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40 | 0 | 1 | 12 | corporate/tests/test_stripe.py | 84,213 | mypy: Enable truthy-bool errors.
Signed-off-by: Anders Kaseorg <anders@zulip.com> | zulip | 11 | Python | 35 | test_stripe.py | def test_invoice_plan_without_stripe_customer(self) -> None:
self.local_upgrade(self.seat_count, True, CustomerPlan.ANNUAL, False, False)
plan = get_current_plan_by_realm(get_realm("zulip"))
assert plan is not None
plan.customer.stripe_customer_id = None
plan.customer.save(update_fields=["stripe_customer_id"])
with self.assertRaises(BillingError) as context:
invoice_plan(plan, timezone_now())
self.assertRegex(
context.exception.error_description,
"Realm zulip has a paid plan without a Stripe customer",
)
| df69e1d9792a5ea7a72e32981f68a46a7fb88ce1 | 89 | https://github.com/zulip/zulip.git | 128 | def test_invoice_plan_without_stripe_customer(self) -> None:
self.local_upgrade(self.seat_count, True, CustomerPlan.ANNUAL, False, False)
plan = get_current_plan_by_realm(get_realm("zulip"))
assert plan is not None
plan.customer.stripe_customer_id = None
plan.customer.save(update_fields=["stripe_customer_id"])
with self.assertRaises(BillingError) as context:
| 21 | 145 | test_invoice_plan_without_stripe_customer |
|
65 | 0 | 8 | 16 | modin/config/envvars.py | 153,295 | REFACTOR-#3900: add flake8-no-implicit-concat plugin and refactor flake8 error codes (#3901)
Signed-off-by: jeffreykennethli <jkli@ponder.io> | modin | 17 | Python | 48 | envvars.py | def _check_vars():
valid_names = {
obj.varname
for obj in globals().values()
if isinstance(obj, type)
and issubclass(obj, EnvironmentVariable)
and not obj.is_abstract
}
found_names = {name for name in os.environ if name.startswith("MODIN_")}
unknown = found_names - valid_names
if unknown:
warnings.warn(
f"Found unknown environment variable{'s' if len(unknown) > 1 else ''},"
+ f" please check {'their' if len(unknown) > 1 else 'its'} spelling: "
+ ", ".join(sorted(unknown))
)
_check_vars()
| e5e9634357e60925a5a70e56a1d4882d269f533a | 87 | https://github.com/modin-project/modin.git | 164 | def _check_vars():
valid_names = {
obj.varname
for obj in globals().values()
if isinstance(obj, type)
and issubclass(obj, EnvironmentVariable)
and not obj.is_abstract
}
found_names = {name for name in os.environ if name.startswith("MODIN_")}
unknown = found_names - valid_names
if unknown:
warnings.warn(
f"Found unknown environment va | 22 | 192 | _check_vars |
|
56 | 0 | 5 | 11 | scapy/contrib/automotive/scanner/enumerator.py | 209,091 | Minor refactoring of Automotive-Scanner show functions | scapy | 12 | Python | 43 | enumerator.py | def _show_negative_response_information(self, **kwargs):
# type: (Any) -> str
filtered = kwargs.get("filtered", True)
s = "%d negative responses were received\n" % \
len(self.results_with_negative_response)
s += "\n"
s += self._show_negative_response_details(**kwargs) or "" + "\n"
if filtered and len(self.negative_response_blacklist):
s += "The following negative response codes are blacklisted: %s\n"\
% [self._get_negative_response_desc(nr)
for nr in self.negative_response_blacklist]
return s + "\n"
| d74d8601575464a017f6e0f0031403b8c18d4429 | 78 | https://github.com/secdev/scapy.git | 161 | def _show_negative_response_information(self, **kwargs):
| 12 | 139 | _show_negative_response_information |
|
6 | 0 | 1 | 3 | homeassistant/components/github/sensor.py | 309,972 | Revamp github integration (#64190)
Co-authored-by: Paulus Schoutsen <balloob@gmail.com>
Co-authored-by: Franck Nijhof <git@frenck.dev>
Co-authored-by: Martin Hjelmare <marhje52@gmail.com> | core | 9 | Python | 6 | sensor.py | def native_value(self) -> StateType:
return self.entity_description.value_fn(self.coordinator.data)
| 6a0c3843e51085e59d6fb69920733485f2f98fe5 | 21 | https://github.com/home-assistant/core.git | 20 | def native_value(self) -> StateType:
return self.entity_description.value_fn(self.coordinat | 7 | 36 | native_value |
|
120 | 0 | 1 | 59 | tests/orion/api/test_task_runs.py | 55,876 | Add sorts for task run name | prefect | 17 | Python | 49 | test_task_runs.py | async def test_read_task_runs_applies_sort(self, flow_run, session, client):
now = pendulum.now()
task_run_1 = await models.task_runs.create_task_run(
session=session,
task_run=schemas.core.TaskRun(
name="Task Run 1",
flow_run_id=flow_run.id,
task_key="my-key",
expected_start_time=now.subtract(minutes=5),
dynamic_key="0",
),
)
task_run_2 = await models.task_runs.create_task_run(
session=session,
task_run=schemas.core.TaskRun(
name="Task Run 2",
flow_run_id=flow_run.id,
task_key="my-key",
expected_start_time=now.add(minutes=5),
dynamic_key="1",
),
)
await session.commit()
response = await client.post(
"/task_runs/filter",
json=dict(
limit=1, sort=schemas.sorting.TaskRunSort.EXPECTED_START_TIME_DESC.value
),
)
assert response.status_code == status.HTTP_200_OK
assert response.json()[0]["id"] == str(task_run_2.id)
response = await client.post(
"/task_runs/filter",
json=dict(
limit=1,
offset=1,
sort=schemas.sorting.TaskRunSort.EXPECTED_START_TIME_DESC.value,
),
)
assert response.status_code == status.HTTP_200_OK
assert response.json()[0]["id"] == str(task_run_1.id)
# name asc
response = await client.post(
"/task_runs/filter",
json=dict(
limit=1,
sort=schemas.sorting.TaskRunSort.NAME_ASC.value,
),
)
assert response.status_code == status.HTTP_200_OK
assert response.json()[0]["id"] == str(task_run_1.id)
# name desc
response = await client.post(
"/task_runs/filter",
json=dict(
limit=1,
sort=schemas.sorting.TaskRunSort.NAME_DESC.value,
),
)
assert response.status_code == status.HTTP_200_OK
assert response.json()[0]["id"] == str(task_run_2.id)
| d225bb1da80d22b32148b68ebc9ff578bfd85c9b | 369 | https://github.com/PrefectHQ/prefect.git | 755 | async def test_read_task_runs_applies_sort(self, flow_run, session, client):
now = pendulum.now()
task_run_1 = await models.task_runs.create_task_run(
session=session,
task_run=schemas.core.TaskRun(
name="Task Run 1",
flow_run_id=flow_run.id,
task_key="my-key",
expected_start_time=now.subtract(minutes=5),
dynamic_key="0",
),
)
task_run_2 = await models.task_runs.create_task_run(
session=session,
task_run=schemas.core.TaskRun(
name="Task Run 2",
flow_run_id=flow_run.id,
task_key="my-key",
expected_start_time=now.add(minutes=5),
dynamic_key="1",
),
)
await session.commit()
response = await client.post(
"/task_runs/filter",
json=dict(
limit=1, sort=schemas.sorting.TaskRunSort.EXPECTED_START_TIME_DESC.value
),
)
assert response.status_code == status.HTTP_200_OK
assert response.json()[0]["id"] == str(task_run_2.id)
response = await client.post(
"/task_runs/filter",
json=dict(
limit=1,
offset=1,
sort=schemas.sorting.TaskRunSort.EXPECTED_START_TIME_DESC.value,
),
)
assert response.status_code == status.HTTP_200_OK
assert response.json()[0]["id"] == str(task_run_1.id)
# name asc
response = await client.post(
| 43 | 577 | test_read_task_runs_applies_sort |
|
12 | 0 | 1 | 6 | test/units/parsing/vault/test_vault.py | 266,349 | Avoid deprecated TestCase functions in unit tests. (#76678)
* Avoid deprecated TestCase functions in unit tests.
* Add assertRaisesRegex for Python 2.7.
* Fix indentation. | ansible | 8 | Python | 12 | test_vault.py | def test_odd_length(self):
b_data = b'123456789abcdefghijklmnopqrstuvwxyz'
self.assertRaisesRegex(vault.AnsibleVaultFormatError,
'.*Vault format unhexlify error.*',
vault._unhexlify,
b_data)
| 97104f1221b64ef36cf42cb90c5a0eff263a2adb | 25 | https://github.com/ansible/ansible.git | 115 | def test_odd_length(self):
b_data = b'123456789abcdefghijklmnopqrstuvwxyz'
se | 7 | 40 | test_odd_length |
|
27 | 0 | 1 | 9 | mindsdb/integrations/handlers/jira_handler/tests/test_jira_handler.py | 118,383 | removing the wrongly commited files and addressing the review comments of PR 4112 | mindsdb | 9 | Python | 24 | test_jira_handler.py | def setUpClass(cls):
cls.kwargs = {
"table_name": "project",
"jira_url": "https://jira.linuxfoundation.org/",
"user_id": "balaceg",
"api_key": "4Rhq&Ehd#KV4an!",
"jira_query": "project = RELENG and status = 'In Progress'"
}
cls.handler = JiraHandler('test_jira_handler', cls.kwargs)
| e308f43952f3e27d3b48ac28dd3eaffeb26e8ee0 | 42 | https://github.com/mindsdb/mindsdb.git | 102 | def setUpClass(cls):
cls.kwargs = {
"table_name": "project",
"jira_url": "https://jira.linuxfoundation.org/",
"user_id": "balaceg",
"api_key": "4Rhq&Ehd#KV4an!",
"jira_query": "project = RELENG and status = 'In Progress'"
}
| 5 | 83 | setUpClass |
|
104 | 0 | 2 | 21 | tests/rest/client/test_login.py | 246,608 | Add type hints to `tests/rest/client` (#12066) | synapse | 12 | Python | 80 | test_login.py | def test_multi_sso_redirect(self) -> None:
# first hit the redirect url, which should redirect to our idp picker
channel = self._make_sso_redirect_request(None)
self.assertEqual(channel.code, 302, channel.result)
location_headers = channel.headers.getRawHeaders("Location")
assert location_headers
uri = location_headers[0]
# hitting that picker should give us some HTML
channel = self.make_request("GET", uri)
self.assertEqual(channel.code, 200, channel.result)
# parse the form to check it has fields assumed elsewhere in this class
html = channel.result["body"].decode("utf-8")
p = TestHtmlParser()
p.feed(html)
p.close()
# there should be a link for each href
returned_idps: List[str] = []
for link in p.links:
path, query = link.split("?", 1)
self.assertEqual(path, "pick_idp")
params = urllib.parse.parse_qs(query)
self.assertEqual(params["redirectUrl"], [TEST_CLIENT_REDIRECT_URL])
returned_idps.append(params["idp"][0])
self.assertCountEqual(returned_idps, ["cas", "oidc", "oidc-idp1", "saml"])
| 64c73c6ac88a740ee480a0ad1f9afc8596bccfa4 | 188 | https://github.com/matrix-org/synapse.git | 292 | def test_multi_sso_redirect(self) -> None:
# first hit the redirect url, which should redirect to our idp picker
channel = self._make_sso_redirect_request(None)
self.assertEqual(channel.code, 302, channel.result)
location_headers = channel.headers.getRawHeaders("Location")
assert location_headers
uri = location_headers[0]
# hitting that picker should give us some HTML
channel = self.make_request("GET", uri)
self.assertEqual(channel.code, 200, channel. | 33 | 316 | test_multi_sso_redirect |
|
89 | 1 | 1 | 4 | tests/utilities/test_importtools.py | 58,239 | Improve tests | prefect | 12 | Python | 52 | test_importtools.py | def reset_sys_modules():
original = sys.modules.copy()
yield
sys.modules = original
@pytest.mark.usefixtures("reset_sys_modules")
@pytest.mark.parametrize(
"working_directory,script_path",
[
# Working directory is not necessary for these imports to work
(__root_path__, TEST_PROJECTS_DIR / "flat-project" / "explicit_relative.py"),
(__root_path__, TEST_PROJECTS_DIR / "flat-project" / "implicit_relative.py"),
(__root_path__, TEST_PROJECTS_DIR / "nested-project" / "implicit_relative.py"),
# They also work with the working directory set
(TEST_PROJECTS_DIR / "flat-project", "explicit_relative.py"),
(TEST_PROJECTS_DIR / "flat-project", "implicit_relative.py"),
(TEST_PROJECTS_DIR / "nested-project", "implicit_relative.py"),
# The tree structure requires the working directory to be at the base of all
# module imports
(TEST_PROJECTS_DIR / "tree-project", Path("imports") / "implicit_relative.py"),
],
) | b950152d8afc439135d5241c27821b1eeaa72a1e | @pytest.mark.usefixtures("reset_sys_modules")
@pytest.mark.parametrize(
"working_directory,script_path",
[
# Working directory is not necessary for these imports to work
(__root_path__, TEST_PROJECTS_DIR / "flat-project" / "explicit_relative.py"),
(__root_path__, TEST_PROJECTS_DIR / "flat-project" / "implicit_relative.py"),
(__root_path__, TEST_PROJECTS_DIR / "nested-project" / "implicit_relative.py"),
# They also work with the working directory set
(TEST_PROJECTS_DIR / "flat-project", "explicit_relative.py"),
(TEST_PROJECTS_DIR / "flat-project", "implicit_relative.py"),
(TEST_PROJECTS_DIR / "nested-project", "implicit_relative.py"),
# The tree structure requires the working directory to be at the base of all
# module imports
(TEST_PROJECTS_DIR / "tree-project", Path("imports") / "implicit_relative.py"),
],
) | 19 | https://github.com/PrefectHQ/prefect.git | 180 | def reset_sys_modules():
original = sys.modules.copy()
yield
sys.modules = original
@pytest.mark.usefixtures("reset_sys_modules")
@pytest.mark.parametrize(
"working_directory,script_path",
[
# Working directory is not necessary for these imports to work
(__root_path__, TEST_PROJECTS_DIR / "flat-project" / "explicit_relative.py"),
(__root_path__, TEST_PROJECTS_DIR / "flat-project" / "implicit_relative.py"),
(__root_path__, TEST_PROJECTS_DIR / "nested-project" / "implicit_relative.py"),
# They also work with the working directory set
(TEST_PROJECTS_DIR / "flat-project", "explicit_relative.py"),
| 12 | 193 | reset_sys_modules |
175 | 1 | 14 | 41 | test/test_extended_models.py | 192,711 | Clean up purely informational fields from Weight Meta-data (#5852)
* Removing `task`, `architecture` and `quantization`
* Fix mypy
* Remove size field
* Remove unused import.
* Fix mypy
* Remove size from schema list.
* update todo
* Simplify with assert
* Adding min_size to all models.
* Update RAFT min size to 128 | vision | 21 | Python | 109 | test_extended_models.py | def test_schema_meta_validation(model_fn):
# TODO: add list of permitted fields
classification_fields = ["categories", "acc@1", "acc@5"]
defaults = {
"all": ["recipe", "num_params", "min_size"],
"models": classification_fields,
"detection": ["categories", "map"],
"quantization": classification_fields + ["backend", "unquantized"],
"segmentation": ["categories", "mIoU", "acc"],
"video": classification_fields,
"optical_flow": [],
}
model_name = model_fn.__name__
module_name = model_fn.__module__.split(".")[-2]
fields = set(defaults["all"] + defaults[module_name])
weights_enum = _get_model_weights(model_fn)
if len(weights_enum) == 0:
pytest.skip(f"Model '{model_name}' doesn't have any pre-trained weights.")
problematic_weights = {}
incorrect_params = []
bad_names = []
for w in weights_enum:
missing_fields = fields - set(w.meta.keys())
if missing_fields:
problematic_weights[w] = missing_fields
if w == weights_enum.DEFAULT:
if module_name == "quantization":
# parameters() count doesn't work well with quantization, so we check against the non-quantized
unquantized_w = w.meta.get("unquantized")
if unquantized_w is not None and w.meta.get("num_params") != unquantized_w.meta.get("num_params"):
incorrect_params.append(w)
else:
if w.meta.get("num_params") != sum(p.numel() for p in model_fn(weights=w).parameters()):
incorrect_params.append(w)
else:
if w.meta.get("num_params") != weights_enum.DEFAULT.meta.get("num_params"):
if w.meta.get("num_params") != sum(p.numel() for p in model_fn(weights=w).parameters()):
incorrect_params.append(w)
if not w.name.isupper():
bad_names.append(w)
assert not problematic_weights
assert not incorrect_params
assert not bad_names
@pytest.mark.parametrize(
"model_fn",
TM.get_models_from_module(models)
+ TM.get_models_from_module(models.detection)
+ TM.get_models_from_module(models.quantization)
+ TM.get_models_from_module(models.segmentation)
+ TM.get_models_from_module(models.video)
+ TM.get_models_from_module(models.optical_flow),
)
@run_if_test_with_extended | f1587a20e3169cdd1c8cae5a1067c0ff52d63320 | @pytest.mark.parametrize(
"model_fn",
TM.get_models_from_module(models)
+ TM.get_models_from_module(models.detection)
+ TM.get_models_from_module(models.quantization)
+ TM.get_models_from_module(models.segmentation)
+ TM.get_models_from_module(models.video)
+ TM.get_models_from_module(models.optical_flow),
)
@run_if_test_with_extended | 341 | https://github.com/pytorch/vision.git | 518 | def test_schema_meta_validation(model_fn):
# TODO: add list of permitted fields
classification_fields = ["categories", "acc@1", "acc@5"]
defaults = {
"all": ["recipe", "num_params", "min_size"],
"models": classification_fields,
"detection": ["categories", "map"],
"quantization": classification_fields + ["backend", "unquantized"],
"segmentation": ["categories", "mIoU", "acc"],
"video": classification_fields,
"optical_flow": [],
}
model_name = model_fn.__name__
module_name = model_fn.__module__.split(".")[-2]
fields = | 45 | 697 | test_schema_meta_validation |
19 | 1 | 2 | 9 | ludwig/backend/ray.py | 8,293 | Allow explicitly plumbing through nics (#2605) | ludwig | 11 | Python | 17 | ray.py | def create_runner(**kwargs):
trainer_kwargs = get_trainer_kwargs(**kwargs)
with spread_env(**trainer_kwargs):
trainer = Trainer(**trainer_kwargs)
trainer.start()
try:
yield trainer
finally:
trainer.shutdown()
@register_ray_trainer("trainer", MODEL_ECD, default=True) | c99cab3a674e31885e5608a4aed73a64b1901c55 | @register_ray_trainer("trainer", MODEL_ECD, default=True) | 43 | https://github.com/ludwig-ai/ludwig.git | 53 | def create_runner(**kwargs):
trainer_kwargs = g | 12 | 97 | create_runner |
8 | 0 | 1 | 2 | tests/flow_runners/test_base.py | 56,067 | Splits flow_runners into files by their execution engine. (PrefectHQ/orion#1948)
Our `subprocess`, Docker, and Kubernetes runners don't share a lot of behavior,
and some of them require complex imports. To make the repo easier to navigate
and make room for additional future FlowRunners, I'm splitting flow_runners and
their tests into a subpackage. All current imports should be preserved, and we
can continue to document them as coming from `prefect.flow_runners`. | prefect | 14 | Python | 8 | test_base.py | def test_flow_runner_networks_config_casts_to_list(self, runner_type):
assert type(runner_type(networks={"a", "b"}).networks) == list
| 9a83d0c051e4a461bab8ecc97312fac7c6061d78 | 25 | https://github.com/PrefectHQ/prefect.git | 14 | def test_flow_runner_networks_config_casts_to_list(self, runner_type):
assert t | 6 | 42 | test_flow_runner_networks_config_casts_to_list |
|
15 | 0 | 1 | 5 | tests/sentry/api/endpoints/test_organization_sentry_functions.py | 94,288 | Sentry Functions: Endpoint to return list of Sentry Functions (#37626)
* feat(integrations): new endpoint for fetching sentry functions for an organization
* ref(integrations): add feature flag to gate endpoint
* ref(integrations): remove extraneous comment | sentry | 11 | Python | 13 | test_organization_sentry_functions.py | def test_get(self):
with Feature("organizations:sentry-functions"):
response = self.client.get(self.url)
assert response.status_code == 200
assert response.data == []
| d2ed8bbdfe259eb0f316227a45b2266f41aa9ea0 | 36 | https://github.com/getsentry/sentry.git | 54 | def test_get(self):
with Feature("organizations:sentry-functions | 9 | 62 | test_get |
|
78 | 0 | 1 | 31 | zerver/tests/test_link_embed.py | 83,620 | actions: Split out zerver.actions.message_send.
Signed-off-by: Anders Kaseorg <anders@zulip.com> | zulip | 16 | Python | 68 | test_link_embed.py | def test_youtube_url_title_replaces_url(self) -> None:
url = "https://www.youtube.com/watch?v=eSJTXC7Ixgg"
with mock_queue_publish("zerver.actions.message_send.queue_json_publish"):
msg_id = self.send_personal_message(
self.example_user("hamlet"),
self.example_user("cordelia"),
content=url,
)
msg = Message.objects.select_related("sender").get(id=msg_id)
event = {
"message_id": msg_id,
"urls": [url],
"message_realm_id": msg.sender.realm_id,
"message_content": url,
}
mocked_data = {"title": "Clearer Code at Scale - Static Types at Zulip and Dropbox"}
self.create_mock_response(url)
with self.settings(TEST_SUITE=False, CACHES=TEST_CACHES):
with self.assertLogs(level="INFO") as info_logs:
with mock.patch(
"zerver.lib.markdown.link_preview.link_embed_data_from_cache",
lambda *args, **kwargs: mocked_data,
):
FetchLinksEmbedData().consume(event)
self.assertTrue(
"INFO:root:Time spent on get_link_embed_data for https://www.youtube.com/watch?v=eSJTXC7Ixgg:"
in info_logs.output[0]
)
msg.refresh_from_db()
expected_content = f
self.assertEqual(expected_content, msg.rendered_content)
| 975066e3f0e3d4c3a356da3dfc1d4472f72717b7 | 181 | https://github.com/zulip/zulip.git | 415 | def test_youtube_url_title_replaces_url(self) -> None:
url = "https://www.youtube.com/watch?v=eSJTXC7Ixgg"
with mock_queue_publish("zerver.actions.message_send.queue_json_publish"):
msg_id = self.send_personal_message(
self.example_user("hamlet"),
self.example_user("cordelia"),
content=url,
)
msg = Message.objects.select_related("sender").get(id=msg_id)
event = {
"message_id": msg_id,
"urls": [url],
"message_realm_id": msg.sender.realm_id,
"message_content": ur | 39 | 329 | test_youtube_url_title_replaces_url |
|
7 | 0 | 1 | 3 | airbyte-integrations/connectors/source-salesforce/integration_tests/integration_test.py | 3,980 | 🐛 Fix Python checker configs and Connector Base workflow (#10505) | airbyte | 11 | Python | 7 | integration_test.py | def _encode_content(text):
base64_bytes = base64.b64encode(text.encode("utf-8"))
return base64_bytes.decode("utf-8")
| bbd13802d81263d5677a4e8599d0b8708889719d | 25 | https://github.com/airbytehq/airbyte.git | 12 | def _encode_content(text):
base64_bytes = base64.b64encode(text.encode("utf-8"))
return base64_bytes.decode("utf-8" | 7 | 45 | _encode_content |
|
14 | 0 | 1 | 4 | dev/breeze/tests/test_commands.py | 48,141 | Seperate provider verification as standalone breeze command (#23454)
This is another step in simplifying and converting to Python all of
the CI/local development tooling.
This PR separates out verification of providers as a separate
breeze command `verify-provider-packages`. It was previously part of
"prepare_provider_packages.py" but it has been now
extracted to a separate in-container python file and it was
wrapped with breeze's `verify-provider-packages` command.
No longer provider verification is run with "preparing provider docs"
nor "preparing provider packages" - it's a standaline command.
This command is also used in CI now to run the tests:
* all provider packages are built and created on CI together with
airflow version
* the packages are installed inside the CI image and providers are
verified
* the 2.1 version of Airflow is installed together with all 2.1
- compatible providers and provider verification is run there too.
This all is much simpler now - we got rediof some 500 lines of bash
code again in favour of breeze python code.
Fixes: #23430 | airflow | 9 | Python | 13 | test_commands.py | def test_get_extra_docker_flags_all():
flags = get_extra_docker_flags(MOUNT_ALL)
assert "empty" not in "".join(flags)
assert len(flags) < 10
| 3ed07474649b1e202f9b106105fef21f7b2cfddc | 27 | https://github.com/apache/airflow.git | 22 | def test_get_extra_docker_flags_all():
flags = get_extra_docker_flags(MOUNT_ALL)
assert "empty" not in "".join(flags)
assert len | 6 | 48 | test_get_extra_docker_flags_all |
|
51 | 0 | 1 | 13 | tests/snuba/api/endpoints/test_organization_group_index.py | 90,269 | ref(tests): Remove `get_valid_response()` (#34822) | sentry | 12 | Python | 40 | test_organization_group_index.py | def test_basic_ignore(self):
group = self.create_group(status=GroupStatus.RESOLVED)
snooze = GroupSnooze.objects.create(group=group, until=timezone.now())
self.login_as(user=self.user)
assert not GroupHistory.objects.filter(
group=group, status=GroupHistoryStatus.IGNORED
).exists()
response = self.get_success_response(qs_params={"id": group.id}, status="ignored")
# existing snooze objects should be cleaned up
assert not GroupSnooze.objects.filter(id=snooze.id).exists()
group = Group.objects.get(id=group.id)
assert group.status == GroupStatus.IGNORED
assert GroupHistory.objects.filter(group=group, status=GroupHistoryStatus.IGNORED).exists()
assert response.data == {"status": "ignored", "statusDetails": {}, "inbox": None}
| 096b5511e244eecd8799b2a0324655207ce8985e | 169 | https://github.com/getsentry/sentry.git | 145 | def test_basic_ignore(self):
group = self.create_group(status=GroupStatus.RESOLVED)
snooze = GroupSnooze.objects.create(group=grou | 28 | 274 | test_basic_ignore |
|
23 | 0 | 1 | 3 | tests/infrastructure/test_process.py | 57,424 | Add tests for process | prefect | 16 | Python | 21 | test_process.py | async def test_process_runs_command(tmp_path):
# Perform a side-effect to demonstrate the command is run
assert await Process(command=["touch", str(tmp_path / "canary")]).run()
assert (tmp_path / "canary").exists()
| cb53fb90654e3adfef19e58a42be16228d0695ec | 36 | https://github.com/PrefectHQ/prefect.git | 31 | async def test_process_runs_command(tmp_path):
# Perform a side-effect to demonstrate the command is run
assert await Process(command=["touch", str(tmp_path / "canary")]).run()
assert (tmp_path / "canary").exists()
| 7 | 66 | test_process_runs_command |
|
18 | 0 | 1 | 6 | yt_dlp/extractor/murrtube.py | 162,581 | [murrtube] Add extractor (#2387)
Authored by: cyberfox1691 | yt-dlp | 13 | Python | 17 | murrtube.py | def _download_gql(self, video_id, op, note=None, fatal=True):
result = self._download_json(
'https://murrtube.net/graphql',
video_id, note, data=json.dumps(op).encode(), fatal=fatal,
headers={'Content-Type': 'application/json'})
return result['data']
| 812283199a2f05046b9b4d59c22a06051b958bf6 | 59 | https://github.com/yt-dlp/yt-dlp.git | 64 | def _download_gql(self, video_id, op, note=None, fatal=True):
result = self._download_json(
'https://mu | 13 | 91 | _download_gql |
|
58 | 0 | 4 | 23 | tests/providers/cncf/kubernetes/operators/test_kubernetes_pod.py | 44,995 | Switch XCom implementation to use run_id (#20975) | airflow | 13 | Python | 43 | test_kubernetes_pod.py | def test_push_xcom_pod_info(self, mock_extract_xcom, dag_maker, do_xcom_push):
mock_extract_xcom.return_value = '{}'
with dag_maker():
KubernetesPodOperator(
namespace="default",
image="ubuntu:16.04",
cmds=["bash", "-cx"],
name="test",
task_id="task",
in_cluster=False,
do_xcom_push=do_xcom_push,
)
DummyOperator(task_id='task_to_pull_xcom')
dagrun = dag_maker.create_dagrun()
tis = {ti.task_id: ti for ti in dagrun.task_instances}
pod = self.run_pod(
tis["task"].task,
context=tis["task"].get_template_context(session=dag_maker.session),
)
pod_name = tis["task_to_pull_xcom"].xcom_pull(task_ids="task", key='pod_name')
pod_namespace = tis["task_to_pull_xcom"].xcom_pull(task_ids="task", key='pod_namespace')
assert pod_name and pod_name == pod.metadata.name
assert pod_namespace and pod_namespace == pod.metadata.namespace
| 0ebd6428e6b484790bfbbe1b8687ef4e6cae10e9 | 169 | https://github.com/apache/airflow.git | 295 | def test_push_xcom_pod_info(self, mock_extract_xcom, dag_maker, do_xcom_push):
mock_extract_xcom.return_value = '{}'
with dag_maker():
KubernetesPodOperator(
namespace="default",
image="ubuntu:16.04",
cmds=["bash", "-cx"],
name="test",
task_id="task",
in_cluster=False,
do_xcom_push=do_xcom_push,
)
DummyOperator(task_id='task_to_pull_xcom')
dagrun = dag_maker.create_dagrun( | 31 | 284 | test_push_xcom_pod_info |
|
10 | 0 | 2 | 2 | label_studio/tasks/serializers.py | 177,718 | feat: DEV-1844: "Last updated by" column in Data Manager (#2119)
* feat: DEV-1696: Add lead time task column in Data Manager
* Fix test for test_get_task
* Fix lead time annotation
* Fix tests
* Add migration for view and fix test
* Fix test
* Fix tests data
* Fix annotation count
* Fix annotation results in tests
* Fix lead_time type to float
* Fix test tasks-all-fields-postgre data
* Change test data for tasks-all-fields-postgre
* Change annotations_results to anystr
* Change predictions_results data for tasks-all-fields-postgre
* Change data in tasks-all-fields-sqlite test
* feat: DEV-1844: "Last updated by" column in Data Manager
* More
* Add more functions
* Add context with user
* Remove updated_by from quickview
* Fix label stream
* Fix tests
* Fix tests
* Update DM
* Fix pagination class
* Fix
Co-authored-by: Konstantin Korotaev <42615530+KonstantinKorotaev@users.noreply.github.com> | label-studio | 10 | Python | 10 | serializers.py | def get_updated_by(self, task):
return [{'user_id': task.updated_by_id}] if task.updated_by_id else []
| c0c3426467785ffe9a8e3026fff1ef6e4faddca3 | 24 | https://github.com/heartexlabs/label-studio.git | 16 | def get_updated_by(self, task):
return [{'user_id': task.updated_by_id}] if task.updated_by_id else []
| 4 | 38 | get_updated_by |
|
6 | 0 | 1 | 3 | tests/components/mqtt/test_camera.py | 313,670 | Speed up mqtt tests (#73423)
Co-authored-by: jbouwh <jan@jbsoft.nl>
Co-authored-by: Jan Bouwhuis <jbouwh@users.noreply.github.com> | core | 11 | Python | 6 | test_camera.py | def camera_platform_only():
with patch("homeassistant.components.mqtt.PLATFORMS", [Platform.CAMERA]):
yield
| 51b4d15c8cb83bb715222841aa48e83f77ef38ff | 18 | https://github.com/home-assistant/core.git | 19 | def camera_platform_only():
with patch("homeassistant.components.mqtt.PLATFORMS", [Platform.CAMERA]):
yield
| 4 | 37 | camera_platform_only |
|
64 | 0 | 5 | 12 | pandas/tests/strings/test_find_replace.py | 166,117 | WARN: PerformanceWarning for non-pyarrow fallback (#46732) | pandas | 12 | Python | 35 | test_find_replace.py | def test_match_na_kwarg(any_string_dtype):
# GH #6609
s = Series(["a", "b", np.nan], dtype=any_string_dtype)
with maybe_perf_warn(any_string_dtype == "string[pyarrow]" and pa_version_under4p0):
result = s.str.match("a", na=False)
expected_dtype = np.bool_ if any_string_dtype == "object" else "boolean"
expected = Series([True, False, False], dtype=expected_dtype)
tm.assert_series_equal(result, expected)
with maybe_perf_warn(any_string_dtype == "string[pyarrow]" and pa_version_under4p0):
result = s.str.match("a")
expected_dtype = "object" if any_string_dtype == "object" else "boolean"
expected = Series([True, False, np.nan], dtype=expected_dtype)
tm.assert_series_equal(result, expected)
| 6d165676daef078988c5a292261d7901295e21d9 | 137 | https://github.com/pandas-dev/pandas.git | 107 | def test_match_na_kwarg(any_string_dtype):
# GH #6609
s = Series(["a", "b", np.nan], dtype=any_string_dtype)
with maybe_perf_warn(any_string_dtype == "string[pyarrow]" and pa_version_under4p0):
result = s.str.match("a", na=False)
expected_dtype = np.bool_ if any_string_dtype == "object" else "boolean"
expected = Series([True, False, False], dtype=expected_dtype)
tm.assert_series_equal(result, expected)
with maybe_perf_warn(any_string_dtype == "string[pyarrow]" and pa_version_under4p0):
result = s.str.match("a")
expected_dtype = | 18 | 230 | test_match_na_kwarg |
|
52 | 0 | 1 | 9 | awx/main/tests/functional/api/test_instance_group.py | 81,022 | Disallows disassociate of hubrid type instances from controlplane instance group
Introduce new pattern for is_valid_removal
Makes disassociate error message a bit more dynamic | awx | 12 | Python | 34 | test_instance_group.py | def test_cannot_remove_controlplane_hybrid_instances(post, controlplane_instance_group, node_type_instance, admin_user):
instance = node_type_instance(hostname='hybrid_node', node_type='hybrid')
controlplane_instance_group.instances.add(instance)
url = reverse('api:instance_group_instance_list', kwargs={'pk': controlplane_instance_group.pk})
r = post(url, {'disassociate': True, 'id': instance.id}, admin_user, expect=400)
assert 'Cannot disassociate hybrid node' in str(r.data)
url = reverse('api:instance_instance_groups_list', kwargs={'pk': instance.pk})
r = post(url, {'disassociate': True, 'id': controlplane_instance_group.id}, admin_user, expect=400)
assert f'Cannot disassociate hybrid instance' in str(r.data)
| dc64168ed40bdf0d59a715ef82b2a6b46c2ab58e | 130 | https://github.com/ansible/awx.git | 75 | def test_cannot_remove_controlplane_hybrid_instances(post, controlplane_instance_group, node_type_instance, admin_user):
instance = node_type_instance(hostname='hybrid_node', node_type='hybrid')
controlplane_instance_group.instances.add(instance)
url = reverse('api:instance_group_instance_list', kwargs={'pk': controlplane_instance_group.pk})
r = post(url, {'disassociate': True, 'id': instance.id}, admin_user, expect=400)
assert 'Cannot disassociate hybrid node' in str(r.data)
url = reverse('api:instance_instance_groups_list', kwargs={'pk': instance.pk})
r = post(url, {'disassociate': True, 'id': controlplane_instance_group.id}, admin_user, expect=400)
assert f'Cannot disassociate hybrid instance' in str(r.data)
| 19 | 212 | test_cannot_remove_controlplane_hybrid_instances |
|
236 | 0 | 1 | 65 | pandas/tests/frame/test_reductions.py | 171,091 | DEPR: Enforce deprecation of numeric_only=None in DataFrame aggregations (#49551)
* WIP
* DEPR: Enforce deprecation of numeric_only=None in DataFrame aggregations
* Partial reverts
* numeric_only in generic/series, fixup
* cleanup
* Remove docs warning
* fixups
* Fixups | pandas | 16 | Python | 124 | test_reductions.py | def test_operators_timedelta64(self):
df = DataFrame(
{
"A": date_range("2012-1-1", periods=3, freq="D"),
"B": date_range("2012-1-2", periods=3, freq="D"),
"C": Timestamp("20120101") - timedelta(minutes=5, seconds=5),
}
)
diffs = DataFrame({"A": df["A"] - df["C"], "B": df["A"] - df["B"]})
# min
result = diffs.min()
assert result[0] == diffs.loc[0, "A"]
assert result[1] == diffs.loc[0, "B"]
result = diffs.min(axis=1)
assert (result == diffs.loc[0, "B"]).all()
# max
result = diffs.max()
assert result[0] == diffs.loc[2, "A"]
assert result[1] == diffs.loc[2, "B"]
result = diffs.max(axis=1)
assert (result == diffs["A"]).all()
# abs
result = diffs.abs()
result2 = abs(diffs)
expected = DataFrame({"A": df["A"] - df["C"], "B": df["B"] - df["A"]})
tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result2, expected)
# mixed frame
mixed = diffs.copy()
mixed["C"] = "foo"
mixed["D"] = 1
mixed["E"] = 1.0
mixed["F"] = Timestamp("20130101")
# results in an object array
result = mixed.min()
expected = Series(
[
pd.Timedelta(timedelta(seconds=5 * 60 + 5)),
pd.Timedelta(timedelta(days=-1)),
"foo",
1,
1.0,
Timestamp("20130101"),
],
index=mixed.columns,
)
tm.assert_series_equal(result, expected)
# excludes non-numeric
result = mixed.min(axis=1, numeric_only=True)
expected = Series([1, 1, 1.0], index=[0, 1, 2])
tm.assert_series_equal(result, expected)
# works when only those columns are selected
result = mixed[["A", "B"]].min(1)
expected = Series([timedelta(days=-1)] * 3)
tm.assert_series_equal(result, expected)
result = mixed[["A", "B"]].min()
expected = Series(
[timedelta(seconds=5 * 60 + 5), timedelta(days=-1)], index=["A", "B"]
)
tm.assert_series_equal(result, expected)
# GH 3106
df = DataFrame(
{
"time": date_range("20130102", periods=5),
"time2": date_range("20130105", periods=5),
}
)
df["off1"] = df["time2"] - df["time"]
assert df["off1"].dtype == "timedelta64[ns]"
df["off2"] = df["time"] - df["time2"]
df._consolidate_inplace()
assert df["off1"].dtype == "timedelta64[ns]"
assert df["off2"].dtype == "timedelta64[ns]"
| b7ea7c6dfd100c40b0bc45aacf6d92c5c22f2e63 | 605 | https://github.com/pandas-dev/pandas.git | 859 | def test_operators_timedelta64(self):
df = DataFrame(
{
"A": date_range("2012-1-1", periods=3, freq="D"),
"B": date_range("2012-1-2", periods=3, freq="D"),
"C": Timestamp("20120101") - timedelta(minutes=5, seconds=5),
}
)
diffs = DataFrame({"A": df["A"] - df["C"], "B": df["A"] - df["B"]})
# min
result = diffs.min()
assert result[0] == diffs.loc[0, "A"]
assert result[1] == diffs.loc[0, "B"]
result = diffs.min(axis=1)
assert (result == diffs.loc[0, "B"]).all()
# max
result = diffs.max()
assert result[0] == diffs.loc[2, "A"]
assert result[1] == diffs.loc[2, "B"]
result = diffs.max(axis=1)
assert (result == diffs["A"]).all()
# abs
result = diffs.abs()
result2 = abs(diffs)
expected = DataFrame({"A": df["A"] - df["C"], "B": df["B"] - df["A"]})
tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result2, expected)
# mixed frame
mixed = diffs.copy()
mixed["C"] = "foo"
mixed["D"] = 1
mixed["E"] = 1.0
mixed["F"] = Timestamp("20130101")
# results in an object array
result = mixed.min()
expected = Series(
[
pd.Timedelta(timedelta(seconds=5 * 60 + 5)),
pd.Timedelta(timedelta(days=-1)),
"foo",
1,
1.0,
Timestamp("20130101"),
],
index=mixed.columns,
)
tm.assert_series_equal(result, expected)
# excludes non-numeric
result = mixed.min(axis=1, numeric_only=True)
expected = Series([1, 1, 1.0], index=[0, 1, 2])
tm.assert_series_equal(result, expected)
# works when only those columns are selected
result = mixed[["A", "B"]].min(1)
expected = Series([timedelta(days=-1)] * 3)
tm.assert_series_equal(result, expected)
result = mixed[["A", "B"]].min()
expected = Series(
[timedelta(seconds=5 * 60 + 5), timedelta(days=-1)], index=["A", "B"]
)
tm.assert_series_equal(result, expected)
# GH 3106
df = DataFrame(
{
"time": date_range("20130102", periods=5),
"time2": date_range("20130105", periods=5),
}
)
df["of | 35 | 1,005 | test_operators_timedelta64 |
|
42 | 0 | 1 | 11 | tests/snuba/api/endpoints/test_organization_events_spans_histogram.py | 87,656 | fix(perf): Remove suspect spans flag (#40963)
Re-pushing up https://github.com/getsentry/sentry/pull/38799 now that
test should be (mostly) fixed. That one closed just before I force
pushed so it's not re-openable 🤷 | sentry | 11 | Python | 34 | test_organization_events_spans_histogram.py | def test_bad_params_outside_range_num_buckets(self):
query = {
"project": [self.project.id],
"span": self.format_span("django.middleware", "2b9cbb96dbf59baa"),
"numBuckets": -1,
}
response = self.do_request(query)
assert response.status_code == 400, "failing for numBuckets"
assert response.data == {
"numBuckets": ["Ensure this value is greater than or equal to 1."]
}, "failing for numBuckets"
| 3dea4b7342328fc3ce74685b481f983c7ee6599a | 65 | https://github.com/getsentry/sentry.git | 127 | def test_bad_params_outside_range_num_buckets(self):
query = {
"project": [self.project.id],
"span": self.format_span("django.middleware", "2b9cbb96dbf59baa"),
"numBuckets": -1,
}
response = self.do_request(query)
assert response.status_code == 400, "failing for numBuckets"
assert response.da | 10 | 114 | test_bad_params_outside_range_num_buckets |
|
28 | 0 | 1 | 14 | tests/openbb_terminal/common/behavioural_analysis/test_finbrain_view.py | 283,343 | Updating some names (#1575)
* quick econ fix
* black
* keys and feature flags
* terminal name :eyes:
* some more replacements
* some more replacements
* edit pyproject
* gst -> openbb
* add example portfolios back to git
* Update api from gst
* sorry. skipping some tests
* another round of names
* another round of test edits
* Missed some .gst refs and update timezone
* water mark stuff
* Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS
* fix more GST to OpenBB Terminal
* Logging : merge conflicts with main
* Revert wrong files
Co-authored-by: Andrew <andrew.kenreich@gmail.com>
Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt>
Co-authored-by: Chavithra PARANA <chavithra@gmail.com> | OpenBBTerminal | 10 | Python | 21 | test_finbrain_view.py | def test_display_sentiment_analysis_empty_df(mocker):
view = "openbb_terminal.common.behavioural_analysis.finbrain_view"
# MOCK EXPORT_DATA
mocker.patch(
target="openbb_terminal.common.behavioural_analysis.finbrain_view.export_data"
)
# MOCK GTFF
mocker.patch.object(target=helper_funcs.obbff, attribute="USE_ION", new=True)
# MOCK GET_SENTIMENT
mocker.patch(
target=f"{view}.finbrain_model.get_sentiment",
return_value=pd.DataFrame(),
)
finbrain_view.display_sentiment_analysis(
ticker="AAPL",
export="",
)
| b71abcfbf4d7e8ac1855522aff0378e13c8b5362 | 67 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 95 | def test_display_sentiment_analysis_empty_df(mocker):
view = "openbb_terminal.common.behavioural_analysis.finbrain_view"
# MOCK EXPORT_DATA
mocker.patch(
target="openbb_terminal.common.behavioural_analysis.finbrain_view.export_data"
)
# MOCK GTFF
mocker.patch.object(target=helper_funcs.obbff, attribute="USE_ION", new=True)
# | 17 | 120 | test_display_sentiment_analysis_empty_df |
|
46 | 0 | 1 | 28 | src/documents/tests/test_barcodes.py | 320,002 | Updates how barcodes are detected, using pikepdf images, instead of converting each page to an image | paperless-ngx | 9 | Python | 33 | test_barcodes.py | def test_get_mime_type(self):
tiff_file = os.path.join(
self.SAMPLE_DIR,
"simple.tiff",
)
pdf_file = os.path.join(
self.SAMPLE_DIR,
"simple.pdf",
)
png_file = os.path.join(
self.BARCODE_SAMPLE_DIR,
"barcode-128-custom.png",
)
tiff_file_no_extension = os.path.join(settings.SCRATCH_DIR, "testfile1")
pdf_file_no_extension = os.path.join(settings.SCRATCH_DIR, "testfile2")
shutil.copy(tiff_file, tiff_file_no_extension)
shutil.copy(pdf_file, pdf_file_no_extension)
self.assertEqual(barcodes.get_file_mime_type(tiff_file), "image/tiff")
self.assertEqual(barcodes.get_file_mime_type(pdf_file), "application/pdf")
self.assertEqual(
barcodes.get_file_mime_type(tiff_file_no_extension),
"image/tiff",
)
self.assertEqual(
barcodes.get_file_mime_type(pdf_file_no_extension),
"application/pdf",
)
self.assertEqual(barcodes.get_file_mime_type(png_file), "image/png")
| 7aa0e5650b290cbc39e37418508863043f0de008 | 161 | https://github.com/paperless-ngx/paperless-ngx.git | 274 | def test_get_mime_type(self):
tiff_file = os.path.join(
self.SAMPLE_DIR,
"simple.tiff",
)
pdf_file = os.path.join(
| 19 | 263 | test_get_mime_type |
|
64 | 0 | 2 | 12 | seaborn/tests/_core/test_moves.py | 41,229 | Reorganize how Stat transform works, following Move patterns | seaborn | 13 | Python | 54 | test_moves.py | def test_two_semantics(self, df):
groupby = GroupBy(["x", "grp2", "grp3"])
res = Dodge()(df, groupby, "x")
levels = categorical_order(df["grp2"]), categorical_order(df["grp3"])
w, n = 0.8, len(levels[0]) * len(levels[1])
shifts = np.linspace(0, w - w / n, n)
shifts -= shifts.mean()
assert_series_equal(res["y"], df["y"])
assert_series_equal(res["width"], df["width"] / n)
for (v2, v3), shift in zip(product(*levels), shifts):
rows = (df["grp2"] == v2) & (df["grp3"] == v3)
assert_series_equal(res.loc[rows, "x"], df.loc[rows, "x"] + shift)
| 9917c46c544fa1f1a4b76cf174206a0f35305916 | 181 | https://github.com/mwaskom/seaborn.git | 148 | def test_two_semantics(self, df):
groupby = GroupBy(["x", "grp2", "grp3"])
res = Dodge()(df, groupby, "x")
levels = categorical_order(df["grp2"]) | 24 | 291 | test_two_semantics |
|
34 | 0 | 2 | 10 | examples/model_compress/pruning/taylorfo_lightning_evaluator.py | 113,360 | [Compression] Evaluator - step 3 Tutorial (#5016) | nni | 11 | Python | 27 | taylorfo_lightning_evaluator.py | def evaluate(self, batch, stage=None):
x, y = batch
logits = self(x)
loss = self.criterion(logits, y)
preds = torch.argmax(logits, dim=1)
acc = accuracy(preds, y)
if stage:
self.log(f"default", loss, prog_bar=False)
self.log(f"{stage}_loss", loss, prog_bar=True)
self.log(f"{stage}_acc", acc, prog_bar=True)
| 5f571327902c84c208482f66c2b293ad1013ee3d | 94 | https://github.com/microsoft/nni.git | 108 | def evaluate(self, batch, stage=None):
x, y = batch
| 17 | 149 | evaluate |
|
24 | 0 | 3 | 7 | awx/main/models/schedules.py | 81,246 | modifying schedules API to return a list of links | awx | 13 | Python | 21 | schedules.py | def get_zoneinfo_with_links(self):
zone_instance = get_zonefile_instance()
return_val = {'zones': sorted(zone_instance.zones), 'links': {}}
for zone_name in return_val['zones']:
if str(zone_name) != str(zone_instance.zones[zone_name]._filename):
return_val['links'][zone_name] = zone_instance.zones[zone_name]._filename
return return_val
| c836fafb61066d54af6f9726b00a83e6ae8451af | 71 | https://github.com/ansible/awx.git | 77 | def get_zoneinfo_with_links(self):
zone_instance = get_zonefile_instance()
return_val = {'zones': sorted(zone_instance.zones), 'links': {}}
for zone_name in return_val['zones']:
if str(zone_name) != str(zone_instance.zones[zone_name]._filename):
return_val['links'][zone_name] = zone_instance.zones[zo | 10 | 117 | get_zoneinfo_with_links |
|
7 | 0 | 2 | 2 | mmdet/utils/util_distribution.py | 244,221 | fix lint (#7793) | mmdetection | 9 | Python | 7 | util_distribution.py | def is_mlu_available():
return hasattr(torch, 'is_mlu_available') and torch.is_mlu_available()
| 24f2fdb38481e6c013a588660c044e410148ce1e | 18 | https://github.com/open-mmlab/mmdetection.git | 13 | def is_mlu_available():
return hasattr(torch, 'is_mlu_available') and torch.is_mlu_a | 3 | 34 | is_mlu_available |
|
24 | 0 | 3 | 7 | pytorch_lightning/trainer/connectors/checkpoint_connector.py | 241,510 | Remove `hpc_save` (#11101) | lightning | 11 | Python | 19 | checkpoint_connector.py | def _hpc_resume_path(self) -> Optional[str]:
if not os.path.isdir(self.trainer.weights_save_path):
return None
dir_path_hpc = str(self.trainer.weights_save_path)
max_version = self.__max_ckpt_version_in_folder(dir_path_hpc, "hpc_ckpt_")
if max_version is not None:
return os.path.join(dir_path_hpc, f"hpc_ckpt_{max_version}.ckpt")
| 4b5761539e45bd0392aa49378cbaaca574006f03 | 65 | https://github.com/Lightning-AI/lightning.git | 73 | def _hpc_resume_path(self) -> Optional[str]:
if not os.path.isdir(self.trainer.weights_save_path):
return None
dir_path_hpc = str(self.trainer.weights_save_path)
| 13 | 107 | _hpc_resume_path |
|
9 | 0 | 1 | 3 | mindsdb/integrations/handlers/informix_handler/tests/test_informix_handler.py | 116,938 | cleaned up whitespace and indentation in test_informix_handler | mindsdb | 9 | Python | 9 | test_informix_handler.py | def test_4_get_tables(self):
tables = self.handler.get_tables()
assert tables.type is RESPONSE_TYPE.TABLE
| 82ba332ccf612ef32880a25167aba5fd69408889 | 22 | https://github.com/mindsdb/mindsdb.git | 23 | def test_4_get_tables(self):
tables = self | 8 | 36 | test_4_get_tables |
|
122 | 0 | 3 | 17 | haystack/utils/doc_store.py | 257,007 | fix launch scripts (#2341) | haystack | 13 | Python | 86 | doc_store.py | def launch_opensearch(sleep=15, delete_existing=False):
# Start an OpenSearch server via docker
logger.debug("Starting OpenSearch...")
# This line is needed since it is not possible to start a new docker container with the name opensearch if there is a stopped image with the same now
# docker rm only succeeds if the container is stopped, not if it is running
if delete_existing:
_ = subprocess.run([f"docker rm --force {OPENSEARCH_CONTAINER_NAME}"], shell=True, stdout=subprocess.DEVNULL)
status = subprocess.run(
[
f'docker start {OPENSEARCH_CONTAINER_NAME} > /dev/null 2>&1 || docker run -d -p 9201:9200 -p 9600:9600 -e "discovery.type=single-node" --name {OPENSEARCH_CONTAINER_NAME} opensearchproject/opensearch:1.2.4'
],
shell=True,
)
if status.returncode:
logger.warning(
"Tried to start OpenSearch through Docker but this failed. "
"It is likely that there is already an existing OpenSearch instance running. "
)
else:
time.sleep(sleep)
| d43801143221e71e868c0ac80795bb0a306778e7 | 77 | https://github.com/deepset-ai/haystack.git | 230 | def launch_opensearch(sleep=15, delete_existing=False):
# Start an OpenSearch server via docker
logger.debug("Starting OpenSearch...")
# This line is needed since it is not possible to start a new docker container with the name opensearch if there is a stopped image with the same now
# docker rm only succeeds if the container is stopped, not if it is running
if delete_existing:
_ = sub | 16 | 142 | launch_opensearch |
|
73 | 0 | 9 | 22 | wagtail/search/backends/database/postgres/postgres.py | 75,504 | Reformat with black | wagtail | 16 | Python | 55 | postgres.py | def prepare_field(self, obj, field):
if isinstance(field, SearchField):
yield (
field,
get_weight(field.boost),
self.prepare_value(field.get_value(obj)),
)
elif isinstance(field, AutocompleteField):
# AutocompleteField does not define a boost parameter, so use a base weight of 'D'
yield (field, "D", self.prepare_value(field.get_value(obj)))
elif isinstance(field, RelatedFields):
sub_obj = field.get_value(obj)
if sub_obj is None:
return
if isinstance(sub_obj, Manager):
sub_objs = sub_obj.all()
else:
if callable(sub_obj):
sub_obj = sub_obj()
sub_objs = [sub_obj]
for sub_obj in sub_objs:
for sub_field in field.fields:
yield from self.prepare_field(sub_obj, sub_field)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 144 | https://github.com/wagtail/wagtail.git | 350 | def prepare_field(self, obj, field):
if isinstance(field, SearchField):
yield (
field,
get_weight(field.boost),
self.prepare_value(field.get_value(obj)),
)
elif isinstance(field, AutocompleteField):
# AutocompleteField does not define a boost parameter, so use a base weight of 'D'
yield (field, "D", self.prepare_value(field.get_value(obj)))
elif isinstance(field, RelatedFields):
sub_obj = field.get_value(obj)
if sub_obj is None:
return
if isinstance(sub_obj, Manager):
sub_objs = sub_obj.all()
else:
if callable(sub_obj):
sub_obj = sub_obj()
sub_objs = [sub_obj]
for sub_obj in sub_objs:
for su | 19 | 225 | prepare_field |
|
70 | 0 | 1 | 34 | tests/jobs/test_scheduler_job.py | 47,368 | Fix regression in pool metrics (#22939)
Co-authored-by: Tanel Kiis <tanel.kiis@reach-u.com>
Co-authored-by: Ash Berlin-Taylor <ash_github@firemirror.com> | airflow | 13 | Python | 46 | test_scheduler_job.py | def test_emit_pool_starving_tasks_metrics(self, mock_stats_gauge, dag_maker):
self.scheduler_job = SchedulerJob(subdir=os.devnull)
session = settings.Session()
dag_id = 'SchedulerJobTest.test_emit_pool_starving_tasks_metrics'
with dag_maker(dag_id=dag_id):
op = DummyOperator(task_id='op', pool_slots=2)
dr = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
ti = dr.get_task_instance(op.task_id, session)
ti.state = State.SCHEDULED
set_default_pool_slots(1)
session.flush()
res = self.scheduler_job._executable_task_instances_to_queued(max_tis=32, session=session)
assert 0 == len(res)
mock_stats_gauge.assert_has_calls(
[
mock.call('scheduler.tasks.starving', 1),
mock.call(f'pool.starving_tasks.{Pool.DEFAULT_POOL_NAME}', 1),
],
any_order=True,
)
mock_stats_gauge.reset_mock()
set_default_pool_slots(2)
session.flush()
res = self.scheduler_job._executable_task_instances_to_queued(max_tis=32, session=session)
assert 1 == len(res)
mock_stats_gauge.assert_has_calls(
[
mock.call('scheduler.tasks.starving', 0),
mock.call(f'pool.starving_tasks.{Pool.DEFAULT_POOL_NAME}', 0),
],
any_order=True,
)
session.rollback()
session.close()
| 0367a92881e88df36dabb81ef837e5256f3db89d | 223 | https://github.com/apache/airflow.git | 360 | def test_emit_pool_starving_tasks_metrics(self, mock_stats_gauge, dag_maker):
self.scheduler_job = SchedulerJob(subdir=os.devnull)
session = settings.Session()
dag_id = 'SchedulerJobTest.test_emit_pool_starving_tasks_metrics'
with dag_maker(dag_id=dag_id):
op = DummyOperator(task_id='op', pool_slots=2)
dr = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED)
ti = dr.get_task_instance(op.task_id, session)
ti.state = State.SCHEDULED
set_default_pool_slots(1)
session.flush()
res = self.scheduler_job._executable_task_instances_to_queued(max_tis=32, session=session)
assert 0 == len(res)
mock_stats_gauge.assert_has_calls(
[
mock.call('scheduler.tasks.starving', 1),
mock.call(f'pool.starving_tasks.{Pool.DEFAULT_POOL_NAME}', 1),
],
any_order=True,
)
mock_stats_gauge.reset_mock()
set_default_pool_slots(2)
session.flush()
res = self.scheduler_job._executable_task_instances_to_queued(max_tis=32, session=session)
assert 1 == len(res)
mock_stats_gauge.assert_has_calls(
[
mock.call('scheduler.tasks.starving', 0),
mock.call(f'pool.starving_tasks.{Pool.DEFAULT_POO | 41 | 369 | test_emit_pool_starving_tasks_metrics |
|
16 | 0 | 1 | 4 | easyocr/DBNet/assets/ops/dcn/modules/deform_conv.py | 123,134 | add dbnet | EasyOCR | 8 | Python | 15 | deform_conv.py | def forward(self, x, offset, mask):
return modulated_deform_conv(x, offset, mask, self.weight, self.bias,
self.stride, self.padding, self.dilation,
self.groups, self.deformable_groups)
| 803b90729d25fda253011c505d0189e8e63cc039 | 48 | https://github.com/JaidedAI/EasyOCR.git | 94 | def forward(self, x, offset, mask):
return modulated_deform_conv(x, offset, mask, self.weight, self.bias,
self.stride, self.padding, self.dilation,
| 13 | 62 | forward |
|
10 | 0 | 2 | 55 | tests/components/bluetooth/test_init.py | 317,001 | Add support for subscribing to bluetooth callbacks by address (#74773) | core | 7 | Python | 8 | test_init.py | async def test_register_callback_by_address(hass, mock_bleak_scanner_start):
mock_bt = []
callbacks = []
| c2fefe03b2dc800f42de695f0b73a8f26621d882 | 318 | https://github.com/home-assistant/core.git | 19 | async def test_register_callback_by_address(hass, mock_bleak_scanner_start):
| 5 | 30 | test_register_callback_by_address |
|
33 | 0 | 5 | 46 | website/homepage/render_html.py | 179,196 | added emojis to navbar; added guides main page | gradio | 14 | Python | 27 | render_html.py | def render_docs():
if os.path.exists("generated/colab_links.json"):
with open("generated/colab_links.json") as demo_links_file:
try:
demo_links = json.load(demo_links_file)
except ValueError:
demo_links = {}
else: # docs will be missing demo links
demo_links = {}
SCREENSHOT_FOLDER = "dist/assets/demo_screenshots"
os.makedirs(SCREENSHOT_FOLDER, exist_ok=True)
| b065879054492fbfdfce9d767f13e02019e7764b | 300 | https://github.com/gradio-app/gradio.git | 111 | def render_docs():
if os.path.exists("ge | 13 | 107 | render_docs |
|
12 | 0 | 1 | 3 | mindsdb/integrations/handlers/mysql_handler/tests/test_mysql_handler.py | 115,670 | Test added | mindsdb | 9 | Python | 12 | test_mysql_handler.py | def test_1_native_query_show_dbs(self):
dbs = self.handler.native_query("SHOW DATABASES;")
assert dbs[' '] is not RESPONSE_TYPE.ERROR
| 414b259284343c26fba31b29121c6462b3666fb9 | 25 | https://github.com/mindsdb/mindsdb.git | 25 | def test_1_native_query_show_dbs(self):
dbs = self.handler.native_query("SHOW DATABASE | 7 | 43 | test_1_native_query_show_dbs |
|
598 | 0 | 54 | 108 | nuitka/build/SconsCompilerSettings.py | 178,702 | macOS: Minor cleanups | Nuitka | 19 | Python | 323 | SconsCompilerSettings.py | def setupCCompiler(env, lto_mode, pgo_mode, job_count):
# This is driven by many branches on purpose and has a lot of things
# to deal with for LTO checks and flags, pylint: disable=too-many-branches,too-many-statements
# Enable LTO for compiler.
_enableLtoSettings(
env=env,
lto_mode=lto_mode,
pgo_mode=pgo_mode,
job_count=job_count,
)
_detectWindowsSDK(env)
_enableC11Settings(env)
if env.gcc_mode:
# Support for gcc and clang, restricting visibility as much as possible.
env.Append(CCFLAGS=["-fvisibility=hidden"])
if not env.c11_mode:
env.Append(CXXFLAGS=["-fvisibility-inlines-hidden"])
if isWin32Windows():
# On Windows, exporting to DLL need to be controlled.
env.Append(LINKFLAGS=["-Wl,--exclude-all-symbols"])
# Make sure we handle import library on our own and put it into the
# build directory.
env.Append(
LINKFLAGS=[
"-Wl,--out-implib,%s" % os.path.join(env.source_dir, "import.lib")
]
)
# Make it clear how to handle integer overflows, namely by wrapping around
# to negative values.
env.Append(CCFLAGS=["-fwrapv"])
if not env.low_memory:
# Avoid IO for compilation as much as possible, this should make the
# compilation more memory hungry, but also faster.
env.Append(CCFLAGS="-pipe")
# Support for clang.
if "clang" in env.the_cc_name:
env.Append(CCFLAGS=["-w"])
env.Append(CPPDEFINES=["_XOPEN_SOURCE"])
# Don't export anything by default, this should create smaller executables.
env.Append(CCFLAGS=["-fvisibility=hidden", "-fvisibility-inlines-hidden"])
if env.debug_mode:
env.Append(CCFLAGS=["-Wunused-but-set-variable"])
# Support for macOS standalone backporting.
if isMacOS():
setEnvironmentVariable(env, "MACOSX_DEPLOYMENT_TARGET", env.macos_min_version)
target_flag = "--target=%s-apple-macos%s" % (
env.macos_target_arch,
env.macos_min_version,
)
env.Append(CCFLAGS=[target_flag])
env.Append(LINKFLAGS=[target_flag])
# The 32 bits MinGW does not default for API level properly, so help it.
if env.mingw_mode:
# Windows XP
env.Append(CPPDEFINES=["_WIN32_WINNT=0x0501"])
# Unicode entry points for programs.
if env.mingw_mode:
env.Append(LINKFLAGS=["-municode"])
# Detect the gcc version
if env.gcc_version is None and env.gcc_mode and not env.clang_mode:
env.gcc_version = myDetectVersion(env, env.the_compiler)
# Older g++ complains about aliasing with Py_True and Py_False, but we don't
# care.
if env.gcc_mode and not env.clang_mode and env.gcc_version < (4, 5):
env.Append(CCFLAGS=["-fno-strict-aliasing"])
# For gcc 4.6 or higher, there are some new interesting functions.
if env.gcc_mode and not env.clang_mode and env.gcc_version >= (4, 6):
env.Append(CCFLAGS=["-fpartial-inlining"])
if env.debug_mode:
env.Append(CCFLAGS=["-Wunused-but-set-variable"])
# Save some memory for gcc by not tracing macro code locations at all.
if (
not env.debug_mode
and env.gcc_mode
and not env.clang_mode
and env.gcc_version >= (5,)
):
env.Append(CCFLAGS=["-ftrack-macro-expansion=0"])
# We don't care about deprecations.
if env.gcc_mode and not env.clang_mode:
env.Append(CCFLAGS=["-Wno-deprecated-declarations"])
# The var-tracking does not scale, disable it. Should we really need it, we
# can enable it. TODO: Does this cause a performance loss?
if env.gcc_mode and not env.clang_mode:
env.Append(CCFLAGS=["-fno-var-tracking"])
# For large files, these can issue warnings about disabling
# itself, while we do not need it really.
if env.gcc_mode and not env.clang_mode and env.gcc_version >= (6,):
env.Append(CCFLAGS=["-Wno-misleading-indentation"])
# Disable output of notes, e.g. on struct alignment layout changes for
# some arches, we don't care.
if env.gcc_mode and not env.clang_mode:
env.Append(CCFLAGS=["-fcompare-debug-second"])
# Prevent using LTO when told not to use it, causes errors with some
# static link libraries.
if (
env.gcc_mode
and not env.clang_mode
and env.static_libpython
and not env.lto_mode
):
env.Append(CCFLAGS=["-fno-lto"])
env.Append(LINKFLAGS=["-fno-lto"])
# Set optimization level for gcc and clang in LTO mode
if env.gcc_mode and env.lto_mode:
if env.debug_mode:
env.Append(LINKFLAGS=["-Og"])
else:
# For LTO with static libpython combined, there are crashes with Python core
# being inlined, so we must refrain from that. On Windows there is no such
# thing, and Nuitka-Python is not affected.
env.Append(
LINKFLAGS=[
"-O3"
if env.nuitka_python or os.name == "nt" or not env.static_libpython
else "-O2"
]
)
# When debugging, optimize less than when optimizing, when not remove
# assertions.
if env.debug_mode:
if env.clang_mode or (env.gcc_mode and env.gcc_version >= (4, 8)):
env.Append(CCFLAGS=["-Og"])
elif env.gcc_mode:
env.Append(CCFLAGS=["-O1"])
elif env.msvc_mode:
env.Append(CCFLAGS=["-O2"])
else:
if env.gcc_mode:
env.Append(
CCFLAGS=[
"-O3"
if env.nuitka_python or os.name == "nt" or not env.static_libpython
else "-O2"
]
)
elif env.msvc_mode:
env.Append(
CCFLAGS=[
"/Ox", # Enable most speed optimization
"/GF", # Eliminate duplicate strings.
"/Gy", # Function level object storage, to allow removing unused ones
]
)
env.Append(CPPDEFINES=["__NUITKA_NO_ASSERT__"])
| 7f9a8a2b207dfdf46e1264d6d9b61466b80875d0 | 716 | https://github.com/Nuitka/Nuitka.git | 1,712 | def setupCCompiler(env, lto_mode, pgo_mode, job_count):
# This is driven by many branches on purpose and has a lot of things
# to deal with for LTO checks and flags, pylint: disable=too-many-branches,too-many-statements
# Enable LTO for compiler.
_enableLtoSettings(
env=env,
lto_mode=lto_mode,
pgo_mode=pgo_mode,
job_count=job_count,
)
_detectWindowsSDK(env)
_enableC11Settings(env)
if env.gcc_mode:
# Support for gcc and clang, restricting visibility as much as possible.
env.Append(CCFLAGS=["-fvisibility=hidden"])
if not env.c11_mode:
env.Append(CXXFLAGS=["-fvisibility-inlines-hidden"])
if isWin32Windows():
# On Windows, exporting to DLL need to be controlled.
env.Append(LINKFLAGS=["-Wl,--exclude-all-symbols"])
# Make sure we handle import library on our own and put it into the
# build directory.
env.Append(
LINKFLAGS=[
"-Wl,--out-implib,%s" % os.path.join(env.source_dir, "import.lib")
]
)
# Make it clear how to handle integer overflows, namely by wrapping around
# to negative values.
env.Append(CCFLAGS=["-fwrapv"])
if not env.low_memory:
# Avoid IO for compilation as much as possible, this should make the
# compilation more memory hungry, but also faster.
env.Append(CCFLAGS="-pipe")
# Support for clang.
if "clang" in env.the_cc_name:
env.Append(CCFLAGS=["-w"])
env.Append(CPPDEFINES=["_XOPEN_SOURCE"])
# Don't export anything by default, this should create smaller executables.
env.Append(CCFLAGS=["-fvisibility=hidden", "-fvisibility-inlines-hidden"])
if env.debug_mode:
env.Append(CCFLAGS=["-Wunused-but-set-variable"])
# Support for macOS standalone backporting.
if isMacOS():
setEnvironmentVariable(env, "MACOSX_DEPLOYMENT_TARGET", env.macos_min_version)
target_flag = "--target=%s-apple-macos%s" % (
env.macos_target_arch,
env.macos_min_version,
)
env.Append(CCFLAGS=[target_flag])
env.Append(LINKFLAGS=[target_flag])
# The 32 bits MinGW does not default for API level properly, so help it.
if env.mingw_mode:
# Windows XP
env.Append(CPPDEFINES=["_WIN32_WINNT=0x0501"])
# Unicode entry points for programs.
if env.mingw_mode:
env.Append(LINKFLAGS=["-municode"])
# Detect the gcc version
if env.gcc_version is None and env. | 37 | 1,239 | setupCCompiler |
|
13 | 0 | 1 | 9 | tests/blocks/test_core.py | 54,260 | Require version | prefect | 11 | Python | 13 | test_core.py | async def test_registering_and_getting_blocks():
with pytest.raises(ValueError, match="(No block spec exists)"):
get_block_spec("is anyone home", "1.0")
| 58b51caba356ad021a3b7b76f61d28a40884ba11 | 66 | https://github.com/PrefectHQ/prefect.git | 22 | async def test_registering_and_getting_blocks():
with pytest.raises(ValueError, match="(No block spec exists)"):
get_block_spec("is anyo | 6 | 44 | test_registering_and_getting_blocks |
|
12 | 0 | 1 | 7 | scripts/tools/initialize_virtualenv.py | 47,799 | add script to initialise virtualenv (#22971)
Co-authored-by: Jarek Potiuk <jarek@potiuk.com> | airflow | 8 | Python | 11 | initialize_virtualenv.py | def get_python_version() -> str:
major = sys.version_info[0]
minor = sys.version_info[1]
return f"{major}.{minor}"
| 03bef084b3f1611e1becdd6ad0ff4c0d2dd909ac | 26 | https://github.com/apache/airflow.git | 24 | def get_python_version() -> str:
major = sys | 6 | 52 | get_python_version |
|
6 | 0 | 1 | 4 | wagtail/admin/viewsets/chooser.py | 77,710 | Add ChooserViewSet | wagtail | 9 | Python | 6 | chooser.py | def chosen_view(self):
return self.chosen_view_class.as_view(
model=self.model,
)
| 4b3c57d72ced0f64378cef26fa12a77bce966ac1 | 19 | https://github.com/wagtail/wagtail.git | 30 | def chosen_view(self):
return self.chosen_view_class.as_view(
| 5 | 30 | chosen_view |
|
64 | 0 | 1 | 35 | src/prefect/orion/database/migrations/versions/postgresql/5f376def75c3_.py | 53,586 | Fix syntax error in autogenerated migration file | prefect | 14 | Python | 42 | 5f376def75c3_.py | def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table(
"block_data",
sa.Column(
"id",
prefect.orion.utilities.database.UUID(),
server_default=sa.text("(GEN_RANDOM_UUID())"),
nullable=False,
),
sa.Column(
"created",
prefect.orion.utilities.database.Timestamp(timezone=True),
server_default=sa.text("CURRENT_TIMESTAMP"),
nullable=False,
),
sa.Column(
"updated",
prefect.orion.utilities.database.Timestamp(timezone=True),
server_default=sa.text("CURRENT_TIMESTAMP"),
nullable=False,
),
sa.Column("name", sa.String(), nullable=False),
sa.Column("blockref", sa.String(), nullable=False),
sa.Column(
"data",
prefect.orion.utilities.database.JSON(astext_type=sa.Text()),
server_default="{}",
nullable=False,
),
sa.PrimaryKeyConstraint("id", name=op.f("pk_block_data")),
)
op.create_index(op.f("ix_block_data__name"), "block_data", ["name"], unique=True)
op.create_index(
op.f("ix_block_data__updated"), "block_data", ["updated"], unique=False
)
# ### end Alembic commands ###
| e41e3a0b19d7fdada1c7feff4dffe9841b39269e | 243 | https://github.com/PrefectHQ/prefect.git | 351 | def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table(
"block_data",
sa.Column(
"id",
prefect.orion.utilities.database.UUID(),
server_default=sa.text("(GEN_RANDOM_UUID())"),
nullable=False,
),
sa.Column(
"created",
prefect.orion.utilities.database.Timestamp(timezone=True),
server_default=sa.text("CURRENT_TIMESTAMP"),
nullable=False,
),
sa.Column(
"updated",
prefect.orion.utilities.database.Timestamp(timezone=True),
| 24 | 389 | upgrade |
|
52 | 1 | 2 | 13 | test/mitmproxy/test_eventsequence.py | 252,350 | Add support for raw UDP. (#5414) | mitmproxy | 12 | Python | 31 | test_eventsequence.py | def test_udp_flow(err):
f = tflow.tudpflow(err=err)
i = eventsequence.iterate(f)
assert isinstance(next(i), layers.udp.UdpStartHook)
assert len(f.messages) == 0
assert isinstance(next(i), layers.udp.UdpMessageHook)
assert len(f.messages) == 1
assert isinstance(next(i), layers.udp.UdpMessageHook)
assert len(f.messages) == 2
if err:
assert isinstance(next(i), layers.udp.UdpErrorHook)
else:
assert isinstance(next(i), layers.udp.UdpEndHook)
@pytest.mark.parametrize(
"resp, err",
[
(False, False),
(True, False),
(False, True),
(True, True),
],
) | cd4a74fae7cbd8119afc3900597f798ec1604db7 | @pytest.mark.parametrize(
"resp, err",
[
(False, False),
(True, False),
(False, True),
(True, True),
],
) | 125 | https://github.com/mitmproxy/mitmproxy.git | 130 | def test_udp_flow(err):
f = tflow.tudpflow(err=err)
i = eventsequence.iterate(f)
assert is | 21 | 244 | test_udp_flow |
15 | 0 | 1 | 5 | keras/legacy_tf_layers/variable_scope_shim_test.py | 274,478 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 9 | Python | 11 | variable_scope_shim_test.py | def testGetVar(self):
vs = variable_scope._get_default_variable_store()
v = vs.get_variable("v", [1])
v1 = vs.get_variable("v", [1])
self.assertIs(v, v1)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 44 | https://github.com/keras-team/keras.git | 42 | def testGetVar(self):
vs = variable_scope._get_default_variable_store()
v = vs.get_variable("v", [1])
v1 = vs.get_varia | 9 | 73 | testGetVar |
|
87 | 0 | 2 | 20 | tools/ci_code_validator/tests/test_tools.py | 3,855 | 🎉 Single py checker (#10246) | airbyte | 18 | Python | 67 | test_tools.py | def test_tool(tmp_path, toml_config_file, cmd, package_dir, expected_file):
cmd = cmd.format(package_dir=package_dir, toml_config_file=toml_config_file)
proc = subprocess.Popen(cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, _ = proc.communicate()
file_log = tmp_path / "temp.log"
file_log.write_bytes(out)
assert file_log.is_file() is True
issues_file = tmp_path / "issues.json"
with requests_mock.Mocker() as m:
m.get('/api/authentication/validate', json={"valid": True})
m.get("/api/rules/search", json={"rules": []})
m.post("/api/rules/create", json={})
parser = LogParser(issues_file, host="http://fake.com/", token="fake_token")
assert getattr(parser, f'from_{cmd.split(" ")[0]}')(file_log) == 0
assert issues_file.is_file() is True
data = json.loads(issues_file.read_text())
for issue in data["issues"]:
issue["primaryLocation"]["filePath"] = "/".join(issue["primaryLocation"]["filePath"].split("/")[-2:])
expected_data = json.loads(Path(expected_file).read_text())
assert json.dumps(data, sort_keys=True, separators=(',', ': ')) == json.dumps(expected_data, sort_keys=True, separators=(',', ': '))
| 61b0e9e196ea07795d47effc670bcb981117c030 | 272 | https://github.com/airbytehq/airbyte.git | 191 | def test_tool(tmp_path, toml_config_file, cmd, package_dir, expected_file):
cmd = cmd.format(package_dir=package_dir, toml_config_file=toml_config_file)
proc = subprocess.Popen(cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, _ = proc.communicate()
file_log = tmp_path / "temp.log"
file_log.write_bytes(out)
assert file_log.is_file() is True
issues_file = tmp_path / "issues.json"
with requests_mock.Mocker() as m:
m.get('/api/authentication/validate', json={"valid": True})
m.get("/api/rules/search", json={"rules": []})
m.post("/api/rules/create", json={})
parser = LogParser(issues_file, host="http://fake.com/", token="fake_token")
assert getattr(parser, f'from_{cmd.split(" ")[0]}')(file_log) == 0
assert issues_file.is_file() is True
data = json.loads(issues_file.read_text())
for issue in data["issues"]:
issue["primaryLocation"]["filePath"] = "/".join(issue["primaryLocation"]["filePath"].split("/")[-2:])
expected_data = json.loads(Path(expected_file).read_text())
assert json.dumps(data, sort_keys=True, separators=(',', ': ')) == json.dumps(expected_data, sort_keys=True, separators=(',', ': '))
| 42 | 474 | test_tool |
|
8 | 0 | 1 | 3 | python/ray/util/ml_utils/tests/test_mlflow.py | 133,181 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | ray | 9 | Python | 8 | test_mlflow.py | def test_experiment_id(self):
self.mlflow_util.setup_mlflow(tracking_uri=self.tracking_uri, experiment_id="0")
assert self.mlflow_util.experiment_id == "0"
| 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | 29 | https://github.com/ray-project/ray.git | 21 | def test_experiment_id(self):
self.mlflow_util.setup_mlflow(tracking_uri=self.tracki | 6 | 49 | test_experiment_id |
|
52 | 0 | 1 | 26 | tests/bulk_create/tests.py | 201,941 | Refs #33476 -- Reformatted code with Black. | django | 12 | Python | 35 | tests.py | def _test_update_conflicts_two_fields(self, unique_fields):
TwoFields.objects.bulk_create(
[
TwoFields(f1=1, f2=1, name="a"),
TwoFields(f1=2, f2=2, name="b"),
]
)
self.assertEqual(TwoFields.objects.count(), 2)
conflicting_objects = [
TwoFields(f1=1, f2=1, name="c"),
TwoFields(f1=2, f2=2, name="d"),
]
TwoFields.objects.bulk_create(
conflicting_objects,
update_conflicts=True,
unique_fields=unique_fields,
update_fields=["name"],
)
self.assertEqual(TwoFields.objects.count(), 2)
self.assertCountEqual(
TwoFields.objects.values("f1", "f2", "name"),
[
{"f1": 1, "f2": 1, "name": "c"},
{"f1": 2, "f2": 2, "name": "d"},
],
)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 180 | https://github.com/django/django.git | 302 | def _test_update_conflicts_two_fields(self, unique_fields):
TwoFields.objects.bulk_create(
[
TwoFields(f1=1, f2=1, name="a"),
TwoFields(f1=2, f2=2, name="b"),
]
)
self.assertEqual(TwoFields.objects.count(), 2)
conflicting_objects = [
TwoFields(f1=1, f2=1, name="c"),
TwoFields(f1=2, f2=2, name="d"),
]
TwoFields.objects.bulk_create(
conflicting_objects,
update_conflicts=True,
unique_fields=unique_fields,
update_fields=["name"],
)
self.assertEqual(TwoFields.objects.count(), 2)
self.assertCountEqual(
TwoFields.objects.values("f1", "f2", "name"),
| 16 | 287 | _test_update_conflicts_two_fields |
|
10 | 0 | 1 | 5 | tests/components/skybell/__init__.py | 303,258 | Add config flow to skybell (#70887) | core | 11 | Python | 10 | __init__.py | def _patch_skybell() -> None:
return patch(
"homeassistant.components.skybell.config_flow.Skybell.async_send_request",
return_value={"id": USER_ID},
)
| a502a8798ff74eb6185473df7f69553fc4663634 | 20 | https://github.com/home-assistant/core.git | 29 | def _patch_skybell() -> None:
return patch(
"homeassistant.components.skybell.config_flow.Skybell.async_send_request",
return_value={"id": USER_ID},
)
| 4 | 35 | _patch_skybell |
|
156 | 0 | 5 | 40 | python/ccxt/async_support/gateio.py | 18,323 | 1.72.78
[ci skip] | ccxt | 12 | Python | 102 | gateio.py | def parse_transaction(self, transaction, currency=None):
#
# deposits
#
# {
# "id": "d33361395",
# "currency": "USDT_TRX",
# "address": "TErdnxenuLtXfnMafLbfappYdHtnXQ5U4z",
# "amount": "100",
# "txid": "ae9374de34e558562fe18cbb1bf9ab4d9eb8aa7669d65541c9fa2a532c1474a0",
# "timestamp": "1626345819",
# "status": "DONE",
# "memo": ""
# }
#
# withdrawals
id = self.safe_string(transaction, 'id')
type = None
amount = self.safe_string(transaction, 'amount')
if id[0] == 'b':
# GateCode handling
type = 'deposit' if Precise.string_gt(amount, '0') else 'withdrawal'
amount = Precise.string_abs(amount)
elif id is not None:
type = self.parse_transaction_type(id[0])
currencyId = self.safe_string(transaction, 'currency')
code = self.safe_currency_code(currencyId)
txid = self.safe_string(transaction, 'txid')
rawStatus = self.safe_string(transaction, 'status')
status = self.parse_transaction_status(rawStatus)
address = self.safe_string(transaction, 'address')
fee = self.safe_number(transaction, 'fee')
tag = self.safe_string(transaction, 'memo')
if tag == '':
tag = None
timestamp = self.safe_timestamp(transaction, 'timestamp')
return {
'info': transaction,
'id': id,
'txid': txid,
'currency': code,
'amount': self.parse_number(amount),
'network': None,
'address': address,
'addressTo': None,
'addressFrom': None,
'tag': tag,
'tagTo': None,
'tagFrom': None,
'status': status,
'type': type,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'updated': None,
'fee': fee,
}
| 6a6664b154a2f3a123e4a750457e1ec39fd74e22 | 260 | https://github.com/ccxt/ccxt.git | 688 | def parse_transaction(self, transaction, currency=None):
#
# deposits
#
# {
# "id": "d33361395",
# "currency": "USDT_TRX",
# "address": "TErdnxenuLtXfnMafLbfappYdHtnXQ5U4z",
# "amount": "100",
# "txid": "ae9374de34e558562fe18cbb1bf9ab4d9eb8aa7669d65541c9fa2a532c1474a0",
# "timestamp": "1626345819",
# "status": "DONE",
# "memo": ""
# }
#
# withdrawals
id = self.safe_string(transaction, 'id')
type = None
amount = self.safe_string(transaction, 'amount')
if id[0] == 'b':
# GateCode handling
type = 'deposit' if Precise.string_gt(amount, '0') else 'withdrawal'
amount = Precise.string_abs(amount)
elif id is not None:
type = self.parse_transaction_type(id[0])
currencyId = self.safe_string(transaction, 'currency')
code = self.safe_currency_code(currencyId)
txid = self.safe_string(transaction, 'txid')
rawSta | 27 | 457 | parse_transaction |
|
72 | 0 | 7 | 22 | erpnext/accounts/doctype/invoice_discounting/test_invoice_discounting.py | 64,869 | style: format code with black | erpnext | 12 | Python | 49 | test_invoice_discounting.py | def create_invoice_discounting(invoices, **args):
args = frappe._dict(args)
inv_disc = frappe.new_doc("Invoice Discounting")
inv_disc.posting_date = args.posting_date or nowdate()
inv_disc.company = args.company or "_Test Company"
inv_disc.bank_account = args.bank_account
inv_disc.short_term_loan = args.short_term_loan
inv_disc.accounts_receivable_credit = args.accounts_receivable_credit
inv_disc.accounts_receivable_discounted = args.accounts_receivable_discounted
inv_disc.accounts_receivable_unpaid = args.accounts_receivable_unpaid
inv_disc.short_term_loan = args.short_term_loan
inv_disc.bank_charges_account = args.bank_charges_account
inv_disc.bank_account = args.bank_account
inv_disc.loan_start_date = args.start or nowdate()
inv_disc.loan_period = args.period or 30
inv_disc.bank_charges = flt(args.bank_charges)
for d in invoices:
inv_disc.append("invoices", {"sales_invoice": d})
inv_disc.insert()
if not args.do_not_submit:
inv_disc.submit()
return inv_disc
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 165 | https://github.com/frappe/erpnext.git | 50 | def create_invoice_discounting(invoices, **args):
args = frappe._dict(args)
inv_disc = frappe.new_doc("Invoice Discounting")
inv_disc.posting_date = args.posting_date or nowdate()
inv_disc.company = args.company or "_Test Company"
inv_disc.bank_account = args.bank_account
inv_disc.short_term_loan = args.short_term_loan
inv_disc.accounts_receivable_credit = args.accounts_receivable_credit
inv_disc.accounts_receivable_discounted = args.accounts_receivable_discounted
inv_disc.accounts_receivable_unpaid = args.accounts_receivable_unpaid
inv_disc.short_term_loan = args.short_term_loan
inv_disc.bank_charges_account = args.bank_charges_account
inv_disc.bank_account = args.bank_account
inv_disc.loan_start_date = args.start or | 27 | 271 | create_invoice_discounting |
|
138 | 0 | 13 | 89 | jina/orchestrate/flow/base.py | 12,415 | fix: success box ui | jina | 17 | Python | 78 | base.py | def _get_address_table(self, address_table):
_protocol = str(self.protocol)
if self.gateway_args.ssl_certfile and self.gateway_args.ssl_keyfile:
_protocol = f'{self.protocol}S'
address_table.add_row(
':link:', 'Protocol', f':closed_lock_with_key: {_protocol}'
)
else:
address_table.add_row(':link:', 'Protocol', _protocol)
_protocol = _protocol.lower()
address_table.add_row(
':house:',
'Local',
f'[link={_protocol}://{self.host}:{self.port}]{self.host}:{self.port}[/]',
)
address_table.add_row(
':lock:',
'Private',
f'[link={_protocol}://{self.address_private}:{self.port}]{self.address_private}:{self.port}[/]',
)
if self.address_public:
address_table.add_row(
':earth_africa:',
'Public',
f'[link={_protocol}://{self.address_public}:{self.port}]{self.address_public}:{self.port}[/]',
)
if self.protocol == GatewayProtocolType.HTTP:
_address = [
f'[link={_protocol}://localhost:{self.port}/docs]Local[/]',
f'[link={_protocol}://{self.address_private}:{self.port}/docs]Private[/]',
]
if self.address_public:
_address.append(
f'[link={_protocol}://{self.address_public}:{self.port}/docs]Public[/]'
)
address_table.add_row(
':speech_balloon:',
'Swagger UI [dim](/docs)[/]',
'·'.join(_address),
)
_address = [
f'[link={_protocol}://localhost:{self.port}/redoc]Local[/]',
f'[link={_protocol}://{self.address_private}:{self.port}/redoc]Private[/]',
]
if self.address_public:
_address.append(
f'[link={_protocol}://{self.address_public}:{self.port}/redoc]Public[/]'
)
address_table.add_row(
':books:',
'Redoc [dim](/redoc)[/]',
'·'.join(_address),
)
if self.gateway_args.expose_graphql_endpoint:
_address = [
f'[link={_protocol}://localhost:{self.port}/graphql]Local[/]',
f'[link={_protocol}://{self.address_private}:{self.port}/graphql]Private[/]',
]
if self.address_public:
_address.append(
f'[link={_protocol}://{self.address_public}:{self.port}/graphql]Public[/]'
)
address_table.add_row(
':strawberry:',
'GraphQL UI [dim](/graphql)[/]',
'·'.join(_address),
)
if self.monitoring:
for name, deployment in self:
_address = [
f'[link=http://localhost:{deployment.args.port_monitoring}]Local[/]',
f'[link=http://{self.address_private}:{deployment.args.port_monitoring}]Private[/]',
]
if self.address_public:
_address.append(
f'[link=http://{self.address_public}:{deployment.args.port_monitoring}]Public[/]'
)
if deployment.args.monitoring:
address_table.add_row(
':bar_chart:',
f'Monitor [b]{name}:{deployment.args.port_monitoring}[/]',
'·'.join(_address),
)
return self[GATEWAY_NAME].args.port_monitoring
else:
return self._common_kwargs.get(
'port_monitoring', __default_port_monitoring__
)
return address_table
| 674e8121fb5dfdac4ce88a8ade1d248d16b75617 | 315 | https://github.com/jina-ai/jina.git | 1,345 | def _get_address_table(self, address_table):
_protocol = str(self.protocol)
if self.gateway_args.ssl_certfile and self.gateway_args.ssl_keyfile:
_protocol = f'{self.protocol}S'
address_table.add_row(
':link:', 'Protocol', f':closed_lock_with_key: {_protocol}'
)
else:
address_table.add_row(':link:', 'Protocol', _protocol)
_protocol = _protocol.lower()
address_table.add_row(
':house:',
'Local',
f'[link={_protocol}://{self.host}:{self.port}]{self.host}:{self.port}[/]',
)
address_table.add_row(
':lock:',
'Private',
f'[link={_protocol}://{self.address_private}:{self.port}]{self.address_private}:{self.port}[/]',
)
if self.address_public:
address_table.add_row(
':earth_africa:',
'Public',
f'[link={_protocol}://{self.address_public}:{self.port}]{self.address_public}:{self.port}[/]',
)
if self.protocol == GatewayProtocolType.HTTP:
_address = [
f'[link={_protocol}://localhost:{self.port}/docs]Local[/]',
f'[link={_protocol}://{self.address_private}:{self.port}/docs]Private[/]',
]
if self.address_public:
_address.append(
f'[link={_protocol}://{self.address_public}:{self.port}/docs]Public[/]'
)
address_table.add_row(
':speech_balloon:',
'Swagger UI [dim](/docs)[/]',
'·'.join(_address),
)
_address = [
f'[link={_protocol}://localhost:{self.port}/redoc]Local[/]',
f'[link={_protocol}://{self.address_private}:{self.port}/redoc]Private[/]',
]
if self.address_public:
_address.append(
f'[link={_protocol} | 30 | 830 | _get_address_table |
|
16 | 0 | 1 | 4 | plugins/dbms/extremedb/enumeration.py | 123,582 | Fixing DeprecationWarning (logger.warn) | sqlmap | 7 | Python | 16 | enumeration.py | def searchColumn(self):
warnMsg = "on eXtremeDB it is not possible to search columns"
logger.warning(warnMsg)
return []
| df4293473d2fb6e887e31522cab5aff95e201581 | 17 | https://github.com/sqlmapproject/sqlmap.git | 36 | def searchColumn(self):
warnMsg = "on eXtremeDB it is not possible to search columns"
logger.warning(warnMsg | 5 | 31 | searchColumn |
|
31 | 0 | 1 | 6 | wagtail/images/tests/test_admin_views.py | 75,114 | Reformat with black | wagtail | 10 | Python | 27 | test_admin_views.py | def test_simple_with_collection_nesting(self):
root_collection = Collection.get_first_root_node()
evil_plans = root_collection.add_child(name="Evil plans")
evil_plans.add_child(name="Eviler plans")
response = self.get()
# "Eviler Plans" should be prefixed with ↳ (↳) and 4 non-breaking spaces.
self.assertContains(response, " ↳ Eviler plans")
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 45 | https://github.com/wagtail/wagtail.git | 72 | def test_simple_with_collection_nesting(self):
root_collection = Collection.get_first_root_node()
evil_plans = root_collection.add_child(name="Evil plans")
evil_plans.add_child(name="Eviler plans")
response = self.get()
# "Eviler Plans" should be prefixed with ↳ (↳) and 4 non-breaking spaces.
| 11 | 81 | test_simple_with_collection_nesting |
|
40 | 0 | 5 | 14 | test/support/network-integration/collections/ansible_collections/ansible/netcommon/plugins/plugin_utils/connection_base.py | 268,503 | Add `use_rsa_sha2_algorithms` option for paramiko (#78789)
Fixes #76737
Fixes #77673
Co-authored-by: Matt Clay <matt@mystile.com> | ansible | 15 | Python | 33 | connection_base.py | def __getattr__(self, name):
try:
return self.__dict__[name]
except KeyError:
if not name.startswith("_"):
plugin = self._sub_plugin.get("obj")
if plugin:
method = getattr(plugin, name, None)
if method is not None:
return method
raise AttributeError(
"'%s' object has no attribute '%s'"
% (self.__class__.__name__, name)
)
| 76b746655a36807fa9198064ca9fe7c6cc00083a | 74 | https://github.com/ansible/ansible.git | 218 | def __getattr__(self, name):
try:
| 14 | 120 | __getattr__ |
|
980 | 0 | 45 | 247 | rllib/agents/sac/tests/test_sac.py | 129,575 | [RLlib] Preparatory PR for multi-agent multi-GPU learner (alpha-star style) #03 (#21652) | ray | 24 | Python | 452 | test_sac.py | def test_sac_loss_function(self):
config = sac.DEFAULT_CONFIG.copy()
# Run locally.
config["num_workers"] = 0
config["learning_starts"] = 0
config["twin_q"] = False
config["gamma"] = 0.99
# Switch on deterministic loss so we can compare the loss values.
config["_deterministic_loss"] = True
# Use very simple nets.
config["Q_model"]["fcnet_hiddens"] = [10]
config["policy_model"]["fcnet_hiddens"] = [10]
# Make sure, timing differences do not affect trainer.train().
config["min_time_s_per_reporting"] = 0
# Test SAC with Simplex action space.
config["env_config"] = {"simplex_actions": True}
map_ = {
# Action net.
"default_policy/fc_1/kernel": "action_model._hidden_layers.0."
"_model.0.weight",
"default_policy/fc_1/bias": "action_model._hidden_layers.0."
"_model.0.bias",
"default_policy/fc_out/kernel": "action_model."
"_logits._model.0.weight",
"default_policy/fc_out/bias": "action_model._logits._model.0.bias",
"default_policy/value_out/kernel": "action_model."
"_value_branch._model.0.weight",
"default_policy/value_out/bias": "action_model."
"_value_branch._model.0.bias",
# Q-net.
"default_policy/fc_1_1/kernel": "q_net."
"_hidden_layers.0._model.0.weight",
"default_policy/fc_1_1/bias": "q_net."
"_hidden_layers.0._model.0.bias",
"default_policy/fc_out_1/kernel": "q_net._logits._model.0.weight",
"default_policy/fc_out_1/bias": "q_net._logits._model.0.bias",
"default_policy/value_out_1/kernel": "q_net."
"_value_branch._model.0.weight",
"default_policy/value_out_1/bias": "q_net."
"_value_branch._model.0.bias",
"default_policy/log_alpha": "log_alpha",
# Target action-net.
"default_policy/fc_1_2/kernel": "action_model."
"_hidden_layers.0._model.0.weight",
"default_policy/fc_1_2/bias": "action_model."
"_hidden_layers.0._model.0.bias",
"default_policy/fc_out_2/kernel": "action_model."
"_logits._model.0.weight",
"default_policy/fc_out_2/bias": "action_model."
"_logits._model.0.bias",
"default_policy/value_out_2/kernel": "action_model."
"_value_branch._model.0.weight",
"default_policy/value_out_2/bias": "action_model."
"_value_branch._model.0.bias",
# Target Q-net
"default_policy/fc_1_3/kernel": "q_net."
"_hidden_layers.0._model.0.weight",
"default_policy/fc_1_3/bias": "q_net."
"_hidden_layers.0._model.0.bias",
"default_policy/fc_out_3/kernel": "q_net."
"_logits._model.0.weight",
"default_policy/fc_out_3/bias": "q_net."
"_logits._model.0.bias",
"default_policy/value_out_3/kernel": "q_net."
"_value_branch._model.0.weight",
"default_policy/value_out_3/bias": "q_net."
"_value_branch._model.0.bias",
"default_policy/log_alpha_1": "log_alpha",
}
env = SimpleEnv
batch_size = 100
obs_size = (batch_size, 1)
actions = np.random.random(size=(batch_size, 2))
# Batch of size=n.
input_ = self._get_batch_helper(obs_size, actions, batch_size)
# Simply compare loss values AND grads of all frameworks with each
# other.
prev_fw_loss = weights_dict = None
expect_c, expect_a, expect_e, expect_t = None, None, None, None
# History of tf-updated NN-weights over n training steps.
tf_updated_weights = []
# History of input batches used.
tf_inputs = []
for fw, sess in framework_iterator(
config, frameworks=("tf", "torch"), session=True):
# Generate Trainer and get its default Policy object.
trainer = sac.SACTrainer(config=config, env=env)
policy = trainer.get_policy()
p_sess = None
if sess:
p_sess = policy.get_session()
# Set all weights (of all nets) to fixed values.
if weights_dict is None:
# Start with the tf vars-dict.
assert fw in ["tf2", "tf", "tfe"]
weights_dict = policy.get_weights()
if fw == "tfe":
log_alpha = weights_dict[10]
weights_dict = self._translate_tfe_weights(
weights_dict, map_)
else:
assert fw == "torch" # Then transfer that to torch Model.
model_dict = self._translate_weights_to_torch(
weights_dict, map_)
# Have to add this here (not a parameter in tf, but must be
# one in torch, so it gets properly copied to the GPU(s)).
model_dict["target_entropy"] = policy.model.target_entropy
policy.model.load_state_dict(model_dict)
policy.target_model.load_state_dict(model_dict)
if fw == "tf":
log_alpha = weights_dict["default_policy/log_alpha"]
elif fw == "torch":
# Actually convert to torch tensors (by accessing everything).
input_ = policy._lazy_tensor_dict(input_)
input_ = {k: input_[k] for k in input_.keys()}
log_alpha = policy.model.log_alpha.detach().cpu().numpy()[0]
# Only run the expectation once, should be the same anyways
# for all frameworks.
if expect_c is None:
expect_c, expect_a, expect_e, expect_t = \
self._sac_loss_helper(input_, weights_dict,
sorted(weights_dict.keys()),
log_alpha, fw,
gamma=config["gamma"], sess=sess)
# Get actual outs and compare to expectation AND previous
# framework. c=critic, a=actor, e=entropy, t=td-error.
if fw == "tf":
c, a, e, t, tf_c_grads, tf_a_grads, tf_e_grads = \
p_sess.run([
policy.critic_loss,
policy.actor_loss,
policy.alpha_loss,
policy.td_error,
policy.optimizer().compute_gradients(
policy.critic_loss[0],
[v for v in policy.model.q_variables() if
"value_" not in v.name]),
policy.optimizer().compute_gradients(
policy.actor_loss,
[v for v in policy.model.policy_variables() if
"value_" not in v.name]),
policy.optimizer().compute_gradients(
policy.alpha_loss, policy.model.log_alpha)],
feed_dict=policy._get_loss_inputs_dict(
input_, shuffle=False))
tf_c_grads = [g for g, v in tf_c_grads]
tf_a_grads = [g for g, v in tf_a_grads]
tf_e_grads = [g for g, v in tf_e_grads]
elif fw == "tfe":
with tf.GradientTape() as tape:
tf_loss(policy, policy.model, None, input_)
c, a, e, t = policy.critic_loss, policy.actor_loss, \
policy.alpha_loss, policy.td_error
vars = tape.watched_variables()
tf_c_grads = tape.gradient(c[0], vars[6:10])
tf_a_grads = tape.gradient(a, vars[2:6])
tf_e_grads = tape.gradient(e, vars[10])
elif fw == "torch":
loss_torch(policy, policy.model, None, input_)
c, a, e, t = policy.get_tower_stats("critic_loss")[0], \
policy.get_tower_stats("actor_loss")[0], \
policy.get_tower_stats("alpha_loss")[0], \
policy.get_tower_stats("td_error")[0]
# Test actor gradients.
policy.actor_optim.zero_grad()
assert all(v.grad is None for v in policy.model.q_variables())
assert all(
v.grad is None for v in policy.model.policy_variables())
assert policy.model.log_alpha.grad is None
a.backward()
# `actor_loss` depends on Q-net vars (but these grads must
# be ignored and overridden in critic_loss.backward!).
assert not all(
torch.mean(v.grad) == 0
for v in policy.model.policy_variables())
assert not all(
torch.min(v.grad) == 0
for v in policy.model.policy_variables())
assert policy.model.log_alpha.grad is None
# Compare with tf ones.
torch_a_grads = [
v.grad for v in policy.model.policy_variables()
if v.grad is not None
]
check(tf_a_grads[2],
np.transpose(torch_a_grads[0].detach().cpu()))
# Test critic gradients.
policy.critic_optims[0].zero_grad()
assert all(
torch.mean(v.grad) == 0.0
for v in policy.model.q_variables() if v.grad is not None)
assert all(
torch.min(v.grad) == 0.0
for v in policy.model.q_variables() if v.grad is not None)
assert policy.model.log_alpha.grad is None
c[0].backward()
assert not all(
torch.mean(v.grad) == 0
for v in policy.model.q_variables() if v.grad is not None)
assert not all(
torch.min(v.grad) == 0 for v in policy.model.q_variables()
if v.grad is not None)
assert policy.model.log_alpha.grad is None
# Compare with tf ones.
torch_c_grads = [v.grad for v in policy.model.q_variables()]
check(tf_c_grads[0],
np.transpose(torch_c_grads[2].detach().cpu()))
# Compare (unchanged(!) actor grads) with tf ones.
torch_a_grads = [
v.grad for v in policy.model.policy_variables()
]
check(tf_a_grads[2],
np.transpose(torch_a_grads[0].detach().cpu()))
# Test alpha gradient.
policy.alpha_optim.zero_grad()
assert policy.model.log_alpha.grad is None
e.backward()
assert policy.model.log_alpha.grad is not None
check(policy.model.log_alpha.grad, tf_e_grads)
check(c, expect_c)
check(a, expect_a)
check(e, expect_e)
check(t, expect_t)
# Store this framework's losses in prev_fw_loss to compare with
# next framework's outputs.
if prev_fw_loss is not None:
check(c, prev_fw_loss[0])
check(a, prev_fw_loss[1])
check(e, prev_fw_loss[2])
check(t, prev_fw_loss[3])
prev_fw_loss = (c, a, e, t)
# Update weights from our batch (n times).
for update_iteration in range(5):
print("train iteration {}".format(update_iteration))
if fw == "tf":
in_ = self._get_batch_helper(obs_size, actions, batch_size)
tf_inputs.append(in_)
# Set a fake-batch to use
# (instead of sampling from replay buffer).
buf = MultiAgentReplayBuffer.get_instance_for_testing()
buf._fake_batch = in_
trainer.train()
updated_weights = policy.get_weights()
# Net must have changed.
if tf_updated_weights:
check(
updated_weights["default_policy/fc_1/kernel"],
tf_updated_weights[-1][
"default_policy/fc_1/kernel"],
false=True)
tf_updated_weights.append(updated_weights)
# Compare with updated tf-weights. Must all be the same.
else:
tf_weights = tf_updated_weights[update_iteration]
in_ = tf_inputs[update_iteration]
# Set a fake-batch to use
# (instead of sampling from replay buffer).
buf = MultiAgentReplayBuffer.get_instance_for_testing()
buf._fake_batch = in_
trainer.train()
# Compare updated model.
for tf_key in sorted(tf_weights.keys()):
if re.search("_[23]|alpha", tf_key):
continue
tf_var = tf_weights[tf_key]
torch_var = policy.model.state_dict()[map_[tf_key]]
if tf_var.shape != torch_var.shape:
check(
tf_var,
np.transpose(torch_var.detach().cpu()),
atol=0.003)
else:
check(tf_var, torch_var, atol=0.003)
# And alpha.
check(policy.model.log_alpha,
tf_weights["default_policy/log_alpha"])
# Compare target nets.
for tf_key in sorted(tf_weights.keys()):
if not re.search("_[23]", tf_key):
continue
tf_var = tf_weights[tf_key]
torch_var = policy.target_model.state_dict()[map_[
tf_key]]
if tf_var.shape != torch_var.shape:
check(
tf_var,
np.transpose(torch_var.detach().cpu()),
atol=0.003)
else:
check(tf_var, torch_var, atol=0.003)
trainer.stop()
| d5bfb7b7da6f8ec505dd8ed69f0be419decfdcc0 | 1,752 | https://github.com/ray-project/ray.git | 5,558 | def test_sac_loss_function(self):
config = sac.DEFAULT_CONFIG.copy()
# Run locally.
config["num_workers"] = 0
config["learning_starts"] = 0
config["twin_q"] = False
config["gamma"] = 0.99
# Switch on deterministic loss so we can compare the loss values.
config["_deterministic_loss"] = True
# Use very simple nets.
config["Q_model"]["fcnet_hiddens"] = [10]
config["policy_model"]["fcnet_hiddens"] = [10]
# Make sure, timing differences do not affect trainer.train().
config["min_time_s_per_reporting"] = 0
# Test SAC with Simplex action space.
config["env_config"] = {"simplex_actions": True}
map_ = {
# Action net.
"default_policy/fc_1/kernel": "action_model._hidden_layers.0."
"_model.0.weight",
"default_policy/fc_1/bias": "action_model._hidden_layers.0."
"_model.0.bias",
"default_policy/fc_out/kernel": "action_model."
"_logits._model.0.weight",
"default_policy/fc_out/bias": "action_model._logits._model.0.bias",
"default_policy/value_out/kernel": "action_model."
"_value_branch._model.0.weig | 122 | 2,879 | test_sac_loss_function |
|
39 | 0 | 4 | 17 | src/prefect/agent.py | 54,456 | Capture 404 errors explicitly so other http errors are not hidden | prefect | 15 | Python | 34 | agent.py | async def work_queue_id_from_name(self) -> Optional[UUID]:
if not self.work_queue_name:
raise ValueError("No work queue name provided.")
try:
work_queue = await self.client.read_work_queue_by_name(self.work_queue_name)
return work_queue.id
except httpx.HTTPStatusError as exc:
if exc.response.status_code == status.HTTP_404_NOT_FOUND:
self.logger.warn(f'No work queue found named "{self.work_queue_name}"')
return None
else:
raise
| ccb4cc008efa24ee39a85830c330f83d1fe2477a | 73 | https://github.com/PrefectHQ/prefect.git | 167 | async def work_queue_id_from_name(self) -> Optional[UUID]:
if not self.work_queue_name:
raise ValueError("No work queue name provided.")
try:
work_queue = await self.client.read_work_queue_by_name(self.work_queue_name)
return work_queue.id
except httpx.HTTPStatusError as exc:
if exc.response.status_code == status.HTTP_404_NOT_FOUND:
self.logger.warn(f'No work queue found n | 19 | 132 | work_queue_id_from_name |
|
31 | 0 | 1 | 6 | tests/utils/test_common.py | 159,093 | Configurable logging for libraries (#10614)
* Make library level logging to be configurable
Fixes https://github.com/RasaHQ/rasa/issues/10203
* Create log level documentation under cheatsheet in Rasa docs
* Add log docs to `rasa shell --debug` (and others) | rasa | 9 | Python | 23 | test_common.py | def test_cli_missing_log_level_default_used():
configure_logging_and_warnings()
rasa_logger = logging.getLogger("rasa")
# Default log level is currently INFO
rasa_logger.level == logging.INFO
matplotlib_logger = logging.getLogger("matplotlib")
# Default log level for libraries is currently ERROR
matplotlib_logger.level == logging.ERROR
| f00148b089d326c952880a0e5e6bd4b2dcb98ce5 | 38 | https://github.com/RasaHQ/rasa.git | 55 | def test_cli_missing_log_level_default_used():
configure_logging_and_warnings()
rasa_logger = logging.getLogger("rasa")
# Default log level is currently INFO
rasa_logger.level == logging.INFO
matplotlib_logger = logging.getLogger("matplotlib")
# Default log level for libraries is currently ERROR
matplotlib_logger.level == logging.ERROR
| 9 | 72 | test_cli_missing_log_level_default_used |
|
47 | 0 | 1 | 9 | tests/test_py_utils.py | 105,458 | Fix to dict conversion of `DatasetInfo`/`Features` (#4741)
* Add custom asdict
* Add test
* One more test
* Comment | datasets | 14 | Python | 30 | test_py_utils.py | def test_asdict():
input = A(x=1, y="foobar")
expected_output = {"x": 1, "y": "foobar"}
assert asdict(input) == expected_output
input = {"a": {"b": A(x=10, y="foo")}, "c": [A(x=20, y="bar")]}
expected_output = {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
assert asdict(input) == expected_output
with pytest.raises(TypeError):
asdict([1, A(x=10, y="foo")])
| 6c398c1098feaa6bac2a9ee5cb7dea63ed8dd37b | 134 | https://github.com/huggingface/datasets.git | 74 | def test_asdict():
input = A(x=1, y="foobar")
expected_output = {"x": 1, "y": "foobar"}
assert asdict(input) == expected_output
| 10 | 240 | test_asdict |
|
7 | 0 | 1 | 2 | homeassistant/components/switchbee/entity.py | 288,190 | Add cover platform for switchbee integration (#78383)
* Added Platform cover for switchbee integration
* added cover to .coveragerc
* Applied code review feedback from other PR
* Addressed comments from other PRs
* rebased
* Re-add carriage return
* Update homeassistant/components/switchbee/cover.py
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* Update homeassistant/components/switchbee/cover.py
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* Update homeassistant/components/switchbee/cover.py
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* Update homeassistant/components/switchbee/cover.py
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* addressed CR comments
* fixes
* fixes
* more fixes
* more fixes
* separate entities for cover and somfy cover
* fixed isort
* more fixes
* more fixes
* Update homeassistant/components/switchbee/cover.py
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* Update homeassistant/components/switchbee/cover.py
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* more fixes
* more fixes
* more
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com> | core | 10 | Python | 7 | entity.py | def _get_coordinator_device(self) -> _DeviceTypeT:
return cast(_DeviceTypeT, self.coordinator.data[self._device.id])
| 75510b8e90162a5b7a530d36d141cbada3df644c | 25 | https://github.com/home-assistant/core.git | 13 | def _get_coordinator_device(self) -> _DeviceTypeT:
return cast(_DeviceTypeT, self.coordinator.data[self._device.id])
| 8 | 38 | _get_coordinator_device |
|
243 | 0 | 1 | 103 | pipenv/patched/notpip/_vendor/idna/uts46data.py | 20,152 | 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 | pipenv | 8 | Python | 146 | uts46data.py | def _seg_59() -> List[Union[Tuple[int, str], Tuple[int, str, str]]]:
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| 6 | 1,068 | _seg_59 |
|
26 | 0 | 1 | 5 | pandas/tests/scalar/timedelta/test_constructors.py | 169,557 | API: Timedelta constructor pytimedelta, Tick preserve reso (#48918)
* BUG: Timedelta.__new__
* remove assertion
* GH refs
* API: Timedelta(td64_obj) retain resolution
* API: Timedelta constructor pytimedelta, Tick preserve reso
* remove debugging variable
* remove duplicate | pandas | 9 | Python | 22 | test_constructors.py | def test_from_pytimedelta_us_reso():
# pytimedelta has microsecond resolution, so Timedelta(pytd) inherits that
td = timedelta(days=4, minutes=3)
result = Timedelta(td)
assert result.to_pytimedelta() == td
assert result._reso == NpyDatetimeUnit.NPY_FR_us.value
| ac05d29cf8cae186e96c83a03e2e80542ce2ad38 | 40 | https://github.com/pandas-dev/pandas.git | 40 | def test_from_pytimedelta_us_reso():
# pytimedelta has microsecond resolution, so Timedelta(pytd) inherits that
td = timedelta(days=4, minutes=3)
result = Timedelta(td)
assert result.to_pytimedelta() == td
assert result._reso == NpyDate | 12 | 65 | test_from_pytimedelta_us_reso |
|
10 | 0 | 1 | 7 | homeassistant/components/logbook/queries/common.py | 300,944 | Add support for selecting device_ids from the logbook (#72039)
Co-authored-by: Paulus Schoutsen <paulus@home-assistant.io> | core | 8 | Python | 10 | common.py | def select_states() -> Select:
return select(
*EVENT_COLUMNS_FOR_STATE_SELECT,
*STATE_COLUMNS,
NOT_CONTEXT_ONLY,
)
| c4fc84ec1e77a18ff392b34389baa86d52388246 | 19 | https://github.com/home-assistant/core.git | 40 | def select_states() -> Select:
return select(
*EVENT_COLUMNS_FOR_S | 6 | 32 | select_states |
|
77 | 0 | 1 | 14 | test/test_table_reader.py | 257,337 | Add `run_batch` method to all nodes and `Pipeline` to allow batch querying (#2481)
* Add run_batch methods for batch querying
* Update Documentation & Code Style
* Fix mypy
* Update Documentation & Code Style
* Fix mypy
* Fix linter
* Fix tests
* Update Documentation & Code Style
* Fix tests
* Update Documentation & Code Style
* Fix mypy
* Fix rest api test
* Update Documentation & Code Style
* Add Doc strings
* Update Documentation & Code Style
* Add batch_size as attribute to nodes supporting batching
* Adapt error messages
* Adapt type of filters in retrievers
* Revert change about truncation_warning in summarizer
* Unify multiple_doc_lists tests
* Use smaller models in extractor tests
* Add return types to JoinAnswers and RouteDocuments
* Adapt return statements in reader's run_batch method
* Allow list of filters
* Adapt error messages
* Update Documentation & Code Style
* Fix tests
* Fix mypy
* Adapt print_questions
* Remove disabling warning about too many public methods
* Add flag for pylint to disable warning about too many public methods in pipelines/base.py and document_stores/base.py
* Add type check
* Update Documentation & Code Style
* Adapt tutorial 11
* Update Documentation & Code Style
* Add query_batch method for DCDocStore
* Update Documentation & Code Style
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | haystack | 14 | Python | 66 | test_table_reader.py | def test_table_reader_batch_single_query_single_doc_list(table_reader):
data = {
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["58", "47", "60"],
"number of movies": ["87", "53", "69"],
"date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
}
table = pd.DataFrame(data)
query = "When was Di Caprio born?"
prediction = table_reader.predict_batch(queries=query, documents=[Document(content=table, content_type="table")])
# Expected output: List of lists of answers
assert isinstance(prediction["answers"], list)
assert isinstance(prediction["answers"][0], list)
assert isinstance(prediction["answers"][0][0], Answer)
assert len(prediction["answers"]) == 1 # Predictions for 5 docs
| 738e008020f146ff9820c290311782f515749c48 | 134 | https://github.com/deepset-ai/haystack.git | 135 | def test_table_reader_batch_single_query_single_doc_list(table_reader):
data = {
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["58", "47", "60"],
"number of movies": ["87", "53", "69"],
"date of birth": ["18 december 1963", "11 november 1974 | 18 | 234 | test_table_reader_batch_single_query_single_doc_list |
|
61 | 0 | 4 | 12 | test/test_nn.py | 102,389 | No-batch-dim support for ConvNd (#70506)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/70506
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33355034
Pulled By: jbschlosser
fbshipit-source-id: 5a42645299b1d82cee7d461826acca1c5b35a71c | pytorch | 18 | Python | 42 | test_nn.py | def test_conv_modules_raise_error_on_incorrect_input_size(self):
for dtype in [torch.bfloat16, torch.double, torch.float]:
modules = [nn.Conv1d(3, 8, 3).to(dtype), nn.ConvTranspose1d(3, 8, 3).to(dtype),
nn.Conv2d(3, 8, 3).to(dtype), nn.ConvTranspose2d(3, 8, 3).to(dtype),
nn.Conv3d(3, 8, 3).to(dtype), nn.ConvTranspose3d(3, 8, 3).to(dtype)]
invalid_input_dims = [(1, 4), (1, 4),
(2, 5), (2, 5),
(3, 6), (3, 6)]
for invalid_dims, module in zip(invalid_input_dims, modules):
for dims in invalid_dims:
input = torch.empty(torch.Size((3, ) * dims))
self.assertRaises(RuntimeError, lambda: module(input))
| 7b8f73dd32a8a893dfb794433ce501e76c53bc89 | 208 | https://github.com/pytorch/pytorch.git | 263 | def test_conv_modules_raise_error_on_incorrect_input_size(self):
for dtype in [torch.bfloat16, torch.double, torch.float]:
modules = [nn.Conv1d(3, 8, 3).to(dtype), nn.ConvTranspose1d(3, 8, 3).to(dtype),
nn.Conv2d(3, 8, 3).to(dtype), nn.ConvTranspose2d(3, 8, 3).to(dtype),
nn.Conv3d(3, 8, 3).to(dtype), nn.ConvTranspose3d(3, 8, 3).to(dtype)]
invalid_input_dims = [(1, 4), (1, 4),
| 26 | 290 | test_conv_modules_raise_error_on_incorrect_input_size |
|
61 | 0 | 1 | 22 | keras/callbacks_test.py | 270,007 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 12 | Python | 36 | callbacks_test.py | def test_TensorBoard_autoTrace_profileTwiceGraphMode(self):
tf.compat.v1.disable_eager_execution()
inp = keras.Input((1,))
out = keras.layers.Dense(units=1)(inp)
model = keras.Model(inp, out)
model.compile(gradient_descent.SGD(1), "mse")
logdir = os.path.join(self.get_temp_dir(), "tb1")
model.fit(
np.zeros((64, 1)),
np.zeros((64, 1)),
batch_size=32,
callbacks=[keras.callbacks.TensorBoard(logdir, profile_batch=1)],
)
# Verifies trace exists in the first logdir.
self.assertEqual(1, self._count_trace_file(logdir=logdir))
logdir = os.path.join(self.get_temp_dir(), "tb2")
model.fit(
np.zeros((64, 1)),
np.zeros((64, 1)),
batch_size=32,
callbacks=[keras.callbacks.TensorBoard(logdir, profile_batch=2)],
)
# Verifies trace exists in the second logdir.
self.assertEqual(1, self._count_trace_file(logdir=logdir))
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 221 | https://github.com/keras-team/keras.git | 253 | def test_TensorBoard_autoTrace_profileTwiceGraphMode(self):
tf.compat.v1.disable_eager_execution()
inp = keras.In | 32 | 340 | test_TensorBoard_autoTrace_profileTwiceGraphMode |
|
25 | 0 | 1 | 7 | dask/dataframe/io/tests/test_hdf.py | 155,999 | If hdf file is empty, don't fail on meta creation (#8809) | dask | 11 | Python | 25 | test_hdf.py | def test_hdf_empty_dataframe():
pytest.importorskip("tables")
# https://github.com/dask/dask/issues/8707
from dask.dataframe.io.hdf import dont_use_fixed_error_message
df = pd.DataFrame({"A": [], "B": []}, index=[])
df.to_hdf("data.h5", format="fixed", key="df", mode="w")
with pytest.raises(TypeError, match=dont_use_fixed_error_message):
dd.read_hdf("data.h5", "df")
| e0d34a54ce4930528bbe3c8ded1d85c0c2be7fe6 | 81 | https://github.com/dask/dask.git | 49 | def test_hdf_empty_dataframe():
pytest.importorskip("tables")
| 21 | 142 | test_hdf_empty_dataframe |
|
75 | 0 | 3 | 11 | ivy/backends/numpy/core/random.py | 213,538 | renamed dev_str arg to dev for all methods. | ivy | 13 | Python | 58 | random.py | def multinomial(population_size, num_samples, batch_size, probs=None, replace=True, dev=None):
if probs is None:
probs = _np.ones((batch_size, population_size,)) / population_size
orig_probs_shape = list(probs.shape)
num_classes = orig_probs_shape[-1]
probs_flat = _np.reshape(probs, (-1, orig_probs_shape[-1]))
probs_flat = probs_flat / _np.sum(probs_flat, -1, keepdims=True)
probs_stack = _np.split(probs_flat, probs_flat.shape[0])
samples_stack = [_np.random.choice(num_classes, num_samples, replace, p=prob[0]) for prob in probs_stack]
samples_flat = _np.stack(samples_stack)
return _np.asarray(_np.reshape(samples_flat, orig_probs_shape[:-1] + [num_samples]))
randint = lambda low, high, shape, dev=None: _np.random.randint(low, high, shape)
seed = lambda seed_value=0: _np.random.seed(seed_value)
shuffle = _np.random.permutation
| d743336b1f3654cd0315f380f43eed4116997c1d | 165 | https://github.com/unifyai/ivy.git | 105 | def multinomial(population_size, num_samples, batch_size, probs=None, replace=True, dev=None):
if probs is None:
probs = _np.ones((batch_size, population_size,)) / population_size
orig_probs_shape = list(probs.shape)
num_classes = orig_probs_shape[-1]
probs_flat = _np.reshape(probs, (-1, orig_probs_shape[-1]))
probs_flat = probs_flat / _np.sum(probs_flat, -1, keepdims=True)
probs_stack = _np.split(probs_flat, probs_flat.shape[0])
samples_stack | 34 | 311 | multinomial |
|
90 | 1 | 1 | 17 | tests/integration/smpc/tensor/tensor_abstraction_test.py | 2,882 | Fix requested changes: Replace block_timeout() -> get(timeout) | PySyft | 13 | Python | 66 | tensor_abstraction_test.py | def test_tensor_abstraction_pointer(get_clients, op_str) -> None:
clients = get_clients(3)
op = getattr(operator, op_str)
data_1 = Tensor(child=np.array([[15, 34], [32, 89]], dtype=DEFAULT_INT_NUMPY_TYPE))
data_2 = Tensor(child=np.array([[567, 98], [78, 25]], dtype=DEFAULT_INT_NUMPY_TYPE))
data_3 = Tensor(
child=np.array([[125, 10], [124, 28]], dtype=DEFAULT_INT_NUMPY_TYPE)
)
tensor_pointer_1 = data_1.send(clients[0])
tensor_pointer_2 = data_2.send(clients[1])
tensor_pointer_3 = data_3.send(clients[2])
# creates an MPCTensor between party 1 and party 2
mpc_1_2 = op(tensor_pointer_1, tensor_pointer_2)
# creates and MPCTensor between party 1,2,3
mpc_1_2_3 = op(mpc_1_2, tensor_pointer_3)
exp_res = op(data_1, data_2)
assert (mpc_1_2.reconstruct(timeout_secs=40) == exp_res.child).all()
exp_res = op(exp_res, data_3)
assert (mpc_1_2_3.reconstruct(timeout_secs=40) == exp_res.child).all()
@pytest.mark.smpc_abstract
@pytest.mark.parametrize("op_str", ["add", "sub", "mul"]) | a8c5abf1494356f854a81631b814e5928bc0eb8b | @pytest.mark.smpc_abstract
@pytest.mark.parametrize("op_str", ["add", "sub", "mul"]) | 213 | https://github.com/OpenMined/PySyft.git | 145 | def test_tensor_abstraction_pointer(get_clients, op_str) -> None:
clients = get_clients(3)
op = getattr(operator, op_str)
data_1 = Tensor(child=np.array([[15, 34], [32, 89]], dtype=DEFAULT_INT_NUMPY_TYPE))
data_2 = Tensor(child=np.array([[567, 98], [78, 25]], dtype=DEFAULT_INT_NUM | 30 | 361 | test_tensor_abstraction_pointer |
14 | 0 | 1 | 4 | packages/python/plotly/plotly/graph_objs/_figure.py | 241,438 | upgrade Plotly.js to 2.13.2 | plotly.py | 8 | Python | 14 | _figure.py | def select_selections(self, selector=None, row=None, col=None, secondary_y=None):
return self._select_annotations_like(
"selections", selector=selector, row=row, col=col, secondary_y=secondary_y
)
| a51932f920c5f2407827f10b89b5569c27c13b4b | 45 | https://github.com/plotly/plotly.py.git | 46 | def select_selections(self, selector=None, row=None, col=None, secondary_y=None):
return self._select_annotations_like(
"selections", selector=selector, r | 7 | 66 | select_selections |
|
63 | 0 | 7 | 18 | asv_bench/benchmarks/array.py | 172,149 | PERF: ArrowExtensionArray.to_numpy (#49973) | pandas | 13 | Python | 38 | array.py | def setup(self, dtype, hasna):
N = 100_000
if dtype == "boolean[pyarrow]":
data = np.random.choice([True, False], N, replace=True)
elif dtype == "float64[pyarrow]":
data = np.random.randn(N)
elif dtype == "int64[pyarrow]":
data = np.arange(N)
elif dtype == "string[pyarrow]":
data = tm.rands_array(10, N)
elif dtype == "timestamp[ns][pyarrow]":
data = pd.date_range("2000-01-01", freq="s", periods=N)
else:
raise NotImplementedError
arr = pd.array(data, dtype=dtype)
if hasna:
arr[::2] = pd.NA
self.arr = arr
| 026a83e06447b749385beddd3d03abe97d48e8f5 | 134 | https://github.com/pandas-dev/pandas.git | 209 | def setup(self, dtype, hasna):
N = 100_000
if dtype == "boolean[pyarrow]":
data = np.random.choice([True, False], N, replace=True)
elif dtype == "float64[pyarrow]":
data = np.random.randn(N)
elif dtype == "int64[pyarrow]":
data = | 22 | 220 | setup |
|
65 | 0 | 3 | 15 | rllib/env/wrappers/tests/test_kaggle_wrapper.py | 143,421 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | ray | 15 | Python | 53 | test_kaggle_wrapper.py | def test_football_env_run_30_steps(self):
from ray.rllib.env.wrappers.kaggle_wrapper import KaggleFootballMultiAgentEnv
env = KaggleFootballMultiAgentEnv()
# use the built-in agents in the kaggle environment
run_right_agent = env.kaggle_env.agents["run_right"]
do_nothing_agent = env.kaggle_env.agents["do_nothing"]
obs = env.reset()
self.assertEqual(list(obs.keys()), ["agent0", "agent1"])
done = {"__all__": False}
num_steps_completed = 0
while not done["__all__"] and num_steps_completed <= 30:
action0 = run_right_agent(structify(obs["agent0"]))[0]
action1 = do_nothing_agent(structify(obs["agent1"]))[0]
action_dict = {"agent0": action0, "agent1": action1}
obs, _, done, _ = env.step(action_dict)
num_steps_completed += 1
| 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | 147 | https://github.com/ray-project/ray.git | 189 | def test_football_env_run_30_steps(self):
from ray.rllib.env.wrappers.kaggle_wrapper import KaggleFootballMultiAgentEnv
env = KaggleFootballMultiAgentEnv()
# use the built-in agents in the kaggle environment
run_right_agent = env.kaggle_env.agents["run_right"]
do_nothing_agent = env.kaggle_env.agents["do_nothing"]
obs = env.reset()
self.assertEqual(list(obs.keys()), ["agent0", "agent1"])
done = {"__all__": False}
num_steps_completed = 0
while not done["__all__"] and num_steps_completed <= 30:
action0 = run_right_agent(structify(obs["agent0"]))[0]
action1 = do_nothing_agent(structify(obs["agent1"]))[0]
action_dict = {"agent0": action0, "agent1": action1}
obs, _, done | 25 | 245 | test_football_env_run_30_steps |
|
14 | 1 | 1 | 4 | lib/matplotlib/tests/test_ticker.py | 108,365 | Improve consistency in LogLocator and LogFormatter API | matplotlib | 10 | Python | 14 | test_ticker.py | def test_bad_locator_subs(sub):
ll = mticker.LogLocator()
with pytest.raises(ValueError):
ll.set_params(subs=sub)
@pytest.mark.parametrize('numticks', [1, 2, 3, 9])
@mpl.style.context('default') | 1bc33e99efc9e4be433f99c6a74c7e3b30147dac | @pytest.mark.parametrize('numticks', [1, 2, 3, 9])
@mpl.style.context('default') | 28 | https://github.com/matplotlib/matplotlib.git | 24 | def test_bad_locator_subs(sub):
ll = mticker.LogLocator()
with pytest.raises(ValueError):
ll.set_params(subs=sub)
@pytest.mark.parametrize('numticks', [1, 2, 3, 9])
@mpl | 15 | 93 | test_bad_locator_subs |
95 | 0 | 8 | 31 | python/ray/autoscaler/_private/_kubernetes/config.py | 130,341 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | ray | 17 | Python | 57 | config.py | def _configure_autoscaler_role_binding(namespace, provider_config):
binding_field = "autoscaler_role_binding"
if binding_field not in provider_config:
logger.info(log_prefix + not_provided_msg(binding_field))
return
binding = provider_config[binding_field]
if "namespace" not in binding["metadata"]:
binding["metadata"]["namespace"] = namespace
elif binding["metadata"]["namespace"] != namespace:
raise InvalidNamespaceError(binding_field, namespace)
for subject in binding["subjects"]:
if "namespace" not in subject:
subject["namespace"] = namespace
elif subject["namespace"] != namespace:
raise InvalidNamespaceError(
binding_field + " subject '{}'".format(subject["name"]), namespace
)
name = binding["metadata"]["name"]
field_selector = "metadata.name={}".format(name)
accounts = (
auth_api()
.list_namespaced_role_binding(namespace, field_selector=field_selector)
.items
)
if len(accounts) > 0:
assert len(accounts) == 1
logger.info(log_prefix + using_existing_msg(binding_field, name))
return
logger.info(log_prefix + not_found_msg(binding_field, name))
auth_api().create_namespaced_role_binding(namespace, binding)
logger.info(log_prefix + created_msg(binding_field, name))
| 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | 215 | https://github.com/ray-project/ray.git | 268 | def _configure_autoscaler_role_binding(namespace, provider_config):
binding_field = "autoscaler_role_binding"
if binding_field not in provider_config:
logger.info(log_prefix + not_provided_msg(binding_field))
return
binding = provider_config[binding_field]
if "namespace" not in binding["metadata"]:
binding["metadata"]["namespace"] = namespace
elif binding["metadata"]["namespace"] != namespace:
raise InvalidNamespaceError(binding_field, namespace)
for subject in binding["subjects"]:
if "namespace" not in subject:
subject["namespace"] = namespace
elif subject["namespace"] != namespace:
raise InvalidNamespaceError(
binding_field + " subject '{}'".format(subject["name"]), namespace
)
name = binding["metadata"]["name"]
field_selector = "metadata.name={}".format(name)
accounts = (
auth_api()
.list_namespaced_role_binding(namespace, field_selector=field_selector)
.items
)
| 23 | 360 | _configure_autoscaler_role_binding |
|
87 | 0 | 1 | 17 | sympy/polys/numberfields/tests/test_primes.py | 197,842 | Improve `PrimeIdeal` reduction methods. | sympy | 14 | Python | 48 | test_primes.py | def test_PrimeIdeal_reduce():
k = QQ.alg_field_from_poly(Poly(x ** 3 + x ** 2 - 2 * x + 8))
Zk = k.maximal_order()
P = k.primes_above(2)
frp = P[2]
# reduce_element
a = Zk.parent(to_col([23, 20, 11]), denom=6)
a_bar_expected = Zk.parent(to_col([11, 5, 2]), denom=6)
a_bar = frp.reduce_element(a)
assert a_bar == a_bar_expected
# reduce_ANP
a = k([QQ(11, 6), QQ(20, 6), QQ(23, 6)])
a_bar_expected = k([QQ(2, 6), QQ(5, 6), QQ(11, 6)])
a_bar = frp.reduce_ANP(a)
assert a_bar == a_bar_expected
# reduce_alg_num
a = k.to_alg_num(a)
a_bar_expected = k.to_alg_num(a_bar_expected)
a_bar = frp.reduce_alg_num(a)
assert a_bar == a_bar_expected
| af44b30d68265acb25340374b648e198fb5570e7 | 196 | https://github.com/sympy/sympy.git | 143 | def test_PrimeIdeal_reduce():
k = QQ.alg_field_from_poly(Poly(x ** 3 + x ** 2 - 2 * x + 8))
Zk = k.maximal_order()
P = k.primes_above(2)
frp = P[2]
# reduce_element
a = Zk.parent(to_col([23, 20, 11]), denom=6)
a_bar_expected = Zk.parent(to_col([11, 5, 2]), denom=6)
a_bar = frp.reduce_element(a)
assert a_bar == a_bar_expected
# reduce_ANP
a = k([QQ(11, 6), QQ(20, 6), QQ(23, 6)])
a_bar_expected = k([QQ(2, 6), QQ(5, 6), QQ(11, 6)])
a_bar = frp.reduce_ANP(a)
assert a_bar == a_bar_expected
# reduce_alg_num
a = k.to_alg_num(a)
a_bar_expected = k.to_alg_num(a_bar_expected)
a_ | 21 | 300 | test_PrimeIdeal_reduce |
|
43 | 0 | 1 | 13 | python3.10.4/Lib/distutils/tests/test_register.py | 223,281 | add python 3.10.4 for windows | XX-Net | 9 | Python | 33 | test_register.py | def test_register_invalid_long_description(self):
description = ':funkie:`str`' # mimic Sphinx-specific markup
metadata = {'url': 'xxx', 'author': 'xxx',
'author_email': 'xxx',
'name': 'xxx', 'version': 'xxx',
'long_description': description}
cmd = self._get_cmd(metadata)
cmd.ensure_finalized()
cmd.strict = True
inputs = Inputs('2', 'tarek', 'tarek@ziade.org')
register_module.input = inputs
self.addCleanup(delattr, register_module, 'input')
self.assertRaises(DistutilsSetupError, cmd.run)
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 88 | https://github.com/XX-net/XX-Net.git | 163 | def test_register_invalid_long_description(self):
description = ':funkie:`str`' # mimic Sphinx-specific markup
metadata = {'url': 'xxx', 'author': 'xxx',
'author_email': 'xxx',
'name': 'xxx', 'version': 'xxx',
'long_description': description}
cmd = self._get_cmd(metadata)
cmd.ensure_finalized()
cmd.strict = True
inputs | 17 | 164 | test_register_invalid_long_description |
|
10 | 0 | 1 | 3 | tests/test_markup.py | 161,832 | fix invalid escapes | rich | 11 | Python | 9 | test_markup.py | def test_markup_escape():
result = str(render("[dim white][url=[/]"))
assert result == "[url="
| 90a7224ee672ca7f58399f3c8bec9d38341b1423 | 17 | https://github.com/Textualize/rich.git | 15 | def test_markup_escape():
result = str(rende | 4 | 33 | test_markup_escape |
|
102 | 0 | 2 | 49 | tests/sentry/lang/javascript/test_processor.py | 86,506 | feat(empty-stacktraces): Tag events with stack traces from JS console errors (#39335)
Add a new tag, `empty_stacktrace.js_console`, to tag JavaScript console errors. | sentry | 19 | Python | 68 | test_processor.py | def test_no_suspected_console_error(self):
project = self.create_project()
release = self.create_release(project=project, version="12.31.12")
data = {
"is_exception": True,
"platform": "javascript",
"project": project.id,
"exception": {
"values": [
{
"type": "SyntaxError",
"mechanism": {
"type": "onerror",
},
"value": ("value"),
"stacktrace": {
"frames": [
{
"abs_path": "http://example.com/foo.js",
"filename": "<anonymous>",
"function": "name",
"lineno": 4,
"colno": 0,
},
{
"abs_path": "http://example.com/foo.js",
"filename": "<anonymous>",
"function": "new name",
"lineno": 4,
"colno": 0,
},
]
},
}
]
},
}
stacktrace_infos = [
stacktrace for stacktrace in find_stacktraces_in_data(data, with_exceptions=True)
]
processor = JavaScriptStacktraceProcessor(
data={"release": release.version, "dist": "foo", "timestamp": 123.4},
project=project,
stacktrace_infos=stacktrace_infos,
)
frames = processor.get_valid_frames()
assert processor.suspected_console_errors(frames) is False
processor.tag_suspected_console_errors(frames)
assert get_tag(processor.data, "empty_stacktrace.js_console") is False
| e0dddfaa4b466e7eccff4ed075cc319fcc922688 | 211 | https://github.com/getsentry/sentry.git | 1,045 | def test_no_suspected_console_error(self):
project = self.create_project()
release = self.create_release(project=project, version="12.31.12")
data = {
"is_exception": True,
"platform": "javascript",
"project": project.id,
"exception": {
"values": [
{
"type": "SyntaxError",
"mechanism": {
"type": "onerror",
},
"value": ("value"),
"stacktrace": {
"frames": [
{
"abs_path": "http://example.com/foo.js",
"filename": "<anonymous>",
"function": "name",
"lineno": 4,
"colno": 0,
},
{
"abs_path": "http://example.com/foo.js",
"filename": "<anonymous>",
"function": "new name",
"lineno": 4,
"colno": 0,
},
]
},
}
]
},
}
stacktrace_infos = [
stacktrace for stacktrace in find_stacktraces_in_data(data, with_exceptions=True) | 20 | 371 | test_no_suspected_console_error |
|
54 | 0 | 4 | 15 | nuitka/importing/Importing.py | 178,301 | Plugins: Massive cleanup and API improvements and Kivy support
* Added method to locate a DLL and to create a DLL entry point
as expected, removing need for imports and making it more
clear as an API.
* The location of modules had already an API, but it wasn'
used where it could be.
* Moved implicit imports and DLL usage for Gi to its plugin,
solving a TODO for it.
* Make sure sure to only yield, and not return, that is just
more error prone.
* Also allow generators for implicit dependencies, such that
generators work in a yield from fashion.
* With this, Kivy apps work on at least Linux. | Nuitka | 11 | Python | 39 | Importing.py | def locateModule(module_name, parent_package, level):
module_package, module_filename, finding = findModule(
module_name=module_name,
parent_package=parent_package,
level=level,
)
assert module_package is None or (
type(module_package) is ModuleName and module_package != ""
), repr(module_package)
if module_filename is not None:
module_filename = os.path.normpath(module_filename)
module_name, module_kind = getModuleNameAndKindFromFilename(module_filename)
assert module_kind is not None, module_filename
module_name = ModuleName.makeModuleNameInPackage(module_name, module_package)
return module_name, module_filename, finding
| 56eb59d93f13815e66d0dea07e7669dfe275fa10 | 100 | https://github.com/Nuitka/Nuitka.git | 131 | def locateModule(module_name, parent_package, level):
module_package, module_filename, finding = findModule(
module_name=module_name,
parent_package=parent_package,
level=level,
)
assert module_package is None or (
type(module_package) is ModuleName and module_package != ""
), repr(module_package)
if module_filename is not None:
module_filename = os.path.normpath(module_filename)
module_name, module_kind = getModuleNameAndKindFromFilename(module_filename)
assert | 17 | 151 | locateModule |
|
45 | 1 | 1 | 6 | python/ray/ml/tests/test_torch_predictor.py | 140,389 | [AIR] Directly convert `TorchPredictor` `ndarray` inputs to tensors (#25190)
If you pass a multidimensional input to `TorchPredictor.predict`, AIR errors. For more information about the error, see #25194.
Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com> | ray | 12 | Python | 37 | test_torch_predictor.py | def test_predict_dataframe():
predictor = TorchPredictor(model=torch.nn.Linear(2, 1, bias=False))
data_batch = pd.DataFrame({"X0": [0.0, 0.0, 0.0], "X1": [0.0, 0.0, 0.0]})
predictions = predictor.predict(data_batch, dtype=torch.float)
assert len(predictions) == 3
assert predictions.to_numpy().flatten().tolist() == [0.0, 0.0, 0.0]
@pytest.mark.parametrize(
("input_dtype", "expected_output_dtype"),
(
(torch.float16, np.float16),
(torch.float64, np.float64),
(torch.int32, np.int32),
(torch.int64, np.int64),
),
) | 692335440b10b487641641d71413d4c03c85a362 | @pytest.mark.parametrize(
("input_dtype", "expected_output_dtype"),
(
(torch.float16, np.float16),
(torch.float64, np.float64),
(torch.int32, np.int32),
(torch.int64, np.int64),
),
) | 114 | https://github.com/ray-project/ray.git | 94 | def test_predict_dataframe():
predictor = TorchPredictor(model=torch.nn.Linear(2, 1, bias=False))
data_batch = pd.DataFrame({"X0": [0.0, 0.0, 0.0], "X1": [0.0, 0.0, 0.0]})
predictions = predictor.predict(data_batch, dtype=torch.float)
assert len(predictions) == 3
assert predictions.to_numpy().flatten().tolist() == [0.0, 0.0, 0.0]
@pytest.mark.parametrize(
("input_dtype", | 27 | 228 | test_predict_dataframe |
109 | 0 | 18 | 43 | src/textual/message_pump.py | 183,689 | combine updates, cache arrangements | textual | 22 | Python | 60 | message_pump.py | async def _process_messages(self) -> None:
_rich_traceback_guard = True
while not self._closed:
try:
message = await self.get_message()
except MessagePumpClosed:
break
except CancelledError:
raise
except Exception as error:
raise error from None
# Combine any pending messages that may supersede this one
while not (self._closed or self._closing):
try:
pending = self.peek_message()
except MessagePumpClosed:
break
if pending is None or not message.can_replace(pending):
break
try:
message = await self.get_message()
except MessagePumpClosed:
break
try:
await self.dispatch_message(message)
except CancelledError:
raise
except Exception as error:
self.app.on_exception(error)
break
finally:
if self._message_queue.empty():
if not self._closed:
event = events.Idle(self)
for _cls, method in self._get_dispatch_methods(
"on_idle", event
):
try:
await invoke(method, event)
except Exception as error:
self.app.on_exception(error)
break
log("CLOSED", self)
| 55543479ad3049c6f9d1507d034c7c5bedf3981a | 192 | https://github.com/Textualize/textual.git | 814 | async def _process_messages(self) -> None:
_rich_traceback_guard = True
while not self._closed:
try:
message = await self.get_message()
except MessagePumpClosed | 27 | 330 | _process_messages |
|
21 | 0 | 1 | 5 | lib/matplotlib/tests/test_contour.py | 110,787 | Support only positional args for data in contour | matplotlib | 11 | Python | 20 | test_contour.py | def test_contour_no_args():
fig, ax = plt.subplots()
data = [[0, 1], [1, 0]]
with pytest.raises(TypeError, match=r"contour\(\) takes from 1 to 4"):
ax.contour(Z=data)
| 756eb1e539aff1aa7c9a73c42b527c6b6f204419 | 49 | https://github.com/matplotlib/matplotlib.git | 36 | def test_contour_no_args():
fig, ax = plt.subplots()
data = [[0, 1], [1, 0]]
with pytest.raises(TypeError, match=r"contour\(\) takes from 1 to 4"):
ax.contour | 12 | 78 | test_contour_no_args |
|
32 | 0 | 1 | 13 | onnx/backend/test/case/node/stringnormalizer.py | 255,088 | Use Python type annotations rather than comments (#3962)
* These have been supported since Python 3.5.
ONNX doesn't support Python < 3.6, so we can use the annotations.
Diffs generated by https://pypi.org/project/com2ann/.
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* Remove MYPY conditional logic in gen_proto.py
It breaks the type annotations and shouldn't be needed.
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* Get rid of MYPY bool from more scripts
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* move Descriptors class above where its referenced in type annotation
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* fixes
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* remove extra blank line
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* fix type annotations
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* fix type annotation in gen_docs
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* fix Operators.md
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* fix TestCoverage.md
Signed-off-by: Gary Miguel <garymiguel@microsoft.com>
* fix protoc-gen-mypy.py
Signed-off-by: Gary Miguel <garymiguel@microsoft.com> | onnx | 12 | Python | 27 | stringnormalizer.py | def export_monday_casesensintive_lower() -> None:
input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
output = np.array([u'tuesday', u'wednesday', u'thursday']).astype(object)
stopwords = [u'monday']
node = onnx.helper.make_node(
'StringNormalizer',
inputs=['x'],
outputs=['y'],
case_change_action='LOWER',
is_case_sensitive=1,
stopwords=stopwords
)
expect(node, inputs=[input], outputs=[output], name='test_strnormalizer_export_monday_casesensintive_lower')
| 83fa57c74edfd13ddac9548b8a12f9e3e2ed05bd | 113 | https://github.com/onnx/onnx.git | 139 | def export_monday_casesensintive_lower() -> None:
input = np.array([u'monday', u'tuesday', u'wednesday', u'thursday']).astype(object)
output = np.array([u'tuesday', u'wednesday', u'thursday'] | 18 | 178 | export_monday_casesensintive_lower |
|
29 | 0 | 1 | 13 | tests/www/views/test_views_tasks.py | 44,086 | Return to the same place when triggering a DAG (#20955) | airflow | 13 | Python | 26 | test_views_tasks.py | def test_dag_details_trigger_origin_dag_details_view(app, admin_client):
app.dag_bag.get_dag('test_graph_view').create_dagrun(
run_type=DagRunType.SCHEDULED,
execution_date=DEFAULT_DATE,
data_interval=(DEFAULT_DATE, DEFAULT_DATE),
start_date=timezone.utcnow(),
state=State.RUNNING,
)
url = 'dag_details?dag_id=test_graph_view'
resp = admin_client.get(url, follow_redirects=True)
params = {'dag_id': 'test_graph_view', 'origin': '/dag_details?dag_id=test_graph_view'}
href = f"/trigger?{html.escape(urllib.parse.urlencode(params))}"
check_content_in_response(href, resp)
| 928dafe6c495bbf3e03d14473753fce915134a46 | 87 | https://github.com/apache/airflow.git | 84 | def test_dag_details_trigger_origin_dag_details_view(app, admin_client):
app.dag_bag.get_dag('test_graph_view').create_da | 30 | 164 | test_dag_details_trigger_origin_dag_details_view |
|
105 | 0 | 3 | 18 | jax/_src/random.py | 122,115 | [typing] use jax.Array annotations in random.py | jax | 13 | Python | 82 | random.py | def _truncated_normal(key, lower, upper, shape, dtype) -> Array:
if shape is None:
shape = lax.broadcast_shapes(np.shape(lower), np.shape(upper))
else:
_check_shape("truncated_normal", shape, np.shape(lower), np.shape(upper))
sqrt2 = np.array(np.sqrt(2), dtype)
lower = lax.convert_element_type(lower, dtype)
upper = lax.convert_element_type(upper, dtype)
a = lax.erf(lower / sqrt2)
b = lax.erf(upper / sqrt2)
if not jnp.issubdtype(dtype, np.floating):
raise TypeError("truncated_normal only accepts floating point dtypes.")
u = uniform(key, shape, dtype, minval=a, maxval=b)
out = sqrt2 * lax.erf_inv(u)
# Clamp the value to the open interval (lower, upper) to make sure that
# rounding (or if we chose `a` for `u`) doesn't push us outside of the range.
return jnp.clip(
out,
lax.nextafter(lax.stop_gradient(lower), np.array(np.inf, dtype=dtype)),
lax.nextafter(lax.stop_gradient(upper), np.array(-np.inf, dtype=dtype)))
| aed46f3312c970de257afbeb6cd775e79dd8e04e | 221 | https://github.com/google/jax.git | 141 | def _truncated_normal(key, lower, upper, shape, dtype) -> Array:
if shape is None:
shape = lax.broadcast_shapes(np.shape(lower), np.shape(upper))
else:
_check_shape("truncated_normal", shape, np.shape(lower), np.shape(upper))
sqrt2 = np.array(np.sqrt(2), dtype)
lower = lax.convert_element_type(lower, dtype)
upper = lax.convert_element_type(upper, dtype)
a = lax.erf(lower / sqrt2)
b = lax.erf(upper / sqrt2)
if not jnp.issubdtype(dtype, np.floating):
raise TypeError("truncated_normal only accepts floating point dtypes.")
u = uniform(key, shape, dtype, minval=a, maxval=b)
out = sqrt2 * lax.erf_inv(u)
# Clamp the value to the open interval (lower, upper) to make sure that
# rounding (or if we chose `a` for `u`) doesn't push us outside of the range.
return jnp.clip(
out,
lax.nextafter(lax.stop_gradient(lower), | 32 | 336 | _truncated_normal |
|
103 | 0 | 7 | 20 | .venv/lib/python3.8/site-packages/pip/_vendor/html5lib/treebuilders/__init__.py | 62,572 | upd; format | transferlearning | 14 | Python | 56 | __init__.py | def getTreeBuilder(treeType, implementation=None, **kwargs):
treeType = treeType.lower()
if treeType not in treeBuilderCache:
if treeType == "dom":
from . import dom
# Come up with a sane default (pref. from the stdlib)
if implementation is None:
from xml.dom import minidom
implementation = minidom
# NEVER cache here, caching is done in the dom submodule
return dom.getDomModule(implementation, **kwargs).TreeBuilder
elif treeType == "lxml":
from . import etree_lxml
treeBuilderCache[treeType] = etree_lxml.TreeBuilder
elif treeType == "etree":
from . import etree
if implementation is None:
implementation = default_etree
# NEVER cache here, caching is done in the etree submodule
return etree.getETreeModule(implementation, **kwargs).TreeBuilder
else:
raise ValueError( % treeType)
return treeBuilderCache.get(treeType)
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 123 | https://github.com/jindongwang/transferlearning.git | 320 | def getTreeBuilder(treeType, implementation=None, **kwargs):
treeType = treeType.lower()
if treeType not in treeBuilderCache:
if treeType == "dom":
from . import dom
# Come up with a sane default (pref. from the stdlib)
if implementation is None:
from xml.dom import minidom
implementation = minidom
# NEVER cache here, caching is done in the dom submodule
return dom.getDomModule(implementation, **kwargs).TreeBuilder
elif treeType == "lxml":
from . import etree_lxml
treeBuilderCache[treeType] = etree_lxml.TreeBuilder
elif treeT | 17 | 211 | getTreeBuilder |
|
631 | 0 | 18 | 111 | sklearn/linear_model/_quantile.py | 260,449 | MAINT Param validation for QuantileRegressor (#23808)
Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> | scikit-learn | 17 | Python | 332 | _quantile.py | def fit(self, X, y, sample_weight=None):
self._validate_params()
X, y = self._validate_data(
X,
y,
accept_sparse=["csc", "csr", "coo"],
y_numeric=True,
multi_output=False,
)
sample_weight = _check_sample_weight(sample_weight, X)
n_features = X.shape[1]
n_params = n_features
if self.fit_intercept:
n_params += 1
# Note that centering y and X with _preprocess_data does not work
# for quantile regression.
# The objective is defined as 1/n * sum(pinball loss) + alpha * L1.
# So we rescale the penalty term, which is equivalent.
alpha = np.sum(sample_weight) * self.alpha
if self.solver == "warn":
warnings.warn(
"The default solver will change from 'interior-point' to 'highs' in "
"version 1.4. Set `solver='highs'` or to the desired solver to silence "
"this warning.",
FutureWarning,
)
solver = "interior-point"
elif self.solver in (
"highs-ds",
"highs-ipm",
"highs",
) and sp_version < parse_version("1.6.0"):
raise ValueError(
f"Solver {self.solver} is only available "
f"with scipy>=1.6.0, got {sp_version}"
)
else:
solver = self.solver
if solver == "interior-point" and sp_version >= parse_version("1.11.0"):
raise ValueError(
f"Solver {solver} is not anymore available in SciPy >= 1.11.0."
)
if sparse.issparse(X) and solver not in ["highs", "highs-ds", "highs-ipm"]:
raise ValueError(
f"Solver {self.solver} does not support sparse X. "
"Use solver 'highs' for example."
)
# make default solver more stable
if self.solver_options is None and solver == "interior-point":
solver_options = {"lstsq": True}
else:
solver_options = self.solver_options
# After rescaling alpha, the minimization problem is
# min sum(pinball loss) + alpha * L1
# Use linear programming formulation of quantile regression
# min_x c x
# A_eq x = b_eq
# 0 <= x
# x = (s0, s, t0, t, u, v) = slack variables >= 0
# intercept = s0 - t0
# coef = s - t
# c = (0, alpha * 1_p, 0, alpha * 1_p, quantile * 1_n, (1-quantile) * 1_n)
# residual = y - X@coef - intercept = u - v
# A_eq = (1_n, X, -1_n, -X, diag(1_n), -diag(1_n))
# b_eq = y
# p = n_features
# n = n_samples
# 1_n = vector of length n with entries equal one
# see https://stats.stackexchange.com/questions/384909/
#
# Filtering out zero sample weights from the beginning makes life
# easier for the linprog solver.
indices = np.nonzero(sample_weight)[0]
n_indices = len(indices) # use n_mask instead of n_samples
if n_indices < len(sample_weight):
sample_weight = sample_weight[indices]
X = _safe_indexing(X, indices)
y = _safe_indexing(y, indices)
c = np.concatenate(
[
np.full(2 * n_params, fill_value=alpha),
sample_weight * self.quantile,
sample_weight * (1 - self.quantile),
]
)
if self.fit_intercept:
# do not penalize the intercept
c[0] = 0
c[n_params] = 0
if solver in ["highs", "highs-ds", "highs-ipm"]:
# Note that highs methods always use a sparse CSC memory layout internally,
# even for optimization problems parametrized using dense numpy arrays.
# Therefore, we work with CSC matrices as early as possible to limit
# unnecessary repeated memory copies.
eye = sparse.eye(n_indices, dtype=X.dtype, format="csc")
if self.fit_intercept:
ones = sparse.csc_matrix(np.ones(shape=(n_indices, 1), dtype=X.dtype))
A_eq = sparse.hstack([ones, X, -ones, -X, eye, -eye], format="csc")
else:
A_eq = sparse.hstack([X, -X, eye, -eye], format="csc")
else:
eye = np.eye(n_indices)
if self.fit_intercept:
ones = np.ones((n_indices, 1))
A_eq = np.concatenate([ones, X, -ones, -X, eye, -eye], axis=1)
else:
A_eq = np.concatenate([X, -X, eye, -eye], axis=1)
b_eq = y
result = linprog(
c=c,
A_eq=A_eq,
b_eq=b_eq,
method=solver,
options=solver_options,
)
solution = result.x
if not result.success:
failure = {
1: "Iteration limit reached.",
2: "Problem appears to be infeasible.",
3: "Problem appears to be unbounded.",
4: "Numerical difficulties encountered.",
}
warnings.warn(
"Linear programming for QuantileRegressor did not succeed.\n"
f"Status is {result.status}: "
+ failure.setdefault(result.status, "unknown reason")
+ "\n"
+ "Result message of linprog:\n"
+ result.message,
ConvergenceWarning,
)
# positive slack - negative slack
# solution is an array with (params_pos, params_neg, u, v)
params = solution[:n_params] - solution[n_params : 2 * n_params]
self.n_iter_ = result.nit
if self.fit_intercept:
self.coef_ = params[1:]
self.intercept_ = params[0]
else:
self.coef_ = params
self.intercept_ = 0.0
return self
| a0623cec4a253ce3b5c5e4cf3b080651c84a53a9 | 655 | https://github.com/scikit-learn/scikit-learn.git | 2,114 | def fit(self, X, y, sample_weight=None):
self._validate_params()
X, y = self._validate_data(
X,
y,
accept_sparse=["csc", "csr", "coo"],
y_numeric=True,
multi_output=False,
)
sample_weight = _check_sample_weight(sample_weight, X)
n_features = X.shape[1]
n_params = n_features
if self.fit_intercept:
n_params += 1
# Note that centering y and X with _preprocess_data does not work
# for quantile regression.
# The objective is defined as 1/n * sum(pinball loss) + alpha * L1.
# So we rescale the penalty term, which is equivalent.
alpha = np.sum(sample_weight) * self.alpha
if self.solver == "warn":
warnings.warn(
"The default solver will change from 'interior-point' to 'highs' in "
"version 1.4. Set `solver='highs'` or to the desired solver to silence "
"this warning.",
FutureWarning,
)
solver = "interior-point"
elif self.solver in (
"highs-ds",
"highs-ipm",
"highs",
) and sp_version < parse_version("1.6.0"):
raise ValueError(
f"Solver {self.solver} is only available "
f"with scipy>=1.6.0, got {sp_version}"
)
else:
solver = self.solver
if solver == "interior-point" and sp_version >= parse_version("1.11.0"):
raise ValueError(
f"Solver {solver} is not anymore available in SciPy >= 1.11.0."
)
if sparse.issparse(X) and solver not in ["highs", "highs-ds", "highs-ipm"]:
raise ValueError(
f"Solver {self.solver} does not support sparse X. "
"Use solver 'highs' for example."
)
# make default solver more stable
if self.solver_options is None and solver == "interior-point":
solver_options = {"lstsq": True}
else:
solver_options = self.solver_options
# After rescaling alpha, the minimization problem is
# min sum(pinb | 64 | 1,110 | fit |
|
208 | 0 | 9 | 45 | sklearn/datasets/tests/test_lfw.py | 261,542 | MAINT bump up CI dependencies (#24803)
[scipy-dev] [pypy] | scikit-learn | 16 | Python | 131 | test_lfw.py | def setup_module():
Image = pytest.importorskip("PIL.Image")
global SCIKIT_LEARN_DATA, SCIKIT_LEARN_EMPTY_DATA, LFW_HOME
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, "lfw_home")
SCIKIT_LEARN_EMPTY_DATA = tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, "lfw_funneled", name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + "_%04d.jpg" % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
img = Image.fromarray(uniface.astype(np.uint8))
img.save(file_path)
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, "lfw_funneled", ".test.swp"), "wb") as f:
f.write(b"Text file to be ignored by the dataset loader.")
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, "pairsDevTrain.txt"), "wb") as f:
f.write(b"10\n")
more_than_two = [name for name, count in counts.items() if count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(("%s\t%d\t%d\n" % (name, first, second)).encode())
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
first_index = np_rng.choice(np.arange(counts[first_name]))
second_index = np_rng.choice(np.arange(counts[second_name]))
f.write(
(
"%s\t%d\t%s\t%d\n"
% (first_name, first_index, second_name, second_index)
).encode()
)
with open(os.path.join(LFW_HOME, "pairsDevTest.txt"), "wb") as f:
f.write(b"Fake place holder that won't be tested")
with open(os.path.join(LFW_HOME, "pairs.txt"), "wb") as f:
f.write(b"Fake place holder that won't be tested")
| 63f92d4adb61aed58d656544cc6caa9d68cb6065 | 460 | https://github.com/scikit-learn/scikit-learn.git | 564 | def setup_module():
Image = pytest.importorskip("PIL.Image")
global SCIKIT_LEARN_DATA, SCIKIT_LEARN_EMPTY_DATA, LFW_HOME
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, "lfw_home")
SCIKIT_LEARN_EMPTY_DATA = tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, "lfw_funneled", name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + "_%04d.jpg" % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
img = Image.fromarray(uniface.astype(np.uint8))
img.save(file_path)
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, "lfw_funneled", ".test.swp"), "wb") as f:
f.write(b"Text file to be ignored by the dataset loader.")
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, "pairsDevTrain.txt"), "wb") as f:
f.write(b"10\n")
more_than_two = [name for name, count in counts.items() if count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(("%s\t%d\t%d\n" % (name, first, second)).encode())
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
first_index = np_rng.choice(np.arange(counts[first_name]))
second_index = np_rng.choice(np.arange(counts[second_name]))
f.write(
(
"%s\t%d\t%s\t%d\n"
% (first_name, first_index, second_name, second_index)
).encode()
)
with open(os.path.join(LFW_HOME, "pairsDevTest.txt"), "wb") as f:
f.write(b"Fake place holder that won't b | 53 | 764 | setup_module |
|
47 | 1 | 1 | 16 | tests/components/plugwise/conftest.py | 313,041 | Cleanup existing Plugwise tests and test fixtures (#66282)
* Cleanup existing Plugwise tests and test fixtures
* More cleanup | core | 11 | Python | 37 | conftest.py | def mock_stretch() -> Generator[None, MagicMock, None]:
chosen_env = "stretch_v31"
with patch(
"homeassistant.components.plugwise.gateway.Smile", autospec=True
) as smile_mock:
smile = smile_mock.return_value
smile.gateway_id = "259882df3c05415b99c2d962534ce820"
smile.heater_id = None
smile.smile_version = "3.1.11"
smile.smile_type = "stretch"
smile.smile_hostname = "stretch98765"
smile.smile_name = "Stretch"
smile.connect.return_value = True
smile.async_update.return_value = _read_json(chosen_env, "all_data")
yield smile
@pytest.fixture | bd920aa43de584f6a4db934902d64b39aabbd6d6 | @pytest.fixture | 85 | https://github.com/home-assistant/core.git | 135 | def mock_stretch() -> Generator[None, MagicMock, None]:
chosen_env = "stretch_v31"
with patch(
"homeassistant.components.plugwise.gateway.Smile", autospec=True
) as smile_mock:
smile = smile_mock.return_value
smile.gateway_id = "259882df3c | 20 | 161 | mock_stretch |
27 | 1 | 4 | 9 | nuitka/utils/FileOperations.py | 178,587 | macOS: Proper adhoc signing of created distribution
* With this homebrew works on M1 and macOS 12 | Nuitka | 11 | Python | 21 | FileOperations.py | def withPreserveFileMode(filenames):
if type(filenames) is str:
filenames = [filenames]
old_modes = {}
for filename in filenames:
old_modes[filename] = os.stat(filename).st_mode
yield
for filename in filenames:
os.chmod(filename, old_modes[filename])
@contextmanager | e188ede8767cda1750cd41c08bed82c00888aebe | @contextmanager | 57 | https://github.com/Nuitka/Nuitka.git | 61 | def withPreserveFileMode(filenames):
if type(filenames) is str:
filenames = [filenames]
old_modes = {}
for filename in filenames:
old_modes[filename] = os.stat(filename).st_mode
yield
| 11 | 94 | withPreserveFileMode |
20 | 0 | 2 | 22 | gamestonk_terminal/economy/fred/prediction/pred_controller.py | 281,475 | Terminal Wide Rich (#1161)
* My idea for how we handle Rich moving forward
* remove independent consoles
* FIxed pylint issues
* add a few vars
* Switched print to console
* More transitions
* Changed more prints
* Replaced all prints
* Fixing tabulate
* Finished replace tabulate
* Finished removing rich from Tabulate
* add Panel around menu
* add GST watermark under feature flag
* Fixed 46 tests
* Delete test_screener[False].yaml
* Delete test_screener[True].yaml
* Fixed the rest of the tests
* add help and source color vars and use rgb
* rich on stocks/options
* update rich on disc, dps, sia
* rich in gov, ins and scr menus
* ba and ca menus with rich
* Fixed import issue
* Fixed some tests
* removed termcolor
* Removed prettytable
* add rich to remaining stocks menus
* FIxed linting issue
* Added James' changes
* Updated dependencies
* Add rich to cryptocurrency menu
* refactor economy and forex
* refactor etf with rich
* refactor mfunds
* refactor rich rest
* not specify style so default color works well on any background
* Fixing mypy issues
* Updated tests
* More test fixes
* James' test fixes
* Updating tests : stocks/screener - fix cassettes using BR
* Updating tests : crypto
* Updating tests : disable DEBUG_MODE
* Updating tests : stocks/fa/yfinance
* minor fixes that escape
* Improve the rich table function (that replaces tabulate :D )
* Fixed bad code
* delete rogue file + dcf fix + NoConsole
* sia mypy
* fuck you linter
* fuck you linter pt 2
* skip hehe
* i hate the black linter
* ubuntu mypy attempt
* Update : rich_config + gtff
* Updating tests : conftest
* Updating tests : stocks
* Update : rich_config
* Updating : rich_config
* make panel configurable for Theodore :b
* colors update
* Merged
* Updating : rich_config + feature_flags
* Updating : rich_config
* Updating tests : stocks
* Updating : feature_flags
Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt>
Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
Co-authored-by: james <jmaslek11@gmail.com>
Co-authored-by: jose-donato <zmcdonato@gmail.com> | OpenBBTerminal | 12 | Python | 18 | pred_controller.py | def print_help(self):
id_string = ""
for s_id, sub_dict in self.current_series.items():
id_string += f" [cyan]{s_id.upper()}[/cyan] : {sub_dict['title']}"
help_string = f
console.print(help_string)
| 82747072c511beb1b2672846ae2ee4aec53eb562 | 36 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 69 | def print_help(self):
id_string = ""
for s_id, sub_dict in self.current_series.items():
id_string += f" [cyan]{s_id.upper()}[/cyan] | 12 | 96 | print_help |
|
78 | 0 | 2 | 18 | modin/test/interchange/dataframe_protocol/hdk/test_protocol.py | 155,297 | REFACTOR-#5303: Fix code scanning alert - Unused local variable (#5304)
Signed-off-by: Myachev <anatoly.myachev@intel.com>
Co-authored-by: Mahesh Vashishtha <mvashishtha@users.noreply.github.com> | modin | 16 | Python | 62 | test_protocol.py | def test_zero_copy_export_for_primitives(data_has_nulls):
data = get_data_of_all_types(
has_nulls=data_has_nulls, include_dtypes=["int", "uint", "float"]
)
at = pa.Table.from_pydict(data)
md_df = from_arrow(at)
protocol_df = md_df.__dataframe__(allow_copy=False)
for i, col in enumerate(protocol_df.get_columns()):
col_arr, _ = primitive_column_to_ndarray(col)
exported_ptr = col_arr.__array_interface__["data"][0]
producer_ptr = at.column(i).chunks[0].buffers()[-1].address
# Verify that the pointers of produce and exported objects point to the same data
assert producer_ptr == exported_ptr
# Can't export `md_df` zero-copy no more as it has delayed 'fillna' operation
md_df = md_df.fillna({"float32": 32.0})
non_zero_copy_protocol_df = md_df.__dataframe__(allow_copy=False)
with pytest.raises(RuntimeError):
primitive_column_to_ndarray(
non_zero_copy_protocol_df.get_column_by_name("float32")
)
| eb99c500a40c5565012e3fe83c5e6ef333d1b487 | 151 | https://github.com/modin-project/modin.git | 178 | def test_zero_copy_export_for_primitives(data_has_nulls):
data = get_data_of_all_types(
has_nulls=data_has_nulls, include_dtypes=["int", "uint", "float"]
)
at = pa.Table.from_pydict(data)
md_df = from_arrow(at)
protocol_df = md_df.__dataframe__(allow_copy=False)
for i, col in enumerate(protocol_df.get_columns()):
col_arr, _ = pri | 35 | 252 | test_zero_copy_export_for_primitives |
|
20 | 0 | 3 | 7 | apps/DeepFaceLive/backend/CameraSource.py | 179,102 | CameraSource now shows names of video input devices in Windows | DeepFaceLive | 11 | Python | 17 | CameraSource.py | def on_cs_device_idx_selected(self, device_idx, device_name):
cs, state = self.get_control_sheet(), self.get_state()
if state.device_idx != device_idx:
state.device_idx = device_idx
self.save_state()
if self.is_started():
self.restart()
| fa7fddca2869dec8fb1c7c9691fb77f1cc8805b6 | 53 | https://github.com/iperov/DeepFaceLive.git | 81 | def on_cs_device_idx_selected(self, device_idx, device_name):
cs, state = self.get_control_sheet(), self.get_state()
if state.device_idx != device_idx:
state.device_idx = devic | 11 | 87 | on_cs_device_idx_selected |
|
38 | 0 | 1 | 12 | wagtail/users/tests/test_admin_views.py | 76,211 | Reformat with black | wagtail | 14 | Python | 31 | test_admin_views.py | def test_user_can_delete_other_superuser(self):
response = self.client.get(
reverse("wagtailusers_users:delete", args=(self.superuser.pk,))
)
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, "wagtailusers/users/confirm_delete.html")
response = self.client.post(
reverse("wagtailusers_users:delete", args=(self.superuser.pk,))
)
# Should redirect back to index
self.assertRedirects(response, reverse("wagtailusers_users:index"))
# Check that the user was deleted
users = get_user_model().objects.filter(email="testsuperuser@email.com")
self.assertEqual(users.count(), 0)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 108 | https://github.com/wagtail/wagtail.git | 136 | def test_user_can_delete_other_superuser(self):
response = self.client.get(
reverse( | 20 | 179 | test_user_can_delete_other_superuser |
|
85 | 0 | 5 | 17 | cps/gdriveutils.py | 172,506 | Refactor rename author/title on gdrive | calibre-web | 16 | Python | 69 | gdriveutils.py | def moveGdriveFolderRemote(origin_file, target_folder):
drive = getDrive(Gdrive.Instance().drive)
previous_parents = ",".join([parent["id"] for parent in origin_file.get('parents')])
children = drive.auth.service.children().list(folderId=previous_parents).execute()
gFileTargetDir = getFileFromEbooksFolder(None, target_folder)
if not gFileTargetDir or gFileTargetDir['title'] != target_folder:
# Folder is not existing, create, and move folder
drive.auth.service.files().patch(fileId=origin_file['id'],
body={'title': target_folder},
fields='title').execute()
#gFileTargetDir = drive.CreateFile(
# {'title': target_folder, 'parents': [{"kind": "drive#fileLink", 'id': getEbooksFolderId()}],
# "mimeType": "application/vnd.google-apps.folder"})
#gFileTargetDir.Upload()
else:
# Move the file to the new folder
drive.auth.service.files().update(fileId=origin_file['id'],
addParents=gFileTargetDir['id'],
removeParents=previous_parents,
fields='id, parents').execute()
# if previous_parents has no children anymore, delete original fileparent
if len(children['items']) == 1:
deleteDatabaseEntry(previous_parents)
drive.auth.service.files().delete(fileId=previous_parents).execute()
| d8f5bdea6df3a0217f49062d4209cedc80caad0e | 192 | https://github.com/janeczku/calibre-web.git | 388 | def moveGdriveFolderRemote(origin_file, target_folder):
drive = getDrive(Gdrive.Instance().drive)
previous_parents = ",".join([parent["id"] for parent in origin_file.get('parents')])
children = drive.auth.service.children().list(folderId=previous_parents).execute()
gFileTargetDir = getFileFromEbooksFolder(None, target_folder)
if not gFileTargetDir or gFileTargetDir['title'] != target_folder:
# Folder is not existing, create, and move folder
drive.auth.service.files().patch(fileId=origin_file['id'],
body={'title': target_folder},
fields='title').execute()
#gFileTargetDir = drive.CreateFile(
# {'title': target_folder, 'parents': [{"kind": "drive#fileLink", 'id': getEbooksFolderId()}],
# "mimeType": "application/vnd.google-apps.folder"})
#gFi | 30 | 328 | moveGdriveFolderRemote |
|
15 | 1 | 1 | 6 | tests/components/hassio/test_addon_manager.py | 290,489 | Move zwave_js addon manager to hassio integration (#81354) | core | 11 | Python | 15 | test_addon_manager.py | def stop_addon_fixture() -> Generator[AsyncMock, None, None]:
with patch(
"homeassistant.components.hassio.addon_manager.async_stop_addon"
) as stop_addon:
yield stop_addon
@pytest.fixture(name="create_backup") | 9ded2325223de3918e3f69aab8732487323b2214 | @pytest.fixture(name="create_backup") | 24 | https://github.com/home-assistant/core.git | 37 | def stop_addon_fixture() -> Generator[AsyncMock, None, None]:
| 8 | 62 | stop_addon_fixture |
26 | 0 | 1 | 14 | ludwig/features/audio_feature.py | 6,750 | Torchaudio fixes (#2007)
* hotfix for shape broadcast issue
* Reverted [1] index on padded audio feature, set up test for feature creation observation
* Changed default audio type since raw takes too long
* Removed debug code | ludwig | 10 | Python | 25 | audio_feature.py | def preprocessing_defaults():
return {
"audio_file_length_limit_in_s": 7.5,
"missing_value_strategy": BACKFILL,
"in_memory": True,
"padding_value": 0,
"norm": None,
"audio_feature": {
TYPE: "fbank",
"window_length_in_s": 0.04,
"window_shift_in_s": 0.02,
"num_filter_bands": 80,
},
}
| 5209b1aed23a98c092a0e2682ed13b7f61623e20 | 54 | https://github.com/ludwig-ai/ludwig.git | 176 | def preprocessing_defaults():
return {
"audio_file_length_limit_in_s": 7.5,
"missing_value_strategy": BACKFILL,
"in_memory": True,
"padding_value": 0,
"norm": None,
"audio_feature": {
TYPE: "fbank",
"window_length_in_s": 0.04,
"window_shift_in_s": 0.02,
"num_filter_bands": 80,
| 3 | 85 | preprocessing_defaults |