52 | 402 | 19 | 0 | 210 | 1,052 |
call
|
Delete extra whitespace
A correction that does not affect the operation.
|
https://github.com/aws/aws-cli.git
|
def call(self, src_files, dest_files):
# :var src_done: True if there are no more files from the source left.
src_done = False
# :var dest_done: True if there are no more files form the dest left.
dest_done = False
# :var src_take: Take the next source file from the generated files if
# true
src_take = True
# :var dest_take: Take the next dest file from the generated files if
# true
dest_take = True
while True:
try:
if (not src_done) and src_take:
src_file = advance_iterator(src_files)
except StopIteration:
src_file = None
src_done = True
try:
if (not dest_done) and dest_take:
dest_file = advance_iterator(dest_files)
except StopIteration:
dest_file = None
dest_done = True
if (not src_done) and (not dest_done):
src_take = True
dest_take = True
compare_keys = self.compare_comp_key(src_file, dest_file)
if compare_keys == 'equal':
should_sync = self._sync_strategy.determine_should_sync(
src_file, dest_file
)
if should_sync:
yield src_file
elif compare_keys == 'less_than':
src_take = True
dest_take = False
should_sync = self._not_at_dest_sync_strategy.determine_should_sync(src_file, None)
if should_sync:
yield src_file
elif compare_keys == 'greater_than':
src_take = False
dest_take = True
should_sync = self._not_at_src_sync_strategy.determine_should_sync(None, dest_file)
if should_sync:
yield dest_file
elif (not src_done) and dest_done:
src_take = True
should_sync = self._not_at_dest_sync_strategy.determine_should_sync(src_file, None)
if should_sync:
yield src_file
elif src_done and (not dest_done):
dest_take = True
should_sync = self._not_at_src_sync_strategy.determine_should_sync(None, dest_file)
if should_sync:
yield dest_file
else:
break
| 239 |
comparator.py
|
Python
|
awscli/customizations/s3/comparator.py
|
8a16d7d8ce5e3f97fb100af7a960224f7f80137d
|
aws-cli
| 22 |
|
8,638 | 37 | 12 | 8 | 140 | 16 | 0 | 55 | 132 |
test_user_window_size
|
Enable dataset window autosizing (#2721)
* set windowed shuffle for large datasets
* documentation
* update to automatic windowing flag
* address reviews
* address reviews
* update logging info and add auto_window flag passthrough
* update tests to use flag passthrough
* more descriptive test class name
* todo to add link to windowing docs
* local test handling for dask import
* handle RayDataset import in local tests
* bad type annotation
* bad type annotation
|
https://github.com/ludwig-ai/ludwig.git
|
def test_user_window_size(self, ray_cluster_small_object_store):
# This pipeline should use the heuristic window size.
ds = self.create_dataset(self.object_store_size * 8)
pipe = ds.pipeline()
rep = next(iter(pipe._base_iterable))()
auto_num_blocks = rep.num_blocks()
# This pipeline should have fewer windows but more blocks per window
# than the autosized pipeline.
pipe = ds.pipeline(window_size_bytes=self.auto_window_size * 2)
rep = next(iter(pipe._base_iterable))()
assert auto_num_blocks < rep.num_blocks()
| 82 |
test_ray.py
|
Python
|
tests/integration_tests/test_ray.py
|
0d19a48cff0958ed77926a0712cbdb6485d4034a
|
ludwig
| 1 |
|
288,716 | 6 | 7 | 3 | 22 | 3 | 0 | 6 | 12 |
_generate_client_device_id
|
Use persistent device id for jellyfin requests (#79840)
|
https://github.com/home-assistant/core.git
|
def _generate_client_device_id() -> str:
return random_uuid_hex()
| 11 |
config_flow.py
|
Python
|
homeassistant/components/jellyfin/config_flow.py
|
5b0a37a44752edbbf785d6a200e3b7a3f5fa2047
|
core
| 1 |
|
176,268 | 22 | 11 | 7 | 77 | 8 | 0 | 27 | 46 |
find_cliques_recursive
|
Fix functions appearing in variables `__all__` but not in docs for NX2.7 (#5289)
* Adjust functions appearing in `__all__` but not in docs
* clean up coloring: merge two modules make interchange private
* fix duplicate name. Probably should be changed
* fix "see also" doc of recursive_simple_cycles
* Rm internal uses of deprecated .
* Fixup warnings filters regex.
* clean up a bit more, make Node & AdjList private classes
Co-authored-by: Ross Barnowski <[email protected]>
Co-authored-by: Mridul Seth <[email protected]>
|
https://github.com/networkx/networkx.git
|
def find_cliques_recursive(G):
if len(G) == 0:
return iter([])
adj = {u: {v for v in G[u] if v != u} for u in G}
Q = []
| 63 |
clique.py
|
Python
|
networkx/algorithms/clique.py
|
17fa9942568bfca34d4a68f8d93c538014f69389
|
networkx
| 5 |
|
210,293 | 17 | 10 | 6 | 82 | 12 | 0 | 21 | 63 |
predict_skeleton_with_mot
|
Pipeline with kpt and act (#5399)
* add keypoint infer and visualize into Pipeline
* add independent action model inference
* add action inference into pipeline, still in working
* test different display frames and normalization methods
* use bbox and scale normalization
* Remove debug info and Optimize code structure
* remove useless visual param
* make action parameters configurable
|
https://github.com/PaddlePaddle/PaddleDetection.git
|
def predict_skeleton_with_mot(self, skeleton_with_mot, run_benchmark=False):
skeleton_list = skeleton_with_mot["skeleton"]
mot_id = skeleton_with_mot["mot_id"]
act_res = self.predict_skeleton(skeleton_list, run_benchmark, repeats=1)
results = list(zip(mot_id, act_res))
return results
| 51 |
action_infer.py
|
Python
|
deploy/python/action_infer.py
|
7018dad10757b6d414f1b00a547244bced596d68
|
PaddleDetection
| 1 |
|
322,141 | 4 | 9 | 2 | 28 | 4 | 0 | 4 | 10 |
istree
|
Update neural search readme and Add Paddle Serving Support (#1558)
* add recall inference similarity
* update examples
* updatea readme
* update dir name
* update neural search readme
* update milvus readme
* update domain adaptive pretraining readme
* fix the mistakes
* update readme
* add recall Paddle Serving Support
* update readme
* update readme and format the code
* reformat the files
* move the files
* reformat the code
* remove redundant code
Co-authored-by: Zeyu Chen <[email protected]>
Co-authored-by: tianxin <[email protected]>
|
https://github.com/PaddlePaddle/PaddleNLP.git
|
def istree(sequence):
return DepTree(sequence).judge_legal()
| 15 |
utils.py
|
Python
|
examples/dependency_parsing/ddparser/utils.py
|
621357338437ee420eabbbf5ab19065bc85e73a5
|
PaddleNLP
| 1 |
|
273,618 | 6 | 6 | 2 | 20 | 5 | 0 | 6 | 20 |
call
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def call(self, inputs, states):
raise NotImplementedError
| 12 |
abstract_rnn_cell.py
|
Python
|
keras/layers/rnn/abstract_rnn_cell.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 1 |
|
177,252 | 53 | 14 | 20 | 223 | 23 | 0 | 73 | 220 |
intersection_all
|
Make all.py generator friendly (#5984)
* Make compose_all generator friendly
* Make disjoint_union_all and intersection_all generator friendly
* Refactor disjoint_union_all to yield relabeled graphs
* Make union_all generator friendly
* Fix intersection_all
* Fix union_all signature
* Allow passing an infinite rename generator to union_all
* Copy over generalizations to binary.py
* Clean up rename
* Simplify first_label in disjoint_union_all
* Simplify disjoint_union_all
* Add missing R.graph.update in intersection_all
|
https://github.com/networkx/networkx.git
|
def intersection_all(graphs):
R = None
for i, G in enumerate(graphs):
G_nodes_set = set(G.nodes)
G_edges_set = set(G.edges(keys=True) if G.is_multigraph() else G.edges())
if i == 0:
# create new graph
R = G.__class__()
node_intersection = G_nodes_set
edge_intersection = G_edges_set
elif G.is_multigraph() != R.is_multigraph():
raise nx.NetworkXError("All graphs must be graphs or multigraphs.")
else:
node_intersection &= G_nodes_set
edge_intersection &= G_edges_set
R.graph.update(G.graph)
if R is None:
raise ValueError("cannot apply intersection_all to an empty list")
R.add_nodes_from(node_intersection)
R.add_edges_from(edge_intersection)
return R
| 132 |
all.py
|
Python
|
networkx/algorithms/operators/all.py
|
50ff08de69c6e9541cd6c029bede5dabf56cfe73
|
networkx
| 6 |
|
60,403 | 12 | 11 | 6 | 61 | 7 | 0 | 12 | 22 |
PrintUsage
|
Balanced joint maximum mean discrepancy for deep transfer learning
|
https://github.com/jindongwang/transferlearning.git
|
def PrintUsage(message):
sys.stderr.write(_USAGE)
if message:
sys.exit('\nFATAL ERROR: ' + message)
else:
sys.exit(1)
| 33 |
cpp_lint.py
|
Python
|
code/deep/BJMMD/caffe/scripts/cpp_lint.py
|
cc4d0564756ca067516f71718a3d135996525909
|
transferlearning
| 2 |
|
249,109 | 91 | 11 | 61 | 464 | 38 | 0 | 159 | 762 |
test_delete_media
|
Use literals in place of `HTTPStatus` constants in tests (#13469)
|
https://github.com/matrix-org/synapse.git
|
def test_delete_media(self) -> None:
download_resource = self.media_repo.children[b"download"]
upload_resource = self.media_repo.children[b"upload"]
# Upload some media into the room
response = self.helper.upload_media(
upload_resource,
SMALL_PNG,
tok=self.admin_user_tok,
expect_code=200,
)
# Extract media ID from the response
server_and_media_id = response["content_uri"][6:] # Cut off 'mxc://'
server_name, media_id = server_and_media_id.split("/")
self.assertEqual(server_name, self.server_name)
# Attempt to access media
channel = make_request(
self.reactor,
FakeSite(download_resource, self.reactor),
"GET",
server_and_media_id,
shorthand=False,
access_token=self.admin_user_tok,
)
# Should be successful
self.assertEqual(
200,
channel.code,
msg=(
"Expected to receive a 200 on accessing media: %s" % server_and_media_id
),
)
# Test if the file exists
local_path = self.filepaths.local_media_filepath(media_id)
self.assertTrue(os.path.exists(local_path))
url = "/_synapse/admin/v1/media/%s/%s" % (self.server_name, media_id)
# Delete media
channel = self.make_request(
"DELETE",
url,
access_token=self.admin_user_tok,
)
self.assertEqual(200, channel.code, msg=channel.json_body)
self.assertEqual(1, channel.json_body["total"])
self.assertEqual(
media_id,
channel.json_body["deleted_media"][0],
)
# Attempt to access media
channel = make_request(
self.reactor,
FakeSite(download_resource, self.reactor),
"GET",
server_and_media_id,
shorthand=False,
access_token=self.admin_user_tok,
)
self.assertEqual(
HTTPStatus.NOT_FOUND,
channel.code,
msg=(
"Expected to receive a HTTPStatus.NOT_FOUND on accessing deleted media: %s"
% server_and_media_id
),
)
# Test if the file is deleted
self.assertFalse(os.path.exists(local_path))
| 297 |
test_media.py
|
Python
|
tests/rest/admin/test_media.py
|
c97042f7eef3748e17c90e48a4122389a89c4735
|
synapse
| 1 |
|
323,136 | 8 | 8 | 6 | 28 | 5 | 0 | 8 | 22 |
is_world_process_zero
|
[Trainer] Add init version of paddlenlp trainer and apply finetune for ernie-1.0 pretraining. (#1761)
* add some datasets for finetune.
* support fine tune for all tastks.
* add trainer prototype.
* init verison for paddlenlp trainer.
* refine trainer.
* update for some details.
* support multi-cards training evaluation.
* support load from ckpt.
* support for export inference model.
* first version of trainer.
* seq cls support clue.
* trainer support for token classification and question answersing tasks.
* fix as reviews.
Co-authored-by: Zeyu Chen <[email protected]>
|
https://github.com/PaddlePaddle/PaddleNLP.git
|
def is_world_process_zero(self) -> bool:
return self.args.process_index == 0
| 16 |
trainer_base.py
|
Python
|
paddlenlp/trainer/trainer_base.py
|
44a290e94d1becd1f09fddc3d873f9e19c9d6919
|
PaddleNLP
| 1 |
|
64,314 | 30 | 14 | 25 | 187 | 11 | 0 | 42 | 19 |
show_job_status
|
feat: Bulk Transaction Processing (#28580)
* feat: Bulk Transaction Processing
* fix: add flags to ignore validations and exception handling correction
* fix: remove duplicate code, added logger functionality and improved notifications
* fix: linting and sider issues
* test: added tests
* fix: linter issues
* fix: failing test case
* fix: sider issues and test cases
* refactor: mapping function calls to create order/invoice
* fix: added more test cases to increase coverage
* fix: test cases
* fix: sider issue
* fix: rename doctype, improve formatting and minor refactor
* fix: update doctype name in hooks and sider issues
* fix: entry log test case
* fix: typos, translations and company name in tests
* fix: linter issues and translations
* fix: linter issue
* fix: split into separate function for marking failed transaction
* fix: typos, retry failed transaction logic and make log read only
* fix: hide retry button when no failed transactions and remove test cases not rrelevant
* fix: sider issues and indentation to tabs
Co-authored-by: Ankush Menat <[email protected]>
|
https://github.com/frappe/erpnext.git
|
def show_job_status(failed_history, deserialized_data, to_doctype):
if not failed_history:
frappe.msgprint(
_("Creation of {0} successful").format(to_doctype),
title="Successful",
indicator="green",
)
if len(failed_history) != 0 and len(failed_history) < len(deserialized_data):
frappe.msgprint(
_().format(
to_doctype
),
title="Partially successful",
indicator="orange",
)
if len(failed_history) == len(deserialized_data):
frappe.msgprint(
_().format(
to_doctype
),
title="Failed",
indicator="red",
)
| 111 |
bulk_transaction.py
|
Python
|
erpnext/utilities/bulk_transaction.py
|
a3e69cf75d27198132d05c7c10475a0297b1e190
|
erpnext
| 5 |
|
43,469 | 9 | 10 | 4 | 56 | 11 | 0 | 9 | 41 |
test_create_queue_exception
|
Implement Azure Service Bus Queue Operators (#24038)
Implemented Azure Service Bus Queue based Operator's to create queue, send message to the queue and receive message(list of message or batch message) and delete queue in azure service
- Added `AzureServiceBusCreateQueueOperator`
- Added `AzureServiceBusSendMessageOperator`
- Added `AzureServiceBusReceiveMessageOperator`
- Added `AzureServiceBusDeleteQueueOperator`
- Added Example DAG
- Added Documentation
- Added hooks and connection type in - provider yaml file
- Added unit Test case, doc strings
|
https://github.com/apache/airflow.git
|
def test_create_queue_exception(self, mock_sb_admin_client):
hook = AdminClientHook(azure_service_bus_conn_id=self.conn_id)
with pytest.raises(TypeError):
hook.create_queue(None)
| 32 |
test_asb.py
|
Python
|
tests/providers/microsoft/azure/hooks/test_asb.py
|
09f38ad3f6872bae5059a1de226362eb358c4a7a
|
airflow
| 1 |
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