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121 | 0 | 9 | 32 | src/textual/_compositor.py | 182,523 | box model | textual | 17 | Python | 85 | _compositor.py | def _get_renders(self) -> Iterable[tuple[Region, Region, Lines]]:
# If a renderable throws an error while rendering, the user likely doesn't care about the traceback
# up to this point.
_rich_traceback_guard = True
if self.map:
widget_regions = sorted(
[
(widget, region, order, clip)
for widget, (region, order, clip) in self.map.items()
if widget.is_visual and widget.visible
],
key=itemgetter(2),
reverse=True,
)
else:
widget_regions = []
for widget, region, _order, clip in widget_regions:
lines = widget._get_lines()
if region in clip:
yield region, clip, lines
elif clip.overlaps(region):
new_region = region.intersection(clip)
delta_x = new_region.x - region.x
delta_y = new_region.y - region.y
splits = [delta_x, delta_x + new_region.width]
lines = lines[delta_y : delta_y + new_region.height]
divide = Segment.divide
lines = [list(divide(line, splits))[1] for line in lines]
yield region, clip, lines
| ef99069cf4cc0782f207a0a53689567bf85110ee | 203 | https://github.com/Textualize/textual.git | 488 | def _get_renders(self) -> Iterable[tuple[Region, Region, Lines]]:
# If a renderable throws an error while rendering, the user likely doesn't care about the traceback
# up to this point.
_rich_traceback_guard = True
if self.map:
widget_regions = sorted(
[
(widget, region, order, clip)
for widget, (region, order, clip) in self.map.items()
if widget.is_visual and widget.visible
],
key=itemgetter(2),
reverse=True,
)
else:
widget_regions = []
for widget, region, _order, clip in widget_regions:
lines = widget._get_lines()
if region in clip:
yield region, clip, lines
elif clip.overlaps(region):
new_region = region.intersection(clip)
delta_x = new_region.x - region.x
delta_y = new_region.y - region.y
splits = [delta_x, delta_x + new_region.width]
lines = lines[delta_y : delta_y + new_region.h | 37 | 306 | _get_renders |
|
30 | 0 | 1 | 8 | tests/models/gpt_neox_japanese/test_tokenization_gpt_neox_japanese.py | 33,785 | Add support for Japanese GPT-NeoX-based model by ABEJA, Inc. (#18814)
* add gpt-neox-japanese model and tokenizer as new model
* Correction to PR's comment for GPT NeoX Japanese
- Fix to be able to use gpu
- Add comment # Copied... at the top of RotaryEmbedding
- Implement nn.Linear instead of original linear class
- Add generation test under @slow
* fix bias treatment for gpt-neox-japanese
* Modidy gpt-neox-japanese following PR
- add doc for bias_dropout_add
- style change following a PR comment
* add document for gpt-neox-japanese
* remove unused import from gpt-neox-japanese
* fix README for gpt-neox-japanese | transformers | 9 | Python | 18 | test_tokenization_gpt_neox_japanese.py | def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b")
ids_1 = tokenizer.encode("γγγγ¨γγ", add_special_tokens=False)
ids_2 = tokenizer.encode("γ©γγγγγΎγγ¦γ", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(ids_1)
encoded_pair = tokenizer.build_inputs_with_special_tokens(ids_1, ids_2)
assert encoded_sentence == ids_1
assert encoded_pair == ids_1 + ids_2
| f5f430e5c80b85b57bb910435e45d84746210133 | 67 | https://github.com/huggingface/transformers.git | 78 | def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b")
ids_1 = tokenizer.encode("γγγγ¨γγ", add | 12 | 110 | test_sequence_builders |
|
82 | 0 | 5 | 20 | pandas/tests/computation/test_eval.py | 164,623 | TST: Don't use autouse fixture in test_eval (#45832) | pandas | 12 | Python | 53 | test_eval.py | def test_pow(self, lhs, rhs, engine, parser):
# odd failure on win32 platform, so skip
ex = "lhs ** rhs"
expected = _eval_single_bin(lhs, "**", rhs, engine)
result = pd.eval(ex, engine=engine, parser=parser)
if (
is_scalar(lhs)
and is_scalar(rhs)
and isinstance(expected, (complex, np.complexfloating))
and np.isnan(result)
):
msg = "(DataFrame.columns|numpy array) are different"
with pytest.raises(AssertionError, match=msg):
tm.assert_numpy_array_equal(result, expected)
else:
tm.assert_almost_equal(result, expected)
ex = "(lhs ** rhs) ** rhs"
result = pd.eval(ex, engine=engine, parser=parser)
middle = _eval_single_bin(lhs, "**", rhs, engine)
expected = _eval_single_bin(middle, "**", rhs, engine)
tm.assert_almost_equal(result, expected)
| a1ce6a0eb07e5f969ab192b792083cb1c1f702d5 | 161 | https://github.com/pandas-dev/pandas.git | 277 | def test_pow(self, lhs, rhs, engine, parser):
# odd failure on win32 platform, so skip
ex = "lhs ** rhs"
expected = _eval_single_bin(lhs, "**", rhs, engine)
result = pd.eval(ex, engine=engine, parser=parser)
if (
is_scalar(lhs)
and is_scalar(rhs)
and isinstance(expected, (complex, np.complexfloating))
and np.isnan(result)
):
msg = "(DataFrame.columns|numpy array) are different"
with pytest.raises(AssertionError, match=msg):
tm.assert_numpy_array_equal(result, expected)
else:
tm.assert_almost_equal(result, expected)
ex = "(lhs ** rhs) ** rhs"
result = pd.eval(ex, engine=engine, parser=parser)
middle = _eval_single_bin(lhs, "**", rhs, engine)
| 27 | 249 | test_pow |
|
30 | 0 | 1 | 5 | tests/test_region_group.py | 182,508 | Convert method inline_ranges to function, remove RegionGroup class | textual | 10 | Python | 23 | test_region_group.py | def test_inline_ranges_fully_overlapping_regions():
regions = [Region(1, 1, 3, 3), Region(2, 2, 1, 1), Region(0, 2, 3, 1)]
assert list(inline_ranges(regions)) == [
InlineRange(1, 1, 3), InlineRange(2, 0, 3), InlineRange(3, 1, 3)
]
| f541c26587ba6e1eb07e5d7b6030082c729e9f2e | 77 | https://github.com/Textualize/textual.git | 45 | def test_inline_ranges_fully_overlapping_regions():
regions | 6 | 103 | test_inline_ranges_fully_overlapping_regions |
|
38 | 0 | 5 | 9 | src/sentry_plugins/slack/client.py | 95,409 | ref(slack plugin): Don't raise errors for unactionable things (#30998)
* ref(slack plugin): Don't raise errors for unactionable things | sentry | 12 | Python | 35 | client.py | def request(self, data):
try:
return self._request(
path=self.webhook, method="post", data=data, json=False, allow_text=True
)
except ApiError as e:
# Ignore 404 and ignorable errors from slack webhooks
if e.text and e.text in IGNORABLE_SLACK_ERRORS or e.code == 404:
return
raise e
| 2a7a0aac94e3b612cb49ca24c8882b7290c788f8 | 61 | https://github.com/getsentry/sentry.git | 136 | def request(self, data):
try:
return self._request(
path=self.webhook, | 14 | 95 | request |
|
22 | 0 | 1 | 5 | modules/deepbooru.py | 152,693 | deepdanbooru interrogator | stable-diffusion-webui | 10 | Python | 20 | deepbooru.py | def get_deepbooru_tags(pil_image, threshold=0.5):
with ProcessPoolExecutor() as executor:
f = executor.submit(_load_tf_and_return_tags, pil_image, threshold)
ret = f.result() # will rethrow any exceptions
return ret | 59a2b9e5afc27d2fda72069ca0635070535d18fe | 39 | https://github.com/AUTOMATIC1111/stable-diffusion-webui.git | 42 | def get_deepbooru_tags(pil_image, threshold=0.5):
with ProcessPoolExecutor() as execut | 10 | 62 | get_deepbooru_tags |
|
19 | 0 | 1 | 5 | sympy/combinatorics/permutations.py | 196,160 | Updated import locations | sympy | 11 | Python | 15 | permutations.py | def __add__(self, other):
rank = (self.rank() + other) % self.cardinality
rv = self.unrank_lex(self.size, rank)
rv._rank = rank
return rv
| 498015021131af4dbb07eb110e5badaba8250c7b | 42 | https://github.com/sympy/sympy.git | 54 | def __add__(self, other):
rank = (self.rank() + other) % self.cardinality
rv = self.unrank_lex(self.size, rank)
| 9 | 68 | __add__ |
|
73 | 1 | 3 | 17 | pandas/tests/io/test_stata.py | 164,631 | TST: Don't use autouse fixture in test_stata (#45831) | pandas | 19 | Python | 57 | test_stata.py | def test_chunked_categorical_partial(datapath):
dta_file = datapath("io", "data", "stata", "stata-dta-partially-labeled.dta")
values = ["a", "b", "a", "b", 3.0]
with StataReader(dta_file, chunksize=2) as reader:
with tm.assert_produces_warning(CategoricalConversionWarning):
for i, block in enumerate(reader):
assert list(block.cats) == values[2 * i : 2 * (i + 1)]
if i < 2:
idx = pd.Index(["a", "b"])
else:
idx = pd.Index([3.0], dtype="float64")
tm.assert_index_equal(block.cats.cat.categories, idx)
with tm.assert_produces_warning(CategoricalConversionWarning):
with StataReader(dta_file, chunksize=5) as reader:
large_chunk = reader.__next__()
direct = read_stata(dta_file)
tm.assert_frame_equal(direct, large_chunk)
@pytest.mark.parametrize("chunksize", (-1, 0, "apple")) | c055dc4e6be9fc1b68d873a1ace286322dadd5e1 | @pytest.mark.parametrize("chunksize", (-1, 0, "apple")) | 174 | https://github.com/pandas-dev/pandas.git | 223 | def test_chunked_categorical_partial(datapath):
dta_file = datapath("io", "data", "stata", "stata-dta-partially-labeled.dta")
values = ["a", "b", "a", "b", 3.0]
with StataReader(dta_file, chunksize=2) as reader:
with tm.assert_produces_warning(CategoricalConversionWarning):
for i, block in enumerate(reader):
assert list(block.cats) == values[2 * i : 2 * (i + 1)]
if i < 2:
idx = pd.Index(["a", "b"])
else:
idx = pd.Index([3.0], dtype="float64")
tm.assert_index_equal(block.cats.cat.categories, idx)
wit | 30 | 319 | test_chunked_categorical_partial |
93 | 0 | 8 | 21 | mmdet/datasets/samplers/batch_sampler.py | 244,407 | [Refactor] Refactor samplers. | mmdetection | 14 | Python | 66 | batch_sampler.py | def __iter__(self) -> Sequence[int]:
for idx in self.sampler:
data_info = self.sampler.dataset.get_data_info(idx)
width, height = data_info['width'], data_info['height']
bucket_id = 0 if width < height else 1
bucket = self._aspect_ratio_buckets[bucket_id]
bucket.append(idx)
# yield a batch of indices in the same aspect ratio group
if len(bucket) == self.batch_size:
yield bucket[:]
del bucket[:]
# yield the rest data and reset the bucket
left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
1]
self._aspect_ratio_buckets = [[] for _ in range(2)]
while len(left_data) > 0:
if len(left_data) <= self.batch_size:
if not self.drop_last:
yield left_data[:]
left_data = []
else:
yield left_data[:self.batch_size]
left_data = left_data[self.batch_size:]
| 19441631117425b5521655b23cd4d885a7858478 | 167 | https://github.com/open-mmlab/mmdetection.git | 346 | def __iter__(self) -> Sequence[int]:
for idx in self.sampler:
data_info = self.sampler.dataset.get_data_info(idx)
width, height = data_info['width'], data_info['height']
bucket_id = 0 if width < height else 1
bucket = self._aspect_ratio_buckets[bucket_id]
bucket.append(idx)
# yield a batch of indices in the same aspect ratio group
if len(bucket) == self.batch_size:
yield bucket[:]
del bucket[:]
# yield the rest data and reset the bucket
left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
1]
se | 21 | 269 | __iter__ |
|
5 | 0 | 1 | 3 | manim/mobject/opengl/opengl_vectorized_mobject.py | 189,767 | Reuse shader wrappers and shader data (#2062)
* reuse shader wrappers and shader data arrays
* Update uniforms
Co-authored-by: Laith Bahodi <70682032+hydrobeam@users.noreply.github.com>
Co-authored-by: Darylgolden <darylgolden@gmail.com> | manim | 7 | Python | 5 | opengl_vectorized_mobject.py | def get_stroke_shader_wrapper(self):
self.update_stroke_shader_wrapper()
return self.stroke_shader_wrapper
| 10a5f40aa5c51bd6822742b707acd2390ce2cc23 | 14 | https://github.com/ManimCommunity/manim.git | 18 | def get_stroke_shader_wrapper(self):
self.update_stroke_shader_wrapper()
return | 4 | 24 | get_stroke_shader_wrapper |
|
23 | 0 | 1 | 8 | python/ray/data/tests/test_dataset.py | 124,140 | [dataset] Support push-based shuffle in groupby operations (#25910)
Allows option for push-based shuffle in groupby operations, to improve scalability to larger Datasets. | ray | 17 | Python | 23 | test_dataset.py | def test_groupby_arrow(ray_start_regular_shared, use_push_based_shuffle):
# Test empty dataset.
agg_ds = (
ray.data.range_table(10)
.filter(lambda r: r["value"] > 10)
.groupby("value")
.count()
)
assert agg_ds.count() == 0
| 68b893369cada42be61d05843a9b7267b4f0b353 | 49 | https://github.com/ray-project/ray.git | 62 | def test_groupby_arrow(ray_start_regular_shared, use_push_based_shuffle):
# Test empty dataset.
agg_ds = (
ray.data.range_table(10)
| 11 | 83 | test_groupby_arrow |
|
177 | 0 | 19 | 49 | mindsdb/api/http/namespaces/config.py | 116,380 | storage | mindsdb | 15 | Python | 105 | config.py | def put(self, name):
params = {}
params.update((request.json or {}).get('params', {}))
params.update(request.form or {})
if len(params) == 0:
abort(400, "type of 'params' must be dict")
files = request.files
temp_dir = None
if files is not None and len(files) > 0:
temp_dir = tempfile.mkdtemp(prefix='integration_files_')
for key, file in files.items():
temp_dir_path = Path(temp_dir)
file_name = Path(file.filename)
file_path = temp_dir_path.joinpath(file_name).resolve()
if temp_dir_path not in file_path.parents:
raise Exception(f'Can not save file at path: {file_path}')
file.save(file_path)
params[key] = file_path
is_test = params.get('test', False)
if is_test:
del params['test']
handler = request.integration_controller.create_tmp_handler(
handler_type=params.get('type'),
connection_data=params
)
status = handler.check_connection()
if temp_dir is not None:
shutil.rmtree(temp_dir)
return status, 200
integration = request.integration_controller.get(name, sensitive_info=False)
if integration is not None:
abort(400, f"Integration with name '{name}' already exists")
try:
engine = params['type']
if engine is not None:
del params['type']
request.integration_controller.add(name, engine, params)
if is_test is False and params.get('publish', False) is True:
stream_controller = StreamController(request.company_id)
if engine in stream_controller.known_dbs and params.get('publish', False) is True:
stream_controller.setup(name)
except Exception as e:
log.error(str(e))
if temp_dir is not None:
shutil.rmtree(temp_dir)
abort(500, f'Error during config update: {str(e)}')
if temp_dir is not None:
shutil.rmtree(temp_dir)
return '', 200
| 25201296fe9944420667cb5fed4d676b869f48ff | 368 | https://github.com/mindsdb/mindsdb.git | 712 | def put(self, name):
params = {}
params.update((request.json or {}).get('params', {}))
params.update(request.form or {})
if len(params) == 0:
abort(400, "type of 'params' must be dict")
files = request.files
temp_dir = None
if files is not None and len(files) > 0:
temp_dir = tempfile.mkdtemp(prefix='integration_files_')
for key, file in files.items():
temp_dir_path = Path(temp_dir)
file_name = Path(file.filename)
file_path = temp_dir_path.joinpath(file_name).resolve()
if temp_dir_path not in file_path.parents:
raise Exception(f'Can not save file at path: {file_path}')
file.save(file_path)
params[key] = file_path
is_test = params.get('test', False)
if is_test:
del params['test']
handler = request.integration_controller.create_tmp_handler(
handler_type=params.get('type'),
connection_data=params
)
status = handler.check_connection()
if temp_dir is not None:
shutil.rmtree(temp_dir)
return status, 200
integration = request.integration_controller.get(name, sensitive_info=False)
if integration is not None:
abort(400, f"Integration with name '{name}' already exists")
try:
engine = params['type']
if engine is not None:
del params['type']
request.integration_controller.add(name, engine, params)
if is_test is False and params.get('publish', False) is True:
stream_controller = StreamController(request.company_id)
if engine in stream_controller.known_dbs and params.get('publish', False) is True:
stream_controller.setup(name)
except Exception as e:
lo | 52 | 616 | put |
|
50 | 0 | 1 | 16 | tests/orion/api/test_task_runs.py | 54,988 | Use status constants instead of hardcoded values
Closes: PrefectHQ/orion#1673 | prefect | 17 | Python | 37 | test_task_runs.py | async def test_set_task_run_state(self, task_run, client, session):
response = await client.post(
f"/task_runs/{task_run.id}/set_state",
json=dict(state=dict(type="RUNNING", name="Test State")),
)
assert response.status_code == status.HTTP_201_CREATED
api_response = OrchestrationResult.parse_obj(response.json())
assert api_response.status == responses.SetStateStatus.ACCEPT
task_run_id = task_run.id
session.expire_all()
run = await models.task_runs.read_task_run(
session=session, task_run_id=task_run_id
)
assert run.state.type == states.StateType.RUNNING
assert run.state.name == "Test State"
assert run.run_count == 1
| 37549d157007f6eef07ed8b1e2e14efb73134840 | 123 | https://github.com/PrefectHQ/prefect.git | 166 | async def test_set_task_run_state(self, task_run, client, session):
response = await client.post(
f"/task_runs/{task_run.id}/set_state",
json=dict(state=dict(type="RUNNING", name="Test State")),
)
assert response.status_code == status.HTTP_201_CREATED
api_response = OrchestrationResult.parse_obj(response.json())
assert api_response.status == respons | 32 | 202 | test_set_task_run_state |
|
27 | 0 | 1 | 4 | keras/dtensor/test_util.py | 270,640 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 9 | Python | 24 | test_util.py | def tearDown(self):
super().tearDown()
# Make sure all async ops finish.
context.async_wait()
# TODO(hthu): Remove the reset once we fixed the CopyToMesh with
# DefaultMesh placement issue.
reset_dtensor()
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 20 | https://github.com/keras-team/keras.git | 68 | def tearDown(self):
super().tearDown()
# | 6 | 40 | tearDown |
|
25 | 0 | 1 | 13 | tests/rest/media/v1/test_media_storage.py | 247,522 | Add type hints to `tests/rest`. (#12208)
Co-authored-by: Patrick Cloke <clokep@users.noreply.github.com> | synapse | 14 | Python | 22 | test_media_storage.py | def default_config(self) -> Dict[str, Any]:
config = default_config("test")
config.update(
{
"spam_checker": [
{
"module": TestSpamChecker.__module__ + ".TestSpamChecker",
"config": {},
}
]
}
)
return config
| 32c828d0f760492711a98b11376e229d795fd1b3 | 46 | https://github.com/matrix-org/synapse.git | 188 | def default_config(self) -> Dict[str, Any]:
config = default_config("test")
config.update(
{
"spam_checker": [
{
"module": TestSpamChecker.__module__ + ".TestSpamChecker",
"config": {},
}
]
}
)
return config
| 9 | 80 | default_config |
|
14 | 0 | 1 | 5 | keras/tests/model_subclassing_test_util.py | 276,547 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 8 | Python | 10 | model_subclassing_test_util.py | def call(self, inputs):
x = self.dense1(inputs)
x = self.bn(x)
x = self.test_net(x)
return self.dense2(x)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 38 | https://github.com/keras-team/keras.git | 41 | def call(self, inputs):
x = self.dense1(inputs)
x = self.bn(x)
x = self.test_net(x)
return self.d | 8 | 61 | call |
|
116 | 0 | 1 | 39 | tests/orion/api/test_deployments.py | 54,916 | Use status constants instead of hardcoded values
Closes: PrefectHQ/orion#1673 | prefect | 19 | Python | 67 | test_deployments.py | async def test_delete_deployment(self, session, client, deployment):
# schedule both an autoscheduled and manually scheduled flow run
# for this deployment id, these should be deleted when the deployment is deleted
flow_run_1 = await models.flow_runs.create_flow_run(
session=session,
flow_run=schemas.core.FlowRun(
flow_id=deployment.flow_id,
deployment_id=deployment.id,
flow_version="1.0",
auto_scheduled=False,
state=schemas.states.Scheduled(
scheduled_time=pendulum.now("UTC"),
message="Flow run scheduled",
),
),
)
flow_run_2 = await models.flow_runs.create_flow_run(
session=session,
flow_run=schemas.core.FlowRun(
flow_id=deployment.flow_id,
deployment_id=deployment.id,
flow_version="1.0",
auto_scheduled=True,
state=schemas.states.Scheduled(
scheduled_time=pendulum.now("UTC"),
message="Flow run scheduled",
),
),
)
await session.commit()
# delete the deployment
response = await client.delete(f"/deployments/{deployment.id}")
assert response.status_code == status.HTTP_204_NO_CONTENT
# make sure it's deleted
response = await client.get(f"/deployments/{deployment.id}")
assert response.status_code == status.HTTP_404_NOT_FOUND
# make sure scheduled flow runs are deleted
n_runs = await models.flow_runs.count_flow_runs(
session,
flow_run_filter=schemas.filters.FlowRunFilter(
id={"any_": [flow_run_1.id, flow_run_2.id]}
),
)
assert n_runs == 0
| 37549d157007f6eef07ed8b1e2e14efb73134840 | 228 | https://github.com/PrefectHQ/prefect.git | 604 | async def test_delete_deployment(self, session, client, deployment):
# schedule both an autoscheduled and manually scheduled flow run
# for this deployment id, these should be deleted when the deployment is deleted
flow_run_1 = await models.flow_runs.create_flow_run(
session=session,
flow_run=schemas.core.FlowRun(
flow_id=deployment.flow_id,
deployment_id=deployment.id,
flow_version="1.0",
auto_scheduled=False,
state=schemas.states.Scheduled(
scheduled_time=pendulum.now("UTC"),
message="Flow run scheduled",
),
),
)
flow_run_2 = await models.flow_runs.create_flow_run(
session=session,
flow_run=schemas.core.FlowRun(
flow_id=deployment.flow_id,
deployment_id=deployment.id,
flow_version="1.0",
auto_scheduled=True,
state=schemas.states.Scheduled(
scheduled_time=pendulum.now("UTC"),
message="Flow run scheduled",
),
),
)
await session.commit()
# delete the deployment
response = await client.delete(f"/deployments/{deployment.id}")
assert response.status_code == status.HTTP_204_NO_CONTENT
# make sure it's deleted
response = await client.get(f"/deployments/{deployment.id}")
assert response.status_code == status.HTTP_404_NOT_FOUND
# make sure scheduled flow runs are deleted
n_runs = await models.flow_runs.count_flo | 39 | 371 | test_delete_deployment |
|
22 | 0 | 1 | 6 | wagtail/admin/tests/test_templatetags.py | 72,176 | Reformat with black | wagtail | 10 | Python | 17 | test_templatetags.py | def test_timesince_last_update_before_today_shows_timeago(self):
dt = timezone.now() - timedelta(weeks=1, days=2)
timesince = timesince_last_update(dt, use_shorthand=False)
self.assertEqual(timesince, "1\xa0week, 2\xa0days ago")
timesince = timesince_last_update(dt)
self.assertEqual(timesince, "1\xa0week ago")
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 55 | https://github.com/wagtail/wagtail.git | 56 | def test_timesince_last_update_before_today_shows_timeago(self):
dt = timezone.now() - timedelta(weeks=1, days=2)
timesince = timesince_last_update(dt, use_shorthand=False)
self.assertEqual(timesince, "1\xa0week, 2\xa0days ago")
timesince = timesince_last_update(dt)
self.assertEqual(timesince, "1\xa0week ago")
| 12 | 93 | test_timesince_last_update_before_today_shows_timeago |
|
38 | 0 | 5 | 11 | lib/matplotlib/bezier.py | 110,252 | DOC: improve grammar and consistency | matplotlib | 10 | Python | 28 | bezier.py | def split_de_casteljau(beta, t):
beta = np.asarray(beta)
beta_list = [beta]
while True:
beta = _de_casteljau1(beta, t)
beta_list.append(beta)
if len(beta) == 1:
break
left_beta = [beta[0] for beta in beta_list]
right_beta = [beta[-1] for beta in reversed(beta_list)]
return left_beta, right_beta
| 9b6abd0b4933811e0a45c2535ab8fd107db65dd9 | 79 | https://github.com/matplotlib/matplotlib.git | 91 | def split_de_casteljau(beta, t):
beta = np.asarray(beta)
beta_list = [beta]
while True:
beta = _de_casteljau1(beta, t)
beta_list.append(beta)
if len(bet | 12 | 125 | split_de_casteljau |
|
290 | 0 | 15 | 66 | lib/matplotlib/patches.py | 110,059 | Simplify some patches path definitions.
- When a Path ends with a CLOSEPOLY, it is not necessary to put a LINETO
to the closing position before it (in fact that can result in an
incorrect line join at that position), and the xy position associated
with the CLOSEPOLY can just be (0, 0), as it is irrelevant.
- For defining the codes arrays, for short paths (such as the patch
shapes here), one can just use list unpacking for shorter definitions.
- Rename the _path and _fillable lists in ArrowStyle to plural names.
- Rely on the default tolerance of
split_bezier_intersecting_with_closedpath (which is 0.01) rather than
re-specifying the same magic value everywhere.
- Remove inapplicable comment re: make_compound_path_from_polys (which
only applies to polygons all of with the same number of sides, which
is not the case when clipping to a bbox). | matplotlib | 15 | Python | 150 | patches.py | def transmute(self, path, mutation_size, linewidth):
if self._beginarrow_head or self._endarrow_head:
head_length = self.head_length * mutation_size
head_width = self.head_width * mutation_size
head_dist = np.hypot(head_length, head_width)
cos_t, sin_t = head_length / head_dist, head_width / head_dist
scaleA = mutation_size if self.scaleA is None else self.scaleA
scaleB = mutation_size if self.scaleB is None else self.scaleB
# begin arrow
x0, y0 = path.vertices[0]
x1, y1 = path.vertices[1]
# If there is no room for an arrow and a line, then skip the arrow
has_begin_arrow = self._beginarrow_head and (x0, y0) != (x1, y1)
verticesA, codesA, ddxA, ddyA = (
self._get_arrow_wedge(x1, y1, x0, y0,
head_dist, cos_t, sin_t, linewidth)
if has_begin_arrow
else ([], [], 0, 0)
)
# end arrow
x2, y2 = path.vertices[-2]
x3, y3 = path.vertices[-1]
# If there is no room for an arrow and a line, then skip the arrow
has_end_arrow = self._endarrow_head and (x2, y2) != (x3, y3)
verticesB, codesB, ddxB, ddyB = (
self._get_arrow_wedge(x2, y2, x3, y3,
head_dist, cos_t, sin_t, linewidth)
if has_end_arrow
else ([], [], 0, 0)
)
# This simple code will not work if ddx, ddy is greater than the
# separation between vertices.
paths = [Path(np.concatenate([[(x0 + ddxA, y0 + ddyA)],
path.vertices[1:-1],
[(x3 + ddxB, y3 + ddyB)]]),
path.codes)]
fills = [False]
if has_begin_arrow:
if self.fillbegin:
paths.append(
Path([*verticesA, (0, 0)], [*codesA, Path.CLOSEPOLY]))
fills.append(True)
else:
paths.append(Path(verticesA, codesA))
fills.append(False)
elif self._beginarrow_bracket:
x0, y0 = path.vertices[0]
x1, y1 = path.vertices[1]
verticesA, codesA = self._get_bracket(x0, y0, x1, y1,
self.widthA * scaleA,
self.lengthA * scaleA,
self.angleA)
paths.append(Path(verticesA, codesA))
fills.append(False)
if has_end_arrow:
if self.fillend:
fills.append(True)
paths.append(
Path([*verticesB, (0, 0)], [*codesB, Path.CLOSEPOLY]))
else:
fills.append(False)
paths.append(Path(verticesB, codesB))
elif self._endarrow_bracket:
x0, y0 = path.vertices[-1]
x1, y1 = path.vertices[-2]
verticesB, codesB = self._get_bracket(x0, y0, x1, y1,
self.widthB * scaleB,
self.lengthB * scaleB,
self.angleB)
paths.append(Path(verticesB, codesB))
fills.append(False)
return paths, fills
| 73622a0173916bfcb4cb7b9b393929be025e18c9 | 575 | https://github.com/matplotlib/matplotlib.git | 1,632 | def transmute(self, path, mutation_size, linewidth):
if self._beginarrow_head or self._endarrow_head:
head_length = self.head_length * mutation_size
head_width = self.head_width * mutation_size
head_dist = np.hypot(head_length, head_width)
cos_t, sin_t = head_length / head_dist, head_width / head_dist
scaleA = mutation_size if self.scaleA is None else self.scaleA
scaleB = mutation_size if self.scaleB is None else self.scaleB
# begin arrow
x0, y0 = path.vertices[ | 54 | 842 | transmute |
|
11 | 0 | 1 | 2 | freqtrade/rpc/replicate/channel.py | 150,519 | DataFrame transmission, strategy follower logic | freqtrade | 9 | Python | 11 | channel.py | async def send(self, data):
# logger.info(f"Serialized Send - {self._wrapped_ws._serialize(data)}")
await self._wrapped_ws.send(data)
| 6f5478cc029bc146e3980affa61dd7956c5cb416 | 17 | https://github.com/freqtrade/freqtrade.git | 32 | async def send(self, data):
# logger.info(f"Serialized Send - {self._wrapped_ws._serialize( | 4 | 32 | send |
|
160 | 0 | 14 | 49 | scapy/contrib/automotive/scanner/executor.py | 209,485 | Add assert to GMLAN Scanner to enforce fast fail on to many open TestSockets
Fix bugs in TestSocket
Fix bugs in the AutomotiveScanner execution_time handling
Simplify test code for UDS_Scanner and reuse ObjectPipes to avoid mass
creation | scapy | 17 | Python | 116 | executor.py | def scan(self, timeout=None):
# type: (Optional[int]) -> None
kill_time = time.time() + (timeout or 0xffffffff)
log_interactive.debug("[i] Set kill_time to %s" % time.ctime(kill_time))
while kill_time > time.time():
test_case_executed = False
log_interactive.debug("[i] Scan paths %s", self.state_paths)
for p, test_case in product(
self.state_paths, self.configuration.test_cases):
log_interactive.info("[i] Scan path %s", p)
terminate = kill_time <= time.time()
if terminate:
log_interactive.debug(
"[-] Execution time exceeded. Terminating scan!")
break
final_state = p[-1]
if test_case.has_completed(final_state):
log_interactive.debug("[+] State %s for %s completed",
repr(final_state), test_case)
continue
try:
if not self.enter_state_path(p):
log_interactive.error(
"[-] Error entering path %s", p)
continue
log_interactive.info(
"[i] Execute %s for path %s", str(test_case), p)
self.execute_test_case(test_case, kill_time)
test_case_executed = True
except (OSError, ValueError, Scapy_Exception) as e:
log_interactive.critical("[-] Exception: %s", e)
if self.configuration.debug:
raise e
if isinstance(e, OSError):
log_interactive.critical(
"[-] OSError occurred, closing socket")
self.socket.close()
if cast(SuperSocket, self.socket).closed and \
self.reconnect_handler is None:
log_interactive.critical(
"Socket went down. Need to leave scan")
raise e
finally:
self.cleanup_state()
if not test_case_executed:
log_interactive.info(
"[i] Execute failure or scan completed. Exit scan!")
break
self.cleanup_state()
self.reset_target()
| e6eaa484b8fa3d10051e82f5a784fe8dedbd5592 | 280 | https://github.com/secdev/scapy.git | 1,044 | def scan(self, timeout=None):
# type: (Optional[int]) -> None
kill_time = time.time() + (timeout or 0xffffffff)
log_interactive.debug("[i] Set kill_time to %s" % time.ctime(kill_time))
while kill_time > time.time():
test_case_executed = False
log_interactive.debug("[i] Scan paths %s", self.state_paths)
for p, test_case in product(
self.state_paths, self.configuration.test_cases):
log_interactive.info("[i] Scan path %s", p)
terminate = kill_time <= time.time()
if terminate:
log_interactive.debug(
"[-] Execution time exceeded. Terminating scan!")
break
final_state = p[-1]
if test_case.has_completed(final_state):
log_interactive.debug("[+] State %s for %s completed",
repr(final_state), test_case)
continue
try:
if not self.enter_state_path(p):
log_interactive.error(
"[-] Error entering path %s", p)
continue
log_interactive.info(
"[i] Execute %s for path %s", str(test_case), p)
self.execute_test_case(test_case, kill_time)
test_case_executed = True
except (OSError, ValueError, Scapy_Exception) as e:
log_interactive.critical("[-] Exception: %s", e)
| 38 | 468 | scan |
|
33 | 0 | 1 | 12 | tests/api/test_auth.py | 246,682 | Replace assertEquals and friends with non-deprecated versions. (#12092) | synapse | 11 | Python | 26 | test_auth.py | def test_get_user_by_req_appservice_valid_token(self):
app_service = Mock(
token="foobar", url="a_url", sender=self.test_user, ip_range_whitelist=None
)
self.store.get_app_service_by_token = Mock(return_value=app_service)
self.store.get_user_by_access_token = simple_async_mock(None)
request = Mock(args={})
request.getClientIP.return_value = "127.0.0.1"
request.args[b"access_token"] = [self.test_token]
request.requestHeaders.getRawHeaders = mock_getRawHeaders()
requester = self.get_success(self.auth.get_user_by_req(request))
self.assertEqual(requester.user.to_string(), self.test_user)
| 02d708568b476f2f7716000b35c0adfa4cbd31b3 | 118 | https://github.com/matrix-org/synapse.git | 113 | def test_get_user_by_req_appservice_valid_token(self):
app_service = Mock(
token="foobar", url="a_url", sender=self.test_user, ip_range_whitelist=None
)
se | 28 | 191 | test_get_user_by_req_appservice_valid_token |
|
42 | 0 | 8 | 13 | python/ray/air/config.py | 141,215 | Enable streaming ingest in AIR (#25428)
This adds the following options to DatasetConfig, which can be used to enable streaming ingest.
```
# Whether the dataset should be streamed into memory using pipelined reads.
# When enabled, get_dataset_shard() returns DatasetPipeline instead of Dataset.
# The amount of memory to use is controlled by `stream_window_size`.
# False by default for all datasets.
use_stream_api: Optional[bool] = None
# Configure the streaming window size in bytes. A typical value is something like
# 20% of object store memory. If set to -1, then an infinite window size will be
# used (similar to bulk ingest). This only has an effect if use_stream_api is set.
# Set to 1.0 GiB by default.
stream_window_size: Optional[float] = None
# Whether to enable global shuffle (per pipeline window in streaming mode). Note
# that this is an expensive all-to-all operation, and most likely you want to use
# local shuffle instead.
# False by default for all datasets.
global_shuffle: Optional[bool] = None
``` | ray | 11 | Python | 27 | config.py | def fill_defaults(self) -> "DatasetConfig":
return DatasetConfig(
fit=self.fit or False,
split=self.split or False,
required=self.required or False,
use_stream_api=self.use_stream_api or False,
stream_window_size=self.stream_window_size
if self.stream_window_size is not None
else 1024 * 1024 * 1024,
global_shuffle=self.global_shuffle or False,
transform=self.transform if self.transform is not None else True,
)
| 78688a0903d01421b000eb37d11607571dd80dfa | 86 | https://github.com/ray-project/ray.git | 162 | def fill_defaults(self) -> "DatasetConfig":
return DatasetConfig(
fit=self.fit or False,
split=self.split or False,
required=self.required or False,
use_stream_api=self.use_stream_api or False,
stream_window_size=self.stream_window_size
if self.stream_window_size is not None
| 10 | 126 | fill_defaults |
|
16 | 0 | 1 | 7 | gamestonk_terminal/stocks/stocks_controller.py | 281,655 | Remember Contexts (#1187)
* Refacotred classes
* Handling for new instance desired
* Added feature flag
* Converted all menu calls | OpenBBTerminal | 9 | Python | 13 | stocks_controller.py | def call_sia(self, _):
from gamestonk_terminal.stocks.sector_industry_analysis.sia_controller import (
SectorIndustryAnalysisController,
)
self.queue = self.load_class(
SectorIndustryAnalysisController, self.ticker, self.queue
)
| 9e671aeba98dacc69ecbbfec1f087aca3b139ee7 | 39 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 73 | def call_sia(self, _):
from game | 11 | 57 | call_sia |
|
110 | 0 | 2 | 43 | test/test_inputs.py | 179,333 | Format The Codebase
- black formatting
- isort formatting | gradio | 15 | Python | 55 | test_inputs.py | def test_in_interface(self):
iface = gr.Interface(lambda x: x[::-1], "textbox", "textbox")
self.assertEqual(iface.process(["Hello"])[0], ["olleH"])
iface = gr.Interface(
lambda sentence: max([len(word) for word in sentence.split()]),
gr.inputs.Textbox(),
gr.outputs.Textbox(),
interpretation="default",
)
scores, alternative_outputs = iface.interpret(
["Return the length of the longest word in this sentence"]
)
self.assertEqual(
scores,
[
[
("Return", 0.0),
(" ", 0),
("the", 0.0),
(" ", 0),
("length", 0.0),
(" ", 0),
("of", 0.0),
(" ", 0),
("the", 0.0),
(" ", 0),
("longest", 0.0),
(" ", 0),
("word", 0.0),
(" ", 0),
("in", 0.0),
(" ", 0),
("this", 0.0),
(" ", 0),
("sentence", 1.0),
(" ", 0),
]
],
)
self.assertEqual(
alternative_outputs,
[[["8"], ["8"], ["8"], ["8"], ["8"], ["8"], ["8"], ["8"], ["8"], ["7"]]],
)
| cc0cff893f9d7d472788adc2510c123967b384fe | 308 | https://github.com/gradio-app/gradio.git | 699 | def test_in_interface(self):
iface = gr.Interface(lambda x: x[::-1], "textbox", "textbox")
self.assertEqual(iface.process(["Hello"])[0], ["olleH"])
iface = gr.Interface(
lambda sentence: max([len(word) for word in sentence.split()]),
gr.inputs.Textbox(),
gr.outputs.Textbox(),
interpretation="default",
)
scores, alternative_outputs = iface.interpret(
["Return the length of the longest word in this sentence"]
)
self.assertEqual(
scores,
[
[
("Return", 0.0),
(" ", 0),
("the", 0.0),
(" ", 0),
("length", 0.0),
(" ", 0),
("of", 0.0),
(" ", 0),
("the", 0.0),
(" ", 0),
("longest", 0.0),
(" ", 0),
("word", 0.0),
(" ", 0),
("in", 0.0),
(" ", 0),
("this", 0.0),
(" ", 0),
("sentence", 1.0),
(" ", 0),
]
],
)
self.assertEqual(
alternative_outputs,
[[["8"], ["8"], ["8"], ["8"], ["8"], ["8" | 20 | 459 | test_in_interface |
|
32 | 0 | 1 | 15 | wagtail/documents/views/chooser.py | 74,903 | Reformat with black | wagtail | 13 | Python | 32 | chooser.py | def render_to_response(self):
return render_modal_workflow(
self.request,
"wagtaildocs/chooser/chooser.html",
None,
self.get_context_data(),
json_data={
"step": "chooser",
"error_label": _("Server Error"),
"error_message": _(
"Report this error to your website administrator with the following information:"
),
"tag_autocomplete_url": reverse("wagtailadmin_tag_autocomplete"),
},
)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 53 | https://github.com/wagtail/wagtail.git | 205 | def render_to_response(self):
return render_modal_workflow(
self.request,
"wagtaildocs/chooser/chooser.html",
None,
self.get_context_data(),
json_data={
"step": "chooser",
"error_label": _("Server Error"),
"error_messag | 8 | 94 | render_to_response |
|
32 | 0 | 3 | 9 | erpnext/setup/doctype/item_group/item_group.py | 67,489 | style: format code with black | erpnext | 15 | Python | 29 | item_group.py | def get_child_item_groups(item_group_name):
item_group = frappe.get_cached_value("Item Group", item_group_name, ["lft", "rgt"], as_dict=1)
child_item_groups = [
d.name
for d in frappe.get_all(
"Item Group", filters={"lft": (">=", item_group.lft), "rgt": ("<=", item_group.rgt)}
)
]
return child_item_groups or {}
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 70 | https://github.com/frappe/erpnext.git | 23 | def get_child_item_groups(item_group_name):
item_group = frappe.get_cached_value("Item Group", item_group_name, ["lft", "rgt"], as_dict=1)
child_item_groups = [
d.name
for d in frappe.get_all(
"Item Group", filters={"lft": (">=", item_group.lft), "rgt": ("<=", item_group.rgt)}
)
]
return child_item_groups | 13 | 116 | get_child_item_groups |
|
23 | 0 | 3 | 4 | nni/nas/oneshot/pytorch/supermodule/base.py | 113,432 | One-shot sub state dict implementation (#5054) | nni | 14 | Python | 21 | base.py | def _save_module_to_state_dict(self, destination, prefix, keep_vars):
for name, module in self._modules.items():
if module is not None:
sub_state_dict(module, destination=destination, prefix=prefix + name + '.', keep_vars=keep_vars)
| 9e2a069d0f38da64d4c945b2c951fa64d19b9d94 | 51 | https://github.com/microsoft/nni.git | 63 | def _save_module_to_state_dict(self, destination, prefix, keep_vars):
for name, module in self._modules.items():
if module is not None:
sub_state_dict(module, destination=destination, prefix=prefix + name + '.', keep_vars=keep_vars)
| 10 | 78 | _save_module_to_state_dict |
|
15 | 0 | 1 | 11 | tests/providers/amazon/aws/sensors/test_redshift_cluster.py | 43,727 | Standardize AWS Redshift naming (#20374)
* Standardize AWS Redshift naming | airflow | 10 | Python | 15 | test_redshift_cluster.py | def test_poke_cluster_not_found(self):
self._create_cluster()
op = RedshiftClusterSensor(
task_id='test_cluster_sensor',
poke_interval=1,
timeout=5,
aws_conn_id='aws_default',
cluster_identifier='test_cluster_not_found',
target_status='cluster_not_found',
)
assert op.poke({})
| 88ea1575079c0e94e1f62df38d6d592b8c827bbd | 47 | https://github.com/apache/airflow.git | 108 | def test_poke_cluster_not_found(self):
self._create_cluster()
op = RedshiftClusterSensor(
task_id='test_cluster_sensor',
poke_interval=1,
timeout=5,
aws_conn_id='aws_default', | 12 | 78 | test_poke_cluster_not_found |
|
10 | 0 | 1 | 3 | python3.10.4/Lib/distutils/tests/test_upload.py | 223,352 | add python 3.10.4 for windows | XX-Net | 10 | Python | 9 | test_upload.py | def _urlopen(self, url):
self.last_open = FakeOpen(url, msg=self.next_msg, code=self.next_code)
return self.last_open
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 31 | https://github.com/XX-net/XX-Net.git | 23 | def _urlopen(self, url):
self.last_open = Fak | 9 | 46 | _urlopen |
|
8 | 0 | 1 | 5 | tests/acceptance/page_objects/issue_list.py | 97,398 | feat(ui): Remove issues from issue stream (#31910)
* Revert "Revert "feat(ui): Remove issues from issue stream" (#31908)"
This reverts commit 4bd69628c4ec1b5a4cd980a4fa8cd1291072d47f.
* fix(acceptance): Fix delete_issues test (#31909)
* add checks for issue-list-removal-action flag and check if realtimeActive to use feature
* update tests
Co-authored-by: Vu Luong <vuluongj20@gmail.com> | sentry | 8 | Python | 8 | issue_list.py | def delete_issues(self):
self.browser.click('[aria-label="More issue actions"]')
self.browser.wait_until('[data-test-id="delete"]')
self.browser.click('[data-test-id="delete"]')
self.browser.click('[data-test-id="confirm-button"]')
| 266dbc5a8172612679c6549a9ca240cfba9dab3d | 37 | https://github.com/getsentry/sentry.git | 35 | def delete_issues(self):
self.browse | 5 | 69 | delete_issues |
|
201 | 0 | 18 | 55 | mindsdb/api/mysql/mysql_proxy/data_types/mysql_datum.py | 117,328 | ML handler supbrocess (#3377)
* log -> logger
dividing components:
app initialize
parse args
set env.MINDSDB_CONFIG_PATH
config
requiers env.MINDSDB_CONFIG_PATH
sets
env.MINDSDB_DB_CON
Config() - makes initialization
log
uses config
initialize_log - makes initialization
database
uses env.MINDSDB_DB_CON
have init() method
file storage
uses config
* partial sync for model storage interfaces
* ml handler in subprocess interface
* fix delete model
* fix: model with error in success status
* fix: remove hf predictor
* fix pg handler
* MLHandlerPersistWrapper keeps wrapper process opened
* predictor with error keeps 'success' status
#3362
* lock for sending tasks to subprocess one by one
* check target of predictor before run learn in subproccess
* fix check target
* fix: json_ai override and problem definition generation
* fix None case
* folder for ml handler tests
* fix: add timeseries_settings key to learn_args
* fixes in lw_handler
* fix: del join_learn_process
* tests for LW handler
* finish unit test for LW
* changes in tests:
- set-project -> to base class
- return of ml handler is dataframe
- draft for project structure test
* merge from staging
* create_validation method to check learn params before send to subprocess
fixes of HF
fixed version of transformers in HF requirements
Co-authored-by: Patricio Cerda Mardini <pcerdam@live.com> | mindsdb | 14 | Python | 88 | mysql_datum.py | def setFromBuff(self, buff):
start = 0
if self.var_len == 'lenenc':
start = 1
ln_enc = buff[0]
if int(ln_enc) <= ONE_BYTE_ENC[0]:
start = 0
end = 1
elif int(ln_enc) == TWO_BYTE_ENC[0]:
end = 3
elif int(ln_enc) == THREE_BYTE_ENC[0]:
end = 4
elif ln_enc:
end = 9
num_str = buff[start:end]
if end > 9:
logger.error('Cant decode integer greater than 8 bytes')
return buff[end - 1:]
for j in range(8 - (end - start)):
num_str += b'\0'
if self.var_type == 'int':
self.value = struct.unpack('i', num_str)
return buff[end:]
if self.var_type in ['byte', 'string']:
length = struct.unpack('Q', num_str)[0]
self.value = buff[end:(length + end)]
return buff[(length + end):]
if self.var_len == 'EOF':
length = len(buff)
self.var_len = str(length)
self.value = buff
return ''
else:
length = self.var_len
if self.type == 'string<NUL>':
for j, x in enumerate(buff):
if int(x) == 0:
length = j + 1
break
length = int(length)
if self.var_type in ['byte', 'string']:
end = length
self.value = buff[:end]
else: # if its an integer
end = length
num_str = buff[:end]
if end > 8:
logger.error('cant decode integer greater than 8 bytes')
return buff[end:]
for j in range(8 - end):
num_str += b'\0'
self.value = struct.unpack('Q', num_str)[0]
if str(self.var_len) == 'NUL':
self.value = self.value[:-1]
return buff[end:]
| 9ce5a21dd6359fd7e8ebf78051ce9e97bd195ec9 | 376 | https://github.com/mindsdb/mindsdb.git | 839 | def setFromBuff(self, buff):
start = 0
if self.var_len == 'lenenc':
start = 1
ln_enc = buff[0]
if int(ln_enc) <= ONE_BYTE_ENC[0]:
start = 0
end = 1
elif int(ln_enc) == TWO_BYTE_ENC[0]:
end = 3
elif int(ln_enc) == THREE_BYTE_ENC[0]:
end = 4
elif ln_enc:
end = 9
num_str = buff[start:end]
if end > 9:
logger.error('Cant decode integer greater than 8 bytes')
return buff[end - 1:]
for j in range(8 - (end - start)):
num_str += b'\0'
if self.var_type == 'int':
self.value = struct.unpack('i', num_str)
return buff[end:]
if self.var_type in ['byte', 'string']:
length = struct.unpack('Q', num_str)[0]
self.value = buff[end:(length + end)]
return buff[(length + end):]
if self.var_len == 'EOF':
length = len(buff)
self.var_len = str(length)
self.value = buff
return ''
else:
length = self.var_len
if self.type == 'string<NUL>':
for j, x in enumerate(buff):
if int(x) == 0:
length = j + 1
break
length = int(length)
if self.var_type in ['byte', 'string']:
end = length
| 26 | 618 | setFromBuff |
|
19 | 0 | 3 | 6 | wagtail/api/v2/views.py | 72,951 | Reformat with black | wagtail | 10 | Python | 18 | views.py | def get_field_serializer_overrides(cls, model):
return {
field.name: field.serializer
for field in cls.get_body_fields(model) + cls.get_meta_fields(model)
if field.serializer is not None
}
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 40 | https://github.com/wagtail/wagtail.git | 65 | def get_field_serializer_overrides(cls, model):
return {
field.name: field.serializer
for | 8 | 60 | get_field_serializer_overrides |
|
174 | 0 | 4 | 20 | rllib/algorithms/impala/impala.py | 134,174 | [RLlib] IMPALA: Move learner thread health-check into better place. (#29541) | ray | 11 | Python | 122 | impala.py | def training_step(self) -> ResultDict:
# First, check, whether our learner thread is still healthy.
if not self._learner_thread.is_alive():
raise RuntimeError("The learner thread died while training!")
# Get references to sampled SampleBatches from our workers.
unprocessed_sample_batches_refs = self.get_samples_from_workers()
# Tag workers that actually produced ready sample batches this iteration.
# Those workers will have to get updated at the end of the iteration.
self.workers_that_need_updates |= unprocessed_sample_batches_refs.keys()
# Send the collected batches (still object refs) to our aggregation workers.
if self.config["num_aggregation_workers"] > 0:
batches = self.process_experiences_tree_aggregation(
unprocessed_sample_batches_refs
)
# Resolve collected batches here on local process (using the mixin buffer).
else:
batches = self.process_experiences_directly(unprocessed_sample_batches_refs)
# Increase sampling counters now that we have the actual SampleBatches on
# the local process (and can measure their sizes).
for batch in batches:
self._counters[NUM_ENV_STEPS_SAMPLED] += batch.count
self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps()
# Concatenate single batches into batches of size `train_batch_size`.
self.concatenate_batches_and_pre_queue(batches)
# Move train batches (of size `train_batch_size`) onto learner queue.
self.place_processed_samples_on_learner_queue()
# Extract most recent train results from learner thread.
train_results = self.process_trained_results()
# Sync worker weights.
with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
self.update_workers_if_necessary()
return train_results
| 305905f469b0c8c351670b276b54a0d3d77bc242 | 126 | https://github.com/ray-project/ray.git | 426 | def training_step(self) -> ResultDict:
# First, check, whether our learner thread is still healthy.
if not self._learner_thread.is_alive():
raise RuntimeError("The learner thread died while training!")
# Get references to sampled SampleBatches from our workers.
unprocessed_sample_batches_refs = self.get_samples_from_workers()
# Tag workers that actually produced ready sample batches this iteration.
# Those workers will have to get updated at the end of the iteration.
self.workers_that_need_updates |= unprocessed_sample_batches_refs.keys()
# Send the collected batches (still object refs) to our aggregation workers.
if self.config["num_aggregation_workers"] > 0:
batches = self.process_experiences_tree_aggregation(
unprocessed_sample_batches_refs
)
# Re | 27 | 223 | training_step |
|
36 | 0 | 1 | 13 | zerver/tests/test_custom_profile_data.py | 84,347 | custom_profile: Apply ProfileDataElementUpdateDict.
We explicitly annotate variables or parameters with
`ProfileDataElementUpdateDict` as necessary.
Signed-off-by: Zixuan James Li <p359101898@gmail.com> | zulip | 10 | Python | 32 | test_custom_profile_data.py | def test_delete_internals(self) -> None:
user_profile = self.example_user("iago")
realm = user_profile.realm
field = CustomProfileField.objects.get(name="Phone number", realm=realm)
data: List[ProfileDataElementUpdateDict] = [
{"id": field.id, "value": "123456"},
]
do_update_user_custom_profile_data_if_changed(user_profile, data)
self.assertTrue(self.custom_field_exists_in_realm(field.id))
self.assertEqual(user_profile.customprofilefieldvalue_set.count(), self.original_count)
do_remove_realm_custom_profile_field(realm, field)
self.assertFalse(self.custom_field_exists_in_realm(field.id))
self.assertEqual(user_profile.customprofilefieldvalue_set.count(), self.original_count - 1)
| 52be020d0cbc5b8042ea89d7653c3b916badd433 | 129 | https://github.com/zulip/zulip.git | 123 | def test_delete_internals(self) -> None:
user_profile = self.example_user("iago")
realm = user_profile.realm
field = CustomProfileField.objects.get(name="Phone number", realm=realm)
| 23 | 210 | test_delete_internals |
|
16 | 0 | 1 | 9 | tests/components/media_player/test_async_helpers.py | 295,432 | Add EntityFeature enum to Media Player (#69119) | core | 14 | Python | 12 | test_async_helpers.py | def supported_features(self):
return (
mp.const.MediaPlayerEntityFeature.VOLUME_SET
| mp.const.MediaPlayerEntityFeature.VOLUME_STEP
| mp.const.MediaPlayerEntityFeature.PLAY
| mp.const.MediaPlayerEntityFeature.PAUSE
| mp.const.MediaPlayerEntityFeature.TURN_OFF
| mp.const.MediaPlayerEntityFeature.TURN_ON
)
| 17403f930f625dd70bbd8ab44565bbc467db886a | 56 | https://github.com/home-assistant/core.git | 103 | def supported_features(self):
return (
mp.const.MediaPlayerEntityFeature.VOLUME_SET
| mp.const.MediaPlayerEntityFeature.VOLUME_STEP
| mp.const.MediaPlayerEntityFeatu | 11 | 88 | supported_features |
|
59 | 0 | 8 | 24 | homeassistant/components/device_tracker/legacy.py | 318,060 | Add StrEnum for device_tracker `SourceType` (#75892)
Add StrEnum for device_tracker SourceType | core | 13 | Python | 38 | legacy.py | async def async_update(self) -> None:
if not self.last_seen:
return
if self.location_name:
self._state = self.location_name
elif self.gps is not None and self.source_type == SourceType.GPS:
zone_state = zone.async_active_zone(
self.hass, self.gps[0], self.gps[1], self.gps_accuracy
)
if zone_state is None:
self._state = STATE_NOT_HOME
elif zone_state.entity_id == zone.ENTITY_ID_HOME:
self._state = STATE_HOME
else:
self._state = zone_state.name
elif self.stale():
self.mark_stale()
else:
self._state = STATE_HOME
self.last_update_home = True
| 2b1e1365fdb3bfe72feb515fcf2e02331caa4088 | 128 | https://github.com/home-assistant/core.git | 271 | async def async_update(self) -> None:
if not self.last_seen:
return
if self.location_name:
self._state = self.location_name
elif self.gps is not None and self.source_type == SourceType.GPS:
zone_state = zone.async_active_zone(
self.hass, self.gps[0], self.gps[1], self.gps_accuracy
)
if zone_state is None:
self._state = STATE_NOT_HOME
elif zone_state.entity_id == zone.ENTITY_ID_HOME:
self._state = STATE_HOME
else:
self._state = zone | 22 | 205 | async_update |
|
80 | 0 | 6 | 27 | projects/image_chat/interactive.py | 195,518 | [image_chat] remove decode methods for handling HTTP requests, add torchvision requirements (#4867)
* [image_chat] Support both bytes and string
* update cache key
* Update config.yml
Co-authored-by: Kurt Shuster <kshuster@meta.com> | ParlAI | 16 | Python | 43 | interactive.py | def interactive_running(self, data):
reply = {}
if type(data["personality"][0]) is bytes:
reply["text"] = data["personality"][0].decode("utf-8")
else:
reply["text"] = data["personality"][0]
if type(data["text"][0]) is bytes:
text = data["text"][0].decode("utf-8")
else:
text = data["text"][0]
if text:
reply["text"] = "\n".join(SHARED["dialog_history"] + [text, reply["text"]])
SHARED["dialog_history"].append(text)
if SHARED["image_feats"] is None:
if type(data["image"][0]) is bytes:
img_data = data["image"][0].decode("utf-8")
_, encoded = img_data.split(",", 1)
encoded = encoded[2:-1]
else:
img_data = data["image"][0]
_, encoded = img_data.split(",", 1)
image = Image.open(io.BytesIO(b64decode(encoded))).convert("RGB")
SHARED["image_feats"] = SHARED["image_loader"].extract(image)
reply["image"] = SHARED["image_feats"]
SHARED["agent"].observe(reply)
model_res = SHARED["agent"].act()
return model_res
| 0f15b897302e7a4a257e6b2edf3c1b811c95081c | 276 | https://github.com/facebookresearch/ParlAI.git | 349 | def interactive_running(self, data):
reply = {}
if type(data["personality"][0]) is bytes:
reply["text"] = data["personality"][0].decode("utf-8")
else:
reply["text"] = data["personality"][0]
if type(data["text"][0]) is bytes:
text = data["text"][0].decode("utf-8")
else:
text = data["text"][0]
if text:
reply["text"] | 26 | 481 | interactive_running |
|
96 | 0 | 4 | 27 | freqtrade/freqai/data_kitchen.py | 149,834 | keep model accessible in memory to avoid loading objects from disk during live/dry | freqtrade | 17 | Python | 65 | data_kitchen.py | def load_data(self) -> Any:
with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp:
self.data = json.load(fp)
self.training_features_list = self.data["training_features_list"]
self.data_dictionary["train_features"] = pd.read_pickle(
self.model_path / str(self.model_filename + "_trained_df.pkl")
)
self.model_path = Path(self.data["model_path"])
self.model_filename = self.data["model_filename"]
# try to access model in memory instead of loading object from disk to save time
if self.live and self.model_filename in self.model_dictionary:
model = self.model_dictionary[self.model_filename]
else:
model = load(self.model_path / str(self.model_filename + "_model.joblib"))
assert model, (
f"Unable to load model, ensure model exists at "
f"{self.model_path} "
)
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
self.pca = pk.load(
open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "rb")
)
return model
| 1fae6c9ef794a014c3e8f1a692bda8b66b46b960 | 189 | https://github.com/freqtrade/freqtrade.git | 337 | def load_data(self) -> Any:
with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp:
self.data = json.load(fp)
self.training_features_list = self.data["training_features_list"]
self.data_dictionary["train_features"] = pd.read_pickle(
self.model_path / str(self.model_filename + "_trained_df.pkl")
)
self.model_path = Path(self.data["model_path"])
self.model_filename = self.data["model_filename"]
# try to access model in memory instead of loading object from disk to save time
if self.live and | 22 | 331 | load_data |
|
21 | 0 | 1 | 8 | wagtail/core/models/__init__.py | 73,791 | Reformat with black | wagtail | 9 | Python | 20 | __init__.py | def __str__(self):
return "Group %d ('%s') has permission '%s' on page %d ('%s')" % (
self.group.id,
self.group,
self.permission_type,
self.page.id,
self.page,
)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 34 | https://github.com/wagtail/wagtail.git | 89 | def __str__(self):
return "Group %d ('%s') has permission '%s' on page %d ('%s')" % (
self.group.id,
self.group,
self.permission_type,
self.page.id,
self.page,
)
| 6 | 50 | __str__ |
|
20 | 0 | 1 | 12 | pandas/io/json/_json.py | 167,427 | TYP: Return annotations for io/{formats,json} (#47516)
* TYP: Return annotations for io/{formats,json}
* flake8
* explicitly check whether width is None | pandas | 9 | Python | 20 | _json.py | def write(self) -> str:
iso_dates = self.date_format == "iso"
return dumps(
self.obj_to_write,
orient=self.orient,
double_precision=self.double_precision,
ensure_ascii=self.ensure_ascii,
date_unit=self.date_unit,
iso_dates=iso_dates,
default_handler=self.default_handler,
indent=self.indent,
)
| 734db4f1fde2566a02b3c7ff661a479b0a71633c | 62 | https://github.com/pandas-dev/pandas.git | 128 | def write(self) -> str:
iso_dates = self.date_format == "iso"
return dumps(
self.obj_to_write,
orient=self.orient,
double_precision=self.double_precision,
ensure_ascii=self.ensure_a | 13 | 90 | write |
|
116 | 0 | 1 | 29 | wagtail/admin/tests/pages/test_edit_page.py | 77,412 | Replace `PageRevision` with generic `Revision` model (#8441) | wagtail | 15 | Python | 85 | test_edit_page.py | def test_edit_post_publish_scheduled_unpublished_page(self):
# Unpublish the page
self.child_page.live = False
self.child_page.save()
go_live_at = timezone.now() + datetime.timedelta(days=1)
expire_at = timezone.now() + datetime.timedelta(days=2)
post_data = {
"title": "I've been edited!",
"content": "Some content",
"slug": "hello-world",
"action-publish": "Publish",
"go_live_at": submittable_timestamp(go_live_at),
"expire_at": submittable_timestamp(expire_at),
}
response = self.client.post(
reverse("wagtailadmin_pages:edit", args=(self.child_page.id,)), post_data
)
# Should be redirected to explorer page
self.assertEqual(response.status_code, 302)
child_page_new = SimplePage.objects.get(id=self.child_page.id)
# The page should not be live anymore
self.assertFalse(child_page_new.live)
# Instead a revision with approved_go_live_at should now exist
self.assertTrue(
Revision.page_revisions.filter(object_id=child_page_new.id)
.exclude(approved_go_live_at__isnull=True)
.exists()
)
# The page SHOULD have the "has_unpublished_changes" flag set,
# because the changes are not visible as a live page yet
self.assertTrue(
child_page_new.has_unpublished_changes,
"A page scheduled for future publishing should have has_unpublished_changes=True",
)
self.assertEqual(child_page_new.status_string, "scheduled")
| 52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c | 194 | https://github.com/wagtail/wagtail.git | 401 | def test_edit_post_publish_scheduled_unpublished_page(self):
# Unpublish the page
self.child_page.live = False
self.child_page.save()
go_ | 37 | 330 | test_edit_post_publish_scheduled_unpublished_page |
|
55 | 0 | 1 | 41 | tests/sentry/api/serializers/test_organization.py | 89,252 | feat(discover-homepage): Default feature flag to True (#41969)
This feature has been GA'd for a while now. Defaulting the feature flag
to True for self hosted releases | sentry | 9 | Python | 51 | test_organization.py | def test_simple(self):
user = self.create_user()
organization = self.create_organization(owner=user)
result = serialize(organization, user)
assert result["id"] == str(organization.id)
assert result["features"] == {
"advanced-search",
"change-alerts",
"crash-rate-alerts",
"custom-event-title",
"custom-symbol-sources",
"data-forwarding",
"dashboards-basic",
"dashboards-edit",
"dashboards-top-level-filter",
"discover-basic",
"discover-query",
"discover-query-builder-as-landing-page",
"event-attachments",
"integrations-alert-rule",
"integrations-chat-unfurl",
"integrations-deployment",
"integrations-event-hooks",
"integrations-incident-management",
"integrations-issue-basic",
"integrations-issue-sync",
"integrations-ticket-rules",
"invite-members",
"invite-members-rate-limits",
"minute-resolution-sessions",
"open-membership",
"relay",
"shared-issues",
"sso-basic",
"sso-saml2",
"symbol-sources",
"team-insights",
"discover-frontend-use-events-endpoint",
"performance-frontend-use-events-endpoint",
"performance-issues-ingest",
}
| d07b2f35a620fd8cec5f17a7a1605024b8b5afff | 118 | https://github.com/getsentry/sentry.git | 470 | def test_simple(self):
user = self.create_user()
organization = self.create_organization(owner=user)
result = serialize(organization, user)
assert result["id"] == str(organization.id)
assert result["features"] == {
"advanced-search",
"change-alerts",
"crash-rate-alerts",
"custom-event-title",
"custom-symbol-sources",
"data-forwarding",
"dashboards-basic",
"dashboards-edit",
"dashboards-top-level-filter",
"discover-basic",
"discover-query",
"discover-query-builder-as-landing-page",
"event-attachments",
"integrations-alert-rule",
"integrations-chat-unfurl",
"integrations-deployment",
"integrations-event-hooks",
"integrations-incident-management",
"integrations-issue-basic",
"integrations-issue-sync",
"integrations-ticket-rules",
"invite-m | 11 | 219 | test_simple |
|
27 | 0 | 2 | 16 | nuitka/freezer/IncludedEntryPoints.py | 178,389 | UI: In case of PermissionError, allow uses to retry
* Esp. on Windows it happens a lot that running programs cannot be
updated by Nuitka, this avoids the cryptic error somewhere ranomly. | Nuitka | 14 | Python | 23 | IncludedEntryPoints.py | def addShlibEntryPoint(module):
target_filename = os.path.join(
getStandaloneDirectoryPath(), module.getFullName().asPath()
)
target_filename += getSharedLibrarySuffix(preferred=False)
target_dir = os.path.dirname(target_filename)
if not os.path.isdir(target_dir):
makePath(target_dir)
copyFile(module.getFilename(), target_filename)
standalone_entry_points.append(
makeExtensionModuleEntryPoint(
source_path=module.getFilename(),
dest_path=target_filename,
package_name=module.getFullName().getPackageName(),
)
)
| 2c20b90946a8aa5ad4ee39ad365ff1b83f182770 | 102 | https://github.com/Nuitka/Nuitka.git | 111 | def addShlibEntryPoint(module):
target_filename = os.path.join(
getStandaloneDirectoryPath(), module.getFullName().asPath()
| 24 | 165 | addShlibEntryPoint |
|
28 | 0 | 2 | 12 | mitmproxy/utils/debug.py | 253,223 | more mypy (#5724)
Co-authored-by: requires.io <support@requires.io>
Co-authored-by: Maximilian Hils <git@maximilianhils.com> | mitmproxy | 11 | Python | 24 | debug.py | def dump_system_info():
mitmproxy_version = version.get_dev_version()
openssl_version = SSL.SSLeay_version(SSL.SSLEAY_VERSION)
if isinstance(openssl_version, bytes):
openssl_version = openssl_version.decode()
data = [
f"Mitmproxy: {mitmproxy_version}",
f"Python: {platform.python_version()}",
f"OpenSSL: {openssl_version}",
f"Platform: {platform.platform()}",
]
return "\n".join(data)
| 0bbb0215c16bbeaf3b048c023ed0ee55f57b0de8 | 59 | https://github.com/mitmproxy/mitmproxy.git | 86 | def dump_system_info():
mitmproxy_version = version.get_dev_version()
openssl_version = SSL.SSLeay_version(SSL.SSLEAY_VERSION)
if isinstance(openssl_version, bytes):
openssl_version = openssl_version.decode()
data = [
f"Mitmproxy: {mitmproxy_version}",
f"Python: {platform.python_version()}",
f"OpenSSL: {openssl_version}",
f"Platform: {platform.platform()}",
]
retu | 15 | 127 | dump_system_info |
|
280 | 1 | 11 | 67 | erpnext/selling/doctype/sales_order/sales_order.py | 69,262 | fix: use default supplier currency if default supplier is enabled | erpnext | 19 | Python | 186 | sales_order.py | def make_purchase_order_for_default_supplier(source_name, selected_items=None, target_doc=None):
from erpnext.setup.utils import get_exchange_rate
if not selected_items:
return
if isinstance(selected_items, str):
selected_items = json.loads(selected_items)
def set_missing_values(source, target):
target.supplier = supplier
target.currency = frappe.db.get_value(
"Supplier", filters={"name": supplier}, fieldname=["default_currency"]
)
company_currency = frappe.db.get_value(
"Company", filters={"name": target.company}, fieldname=["default_currency"]
)
target.conversion_rate = get_exchange_rate(target.currency, company_currency, args="for_buying")
target.apply_discount_on = ""
target.additional_discount_percentage = 0.0
target.discount_amount = 0.0
target.inter_company_order_reference = ""
target.shipping_rule = ""
default_price_list = frappe.get_value("Supplier", supplier, "default_price_list")
if default_price_list:
target.buying_price_list = default_price_list
if any(item.delivered_by_supplier == 1 for item in source.items):
if source.shipping_address_name:
target.shipping_address = source.shipping_address_name
target.shipping_address_display = source.shipping_address
else:
target.shipping_address = source.customer_address
target.shipping_address_display = source.address_display
target.customer_contact_person = source.contact_person
target.customer_contact_display = source.contact_display
target.customer_contact_mobile = source.contact_mobile
target.customer_contact_email = source.contact_email
else:
target.customer = ""
target.customer_name = ""
target.run_method("set_missing_values")
target.run_method("calculate_taxes_and_totals")
def update_item(source, target, source_parent):
target.schedule_date = source.delivery_date
target.qty = flt(source.qty) - (flt(source.ordered_qty) / flt(source.conversion_factor))
target.stock_qty = flt(source.stock_qty) - flt(source.ordered_qty)
target.project = source_parent.project
suppliers = [item.get("supplier") for item in selected_items if item.get("supplier")]
suppliers = list(dict.fromkeys(suppliers)) # remove duplicates while preserving order
items_to_map = [item.get("item_code") for item in selected_items if item.get("item_code")]
items_to_map = list(set(items_to_map))
if not suppliers:
frappe.throw(
_("Please set a Supplier against the Items to be considered in the Purchase Order.")
)
purchase_orders = []
for supplier in suppliers:
doc = get_mapped_doc(
"Sales Order",
source_name,
{
"Sales Order": {
"doctype": "Purchase Order",
"field_no_map": [
"address_display",
"contact_display",
"contact_mobile",
"contact_email",
"contact_person",
"taxes_and_charges",
"shipping_address",
"terms",
],
"validation": {"docstatus": ["=", 1]},
},
"Sales Order Item": {
"doctype": "Purchase Order Item",
"field_map": [
["name", "sales_order_item"],
["parent", "sales_order"],
["stock_uom", "stock_uom"],
["uom", "uom"],
["conversion_factor", "conversion_factor"],
["delivery_date", "schedule_date"],
],
"field_no_map": [
"rate",
"price_list_rate",
"item_tax_template",
"discount_percentage",
"discount_amount",
"pricing_rules",
],
"postprocess": update_item,
"condition": lambda doc: doc.ordered_qty < doc.stock_qty
and doc.supplier == supplier
and doc.item_code in items_to_map,
},
},
target_doc,
set_missing_values,
)
doc.insert()
frappe.db.commit()
purchase_orders.append(doc)
return purchase_orders
@frappe.whitelist() | 77fdc37cb75d465a7a5297fc89bba31b8193ebeb | @frappe.whitelist() | 305 | https://github.com/frappe/erpnext.git | 177 | def make_purchase_order_for_default_supplier(source_name, selected_items=None, target_doc=None):
from erpnext.setup.utils import get_exchange_rate
if not selected_items:
return
if isinstance(selected_items, str):
selected_items = json.loads(selected_items)
def set_missing_values(source, target):
target.supplier = supplier
target.currency = frappe.db.get_value(
"Supplier", filters={"name": supplier}, fieldname=["default_currency"]
)
company_currency = frappe.db.get_value(
"Company", filters={"name": target.company}, fieldname=["default_currency"]
)
target.conversion_rate = get_exchange_rate(target.currency, company_currency, args="for_buying")
target.apply_discount_on = ""
target.additional_discount_percentage = 0.0
target.discount_amount = 0.0
target.inter_company_order_reference = ""
target.shipping_rule = ""
default_price_list = frappe.get_value("Supplier", supplier, "default_price_list")
if default_price_list:
target.buying_price_list = default_price_list
if any(item.delivered_by_supplier == 1 for item in source.items):
if source.shipping_address_name:
target.shipping_address = source.shipping_address_name
target.shipping_address_display = source.shipping_address
else:
target.shipping_address = source.customer_address
target.shipping_address_display = source.address_display
target.customer_contact_person = source.contact_person
target.customer_contact_display = source. | 80 | 1,020 | make_purchase_order_for_default_supplier |
138 | 0 | 9 | 51 | erpnext/accounts/report/tds_payable_monthly/tds_payable_monthly.py | 69,635 | fix: Tax withholding net total for PI in reports | erpnext | 13 | Python | 95 | tds_payable_monthly.py | def get_tds_docs(filters):
tds_documents = []
purchase_invoices = []
payment_entries = []
journal_entries = []
tax_category_map = frappe._dict()
invoice_net_total_map = frappe._dict()
or_filters = frappe._dict()
journal_entry_party_map = frappe._dict()
bank_accounts = frappe.get_all("Account", {"is_group": 0, "account_type": "Bank"}, pluck="name")
tds_accounts = frappe.get_all(
"Tax Withholding Account", {"company": filters.get("company")}, pluck="account"
)
query_filters = {
"account": ("in", tds_accounts),
"posting_date": ("between", [filters.get("from_date"), filters.get("to_date")]),
"is_cancelled": 0,
"against": ("not in", bank_accounts),
}
if filters.get("supplier"):
del query_filters["account"]
del query_filters["against"]
or_filters = {"against": filters.get("supplier"), "party": filters.get("supplier")}
tds_docs = frappe.get_all(
"GL Entry",
filters=query_filters,
or_filters=or_filters,
fields=["voucher_no", "voucher_type", "against", "party"],
)
for d in tds_docs:
if d.voucher_type == "Purchase Invoice":
purchase_invoices.append(d.voucher_no)
elif d.voucher_type == "Payment Entry":
payment_entries.append(d.voucher_no)
elif d.voucher_type == "Journal Entry":
journal_entries.append(d.voucher_no)
tds_documents.append(d.voucher_no)
if purchase_invoices:
get_doc_info(purchase_invoices, "Purchase Invoice", tax_category_map, invoice_net_total_map)
if payment_entries:
get_doc_info(payment_entries, "Payment Entry", tax_category_map)
if journal_entries:
journal_entry_party_map = get_journal_entry_party_map(journal_entries)
get_doc_info(journal_entries, "Journal Entry", tax_category_map)
return (
tds_documents,
tds_accounts,
tax_category_map,
journal_entry_party_map,
invoice_net_total_map,
)
| 3eb1ed19a19a0e26e9814d70267530769bf8b274 | 320 | https://github.com/frappe/erpnext.git | 87 | def get_tds_docs(filters):
tds_documents = []
purchase_invoices = []
| 26 | 549 | get_tds_docs |
|
11 | 0 | 1 | 14 | saleor/graphql/order/tests/test_order_invoices.py | 26,349 | Migrate order id from int to UUID (#9324)
* Add migration to change order id from int to UUID (#9281)
* Change order token to uuid
* Migrate order id to uuid
* Fix failing tests
* Apply code review suggestions
* Fix payment migration dependencies
* Fix typo in order migration name
* Handle old order ids for order queries
* Hanlde old order ids for order mutations
* Add order relation to GiftCardEvent model
* Deprecate order token related queries and fields (#9295)
* Deprecate order.token field
* Update description of orderByToken query
* Update prepare_order_search_document_value method
* Update changelog
* Update schema file | saleor | 11 | Python | 10 | test_order_invoices.py | def test_order_query_invoices_customer_user_by_token(api_client, fulfilled_order):
query =
response = api_client.post_graphql(query, {"token": fulfilled_order.id})
assert_no_permission(response)
| 41b87559118f560c223f83d405efe9b406701d17 | 30 | https://github.com/saleor/saleor.git | 20 | def test_order_query_invoices_customer_user_by_token(api_client, fulfilled_order):
query =
response = api_client | 8 | 51 | test_order_query_invoices_customer_user_by_token |
|
20 | 0 | 1 | 9 | lib/training/preview_tk.py | 101,989 | GUI - Preview updates
- Training preview. Embed preview pop-out window
- Bugfix - convert/extract previews | faceswap | 13 | Python | 19 | preview_tk.py | def _add_save_button(self) -> None:
logger.debug("Adding save button")
button = tk.Button(self._frame,
text="Save",
cursor="hand2",
command=lambda: self.save_var.set(True))
button.pack(side=tk.LEFT)
logger.debug("Added save burron: '%s'", button)
| 2e8ef5e3c8f2df0f1cca9b342baa8aaa6f620650 | 63 | https://github.com/deepfakes/faceswap.git | 133 | def _add_save_button(self) -> None:
logger.debug("Adding save button")
button = tk.Button(self._frame,
text="Save",
cursor="hand2",
command=lambda: self.save_var.set(True))
button.pack(side=tk.LEFT)
logger.debug("Added save burron: '%s'", button)
| 16 | 107 | _add_save_button |
|
49 | 0 | 3 | 15 | haystack/modeling/model/prediction_head.py | 256,244 | Apply black formatting (#2115)
* Testing black on ui/
* Applying black on docstores
* Add latest docstring and tutorial changes
* Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too
* Remove comments
* Relax constraints on pydoc-markdown
* Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade
* Fix a couple of bugs
* Add a type: ignore that was missing somehow
* Give path to black
* Apply Black
* Apply Black
* Relocate a couple of type: ignore
* Update documentation
* Make Linux CI run after applying Black
* Triggering Black
* Apply Black
* Remove dependency, does not work well
* Remove manually double trailing commas
* Update documentation
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | haystack | 16 | Python | 40 | prediction_head.py | def resize_input(self, input_dim):
if "feed_forward" not in dir(self):
return
else:
old_dims = self.feed_forward.layer_dims
if input_dim == old_dims[0]:
return
new_dims = [input_dim] + old_dims[1:]
logger.info(
f"Resizing input dimensions of {type(self).__name__} ({self.task_name}) "
f"from {old_dims} to {new_dims} to match language model"
)
self.feed_forward = FeedForwardBlock(new_dims)
self.layer_dims[0] = input_dim
self.feed_forward.layer_dims[0] = input_dim
| a59bca366174d9c692fa19750c24d65f47660ef7 | 82 | https://github.com/deepset-ai/haystack.git | 214 | def resize_input(self, input_dim):
if "feed_forward" not in dir(self):
return
else:
old_dims = self.feed_forward.layer_dims
if input_dim == old_dims[0]:
return
new_dims = [input_dim] + old_dims[1:]
logger.info(
f"Resizing input dimensions of {type(self).__name__} ({self.task_name}) "
| 14 | 162 | resize_input |
|
9 | 0 | 2 | 4 | homeassistant/components/apple_tv/media_player.py | 304,127 | Improve entity type hints [a] (#76986) | core | 12 | Python | 9 | media_player.py | async def async_media_stop(self) -> None:
if self.atv:
await self.atv.remote_control.stop()
| 65eb1584f765dcc2ec502bd8a9fa8d2f23d47cfd | 23 | https://github.com/home-assistant/core.git | 34 | async def async_media_stop(self) -> None:
if self.atv:
await self.atv.remote_control.stop()
| 5 | 42 | async_media_stop |
|
10 | 0 | 1 | 4 | wagtail/admin/tests/api/test_pages.py | 71,349 | Reformat with black | wagtail | 11 | Python | 10 | test_pages.py | def get_response(self, page_id, **params):
return self.client.get(
reverse("wagtailadmin_api:pages:detail", args=(page_id,)), params
)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 31 | https://github.com/wagtail/wagtail.git | 34 | def get_response(self, page_id, **params):
return self.client.get(
reverse("wagtailadmin_a | 8 | 47 | get_response |
|
9 | 0 | 1 | 3 | test/mitmproxy/contentviews/test_http3.py | 252,741 | add HTTP/3 content view | mitmproxy | 9 | Python | 9 | test_http3.py | def test_render_priority():
v = http3.ViewHttp3()
assert not v.render_priority(b"random stuff")
| f6ac5006982fd18dfe9f9a67c3534300c7ba8192 | 20 | https://github.com/mitmproxy/mitmproxy.git | 14 | def test_render_priority():
v = http3.ViewHttp3()
assert not v.render_priority(b"rando | 5 | 35 | test_render_priority |
|
27 | 0 | 1 | 34 | tests/helpers/test_event.py | 300,624 | Fail template functions when no default specified (#71687) | core | 9 | Python | 21 | test_event.py | async def test_track_template_result_none(hass):
specific_runs = []
wildcard_runs = []
wildercard_runs = []
template_condition = Template("{{state_attr('sensor.test', 'battery')}}", hass)
template_condition_var = Template(
"{{(state_attr('sensor.test', 'battery')|int(default=0)) + test }}", hass
)
| 4885331509eeffe50f42d76b234996467b06170f | 205 | https://github.com/home-assistant/core.git | 55 | async def test_track_template_result_none(hass):
specific_runs = []
wildcard_runs = []
wildercard_runs = []
template_condition = | 8 | 63 | test_track_template_result_none |
|
27 | 0 | 5 | 7 | networkx/algorithms/clique.py | 176,268 | Fix functions appearing in variables `__all__` but not in docs for NX2.7 (#5289)
* Adjust functions appearing in `__all__` but not in docs
* clean up coloring: merge two modules make interchange private
* fix duplicate name. Probably should be changed
* fix "see also" doc of recursive_simple_cycles
* Rm internal uses of deprecated .
* Fixup warnings filters regex.
* clean up a bit more, make Node & AdjList private classes
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
Co-authored-by: Mridul Seth <seth.mridul@gmail.com> | networkx | 11 | Python | 22 | clique.py | def find_cliques_recursive(G):
if len(G) == 0:
return iter([])
adj = {u: {v for v in G[u] if v != u} for u in G}
Q = []
| 17fa9942568bfca34d4a68f8d93c538014f69389 | 63 | https://github.com/networkx/networkx.git | 46 | def find_cliques_recursive(G):
if len(G) == 0:
return iter([])
adj = {u: {v for v in G[u] if v != u} | 8 | 77 | find_cliques_recursive |
|
11 | 0 | 2 | 5 | synapse/server.py | 249,846 | Add a type hint for `get_device_handler()` and fix incorrect types. (#14055)
This was the last untyped handler from the HomeServer object. Since
it was being treated as Any (and thus unchecked) it was being used
incorrectly in a few places. | synapse | 10 | Python | 10 | server.py | def get_device_handler(self) -> DeviceWorkerHandler:
if self.config.worker.worker_app:
return DeviceWorkerHandler(self)
else:
return DeviceHandler(self)
| 6d47b7e32589e816eb766446cc1ff19ea73fc7c1 | 28 | https://github.com/matrix-org/synapse.git | 46 | def get_device_handler(self) -> DeviceWorkerHandler:
if self.config.worker.worker_app:
return DeviceWorkerHandler(self)
else:
return DeviceHandler(self)
| 7 | 46 | get_device_handler |
|
78 | 0 | 1 | 31 | tests/utils/test_py27hash_fix.py | 213,106 | fix: Py27hash fix (#2182)
* Add third party py27hash code
* Add Py27UniStr and unit tests
* Add py27hash_fix utils and tests
* Add to_py27_compatible_template and tests
* Apply py27hash fix to wherever it is needed
* Apply py27hash fix, all tests pass except api_with_any_method_in_swagger
* apply py27hash fix in openapi + run black
* remove py27 testing
* remove other py27 references
* black fixes
* fixes/typos
* remove py27 from tox.ini
* refactoring
* third party notice
* black
* Fix py27hash fix to deal with null events
* Fix Py27UniStr repr for unicode literals
* black reformat
* Update _template_has_api_resource to check data type more defensively
* Apply py27Dict in _get_authorizers
* Apply Py27Dict to authorizers and gateway responses which will go into swagger
* Update to_py27_compatible_template to handle parameter_values; Add Py27LongInt class
* Rename _convert_to_py27_dict to _convert_to_py27_type
* Apply Py27UniStr to path param name
* Handle HttpApi resource under to_py27_compatible_template
* Fix InvalidDocumentException to not sort different exceptions
* black reformat
* Remove unnecessary test files
Co-authored-by: Wing Fung Lau <4760060+hawflau@users.noreply.github.com> | serverless-application-model | 21 | Python | 45 | test_py27hash_fix.py | def test_resources_api(self):
template = {
"Resources": {
"Api": {"Type": "AWS::Serverless::Api", "Properties": {"Name": "MyApi"}},
"HttpApi": {"Type": "AWS::Serverless::HttpApi"},
"Function": {
"Type": "AWS::Serverless::Function",
"Properties": {
"FunctionName": {"Ref": "MyFunctionName"},
"Events": {
"ApiEvent": {"Type": "Api", "Properties": {"Path": "/user", "Method": "GET"}},
"SecondApiEvent": {"Type": "Api", "Properties": {"Path": "/admin", "Method": "GET"}},
},
},
},
"StateMachine": {
"Type": "AWS::Serverless::StateMachine",
"Condition": "ShouldAddStateMachine",
"Properties": {
"Event": {
"ApiEvent": {"Type": "Api", "Properties": {"Path": "/state-machine", "Method": "GET"}}
}
},
},
}
}
to_py27_compatible_template(template)
self.assertIsInstance(template["Resources"], Py27Dict)
self.assertNotIsInstance(template["Resources"]["Api"], Py27Dict)
self.assertIsInstance(template["Resources"]["Api"]["Properties"], Py27Dict)
self.assertIsInstance(template["Resources"]["Api"]["Properties"]["Name"], Py27UniStr)
| a5db070f446b7cfebdaa6ad2e3dcf78f6105a272 | 211 | https://github.com/aws/serverless-application-model.git | 567 | def test_resources_api(self):
template = {
"Resources": {
"Api": {"Type": "AWS::Serverless::Api", "Properties": {"Name": "MyApi"}},
"HttpApi": {"Type": "AWS::Serverless::HttpApi"},
"Function": {
"Type": "AWS::Serverless::Function",
"Properties": {
"FunctionName": {"Ref": "MyFunctionName"},
"Events": {
"ApiEvent": {"Type": "Api", "Properties": {"Path": "/user", "Method": "GET"}},
"SecondApiEvent": {"Type": "Api", "Properties": {"Path": "/admin", "Method": "GET"}},
},
},
},
"StateMachine": {
"Type": "AWS::Serverless::StateMachine",
"Condition": "ShouldAddStateMachine",
"Properties": {
"Event": {
"ApiEvent": {"Type": "Api", "Properties": {"Path": "/state-machine", "Method": "GET"}}
}
},
},
}
}
to_py27_compatible_template(template)
self.assertIsInstance(template["Resources"], Py27Dict)
self.as | 8 | 415 | test_resources_api |
|
158 | 0 | 1 | 108 | wagtail/contrib/forms/tests/test_forms.py | 72,997 | Reformat with black | wagtail | 13 | Python | 79 | test_forms.py | def setUp(self):
# Create a form page
home_page = Page.objects.get(url_path="/home/")
self.form_page = home_page.add_child(
instance=FormPage(
title="Contact us",
slug="contact-us",
to_address="to@email.com",
from_address="from@email.com",
subject="The subject",
)
)
FormField.objects.create(
page=self.form_page,
sort_order=1,
label="Your name",
field_type="singleline",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your message",
field_type="multiline",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your birthday",
field_type="date",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your birthtime :)",
field_type="datetime",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=1,
label="Your email",
field_type="email",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your homepage",
field_type="url",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your favourite number",
field_type="number",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your favourite text editors",
field_type="multiselect",
required=True,
choices="vim,nano,emacs",
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your favourite Python IDEs",
field_type="dropdown",
required=True,
choices="PyCharm,vim,nano",
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="α½our favourite Ξ‘ython ΓΓΓ", # unicode example
help_text="Choose one",
field_type="radio",
required=True,
choices="PyCharm,vim,nano",
)
FormField.objects.create(
page=self.form_page,
sort_order=3,
label="Your choices",
field_type="checkboxes",
required=False,
choices="foo,bar,baz",
)
FormField.objects.create(
page=self.form_page,
sort_order=3,
label="I agree to the Terms of Use",
field_type="checkbox",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=1,
label="A Hidden Field",
field_type="hidden",
required=False,
)
# Create a form builder
self.fb = FormBuilder(self.form_page.get_form_fields())
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 462 | https://github.com/wagtail/wagtail.git | 1,249 | def setUp(self):
# Create a form page
home_page = Page.objects.get(url_path="/home/")
self.form_page = home_page.add_child(
instance=FormPage(
title="Contact us",
slug="contact-us",
to_address="to@email.com",
from_address="from@email.com",
subject="The subject",
)
)
FormField.objects.create(
page=self.form_page,
sort_order=1,
label="Your name",
field_type="singleline",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your message",
field_type="multiline",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your birthday",
field_type="date",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your birthtime :)",
field_type="datetime",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=1,
label="Your email",
field_type="email",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your homepage",
field_type="url",
required=True,
)
FormField.objects.create(
page=self.form_page,
sort_order=2,
label="Your favourite number",
field_type="number",
require | 28 | 721 | setUp |
|
28 | 0 | 2 | 9 | .venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py | 63,399 | upd; format | transferlearning | 10 | Python | 23 | pyparsing.py | def __setstate__(self, state):
self.__toklist = state[0]
self.__tokdict, par, inAccumNames, self.__name = state[1]
self.__accumNames = {}
self.__accumNames.update(inAccumNames)
if par is not None:
self.__parent = wkref(par)
else:
self.__parent = None
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 66 | https://github.com/jindongwang/transferlearning.git | 91 | def __setstate__(self, state):
self.__toklist = state[0]
self.__tokdict, par, inAccumNames, self.__name = state[1]
self.__accumNames = {}
self.__accumNames.update(inAccumNames)
if par is not None:
| 12 | 102 | __setstate__ |
|
13 | 0 | 1 | 3 | test/fx_acc/test_acc_tracer.py | 102,559 | [fx2trt] break down div (#71172)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71172
Break down div to smaller ops to make those div ops look like all other elementwise ops.
Use operator div ops instead of torch div if possible to avoid converting literal numbers to torch tensor (like in the following).
```
a = 1
b = 2
// `c` would be 0.5
c = a / b
// `c` would be torch.tensor([0.5])
c = torch.div(a, b)
```
The problem we saw on shufflenet is that there's size op followed by a div op which results in int64 tensors in acc traced graph (acc tracer turns operator.div to acc_ops.div which uses torch.div). And trt splitter splits out the reshape op that consumes the div op because we have a rule to split out ops that takes in int64 tensors as inputs.
Test Plan: Unit tests.
Reviewed By: wushirong
Differential Revision: D33482231
fbshipit-source-id: 508a171520c4e5b4188cfc5c30c1370ba9db1c55 | pytorch | 12 | Python | 10 | test_acc_tracer.py | def test_trunc_div(self):
self._make_acc_op_function_test(acc_ops.trunc_div, lambda x: torch.div(x, 2, rounding_mode="trunc"))
self._make_acc_op_function_test(acc_ops.trunc_div, lambda x: torch.floor_divide(x, 2))
| 54fe2741a1b16e36f714fa167f8f692886fd6640 | 49 | https://github.com/pytorch/pytorch.git | 26 | def test_trunc_div(self):
self._make_acc_op_function_test(acc_ops.trunc_div, lambda x: torch.div(x, 2, rounding_mode="tr | 10 | 76 | test_trunc_div |
|
82 | 0 | 8 | 32 | homeassistant/components/intent_script/__init__.py | 311,659 | Add support to reprompt user (#65256) | core | 14 | Python | 51 | __init__.py | async def async_handle(self, intent_obj):
speech = self.config.get(CONF_SPEECH)
reprompt = self.config.get(CONF_REPROMPT)
card = self.config.get(CONF_CARD)
action = self.config.get(CONF_ACTION)
is_async_action = self.config.get(CONF_ASYNC_ACTION)
slots = {key: value["value"] for key, value in intent_obj.slots.items()}
if action is not None:
if is_async_action:
intent_obj.hass.async_create_task(
action.async_run(slots, intent_obj.context)
)
else:
await action.async_run(slots, intent_obj.context)
response = intent_obj.create_response()
if speech is not None:
response.async_set_speech(
speech[CONF_TEXT].async_render(slots, parse_result=False),
speech[CONF_TYPE],
)
if reprompt is not None and reprompt[CONF_TEXT].template:
response.async_set_reprompt(
reprompt[CONF_TEXT].async_render(slots, parse_result=False),
reprompt[CONF_TYPE],
)
if card is not None:
response.async_set_card(
card[CONF_TITLE].async_render(slots, parse_result=False),
card[CONF_CONTENT].async_render(slots, parse_result=False),
card[CONF_TYPE],
)
return response
| 076faaa4a4f231eb5b7b7c72fa20c239c7cc391c | 239 | https://github.com/home-assistant/core.git | 430 | async def async_handle(self, intent_obj):
speech = self.config.get(CONF_SPEECH)
reprompt = self.config.get(CONF_REPROMPT)
card = self.config.get(CONF_CARD)
action = self.config.get(CONF_ACTION)
is_async_action = self.config.get(CONF_ASYNC_ACTION)
slots = {key: value["value"] for key, value in intent_obj.slots.items()}
if action is not None:
if is_async_action:
intent_obj.hass.async_create_task(
action.async_run(slots, intent_obj.context)
)
else:
await action.async_run(slots, intent_obj.context)
response = intent_obj.create_response()
if speech is not None:
response.async_set_speech(
speech[CONF_TEXT].async_render(slots, parse_result=False),
speech[CONF_TYPE],
)
if | 35 | 365 | async_handle |
|
41 | 0 | 1 | 25 | tests/storages/test_mixed_storage.py | 191,024 | Reformat to 80 chars and mypy.ini | thumbor | 14 | Python | 29 | test_mixed_storage.py | async def test_mixed_storage_put_includes_path(self):
await self.storage.put("path1", "contents")
await self.storage.put_crypto("path1")
await self.storage.put_detector_data("path1", "detector")
expect(self.storage.file_storage.storage["path1"]["path"]).to_equal(
"path1"
)
expect(
self.storage.file_storage.storage["path1"]["contents"]
).to_equal("contents")
contents = await self.storage.get("path1")
expect(contents).to_equal("contents")
expect(self.storage.file_storage.storage["path1"]).not_to_include(
"crypto"
)
expect(self.storage.crypto_storage.storage["path1"]).not_to_include(
"contents"
)
expect(
self.storage.crypto_storage.storage["path1"]["crypto"]
).to_equal("security-key")
contents = await self.storage.get_crypto("path1")
expect(contents).to_equal("security-key")
contents = await self.storage.get_detector_data("path1")
expect(contents).to_equal("detector")
| 301124c5b377fa56b940d298900dbc5816dbc24e | 195 | https://github.com/thumbor/thumbor.git | 228 | async def test_mixed_storage_put_includes_path(self):
await self.storage.put("path1", "contents")
await self.storage.put_crypto("path1")
await self.storage.put_detector_data("path1", "detector")
exp | 15 | 358 | test_mixed_storage_put_includes_path |
|
142 | 0 | 1 | 53 | tests/components/media_player/test_browse_media.py | 299,724 | Skip signing URL that we know requires no auth (#71208) | core | 11 | Python | 68 | test_browse_media.py | async def test_process_play_media_url(hass, mock_sign_path):
await async_process_ha_core_config(
hass,
{"internal_url": "http://example.local:8123"},
)
hass.config.api = Mock(use_ssl=False, port=8123, local_ip="192.168.123.123")
# Not changing a url that is not a hass url
assert (
async_process_play_media_url(hass, "https://not-hass.com/path")
== "https://not-hass.com/path"
)
# Not changing a url that is not http/https
assert (
async_process_play_media_url(hass, "file:///tmp/test.mp3")
== "file:///tmp/test.mp3"
)
# Testing signing hass URLs
assert (
async_process_play_media_url(hass, "/path")
== "http://example.local:8123/path?authSig=bla"
)
assert (
async_process_play_media_url(hass, "http://example.local:8123/path")
== "http://example.local:8123/path?authSig=bla"
)
assert (
async_process_play_media_url(hass, "http://192.168.123.123:8123/path")
== "http://192.168.123.123:8123/path?authSig=bla"
)
with pytest.raises(HomeAssistantError), patch(
"homeassistant.components.media_player.browse_media.get_url",
side_effect=NoURLAvailableError,
):
async_process_play_media_url(hass, "/path")
# Test skip signing URLs that have a query param
assert (
async_process_play_media_url(hass, "/path?hello=world")
== "http://example.local:8123/path?hello=world"
)
assert (
async_process_play_media_url(
hass, "http://192.168.123.123:8123/path?hello=world"
)
== "http://192.168.123.123:8123/path?hello=world"
)
# Test skip signing URLs if they are known to require no auth
assert (
async_process_play_media_url(hass, "/api/tts_proxy/bla")
== "http://example.local:8123/api/tts_proxy/bla"
)
assert (
async_process_play_media_url(
hass, "http://example.local:8123/api/tts_proxy/bla"
)
== "http://example.local:8123/api/tts_proxy/bla"
)
with pytest.raises(ValueError):
async_process_play_media_url(hass, "hello")
| 0926470ef0f7bf7fd11da09a9d101ea17a4b4c00 | 177 | https://github.com/home-assistant/core.git | 436 | async def test_process_play_media_url(hass, mock_sign_path):
await async_process_ha_core_config(
hass,
{"internal_url": "http://example.local:8123"},
)
hass.config.api = Mock(use_ssl=False, port=8123, local_ip="192.168.123.123")
# Not changing a url that is not a hass url
assert (
async_process_play_media_url(hass, "https://not-hass.com/path")
== "https://not-hass.com/path"
)
# Not changing a url that is not http/https
assert (
async_process_play_media_url(hass, "file:///tmp/test.mp3")
== "file:///tmp/test.mp3"
)
# Testing signing hass URLs
assert (
async_process_play_media_url(hass, "/path")
== "http://example.local:8123/path?authSig=bla"
)
assert (
async_process_play_media_url(hass, "http://example.local:8123/path")
== "http://example.local:8123/path?authSig=bla"
)
assert (
async_process_play_media_url(hass, "http://192.168.123.123:8123/path")
== "http://192.168.123.123:8123/path?authSig=bla"
)
with pytest.raises(HomeAssistantError), patch(
"homeassistant.components.media_player.browse_media.get_url",
side_effect=NoURLAvailableError,
):
async_process_play_media_url(hass, "/path")
# Test skip signing URLs that have a query param
assert (
async_process_play_media_url(hass, "/path?hello=world")
== "http://example.local:8123/path?hello=world"
)
assert (
async_process_play_media_url(
hass, "http://192.168.123.123:8123/path?hello=world"
)
== "http://192.168.123.123:8123/path?hel | 18 | 322 | test_process_play_media_url |
|
43 | 0 | 2 | 17 | keras/layers/rnn/cudnn_test.py | 273,832 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 12 | Python | 25 | cudnn_test.py | def test_trainability(self):
input_size = 10
units = 2
for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
layer = layer_class(units)
layer.build((None, None, input_size))
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 0)
layer.trainable = False
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 3)
self.assertEqual(len(layer.trainable_weights), 0)
layer.trainable = True
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 0)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 173 | https://github.com/keras-team/keras.git | 206 | def test_trainability(self):
input_size = 10
units = 2
for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
layer = layer_class(units)
layer.build((None, None, input_size))
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqu | 17 | 266 | test_trainability |
|
44 | 1 | 1 | 16 | tests/infrastructure/test_process.py | 60,007 | Monitor process after kill and return early when possible (#7746) | prefect | 11 | Python | 37 | test_process.py | async def test_process_kill_sends_sigterm_then_sigkill(monkeypatch):
os_kill = MagicMock()
monkeypatch.setattr("os.kill", os_kill)
infrastructure_pid = f"{socket.gethostname()}:12345"
grace_seconds = 2
process = Process(command=["noop"])
await process.kill(
infrastructure_pid=infrastructure_pid, grace_seconds=grace_seconds
)
os_kill.assert_has_calls(
[
call(12345, signal.SIGTERM),
call(12345, 0),
call(12345, signal.SIGKILL),
]
)
@pytest.mark.skipif(
sys.platform == "win32",
reason="SIGTERM/SIGKILL are only used in non-Windows environments",
) | b326ebbcca00b79c82ef92ad4d823044dab40e5f | @pytest.mark.skipif(
sys.platform == "win32",
reason="SIGTERM/SIGKILL are only used in non-Windows environments",
) | 80 | https://github.com/PrefectHQ/prefect.git | 128 | async def test_process_kill_sends_sigterm_then_sigkill(monkeypatch):
os_kill = MagicMock()
monkeypatch.setattr("os.kill", os_kill)
infrastructure_pid = f"{socket.gethostname()}:12345"
grace_seconds = 2
process = Process(command=["noop"])
await process.kill(
infrastructure_pid=infrastructure_pid, grace_seconds=grace_seconds
)
os_kill.assert_has_calls(
[
| 24 | 170 | test_process_kill_sends_sigterm_then_sigkill |
90 | 0 | 2 | 14 | tests/unit/test_normalize_icon_type.py | 262,801 | Icon translation using PIL (#6697)
Convert icons into the correct platform dependent format using PIL/Pillow if installed. | pyinstaller | 14 | Python | 70 | test_normalize_icon_type.py | def test_normalize_icon_pillow(tmp_path):
data_dir = str(Path(PyInstaller.__file__).with_name("bootloader") / "images")
workpath = str(tmp_path)
pytest.importorskip("PIL", reason="Needs PIL / Pillow for this test")
# Alternative image - output is a different file with the correct suffix
icon = os.path.join(data_dir, 'github_logo.png')
ret = normalize_icon_type(icon, ("ico",), "ico", workpath)
_, ret_filetype = os.path.splitext(ret)
if ret_filetype != ".ico":
pytest.fail("icon validation didn't convert to the right format", False)
# Some random non-image file: Raises an image conversion error
icon = os.path.join(data_dir, 'pyi_icon.notanicon')
with open(icon, "w") as f:
f.write("this is in fact, not an icon")
with pytest.raises(ValueError):
normalize_icon_type(icon, ("ico",), "ico", workpath)
| 3aad9af18641aa2181dd86cececc2aeb8a0dba06 | 140 | https://github.com/pyinstaller/pyinstaller.git | 146 | def test_normalize_icon_pillow(tmp_path):
data_dir = str(Path(PyInstaller.__file__).with_name("bootloader") / "images")
workpath = str(tmp_path)
pytest.importorskip("PIL", reason="Needs PIL / Pillow for this test")
# Alternative image - output is a different file with the correct suffix
icon = os.path.join(data_dir, 'github_logo.png')
ret = normalize_icon_type(icon, ("ico",), "ico", workpath)
_, ret_filetype = os.path.splitext(ret)
if ret_filetype != ".ico":
pytest.fail("icon validation didn't convert to the right format", False)
# Some random non-image file: Raises an image conversion error
icon = | 27 | 248 | test_normalize_icon_pillow |
|
18 | 0 | 1 | 4 | wagtail/admin/tests/api/test_pages.py | 71,326 | Reformat with black | wagtail | 11 | Python | 17 | test_pages.py | def test_meta_parent_id_doesnt_show_root_page(self):
# Root page is visible in the admin API
response = self.get_response(2)
content = json.loads(response.content.decode("UTF-8"))
self.assertIsNotNone(content["meta"]["parent"])
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 40 | https://github.com/wagtail/wagtail.git | 45 | def test_meta_parent_id_doesnt_show_root_page(self):
# Root page is visible in the admin API
response = self.get_response(2)
content = json.loads(response.content.decode("UTF-8"))
self.assertIsNotN | 9 | 71 | test_meta_parent_id_doesnt_show_root_page |
|
10 | 0 | 1 | 8 | src/prefect/orion/orchestration/core_policy.py | 60,024 | Fix scheduled time copy for flows (#7770) | prefect | 6 | Python | 10 | core_policy.py | def priority():
return [
HandleFlowTerminalStateTransitions,
PreventRedundantTransitions,
CopyScheduledTime,
WaitForScheduledTime,
RetryFailedFlows,
]
| 40201d8b8c49047cb897b8b6664635dcfffe9413 | 17 | https://github.com/PrefectHQ/prefect.git | 78 | def priority():
return [
HandleFlowTerminalStateTransitions,
PreventRedundantTransitions,
CopyScheduledTime,
WaitForScheduledTime,
RetryFailedFl | 6 | 23 | priority |
|
26 | 0 | 1 | 7 | lib/mpl_toolkits/mplot3d/art3d.py | 109,922 | Improve mpl_toolkit documentation | matplotlib | 10 | Python | 20 | art3d.py | def set_3d_properties(self, zs=0, zdir='z'):
xs = self.get_xdata()
ys = self.get_ydata()
zs = cbook._to_unmasked_float_array(zs).ravel()
zs = np.broadcast_to(zs, len(xs))
self._verts3d = juggle_axes(xs, ys, zs, zdir)
self.stale = True
| df6f95703b60348e01603f98a439b133da2938a0 | 72 | https://github.com/matplotlib/matplotlib.git | 75 | def set_3d_properties(self, zs=0, zdir='z'):
xs = self.get_xdata()
ys = self.get_ydata()
zs = cbook._to_unmasked_float_array(zs).ravel()
zs = np.broadcast_to(zs, len(xs))
self._ve | 17 | 116 | set_3d_properties |
|
35 | 0 | 3 | 10 | saleor/core/utils/random_data.py | 27,490 | Update sample products set (#9796)
* Update products data set
* Fix image filenames
* Replave `default_variant` with `default: true`
* Fix fake user creation and attribute-product assignment
* Drop preorders creation
* Optimize images
Shout out to https://github.com/ImageOptim/ImageOptim team
* Load menus from JSON
* Reduce the number of pregenerated sales
* Fix one of the images
* Apply code review changes
* Fix attr values when loading data from json dump
* Don't test user existence twice
* Add some product descriptions
* Fix failing tests
* Simplify channel query
* Reduce files sizes
* Use relative imports
Co-authored-by: Patryk Zawadzki <patrys@room-303.com>
Co-authored-by: Krzysztof Wolski <krzysztof.k.wolski@gmail.com>
Co-authored-by: IKarbowiak <iga.karbowiak@mirumee.com> | saleor | 12 | Python | 28 | random_data.py | def create_product_variant_channel_listings(product_variant_channel_listings_data):
channel_USD = Channel.objects.get(slug=settings.DEFAULT_CHANNEL_SLUG)
channel_PLN = Channel.objects.get(slug="channel-pln")
for variant_channel_listing in product_variant_channel_listings_data:
pk = variant_channel_listing["pk"]
defaults = dict(variant_channel_listing["fields"])
defaults["variant_id"] = defaults.pop("variant")
channel = defaults.pop("channel")
defaults["channel_id"] = channel_USD.pk if channel == 1 else channel_PLN.pk
ProductVariantChannelListing.objects.update_or_create(pk=pk, defaults=defaults)
| 08aa724176be00d7aaf654f14e9ae99dd4327f97 | 100 | https://github.com/saleor/saleor.git | 85 | def create_product_variant_channel_listings(product_variant_channel_listings_data):
channel_USD = Channel.objects.get(slug=settings.DEFAULT_CHANNEL_SLUG)
channel_PLN = Channel.objects.get(slug="channel-pln")
for variant_channel_listing in product_variant_channel_listings_data:
pk = variant_channel_listing["pk"]
defaults = dict(variant_channel_listing["fields"])
| 18 | 168 | create_product_variant_channel_listings |
|
30 | 0 | 1 | 13 | tests/strategy/test_strategy_loading.py | 149,113 | Add test for can_short strategy attribute | freqtrade | 11 | Python | 21 | test_strategy_loading.py | def test_strategy_can_short(caplog, default_conf):
caplog.set_level(logging.INFO)
default_conf.update({
'strategy': CURRENT_TEST_STRATEGY,
})
strat = StrategyResolver.load_strategy(default_conf)
assert isinstance(strat, IStrategy)
default_conf['strategy'] = 'StrategyTestV3Futures'
with pytest.raises(ImportError, match=""):
StrategyResolver.load_strategy(default_conf)
default_conf['trading_mode'] = 'futures'
strat = StrategyResolver.load_strategy(default_conf)
assert isinstance(strat, IStrategy)
| 20fc9459f23979f57d7925175dee376cd69acef0 | 86 | https://github.com/freqtrade/freqtrade.git | 73 | def test_strategy_can_short(caplog, default_conf):
caplog.set_level(logging.INFO)
default_conf.update({
'strategy': CURRENT_TEST_STRATEGY,
})
strat = StrategyResolver.load_strategy(default_conf)
assert isinstance(strat, IStrategy)
default_conf['strategy'] = 'StrategyTestV3Futures'
with pytest.raises(ImportError, match=""):
StrategyResolver.load_strategy(default_conf)
default_conf['trading_mode'] = 'futures'
strat = StrategyResolver.load_strategy(default_conf)
as | 17 | 147 | test_strategy_can_short |
|
50 | 0 | 5 | 14 | python3.10.4/Lib/asyncio/subprocess.py | 220,794 | add python 3.10.4 for windows | XX-Net | 13 | Python | 41 | subprocess.py | async def _feed_stdin(self, input):
debug = self._loop.get_debug()
self.stdin.write(input)
if debug:
logger.debug(
'%r communicate: feed stdin (%s bytes)', self, len(input))
try:
await self.stdin.drain()
except (BrokenPipeError, ConnectionResetError) as exc:
# communicate() ignores BrokenPipeError and ConnectionResetError
if debug:
logger.debug('%r communicate: stdin got %r', self, exc)
if debug:
logger.debug('%r communicate: close stdin', self)
self.stdin.close()
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 90 | https://github.com/XX-net/XX-Net.git | 183 | async def _feed_stdin(self, input):
debug = self._loop.get_debug()
self.stdin.write(input)
if debug:
logger.debug(
'%r communicate: feed stdin | 15 | 151 | _feed_stdin |
|
31 | 0 | 1 | 9 | tests/snuba/api/endpoints/test_organization_events_v2.py | 92,712 | tests(discover): Improve stability of eventsv2 tests (#36641)
Same motivation as #36619, this aims to improve the stability of the eventsv2
tests by moving the event timestamps further in the past. | sentry | 12 | Python | 28 | test_organization_events_v2.py | def test_performance_view_feature(self):
self.store_event(
data={"event_id": "a" * 32, "timestamp": self.ten_mins_ago, "fingerprint": ["group1"]},
project_id=self.project.id,
)
query = {"field": ["id", "project.id"], "project": [self.project.id]}
response = self.do_request(query)
assert response.status_code == 200
assert len(response.data["data"]) == 1
| ef5a739249de199b25d2cba7a2ee52820d9f34de | 87 | https://github.com/getsentry/sentry.git | 94 | def test_performance_view_feature(self):
self.store_event(
data={"event_id": "a" * 32, "timestamp": self.ten_mins_ago, "fingerprint": ["group1"]},
project_id=self.project.id,
)
query = {"field": ["id", "project.id"], "project": [self.project.id]}
response = self.do_request(query)
assert response.status_code == 200
assert len(response. | 13 | 149 | test_performance_view_feature |
|
87 | 0 | 5 | 23 | ppocr/postprocess/drrg_postprocess.py | 25,221 | add drrg | PaddleOCR | 15 | Python | 58 | drrg_postprocess.py | def __call__(self, preds, shape_list):
edges, scores, text_comps = preds
if edges is not None:
if isinstance(edges, paddle.Tensor):
edges = edges.numpy()
if isinstance(scores, paddle.Tensor):
scores = scores.numpy()
if isinstance(text_comps, paddle.Tensor):
text_comps = text_comps.numpy()
assert len(edges) == len(scores)
assert text_comps.ndim == 2
assert text_comps.shape[1] == 9
vertices, score_dict = graph_propagation(edges, scores, text_comps)
clusters = connected_components(vertices, score_dict, self.link_thr)
pred_labels = clusters2labels(clusters, text_comps.shape[0])
text_comps, pred_labels = remove_single(text_comps, pred_labels)
boundaries = comps2boundaries(text_comps, pred_labels)
else:
boundaries = []
boundaries, scores = self.resize_boundary(
boundaries, (1 / shape_list[0, 2:]).tolist()[::-1])
boxes_batch = [dict(points=boundaries, scores=scores)]
return boxes_batch
| 1f9400dd7374ce9cc47981372e324ff412e53ba3 | 207 | https://github.com/PaddlePaddle/PaddleOCR.git | 324 | def __call__(self, preds, shape_list):
edges, scores, text_comps = preds
if edges is not None:
if isinstance(edges, paddle.Tensor):
edges = edges.numpy()
if isinstance(scores, paddle.Tensor):
scores = scores.numpy()
if isinstance(text_comps, paddle.Tensor):
text_comps = text_comps.numpy()
assert len(edges) == len(scores)
assert text_comps.ndim == 2
assert text_comps.shape[1] == 9
vertices, score_dict = graph_propagation(edges, scores, text_comps)
clusters = connected_components(vertices, score_ | 30 | 318 | __call__ |
|
49 | 0 | 1 | 5 | jax/_src/lax/fft.py | 120,172 | [MHLO] Add MHLO lowerings for FFT ops.
PiperOrigin-RevId: 441768017 | jax | 11 | Python | 39 | fft.py | def _fft_batching_rule(batched_args, batch_dims, fft_type, fft_lengths):
x, = batched_args
bd, = batch_dims
x = batching.moveaxis(x, bd, 0)
return fft(x, fft_type, fft_lengths), 0
fft_p = Primitive('fft')
fft_p.def_impl(_fft_impl)
fft_p.def_abstract_eval(fft_abstract_eval)
xla.register_translation(fft_p, _fft_translation_rule)
mlir.register_lowering(fft_p, _fft_lowering)
ad.deflinear2(fft_p, _fft_transpose_rule)
batching.primitive_batchers[fft_p] = _fft_batching_rule
if pocketfft:
xla.register_translation(fft_p, _fft_translation_rule_cpu, platform='cpu')
if jax._src.lib.version >= (0, 3, 6):
mlir.register_lowering(fft_p, _fft_lowering_cpu, platform='cpu')
| 4806c29bf784c22ea10b7c87b1d03f2f42c662d4 | 42 | https://github.com/google/jax.git | 49 | def _fft_batching_rule(batched_args, batch_dims, fft_type, fft_lengths):
x, = batched_args
bd, = batch_dims
x = batching.moveaxis(x, bd, 0)
return fft(x, fft_type, fft_lengths), 0
fft_p = Primitive('fft')
fft_p.def_impl(_fft_impl)
fft_p.def_abstract_eval(fft_abstract_eval)
xla.register_translation(fft_p, _fft_translation_rule)
mlir.register_lowering(fft_p, _fft_lowering)
ad.deflinear2(fft_p, _fft_transpose_rule)
batching.primitive_batchers[fft_p] = _fft_batching_rule
if pocketfft:
xla.register_translation(fft_p, _fft_translation_rule_cpu, platform='cpu')
if j | 34 | 208 | _fft_batching_rule |
|
18 | 0 | 1 | 12 | tests/events/test_utils.py | 246,712 | Replace assertEquals and friends with non-deprecated versions. (#12092) | synapse | 15 | Python | 16 | test_utils.py | def test_event_fields_works_with_nested_keys(self):
self.assertEqual(
self.serialize(
MockEvent(
sender="@alice:localhost",
room_id="!foo:bar",
content={"body": "A message"},
),
["content.body"],
),
{"content": {"body": "A message"}},
)
| 02d708568b476f2f7716000b35c0adfa4cbd31b3 | 50 | https://github.com/matrix-org/synapse.git | 166 | def test_event_fields_works_with_nested_keys(self):
self.assertEqual(
| 8 | 89 | test_event_fields_works_with_nested_keys |
|
194 | 1 | 2 | 33 | test/nodes/test_table_reader.py | 258,390 | refactor: Remove duplicate code in TableReader (#3708)
* Refactor table reader to use util functions to reduce code duplication.
* Expanding the tests for the table reader
* Adding types
* Updating tests to work for RCIReader
* Fix bug in RCIReader. Saving the wrong queries list.
* Update _flatten_inputs to not change input variable
* Remove duplicate code | haystack | 14 | Python | 123 | test_table_reader.py | def test_table_reader_batch_single_query_single_doc_list(table_reader_and_param, table1, table2):
table_reader, param = table_reader_and_param
query = "When was Di Caprio born?"
prediction = table_reader.predict_batch(
queries=[query],
documents=[Document(content=table1, content_type="table"), Document(content=table2, 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 prediction["queries"] == ["When was Di Caprio born?", "When was Di Caprio born?"]
# Check number of answers for each document
num_ans_reference = {
"tapas_small": {"num_answers": [1, 1]},
"rci": {"num_answers": [10, 10]},
"tapas_scored": {"num_answers": [3, 3]},
}
assert len(prediction["answers"]) == 2
for i, ans_list in enumerate(prediction["answers"]):
assert len(ans_list) == num_ans_reference[param]["num_answers"][i]
# Check first answer from the 1ST document
score_reference = {"tapas_small": {"score": 1.0}, "rci": {"score": -6.5301}, "tapas_scored": {"score": 0.50568}}
assert prediction["answers"][0][0].score == pytest.approx(score_reference[param]["score"], rel=1e-3)
assert prediction["answers"][0][0].answer == "11 november 1974"
assert prediction["answers"][0][0].offsets_in_context[0].start == 7
assert prediction["answers"][0][0].offsets_in_context[0].end == 8
# Check first answer from the 2ND Document
ans_reference = {
"tapas_small": {"answer": "5 april 1980", "start": 7, "end": 8, "score": 0.86314},
"rci": {"answer": "15 september 1960", "start": 11, "end": 12, "score": -7.9429},
"tapas_scored": {"answer": "5", "start": 10, "end": 11, "score": 0.11485},
}
assert prediction["answers"][1][0].score == pytest.approx(ans_reference[param]["score"], rel=1e-3)
assert prediction["answers"][1][0].answer == ans_reference[param]["answer"]
assert prediction["answers"][1][0].offsets_in_context[0].start == ans_reference[param]["start"]
assert prediction["answers"][1][0].offsets_in_context[0].end == ans_reference[param]["end"]
@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True) | 756e0114e661767e3c59bb24cbd66c41ad4a5903 | @pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True) | 482 | https://github.com/deepset-ai/haystack.git | 336 | def test_table_reader_batch_single_query_single_doc_list(table_reader_and_param, table1, table2):
table_reader, param = table_reader_and_param
query = "When was Di Caprio born?"
prediction = table_reader.predict_batch(
queries=[query],
documents=[Document(content=table1, content_type="table"), Document(content=table2, 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 prediction["queries"] == ["When was Di Caprio born?", "When was Di Caprio born?"]
# Check number of answers for each document
num_ans_reference = {
"tapas_small": {"num_answers": [1, 1]},
"rci": {"num_answers": [10, 10]},
"tapas_scored": {"num_answers": [3, 3]},
}
assert len(prediction["answers"]) == 2
for i, ans_list in enumerate(prediction["answers"]):
assert len(ans_list) == num_ans_reference[param]["num_answers"][i]
# Check first answer from the 1ST document
score_reference = {"tapas_small": {"score": 1.0}, "rci": {"score": -6.5301}, "tapas_scored": {"score": 0.50568}}
assert prediction["answers"][0][0].score == pytest.approx(score_reference[param]["score"], rel=1e-3)
assert prediction["answers"][0][0].answer == "11 november 1974"
assert prediction["answers"][0][0].offsets_in_context[0].start == 7
assert prediction["answers"][0][0].offsets_in_context[0].end == 8
# Check first answer from the 2ND Document
ans_reference = {
"tapas_small" | 35 | 806 | test_table_reader_batch_single_query_single_doc_list |
23 | 0 | 1 | 10 | tests/models/groupvit/test_modeling_groupvit.py | 31,776 | Adding GroupViT Models (#17313)
* add group vit and fixed test (except slow)
* passing slow test
* addressed some comments
* fixed test
* fixed style
* fixed copy
* fixed segmentation output
* fixed test
* fixed relative path
* fixed copy
* add ignore non auto configured
* fixed docstring, add doc
* fixed copies
* Apply suggestions from code review
merge suggestions
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* resolve comment, renaming model
* delete unused attr
* use fix copies
* resolve comments
* fixed attn
* remove unused vars
* refactor tests
* resolve final comments
* add demo notebook
* fixed inconsitent default
* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* rename stage->stages
* Create single GroupViTEncoderLayer class
* Update conversion script
* Simplify conversion script
* Remove cross-attention class in favor of GroupViTAttention
* Convert other model as well, add processor to conversion script
* addressing final comment
* fixed args
* Update src/transformers/models/groupvit/modeling_groupvit.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local> | transformers | 10 | Python | 21 | test_modeling_groupvit.py | def create_and_check_model(self, config, pixel_values):
model = GroupViTVisionModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.num_output_groups[-1], self.hidden_size)
)
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
| 6c8f4c9a938a09749ea1b19a5fa2a8dd27e99a29 | 93 | https://github.com/huggingface/transformers.git | 93 | def create_and_check_model(self, config, pixel_values):
model = GroupViTVisionModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
resul | 20 | 144 | create_and_check_model |
|
7 | 0 | 1 | 2 | tests/components/fibaro/test_config_flow.py | 290,682 | Normalize url entered in fibaro integration setup dialog (#81996)
* Normalize url entered in fibaro integration setup dialog
* Improvements as suggested in code review
* Fix spelling in comments | core | 9 | Python | 7 | test_config_flow.py | async def test_normalize_url_add_api():
assert _normalize_url("http://192.168.1.1/") == "http://192.168.1.1/api/"
| ff1ec7a028f747de1f96521eb3df6f98d7426434 | 12 | https://github.com/home-assistant/core.git | 13 | async def test_normalize_url_add_api():
assert _normalize_url("http://192.168.1.1/") == "http://192.168.1.1/api/"
| 2 | 28 | test_normalize_url_add_api |
|
36 | 0 | 4 | 11 | tests/utils/test_ffmpeg.py | 30,377 | properly patch is file func | spotify-downloader | 12 | Python | 24 | test_ffmpeg.py | def test_get_local_ffmpeg(monkeypatch):
monkeypatch.setattr(pathlib.Path, "isfile", lambda *_: True)
platform_str = platform.system()
local_ffmpeg = get_local_ffmpeg()
assert local_ffmpeg is not None
if platform_str == "Linux":
assert str(local_ffmpeg).endswith("ffmpeg")
elif platform_str == "Darwin":
assert str(local_ffmpeg).endswith("ffmpeg")
elif platform_str == "Windows":
assert str(local_ffmpeg).endswith("ffmpeg.exe")
| 7595c08e122c43aa55eea9be9fabd2de2c1d7b9a | 84 | https://github.com/spotDL/spotify-downloader.git | 81 | def test_get_local_ffmpeg(monkeypatch):
monkeypatch.setattr(pathlib.Path, "isfile", lambda *_: True)
platform_str = platform.system()
local_ffmpeg = | 13 | 151 | test_get_local_ffmpeg |
|
70 | 0 | 4 | 13 | references/classification/sampler.py | 191,896 | only set random generator if shuffle=true (#5135)
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com> | vision | 16 | Python | 52 | sampler.py | def __iter__(self):
if self.shuffle:
# Deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = list(range(len(self.dataset)))
# Add extra samples to make it evenly divisible
indices = [ele for ele in indices for i in range(self.repetitions)]
indices += indices[: (self.total_size - len(indices))]
assert len(indices) == self.total_size
# Subsample
indices = indices[self.rank : self.total_size : self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices[: self.num_selected_samples])
| e65a857b5487a8493bc8a80a95d64d9f049de347 | 145 | https://github.com/pytorch/vision.git | 194 | def __iter__(self):
if self.shuffle:
# Deterministically sh | 26 | 230 | __iter__ |
|
32 | 0 | 5 | 14 | jina/helper.py | 12,267 | fix: close loop from run_async (#4734)
* fix: close loop from run_async
* fix: check if the loop was just created
* style: fix overload and cli autocomplete
* fix: context manage loop
* fix: style
* fix(loop): don't create new loops
* fix: create loop if not existing
* fix: close asyncio loop after fork
* fix: better loop closing
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
Co-authored-by: Deepankar Mahapatro <deepankar.mahapatro@jina.ai> | jina | 16 | Python | 31 | helper.py | def _update_policy():
if __windows__:
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
elif 'JINA_DISABLE_UVLOOP' in os.environ:
return
else:
try:
import uvloop
if not isinstance(asyncio.get_event_loop_policy(), uvloop.EventLoopPolicy):
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
except ModuleNotFoundError:
warnings.warn(
'Install `uvloop` via `pip install "jina[uvloop]"` for better performance.'
)
| c1f0ae51ed4ef76ff9aaa976d234670a296eac07 | 65 | https://github.com/jina-ai/jina.git | 142 | def _update_policy():
if __windows__:
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
elif 'JINA_DISABLE_UVLOOP' in os.environ:
return
else:
try:
import uvloop
if not isinstance(asyncio.get_event_loop_policy( | 14 | 115 | _update_policy |
|
14 | 0 | 2 | 7 | bypy/macos/__main__.py | 102,898 | Refactor: More f-string for bypy scripts | kitty | 13 | Python | 14 | __main__.py | def install_dylib(self, path, set_id=True):
shutil.copy2(path, self.frameworks_dir)
if set_id:
self.set_id(
join(self.frameworks_dir, basename(path)),
f'{self.FID}/{basename(path)}')
self.fix_dependencies_in_lib(join(self.frameworks_dir, basename(path)))
| 74e70d2548255b26983c34e81bf3c7f85caa778b | 59 | https://github.com/kovidgoyal/kitty.git | 75 | def install_dylib(self, path, set_id=True):
shutil.copy2(path, self.frameworks_dir)
if set_id:
self.set_id(
join(self.frameworks_dir, basename(path)),
f'{self.FID}/{basename(path)}')
self.fix_dependencies_in_lib(join(self.frameworks_dir, basename(path)))
| 11 | 106 | install_dylib |
|
10 | 0 | 1 | 6 | src/prefect/orion/database/orm_models.py | 53,233 | fix logging override bug | prefect | 13 | Python | 10 | orm_models.py | def versions_dir(self) -> Path:
return (
Path(prefect.orion.database.__file__).parent
/ "migrations/versions/postgresql"
)
| b937ae2f19021dabcb8b2548b3c204d6eb34a3e8 | 25 | https://github.com/PrefectHQ/prefect.git | 53 | def versions_dir(self) -> Path:
return (
Path(prefect.orion.database.__file__).parent
/ "migrations/version | 8 | 44 | versions_dir |
|
168 | 1 | 1 | 34 | sklearn/tree/tests/test_tree.py | 258,952 | MNT Update black to stable version (#22474) | scikit-learn | 12 | Python | 97 | test_tree.py | def check_class_weights(name):
TreeClassifier = CLF_TREES[name]
# Iris is balanced, so no effect expected for using 'balanced' weights
clf1 = TreeClassifier(random_state=0)
clf1.fit(iris.data, iris.target)
clf2 = TreeClassifier(class_weight="balanced", random_state=0)
clf2.fit(iris.data, iris.target)
assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
# Make a multi-output problem with three copies of Iris
iris_multi = np.vstack((iris.target, iris.target, iris.target)).T
# Create user-defined weights that should balance over the outputs
clf3 = TreeClassifier(
class_weight=[
{0: 2.0, 1: 2.0, 2: 1.0},
{0: 2.0, 1: 1.0, 2: 2.0},
{0: 1.0, 1: 2.0, 2: 2.0},
],
random_state=0,
)
clf3.fit(iris.data, iris_multi)
assert_almost_equal(clf2.feature_importances_, clf3.feature_importances_)
# Check against multi-output "auto" which should also have no effect
clf4 = TreeClassifier(class_weight="balanced", random_state=0)
clf4.fit(iris.data, iris_multi)
assert_almost_equal(clf3.feature_importances_, clf4.feature_importances_)
# Inflate importance of class 1, check against user-defined weights
sample_weight = np.ones(iris.target.shape)
sample_weight[iris.target == 1] *= 100
class_weight = {0: 1.0, 1: 100.0, 2: 1.0}
clf1 = TreeClassifier(random_state=0)
clf1.fit(iris.data, iris.target, sample_weight)
clf2 = TreeClassifier(class_weight=class_weight, random_state=0)
clf2.fit(iris.data, iris.target)
assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
# Check that sample_weight and class_weight are multiplicative
clf1 = TreeClassifier(random_state=0)
clf1.fit(iris.data, iris.target, sample_weight**2)
clf2 = TreeClassifier(class_weight=class_weight, random_state=0)
clf2.fit(iris.data, iris.target, sample_weight)
assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
@pytest.mark.parametrize("name", CLF_TREES) | 1fc86b6aacd89da44a3b4e8abf7c3e2ba4336ffe | @pytest.mark.parametrize("name", CLF_TREES) | 374 | https://github.com/scikit-learn/scikit-learn.git | 323 | def check_class_weights(name):
TreeClassifier = CLF_TREES[name]
# Iris is balanced, so no effect expected for using 'balanced' weights
clf1 = TreeClassifier(random_state=0)
clf1.fit(iris.data, iris.target)
clf2 = TreeClassifier(class_weight="balanced", random_state=0)
clf2.fit(iris.data, iris.target)
assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
# Make a multi-output problem with three copies of Iris
iris_multi = np.vstack((iris.target, iris.target, iris.target)).T
# Create user-defined weights that should balance over the outputs
clf3 = TreeClassifier(
class_weight=[
{0: 2.0, 1: 2.0, 2: 1.0},
{0: 2.0, 1: 1.0, 2: 2.0},
{0: 1.0, 1: 2.0, 2: 2.0},
],
random_state=0,
)
clf3.fit(iris.data, iris_multi)
assert_almost_equal(clf2.feature_importances_, clf3.feature_importances_)
# Check against multi-output "auto" which should | 26 | 546 | check_class_weights |
24 | 0 | 1 | 8 | modules/deepbooru.py | 152,833 | refactored the deepbooru module to improve speed on running multiple interogations in a row. Added the option to generate deepbooru tags for textual inversion preproccessing. | stable-diffusion-webui | 8 | Python | 18 | deepbooru.py | def release_process():
from modules import shared # prevents circular reference
shared.deepbooru_process_queue.put("QUIT")
shared.deepbooru_process.join()
shared.deepbooru_process_queue = None
shared.deepbooru_process = None
shared.deepbooru_process_return = None
shared.deepbooru_process_manager = None
| 1f92336be768d235c18a82acb2195b7135101ae7 | 44 | https://github.com/AUTOMATIC1111/stable-diffusion-webui.git | 49 | def release_process():
| 9 | 79 | release_process |
|
42 | 0 | 1 | 10 | pandas/tests/frame/constructors/test_from_records.py | 168,545 | Revert Interval/IntervalIndex/interval_range.inclusive deprecation (#48116)
* Revert "Cln tests interval wrt inclusive (#47775)"
This reverts commit 2d6e0b251955d3a2c0c88f7e6ddb57b335ed09b7.
* Revert "CLN: Rename private variables to inclusive (#47655)"
This reverts commit 102b3ca2119df822e2b0f346fa936d0fe9f17501.
* Revert "TYP: Improve typing interval inclusive (#47646)"
This reverts commit 55064763e8ba55f6ff5370a8dd083767a189d7a4.
* Revert "DEPR: Deprecate set_closed and add set_incluive (#47636)"
This reverts commit bd4ff395cbbf4cbde1fc8f1f746cae064a401638.
* Revert "DEPR: Remove deprecation from private class IntervalTree (#47637)"
This reverts commit f6658ef9fdef5972214fdc338e2c6b5ee308dbf4.
* Revert "Revert inclusive default change of IntervalDtype (#47367)"
This reverts commit d9dd1289e07d86928d144e53beb3d5b8ab3c2215.
* Revert "ENH: consistency of input args for boundaries - Interval (#46522)"
This reverts commit 7e23a37e1c5bda81234801a6584563e2880769eb.
* Revert "ENH: consistency of input args for boundaries - pd.interval_range (#46355)"
This reverts commit 073b3535d7a5171102e5915c38b57c21d13795ae.
* Fix ArrowIntervalType manually
* Remove unused import
* Fix doctest and leftover usage
* Fix remaining tests
* Fix wording in doctoring
Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com> | pandas | 12 | Python | 36 | test_from_records.py | def test_from_records_series_categorical_index(self):
# GH#32805
index = CategoricalIndex(
[Interval(-20, -10), Interval(-10, 0), Interval(0, 10)]
)
series_of_dicts = Series([{"a": 1}, {"a": 2}, {"b": 3}], index=index)
frame = DataFrame.from_records(series_of_dicts, index=index)
expected = DataFrame(
{"a": [1, 2, np.NaN], "b": [np.NaN, np.NaN, 3]}, index=index
)
tm.assert_frame_equal(frame, expected)
| 252ae0555abf488522f947107dcdee684be6ac8a | 119 | https://github.com/pandas-dev/pandas.git | 119 | def test_from_records_series_categorical_index(self):
# GH#32805
index = CategoricalIndex(
[Interval(-20, -10), Interval(-10, 0), Interval(0, 10)]
)
series_of_dicts = Series([{"a": 1}, {"a": 2}, {"b": 3}], index=index)
frame = DataFrame.from_records(series_of_dicts, index=index)
expe | 15 | 183 | test_from_records_series_categorical_index |
|
37 | 0 | 5 | 9 | awx/main/models/ha.py | 80,599 | Consume control capacity (#11665)
* Select control node before start task
Consume capacity on control nodes for controlling tasks and consider
remainging capacity on control nodes before selecting them.
This depends on the requirement that control and hybrid nodes should all
be in the instance group named 'controlplane'. Many tests do not satisfy that
requirement. I'll update the tests in another commit.
* update tests to use controlplane
We don't start any tasks if we don't have a controlplane instance group
Due to updates to fixtures, update tests to set node type and capacity
explicitly so they get expected result.
* Fixes for accounting of control capacity consumed
Update method is used to account for currently consumed capacity for
instance groups in the in-memory capacity tracking data structure we initialize in
after_lock_init and then update via calculate_capacity_consumed (both in
task_manager.py)
Also update fit_task_to_instance to consider control impact on instances
Trust that these functions do the right thing looking for a
node with capacity, and cut out redundant check for the whole group's
capacity per Alan's reccomendation.
* Refactor now redundant code
Deal with control type tasks before we loop over the preferred instance
groups, which cuts out the need for some redundant logic.
Also, fix a bug where I was missing assigning the execution node in one case!
* set job explanation on tasks that need capacity
move the job explanation for jobs that need capacity to a function
so we can re-use it in the three places we need it.
* project updates always run on the controlplane
Instance group ordering makes no sense on project updates because they
always need to run on the control plane.
Also, since hybrid nodes should always run the control processes for the
jobs running on them as execution nodes, account for this when looking for a
execution node.
* fix misleading message
the variables and wording were both misleading, fix to be more accurate
description in the two different cases where this log may be emitted.
* use settings correctly
use settings.DEFAULT_CONTROL_PLANE_QUEUE_NAME instead of a hardcoded
name
cache the controlplane_ig object during the after lock init to avoid
an uneccesary query
eliminate mistakenly duplicated AWX_CONTROL_PLANE_TASK_IMPACT and use
only AWX_CONTROL_NODE_TASK_IMPACT
* add test for control capacity consumption
add test to verify that when there are 2 jobs and only capacity for one
that one will move into waiting and the other stays in pending
* add test for hybrid node capacity consumption
assert that the hybrid node is used for both control and execution and
capacity is deducted correctly
* add test for task.capacity_type = control
Test that control type tasks have the right capacity consumed and
get assigned to the right instance group
Also fix lint in the tests
* jobs_running not accurate for control nodes
We can either NOT use "idle instances" for control nodes, or we need
to update the jobs_running property on the Instance model to count
jobs where the node is the controller_node.
I didn't do that because it may be an expensive query, and it would be
hard to make it match with jobs_running on the InstanceGroup which
filters on tasks assigned to the instance group.
This change chooses to stop considering "idle" control nodes an option,
since we can't acurrately identify them.
The way things are without any change, is we are continuing to over consume capacity on control nodes
because this method sees all control nodes as "idle" at the beginning
of the task manager run, and then only counts jobs started in that run
in the in-memory tracking. So jobs which last over a number of task
manager runs build up consuming capacity, which is accurately reported
via Instance.consumed_capacity
* Reduce default task impact for control nodes
This is something we can experiment with as far as what users
want at install time, but start with just 1 for now.
* update capacity docs
Describe usage of the new setting and the concept of control impact.
Co-authored-by: Alan Rominger <arominge@redhat.com>
Co-authored-by: Rebeccah <rhunter@redhat.com> | awx | 16 | Python | 24 | ha.py | def consumed_capacity(self):
capacity_consumed = 0
if self.node_type in ('hybrid', 'execution'):
capacity_consumed += sum(x.task_impact for x in UnifiedJob.objects.filter(execution_node=self.hostname, status__in=('running', 'waiting')))
if self.node_type in ('hybrid', 'control'):
capacity_consumed += sum(
settings.AWX_CONTROL_NODE_TASK_IMPACT for x in UnifiedJob.objects.filter(controller_node=self.hostname, status__in=('running', 'waiting'))
)
return capacity_consumed
| 604cbc17376620dc67df35386421835d43732a4e | 94 | https://github.com/ansible/awx.git | 112 | def consumed_capacity(self):
capacity_consumed = 0
if self.node_type in ('hybrid', 'execution'):
| 16 | 155 | consumed_capacity |
|
12 | 0 | 1 | 5 | erpnext/regional/report/e_invoice_summary/e_invoice_summary.py | 64,116 | revert: "refactor!: drop e-invoicing integration from erpnext (#26940)"
This reverts commit c335962827e4927f7ada084e9ba4ab2db15e3eb6. | erpnext | 8 | Python | 10 | e_invoice_summary.py | def execute(filters=None):
validate_filters(filters)
columns = get_columns()
data = get_data(filters)
return columns, data
| c5782b0e7107ae9ccabc923d480d49d602bafb39 | 26 | https://github.com/frappe/erpnext.git | 7 | def execute(filters=None):
validate_filters(filters)
columns = get_columns()
data = get_data(filters)
| 7 | 44 | execute |
|
236 | 0 | 2 | 22 | tests/test_modeling_utils.py | 334,400 | check with other device | diffusers | 17 | Python | 144 | test_modeling_utils.py | def test_sample(self):
generator = torch.manual_seed(0)
# 1. Load models
scheduler = GaussianDDPMScheduler.from_config("fusing/ddpm-lsun-church")
model = UNetModel.from_pretrained("fusing/ddpm-lsun-church").to(torch_device)
# 2. Sample gaussian noise
image = scheduler.sample_noise((1, model.in_channels, model.resolution, model.resolution), device=torch_device, generator=generator)
# 3. Denoise
for t in reversed(range(len(scheduler))):
# i) define coefficients for time step t
clipped_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clipped_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
image_coeff = (1 - scheduler.get_alpha_prod(t - 1)) * torch.sqrt(scheduler.get_alpha(t)) / (1 - scheduler.get_alpha_prod(t))
clipped_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
# ii) predict noise residual
with torch.no_grad():
noise_residual = model(image, t)
# iii) compute predicted image from residual
# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
pred_mean = torch.clamp(pred_mean, -1, 1)
prev_image = clipped_coeff * pred_mean + image_coeff * image
# iv) sample variance
prev_variance = scheduler.sample_variance(t, prev_image.shape, device=torch_device, generator=generator)
# v) sample x_{t-1} ~ N(prev_image, prev_variance)
sampled_prev_image = prev_image + prev_variance
image = sampled_prev_image
# Note: The better test is to simply check with the following lines of code that the image is sensible
# import PIL
# import numpy as np
# image_processed = image.cpu().permute(0, 2, 3, 1)
# image_processed = (image_processed + 1.0) * 127.5
# image_processed = image_processed.numpy().astype(np.uint8)
# image_pil = PIL.Image.fromarray(image_processed[0])
# image_pil.save("test.png")
assert image.shape == (1, 3, 256, 256)
image_slice = image[0, -1, -3:, -3:].cpu()
import ipdb; ipdb.set_trace()
assert (image_slice - torch.tensor([[-0.0598, -0.0611, -0.0506], [-0.0726, 0.0220, 0.0103], [-0.0723, -0.1310, -0.2458]])).abs().sum() < 1e-3
| b76eea041234a75ebb5451a7c2aba3eb7f844f9f | 369 | https://github.com/huggingface/diffusers.git | 578 | def test_sample(self):
generator = torch.manual_seed(0)
# 1. Load models
scheduler = GaussianDDPMScheduler.from_config("fusing/ddpm-lsun-church")
model = UNetModel.from_pretrained("fusing/ddpm-lsun-church").to(torch_device)
# 2. Sample gaussian noise
image = scheduler.sample_noise((1, model.in_channels, model.resolution, model.resolution), device=torch_device, generator=generator)
# 3. Denoise
for t in reversed(range(len(scheduler))):
# i) define coefficients for time step t
clipped_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clipped_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
image_coeff = (1 - scheduler.get_alpha_prod(t - 1)) * torch.sqrt(scheduler.get_alpha(t)) / (1 - scheduler.get_alpha_prod(t))
clipped_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
# ii) predict noise residual
with torch.no_grad():
noise_residual = model(image, t)
# iii) compute predicted image from residual
# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
pred_mean = torch.clamp(pred_mean, -1, 1)
prev_image = cl | 46 | 555 | test_sample |
|
325 | 0 | 30 | 77 | mindsdb/api/mysql/mysql_proxy/executor/executor_commands.py | 114,976 | del datastore | mindsdb | 17 | Python | 168 | executor_commands.py | def answer_create_predictor(self, statement):
integration_name = None
struct = {
'predictor_name': statement.name.parts[-1],
'select': statement.query_str,
'predict': [x.parts[-1] for x in statement.targets]
}
if len(struct['predict']) > 1:
raise SqlApiException("Only one field can be in 'PREDICT'")
if isinstance(statement.integration_name, Identifier):
struct['integration_name'] = statement.integration_name.parts[-1]
if statement.using is not None:
struct['using'] = statement.using
if statement.datasource_name is not None:
struct['datasource_name'] = statement.datasource_name.parts[-1]
if statement.order_by is not None:
struct['order_by'] = [x.field.parts[-1] for x in statement.order_by]
if len(struct['order_by']) > 1:
raise SqlApiException("Only one field can be in 'OPRDER BY'")
if statement.group_by is not None:
struct['group_by'] = [x.parts[-1] for x in statement.group_by]
if statement.window is not None:
struct['window'] = statement.window
if statement.horizon is not None:
struct['horizon'] = statement.horizon
model_interface = self.session.model_interface
models = model_interface.get_models()
model_names = [x['name'] for x in models]
if struct['predictor_name'] in model_names:
raise SqlApiException(f"Predictor with name '{struct['predictor_name']}' already exists. Each predictor must have unique name.")
predictor_name = struct['predictor_name']
integration_name = struct.get('integration_name')
if integration_name is not None:
handler = self.session.integration_controller.get_handler(integration_name)
# TODO
# raise ErBadDbError(f"Unknown datasource: {integration_name}")
result = handler.native_query(struct['select'])
if result.get('type') != RESPONSE_TYPE.TABLE:
raise Exception(f'Error during query: {result.get("error_message")}')
ds_data_df = result['data_frame']
ds_column_names = list(ds_data_df.columns)
predict = self._check_predict_columns(struct['predict'], ds_column_names)
for i, p in enumerate(predict):
predict[i] = get_column_in_case(ds_column_names, p)
else:
predict = struct['predict']
timeseries_settings = {}
for w in ['order_by', 'group_by', 'window', 'horizon']:
if w in struct:
timeseries_settings[w] = struct.get(w)
kwargs = struct.get('using', {})
if len(timeseries_settings) > 0:
if 'timeseries_settings' not in kwargs:
kwargs['timeseries_settings'] = timeseries_settings
else:
if isinstance(kwargs.get('timeseries_settings'), str):
kwargs['timeseries_settings'] = json.loads(kwargs['timeseries_settings'])
kwargs['timeseries_settings'].update(timeseries_settings)
# Cast all column names to same case
if isinstance(kwargs.get('timeseries_settings'), dict):
order_by = kwargs['timeseries_settings'].get('order_by')
if order_by is not None:
for i, col in enumerate(order_by):
new_name = get_column_in_case(ds_column_names, col)
if new_name is None:
raise Exception(
f'Cant get appropriate cast column case. Columns: {ds_column_names}, column: {col}'
)
kwargs['timeseries_settings']['order_by'][i] = new_name
group_by = kwargs['timeseries_settings'].get('group_by')
if group_by is not None:
for i, col in enumerate(group_by):
new_name = get_column_in_case(ds_column_names, col)
kwargs['timeseries_settings']['group_by'][i] = new_name
if new_name is None:
raise Exception(
f'Cant get appropriate cast column case. Columns: {ds_column_names}, column: {col}'
)
model_interface.learn(predictor_name, ds_data_df, predict, kwargs=kwargs)
return ExecuteAnswer(ANSWER_TYPE.OK)
| 18134ada88727d64eb2b3f04f303e3b66e43a1ec | 642 | https://github.com/mindsdb/mindsdb.git | 1,257 | def answer_create_predictor(self, statement):
integration_name = None
struct = {
'predictor_name': statement.name.parts[-1],
'select': statement.query_str,
'predict': [x.parts[-1] for x in statement.targets]
}
if len(struct['predict']) > 1:
raise SqlApiException("Only one field can be in 'PREDICT'")
if isinstance(statement.integration_name, Identifier):
struct['integration_name'] = statement.integration_name.parts[-1]
if statement.using is not None:
struct['using'] = statement.using
if statement.datasource_name is not None:
struct['datasource_name'] = statement.datasource_name.parts[-1]
if statement.order_by is not None:
struct['order_by'] = [x.field.parts[-1] for x in statement.order_by]
if len(struct['order_by']) > 1:
raise SqlApiException("Only one field can be in 'OPRDER BY'")
if statement.group_by is not None:
struct['group_by'] = [x.parts[-1] for x in statement.group_by]
if statement.window is not None:
struct['window'] = statement.window
if statement.horizon is not None:
struct['horizon'] = statement.horizon
m | 60 | 1,101 | answer_create_predictor |
|
10 | 0 | 1 | 7 | test/mitmproxy/data/addonscripts/configure.py | 251,781 | make it black! | mitmproxy | 9 | Python | 10 | configure.py | def load(self, loader):
loader.add_option(
name="optionaddon",
typespec=Optional[int],
default=None,
help="Option Addon",
)
| b3587b52b25077f68116b9852b041d33e7fc6601 | 31 | https://github.com/mitmproxy/mitmproxy.git | 67 | def load(self, loader):
loader.add_option(
name="optionaddon",
typespec=Optional[int],
default=None,
help="Option Addon",
)
| 10 | 48 | load |
|
25 | 0 | 1 | 10 | tests/unit/types/request/test_request.py | 12,707 | feat: allow to access parameters of data request wo loading data (#4991) | jina | 10 | Python | 18 | test_request.py | def test_status():
r = DataRequest()
r.docs.extend([Document()])
r.add_exception(ValueError('intentional_error'))
byte_array = DataRequestProto.SerializeToString(r)
deserialized_request = DataRequestProto.FromString(byte_array)
assert not deserialized_request.is_decompressed_with_data
assert deserialized_request.status.code == jina_pb2.StatusProto.ERROR
assert deserialized_request.is_decompressed_wo_data
assert not deserialized_request.is_decompressed_with_data
| c3849c6fee4a65a77a82b2cfda9670d727ff0f53 | 72 | https://github.com/jina-ai/jina.git | 51 | def test_status():
r = DataRequest()
r.docs.extend([Document()])
r.add_exception(ValueError('intentional_ | 20 | 120 | test_status |
|
16 | 0 | 3 | 6 | sympy/combinatorics/fp_groups.py | 197,297 | Remove abbreviations in documentation | sympy | 11 | Python | 14 | fp_groups.py | def equals(self, word1, word2):
if self.reduce(word1*word2**-1) == self.identity:
return True
elif self._rewriting_system.is_confluent:
return False
return None
| 65be461082dda54c8748922f9c29a19af1279fe1 | 40 | https://github.com/sympy/sympy.git | 66 | def equals(self, word1, word2):
if self.reduce(word1*word2**-1) == self.identity:
re | 8 | 64 | equals |
|
11 | 0 | 1 | 5 | tests/auth_tests/test_management.py | 201,378 | Refs #33476 -- Reformatted code with Black. | django | 10 | Python | 11 | test_management.py | def test_existing(self):
User.objects.create(username="joe")
management.get_system_username = lambda: "joe"
self.assertEqual(management.get_default_username(), "")
self.assertEqual(management.get_default_username(check_db=False), "joe")
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 49 | https://github.com/django/django.git | 38 | def test_existing(self):
User.objects.create(username="joe")
management.get_system_username = lambda: "joe"
self.assertEqual(management.get_default_username(), "")
self.assertEqual(management.get_default | 11 | 86 | test_existing |
|
10 | 0 | 3 | 4 | django/db/models/sql/query.py | 205,843 | Refs #33476 -- Reformatted code with Black. | django | 10 | Python | 9 | query.py | def has_select_fields(self):
return bool(
self.select or self.annotation_select_mask or self.extra_select_mask
)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 20 | https://github.com/django/django.git | 34 | def has_select_fields(self):
return bool(
self.select or self.annotation_select_mask or self.extra_select_mask
)
| 6 | 32 | has_select_fields |
|
66 | 0 | 5 | 25 | ppstructure/kie/predict_kie_token_ser_re.py | 25,283 | add ser to ppstructure system | PaddleOCR | 11 | Python | 50 | predict_kie_token_ser_re.py | def __call__(self, img):
starttime = time.time()
ser_results, ser_inputs, ser_elapse = self.ser_engine(img)
if self.predictor is None:
return ser_results, ser_elapse
re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
if self.use_visual_backbone == False:
re_input.pop(4)
for idx in range(len(self.input_tensor)):
self.input_tensor[idx].copy_from_cpu(re_input[idx])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = dict(
loss=outputs[1],
pred_relations=outputs[2],
hidden_states=outputs[0], )
post_result = self.postprocess_op(
preds,
ser_results=ser_results,
entity_idx_dict_batch=entity_idx_dict_batch)
elapse = time.time() - starttime
return post_result, elapse
| d4a4c07c561421832f3207e41a0eba3460d431d7 | 173 | https://github.com/PaddlePaddle/PaddleOCR.git | 277 | def __call__(self, img):
starttime = time.time()
ser_results, ser_inputs, ser_elapse = self.ser_engine(img)
if self.predictor is None:
return ser_results, ser_elapse
re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
if self.use_visual_backbone == False:
| 35 | 266 | __call__ |
|
70 | 0 | 7 | 21 | datasets/exams/exams.py | 105,478 | Fix bug and checksums in exams dataset (#4853)
* Fix KeyError in exams dataset
* Update metadata JSON
* Fix dataset card | datasets | 20 | Python | 50 | exams.py | def _generate_examples(self, filepath, files=None):
if self.config.name == "alignments":
with open(filepath, encoding="utf-8") as f:
for id_, line in enumerate(f):
line_dict = json.loads(line.strip())
in_id, out_list = list(line_dict.items())[0]
yield id_, {"source_id": in_id, "target_id_list": out_list}
else:
for path, f in files:
if path == filepath:
for id_, line in enumerate(f):
line_dict = json.loads(line.strip())
for choice in line_dict["question"]["choices"]:
choice["para"] = choice.get("para", "")
yield id_, {
"id": line_dict["id"],
"question": line_dict["question"],
"answerKey": line_dict["answerKey"],
"info": line_dict["info"],
}
break
| b88a656cf94c4ad972154371c83c1af759fde522 | 175 | https://github.com/huggingface/datasets.git | 457 | def _generate_examples(self, filepath, files=None):
if self.config.name == "alignments":
with open(filepath, encoding="utf-8") as f:
for id_, line in enumerate(f):
line_dict = json.loads(line.strip())
in_id, out_list = list(line_dict.items())[0]
yield id_, {"source_id": in_id, "target_id_list": out_list}
else:
for path, f in files:
if path == filepath:
for id_, line in enumerate(f):
line_dict = json.loads(line.strip())
for choice in line_dict["question"]["choices"]:
choice["para"] = choice.get("para", "")
yield id_, {
"id": line_dict["id"],
| 23 | 300 | _generate_examples |
|
187 | 0 | 2 | 60 | python/ccxt/coinbasepro.py | 17,935 | 1.72.67
[ci skip] | ccxt | 20 | Python | 110 | coinbasepro.py | def fetch_markets(self, params={}):
response = self.publicGetProducts(params)
#
# [
# {
# "id":"ZEC-BTC",
# "base_currency":"ZEC",
# "quote_currency":"BTC",
# "base_min_size":"0.01000000",
# "base_max_size":"1500.00000000",
# "quote_increment":"0.00000100",
# "base_increment":"0.00010000",
# "display_name":"ZEC/BTC",
# "min_market_funds":"0.001",
# "max_market_funds":"30",
# "margin_enabled":false,
# "post_only":false,
# "limit_only":false,
# "cancel_only":false,
# "trading_disabled":false,
# "status":"online",
# "status_message":""
# }
# ]
#
result = []
for i in range(0, len(response)):
market = response[i]
id = self.safe_string(market, 'id')
baseId = self.safe_string(market, 'base_currency')
quoteId = self.safe_string(market, 'quote_currency')
base = self.safe_currency_code(baseId)
quote = self.safe_currency_code(quoteId)
status = self.safe_string(market, 'status')
result.append(self.extend(self.fees['trading'], {
'id': id,
'symbol': base + '/' + quote,
'base': base,
'quote': quote,
'settle': None,
'baseId': baseId,
'quoteId': quoteId,
'settleId': None,
'type': 'spot',
'spot': True,
'margin': self.safe_value(market, 'margin_enabled'),
'swap': False,
'future': False,
'option': False,
'active': (status == 'online'),
'contract': False,
'linear': None,
'inverse': None,
'contractSize': None,
'expiry': None,
'expiryDatetime': None,
'strike': None,
'optionType': None,
'precision': {
'amount': self.safe_number(market, 'base_increment'),
'price': self.safe_number(market, 'quote_increment'),
},
'limits': {
'leverage': {
'min': None,
'max': None,
},
'amount': {
'min': self.safe_number(market, 'base_min_size'),
'max': self.safe_number(market, 'base_max_size'),
},
'price': {
'min': None,
'max': None,
},
'cost': {
'min': self.safe_number(market, 'min_market_funds'),
'max': self.safe_number(market, 'max_market_funds'),
},
},
'info': market,
}))
return result
| a485e7e15458907349ee510684112af2430e39e4 | 341 | https://github.com/ccxt/ccxt.git | 1,496 | def fetch_markets(self, params={}):
response = self.publicGetProducts(params)
#
# [
# {
# "id":"ZEC-BTC",
# "base_currency":"ZEC",
# "quote_currency":"BTC",
# "base_min_size":"0.01000000",
# | 23 | 609 | fetch_markets |