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1 | 37 | def test_gpu_stats_monitor_no_queries(tmpdir):
model = BoringModel()
with pytest.deprecated_call(match="GPUStatsMonitor` callback was deprecated in v1.5"):
gpu_stats = GPUStatsMonitor(
memory_utilization=False,
gpu_utilization=False,
intra_step_time=True,
inter_step_time=True,
)
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=2,
limit_val_batches=0,
log_every_n_steps=1,
accelerator="gpu",
devices=1,
callbacks=[gpu_stats],
)
with mock.patch("pytorch_lightning.loggers.tensorboard.TensorBoardLogger.log_metrics") as log_metrics_mock:
trainer.fit(model)
assert log_metrics_mock.mock_calls[1:] == [
mock.call({"batch_time/intra_step (ms)": mock.ANY}, step=0),
mock.call({"batch_time/inter_step (ms)": mock.ANY}, step=1),
mock.call({"batch_time/intra_step (ms)": mock.ANY}, step=1),
]
@pytest.mark.skipif(torch.cuda.is_available(), reason="test requires CPU machine") | tests/callbacks/test_gpu_stats_monitor.py | 283 | @pytest.mark.skipif(torch.cuda.is_available(), reason="test requires CPU machine") | lightning | {
"docstring": "Test GPU logger doesn't fail if no \"nvidia-smi\" queries are to be performed.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | 59 | Python | 49 | 4710a8128b52179be2b1fa46b17677eda7b849ea | test_gpu_stats_monitor.py | 241,709 | 26 | 159 | test_gpu_stats_monitor_no_queries | https://github.com/Lightning-AI/lightning.git | Update test_gpu_stats_monitor.py to use `devices` instead of `gpus` or `ipus` (#11340) | 224 | 1 | 69,662 | 12 |
6 | 33 | def _unpack_observation(self, obs_batch):
unpacked = _unpack_obs(
np.array(obs_batch, dtype=np.float32),
self.observation_space.original_space,
tensorlib=np,
)
if isinstance(unpacked[0], dict):
assert "obs" in unpacked[0]
unpacked_obs = [np.concatenate(tree.flatten(u["obs"]), 1) for u in unpacked]
else:
unpacked_obs = unpacked
obs = np.concatenate(unpacked_obs, axis=1).reshape(
[len(obs_batch), self.n_agents, self.obs_size]
)
if self.has_action_mask:
action_mask = np.concatenate(
[o["action_mask"] for o in unpacked], axis=1
).reshape([len(obs_batch), self.n_agents, self.n_actions])
else:
action_mask = np.ones(
[len(obs_batch), self.n_agents, self.n_actions], dtype=np.float32
)
if self.has_env_global_state:
state = np.concatenate(tree.flatten(unpacked[0][ENV_STATE]), 1)
else:
state = None
return obs, action_mask, state
| rllib/agents/qmix/qmix_policy.py | 338 | ray | {
"docstring": "Unpacks the observation, action mask, and state (if present)\n from agent grouping.\n\n Returns:\n obs (np.ndarray): obs tensor of shape [B, n_agents, obs_size]\n mask (np.ndarray): action mask, if any\n state (np.ndarray or None): state tensor of shape [B, state_size]\n or None if it is not in the batch\n ",
"language": "en",
"n_whitespaces": 116,
"n_words": 47,
"vocab_size": 34
} | 75 | Python | 50 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | qmix_policy.py | 133,808 | 27 | 223 | _unpack_observation | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 332 | 0 | 30,117 | 15 |
|
3 | 15 | def test_create_accessible(self):
response, page = self._create_page(Page.objects.get(pk=2))
self.assertIsNotNone(page.url)
self.assertTrue(
any(
"View live" in message.message and page.url in message.message
for message in response.context["messages"]
)
)
| wagtail/admin/tests/pages/test_edit_page.py | 105 | wagtail | {
"docstring": "\n Create a page under the site root, check the flash message has a valid\n \"View live\" button.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 17,
"vocab_size": 15
} | 23 | Python | 19 | d10f15e55806c6944827d801cd9c2d53f5da4186 | test_edit_page.py | 71,575 | 9 | 63 | test_create_accessible | https://github.com/wagtail/wagtail.git | Reformat with black | 110 | 0 | 15,690 | 12 |
|
2 | 12 | def q_sample(self, x_start, t, noise=None):
if noise is None:
# noise = th.randn_like(x_start)
noise = paddle.randn(x_start.shape, x_start.dtype)
assert noise.shape == x_start.shape
return (_extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
_extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
| modules/image/text_to_image/disco_diffusion_cnclip_vitb16/reverse_diffusion/model/gaussian_diffusion.py | 109 | PaddleHub | {
"docstring": "\n Diffuse the data for a given number of diffusion steps.\n\n In other words, sample from q(x_t | x_0).\n\n :param x_start: the initial data batch.\n :param t: the number of diffusion steps (minus 1). Here, 0 means one step.\n :param noise: if specified, the split-out normal noise.\n :return: A noisy version of x_start.\n ",
"language": "en",
"n_whitespaces": 102,
"n_words": 52,
"vocab_size": 42
} | 33 | Python | 26 | f4d6e64cdc132ae868699a0ba442f4ab1d304a14 | gaussian_diffusion.py | 49,786 | 6 | 73 | q_sample | https://github.com/PaddlePaddle/PaddleHub.git | add disco_diffusion_cnclip_vitb16 module | 98 | 0 | 9,909 | 11 |
|
7 | 19 | def parse_query_string(query_string, operator=None, zero_terms=MATCH_NONE):
filters, query_string = separate_filters_from_query(query_string)
is_phrase = False
tokens = []
for part in query_string.split('"'):
part = part.strip()
if part:
if is_phrase:
tokens.append(Phrase(part))
else:
tokens.append(
PlainText(part, operator=operator or PlainText.DEFAULT_OPERATOR)
)
is_phrase = not is_phrase
if tokens:
if operator == "or":
search_query = OR(tokens)
else:
search_query = AND(tokens)
else:
search_query = zero_terms
return filters, search_query
| wagtail/search/utils.py | 193 | wagtail | {
"docstring": "\n This takes a query string typed in by a user and extracts the following:\n\n - Quoted terms (for phrase search)\n - Filters\n\n For example, the following query:\n\n `hello \"this is a phrase\" live:true` would be parsed into:\n\n filters: {'live': 'true'}\n tokens: And([PlainText('hello'), Phrase('this is a phrase')])\n ",
"language": "en",
"n_whitespaces": 75,
"n_words": 46,
"vocab_size": 40
} | 57 | Python | 38 | d10f15e55806c6944827d801cd9c2d53f5da4186 | utils.py | 75,890 | 22 | 115 | parse_query_string | https://github.com/wagtail/wagtail.git | Reformat with black | 231 | 0 | 16,438 | 19 |
|
1 | 20 | def test_memory_usage_completed_flows(tctx):
gc.collect()
flow_count = flows_tracked()
server = Placeholder(Server)
assert (
Playbook(http.HttpLayer(tctx, HTTPMode.regular), hooks=False)
>> DataReceived(tctx.client, b"GET http://example.com/ HTTP/1.1\r\nHost: example.com\r\n\r\n")
<< OpenConnection(server)
>> reply(None)
<< SendData(server, b"GET / HTTP/1.1\r\nHost: example.com\r\n\r\n")
>> DataReceived(server, b"HTTP/1.1 204 No Content\r\n\r\n")
<< SendData(tctx.client, b"HTTP/1.1 204 No Content\r\n\r\n")
)
gc.collect()
assert flows_tracked() == flow_count
| test/mitmproxy/proxy/layers/http/test_http.py | 179 | mitmproxy | {
"docstring": "Make sure that flows are not kept in memory after they are completed.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 12
} | 48 | Python | 32 | 035b3bf37d9785fef45e81eb9c0c47fc53ab24d2 | test_http.py | 251,123 | 15 | 105 | test_memory_usage_completed_flows | https://github.com/mitmproxy/mitmproxy.git | drop HTTP streams that are completed, fix #4456 | 149 | 0 | 73,601 | 17 |
|
1 | 3 | def test_bad_csrf_cookie_length(self):
self._check_bad_or_missing_cookie(16 * "a", "CSRF cookie has incorrect length.")
| tests/csrf_tests/tests.py | 32 | django | {
"docstring": "\n If the CSRF cookie has an incorrect length in a POST request, the\n middleware rejects the incoming request.\n ",
"language": "en",
"n_whitespaces": 40,
"n_words": 18,
"vocab_size": 16
} | 10 | Python | 10 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 202,430 | 2 | 16 | test_bad_csrf_cookie_length | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 24 | 0 | 50,129 | 9 |
|
1 | 19 | def test_dynamic_path(self):
doc = Document.objects.create(
title="does not matter",
created=timezone.make_aware(datetime.datetime(2020, 6, 25, 7, 36, 51, 153)),
mime_type="application/pdf",
pk=2,
checksum="2",
storage_path=StoragePath.objects.create(path="TestFolder/{created}"),
)
self.assertEqual(generate_filename(doc), "TestFolder/2020-06-25.pdf")
| src/documents/tests/test_file_handling.py | 127 | paperless-ngx | {
"docstring": "\n GIVEN:\n - A document with a defined storage path\n WHEN:\n - the filename is generated for the document\n THEN:\n - the generated filename uses the defined storage path for the document\n ",
"language": "en",
"n_whitespaces": 93,
"n_words": 31,
"vocab_size": 17
} | 22 | Python | 22 | 69ef26dab04d51e7e102dcb33cd98ddc6ad975fd | test_file_handling.py | 319,622 | 10 | 81 | test_dynamic_path | https://github.com/paperless-ngx/paperless-ngx.git | Feature: Dynamic document storage pathes (#916)
* Added devcontainer
* Add feature storage pathes
* Exclude tests and add versioning
* Check escaping
* Check escaping
* Check quoting
* Echo
* Escape
* Escape :
* Double escape \
* Escaping
* Remove if
* Escape colon
* Missing \
* Esacpe :
* Escape all
* test
* Remove sed
* Fix exclude
* Remove SED command
* Add LD_LIBRARY_PATH
* Adjusted to v1.7
* Updated test-cases
* Remove devcontainer
* Removed internal build-file
* Run pre-commit
* Corrected flak8 error
* Adjusted to v1.7
* Updated test-cases
* Corrected flak8 error
* Adjusted to new plural translations
* Small adjustments due to code-review backend
* Adjusted line-break
* Removed PAPERLESS prefix from settings variables
* Corrected style change due to search+replace
* First documentation draft
* Revert changes to Pipfile
* Add sphinx-autobuild with keep-outdated
* Revert merge error that results in wrong storage path is evaluated
* Adjust styles of generated files ...
* Adds additional testing to cover dynamic storage path functionality
* Remove unnecessary condition
* Add hint to edit storage path dialog
* Correct spelling of pathes to paths
* Minor documentation tweaks
* Minor typo
* improving wrapping of filter editor buttons with new storage path button
* Update .gitignore
* Fix select border radius in non input-groups
* Better storage path edit hint
* Add note to edit storage path dialog re document_renamer
* Add note to bulk edit storage path re document_renamer
* Rename FILTER_STORAGE_DIRECTORY to PATH
* Fix broken filter rule parsing
* Show default storage if unspecified
* Remove note re storage path on bulk edit
* Add basic validation of filename variables
Co-authored-by: Markus Kling <[email protected]>
Co-authored-by: Trenton Holmes <[email protected]>
Co-authored-by: Michael Shamoon <[email protected]>
Co-authored-by: Quinn Casey <[email protected]> | 116 | 0 | 116,979 | 13 |
|
4 | 19 | def appell_poly(n, seq, v, f, K, x=None, polys=False):
if n < 0:
raise ValueError(
"Cannot generate Appell sequence polynomial of order %s" % n)
poly = DMP(dup_appell(int(n), seq, v, f, K), K)
if x is not None:
poly = Poly.new(poly, x)
else:
poly = PurePoly.new(poly, Dummy('x'))
return poly if polys else poly.as_expr()
@public | sympy/polys/appellseqs.py | 151 | @public | sympy | {
"docstring": "Generates the nth polynomial in `x` of the Appell sequence with\n parameters `seq`, `v` and `f`.\n\n Parameters\n ==========\n\n n : int\n Order of the polynomial.\n seq : iterable\n v : Expr\n f : callable\n K : Domain\n Domain in which to perform computations and in which the coefficients\n of the specified sequence's polynomials lie in.\n x : optional\n polys : bool, optional\n If True, return a Poly, otherwise (default) return an expression.\n ",
"language": "en",
"n_whitespaces": 133,
"n_words": 72,
"vocab_size": 53
} | 53 | Python | 43 | e875bdb804b0285e4a9bd8de0158436e792c03cb | appellseqs.py | 199,617 | 10 | 97 | appell_poly | https://github.com/sympy/sympy.git | Initial definition of Appell sequences | 102 | 1 | 49,295 | 14 |
2 | 8 | def config_for_enable_caching_device(rnn_cell):
default_enable_caching_device = (
tf.compat.v1.executing_eagerly_outside_functions()
)
if rnn_cell._enable_caching_device != default_enable_caching_device:
return {"enable_caching_device": rnn_cell._enable_caching_device}
return {}
| keras/layers/rnn/rnn_utils.py | 64 | keras | {
"docstring": "Return the dict config for RNN cell wrt to enable_caching_device field.\n\n Since enable_caching_device is a internal implementation detail for speed up\n the RNN variable read when running on the multi remote worker setting, we\n don't want this config to be serialized constantly in the JSON. We will only\n serialize this field when a none default value is used to create the cell.\n Args:\n rnn_cell: the RNN cell for serialize.\n\n Returns:\n A dict which contains the JSON config for enable_caching_device value or\n empty dict if the enable_caching_device value is same as the default value.\n ",
"language": "en",
"n_whitespaces": 129,
"n_words": 93,
"vocab_size": 62
} | 16 | Python | 15 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | rnn_utils.py | 274,086 | 7 | 37 | config_for_enable_caching_device | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 45 | 0 | 81,176 | 11 |
|
1 | 2 | def size(self):
return self["size"]
| packages/python/plotly/plotly/graph_objs/bar/marker/_pattern.py | 22 | plotly.py | {
"docstring": "\n Sets the size of unit squares of the pattern fill in pixels,\n which corresponds to the interval of repetition of the pattern.\n\n The 'size' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|float|numpy.ndarray\n ",
"language": "en",
"n_whitespaces": 125,
"n_words": 57,
"vocab_size": 44
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _pattern.py | 228,787 | 2 | 11 | size | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 60,460 | 7 |
|
4 | 18 | def _cmp_op(self, other, op_name):
lhs_dtype_class = self._get_dtype_cmp_class(self._dtype)
rhs_dtype_class = self._get_dtype_cmp_class(other._dtype)
res_dtype = get_dtype(bool)
# In HDK comparison with NULL always results in NULL,
# but in pandas it is True for 'ne' comparison and False
# for others.
# Also pandas allows 'eq' and 'ne' comparison for values
# of incompatible types which doesn't work in HDK.
if lhs_dtype_class != rhs_dtype_class:
if op_name == "eq" or op_name == "ne":
return LiteralExpr(op_name == "ne")
else:
raise TypeError(
f"Invalid comparison between {self._dtype} and {other._dtype}"
)
else:
cmp = OpExpr(self.binary_operations[op_name], [self, other], res_dtype)
return build_if_then_else(
self.is_null(), LiteralExpr(op_name == "ne"), cmp, res_dtype
)
| modin/experimental/core/execution/native/implementations/hdk_on_native/expr.py | 192 | modin | {
"docstring": "\n Build a comparison expression.\n\n Parameters\n ----------\n other : BaseExpr\n A value to compare with.\n op_name : str\n The comparison operation name.\n\n Returns\n -------\n BaseExpr\n The resulting comparison expression.\n ",
"language": "en",
"n_whitespaces": 125,
"n_words": 28,
"vocab_size": 22
} | 99 | Python | 70 | e5b1888cd932909e49194d58035da34b210b91c4 | expr.py | 154,585 | 16 | 106 | _cmp_op | https://github.com/modin-project/modin.git | FEAT-#4946: Replace OmniSci with HDK (#4947)
Co-authored-by: Iaroslav Igoshev <[email protected]>
Signed-off-by: Andrey Pavlenko <[email protected]> | 310 | 0 | 36,095 | 15 |
|
1 | 8 | def mock_update_empty_fixture(mock_update):
mock_update.return_value = None
yield mock_update
@pytest.mark.parametrize(
"data,options",
[(MOCK_CONFIG, {})],
)
@pytest.mark.usefixtures("mock_update", "mock_config") | tests/components/google_travel_time/test_sensor.py | 75 | @pytest.mark.parametrize(
"data,options",
[(MOCK_CONFIG, {})],
)
@pytest.mark.usefixtures("mock_update", "mock_config") | core | {
"docstring": "Mock an update to the sensor with an empty response.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 14 | Python | 14 | beb30a1ff199596163c655e8ae745a0f1649b78a | test_sensor.py | 292,225 | 3 | 13 | mock_update_empty_fixture | https://github.com/home-assistant/core.git | Add google_travel_time sensor tests (#66568)
Co-authored-by: Paulus Schoutsen <[email protected]> | 26 | 1 | 91,325 | 9 |
1 | 3 | async def count_real_users(self) -> int:
| synapse/storage/databases/main/registration.py | 17 | synapse | {
"docstring": "Counts all users without a special user_type registered on the homeserver.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 5 | Python | 5 | 1783156dbcf4164692e66275d1c29857c434995b | registration.py | 248,017 | 4 | 22 | count_real_users | https://github.com/matrix-org/synapse.git | Add some type hints to datastore (#12423)
* Add some type hints to datastore
* newsfile
* change `Collection` to `List`
* refactor return type of `select_users_txn`
* correct type hint in `stream.py`
* Remove `Optional` in `select_users_txn`
* remove not needed return type in `__init__`
* Revert change in `get_stream_id_for_event_txn`
* Remove import from `Literal` | 12 | 0 | 72,048 | 6 |
|
15 | 34 | def sign(e, x):
if not isinstance(e, Basic):
raise TypeError("e should be an instance of Basic")
if e.is_positive:
return 1
elif e.is_negative:
return -1
elif e.is_zero:
return 0
elif not e.has(x):
from sympy.simplify import logcombine
e = logcombine(e)
return _sign(e)
elif e == x:
return 1
elif e.is_Mul:
a, b = e.as_two_terms()
sa = sign(a, x)
if not sa:
return 0
return sa * sign(b, x)
elif isinstance(e, exp):
return 1
elif e.is_Pow:
if e.base == S.Exp1:
return 1
s = sign(e.base, x)
if s == 1:
return 1
if e.exp.is_Integer:
return s**e.exp
elif isinstance(e, log):
return sign(e.args[0] - 1, x)
# if all else fails, do it the hard way
c0, e0 = mrv_leadterm(e, x)
return sign(c0, x)
@debug
@timeit
@cacheit | sympy/series/gruntz.py | 339 | @debug
@timeit
@cacheit | sympy | {
"docstring": "\n Returns a sign of an expression e(x) for x->oo.\n\n ::\n\n e > 0 for x sufficiently large ... 1\n e == 0 for x sufficiently large ... 0\n e < 0 for x sufficiently large ... -1\n\n The result of this function is currently undefined if e changes sign\n arbitrarily often for arbitrarily large x (e.g. sin(x)).\n\n Note that this returns zero only if e is *constantly* zero\n for x sufficiently large. [If e is constant, of course, this is just\n the same thing as the sign of e.]\n ",
"language": "en",
"n_whitespaces": 139,
"n_words": 89,
"vocab_size": 50
} | 121 | Python | 73 | f757f3daae6e11ea0cfb7dadc133274d8d74315f | gruntz.py | 196,818 | 35 | 209 | sign | https://github.com/sympy/sympy.git | Reordered imports 2 | 330 | 1 | 48,196 | 13 |
4 | 24 | def statistics(self, refresh=False, approximate=False):
# Prepare array with arguments for capi function
smin, smax, smean, sstd = c_double(), c_double(), c_double(), c_double()
stats_args = [
self._ptr,
c_int(approximate),
byref(smin),
byref(smax),
byref(smean),
byref(sstd),
c_void_p(),
c_void_p(),
]
if refresh or self._stats_refresh:
func = capi.compute_band_statistics
else:
# Add additional argument to force computation if there is no
# existing PAM file to take the values from.
force = True
stats_args.insert(2, c_int(force))
func = capi.get_band_statistics
# Computation of statistics fails for empty bands.
try:
func(*stats_args)
result = smin.value, smax.value, smean.value, sstd.value
except GDALException:
result = (None, None, None, None)
self._stats_refresh = False
return result
| django/contrib/gis/gdal/raster/band.py | 241 | django | {
"docstring": "\n Compute statistics on the pixel values of this band.\n\n The return value is a tuple with the following structure:\n (minimum, maximum, mean, standard deviation).\n\n If approximate=True, the statistics may be computed based on overviews\n or a subset of image tiles.\n\n If refresh=True, the statistics will be computed from the data directly,\n and the cache will be updated where applicable.\n\n For empty bands (where all pixel values are nodata), all statistics\n values are returned as None.\n\n For raster formats using Persistent Auxiliary Metadata (PAM) services,\n the statistics might be cached in an auxiliary file.\n ",
"language": "en",
"n_whitespaces": 178,
"n_words": 93,
"vocab_size": 68
} | 98 | Python | 77 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | band.py | 204,002 | 25 | 156 | statistics | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 369 | 0 | 50,606 | 12 |
|
6 | 23 | def logical_or(self, other, context=None):
if context is None:
context = getcontext()
other = _convert_other(other, raiseit=True)
if not self._islogical() or not other._islogical():
return context._raise_error(InvalidOperation)
# fill to context.prec
(opa, opb) = self._fill_logical(context, self._int, other._int)
# make the operation, and clean starting zeroes
result = "".join([str(int(a)|int(b)) for a,b in zip(opa,opb)])
return _dec_from_triple(0, result.lstrip('0') or '0', 0)
| python3.10.4/Lib/_pydecimal.py | 197 | XX-Net | {
"docstring": "Applies an 'or' operation between self and other's digits.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 54 | Python | 45 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | _pydecimal.py | 219,615 | 9 | 122 | logical_or | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 139 | 0 | 55,652 | 14 |
|
1 | 21 | def test_localize_pk_shortcut(self):
holder = Holder.objects.create(pk=123456789, dummy=42)
inner = Inner.objects.create(pk=987654321, holder=holder, dummy=42, readonly="")
response = self.client.get(
reverse("admin:admin_inlines_holder_change", args=(holder.id,))
)
inner_shortcut = "r/%s/%s/" % (
ContentType.objects.get_for_model(inner).pk,
inner.pk,
)
self.assertContains(response, inner_shortcut)
| tests/admin_inlines/tests.py | 153 | django | {
"docstring": "\n The \"View on Site\" link is correct for locales that use thousand\n separators.\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 13,
"vocab_size": 13
} | 28 | Python | 24 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 207,245 | 11 | 97 | test_localize_pk_shortcut | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 117 | 0 | 51,910 | 13 |
|
1 | 5 | def convert_field_to_list_or_connection(field, registry=None):
model = field.related_model
| netbox/netbox/graphql/__init__.py | 26 | netbox | {
"docstring": "\n From graphene_django.converter.py we need to monkey-patch this to return\n our ObjectListField with filtering support instead of DjangoListField\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 17,
"vocab_size": 16
} | 6 | Python | 6 | 99cf1b16718ca8bc037f546c41a9258bcc89b495 | __init__.py | 265,857 | 4 | 22 | convert_field_to_list_or_connection | https://github.com/netbox-community/netbox.git | 8245 add graphql filtering at all levels (#10618)
* 8245 monkey-patch graphene-django to support filtering at all levels
* 8245 fix tests
* 8245 fix tests | 12 | 0 | 78,216 | 7 |
|
3 | 23 | def integration_reduction(facets, index, a, b, expr, dims, degree):
expr = _sympify(expr)
if expr.is_zero:
return expr
value = S.Zero
x0 = facets[index].points[0]
m = len(facets)
gens = (x, y)
inner_product = diff(expr, gens[0]) * x0[0] + diff(expr, gens[1]) * x0[1]
if inner_product != 0:
value += integration_reduction(facets, index, a, b,
inner_product, dims, degree - 1)
value += left_integral2D(m, index, facets, x0, expr, gens)
return value/(len(dims) + degree - 1)
| sympy/integrals/intpoly.py | 208 | sympy | {
"docstring": "Helper method for main_integrate. Returns the value of the input\n expression evaluated over the polytope facet referenced by a given index.\n\n Parameters\n ===========\n\n facets :\n List of facets of the polytope.\n index :\n Index referencing the facet to integrate the expression over.\n a :\n Hyperplane parameter denoting direction.\n b :\n Hyperplane parameter denoting distance.\n expr :\n The expression to integrate over the facet.\n dims :\n List of symbols denoting axes.\n degree :\n Degree of the homogeneous polynomial.\n\n Examples\n ========\n\n >>> from sympy.abc import x, y\n >>> from sympy.integrals.intpoly import integration_reduction,\\\n hyperplane_parameters\n >>> from sympy import Point, Polygon\n >>> triangle = Polygon(Point(0, 3), Point(5, 3), Point(1, 1))\n >>> facets = triangle.sides\n >>> a, b = hyperplane_parameters(triangle)[0]\n >>> integration_reduction(facets, 0, a, b, 1, (x, y), 0)\n 5\n ",
"language": "en",
"n_whitespaces": 240,
"n_words": 125,
"vocab_size": 79
} | 68 | Python | 43 | 498015021131af4dbb07eb110e5badaba8250c7b | intpoly.py | 196,330 | 14 | 145 | integration_reduction | https://github.com/sympy/sympy.git | Updated import locations | 153 | 0 | 47,830 | 11 |
|
9 | 32 | def forward_test(self, aug_batch_imgs, aug_batch_data_samples, **kwargs):
num_augs = len(aug_batch_data_samples)
batch_size = len(aug_batch_data_samples[0])
aug_batch_img_metas = []
for aug_index in range(num_augs):
batch_img_metas = []
for batch_index in range(batch_size):
single_data_sample = aug_batch_data_samples[aug_index][
batch_index]
batch_img_metas.append(single_data_sample.meta)
aug_batch_img_metas.append(batch_img_metas)
for var, name in [(aug_batch_imgs, 'imgs'),
(aug_batch_img_metas, 'img_metas')]:
if not isinstance(var, list):
raise TypeError('{} must be a list, but got {}'.format(
name, type(var)))
num_augs = len(aug_batch_imgs)
if num_augs != len(aug_batch_img_metas):
raise ValueError(
'num of augmentations ({}) != num of image meta ({})'.format(
len(aug_batch_imgs), len(aug_batch_img_metas)))
# NOTE the batched image size information may be useful, e.g.
# in DETR, this is needed for the construction of masks, which is
# then used for the transformer_head.
for batch_img, batch_img_metas in zip(aug_batch_imgs,
aug_batch_img_metas):
batch_size = len(batch_img_metas)
for img_id in range(batch_size):
batch_img_metas[img_id]['batch_input_shape'] = \
tuple(batch_img.size()[-2:])
if num_augs == 1:
return self.simple_test(aug_batch_imgs[0], aug_batch_img_metas[0],
**kwargs)
else:
assert 'proposals' not in kwargs, '`self.aug_test` do not ' \
'support pre-difined proposals'
aug_results = self.aug_test(aug_batch_imgs, aug_batch_img_metas,
**kwargs)
return aug_results
| mmdet/models/detectors/base.py | 394 | mmdetection | {
"docstring": "\n Args:\n aug_batch_imgs (List[Tensor]): the outer list indicates test-time\n augmentations, the Tensor should have a shape NxCxHxW.\n We only support batch size = 1 when do the augtest.\n aug_batch_data_samples (List[List[:obj:`GeneralData`]]): the\n outer list indicates test-time augmentations and inner list\n indicates batch dimension. We only support batch size = 1 when\n do the augtest.\n\n Returns:\n list(obj:`InstanceData`): Detection results of the\n input images. Each item usually contains\\\n following keys.\n\n - scores (Tensor): Classification scores, has a shape\n (num_instance,)\n - labels (Tensor): Labels of bboxes, has a shape\n (num_instances,).\n - bboxes (Tensor): Has a shape (num_instances, 4),\n the last dimension 4 arrange as (x1, y1, x2, y2).\n ",
"language": "en",
"n_whitespaces": 327,
"n_words": 103,
"vocab_size": 68
} | 151 | Python | 105 | 9c5b3331ac8edbfa328922fbab45c382380da540 | base.py | 244,367 | 36 | 247 | forward_test | https://github.com/open-mmlab/mmdetection.git | Simplify api of one-stage detector | 710 | 0 | 70,356 | 15 |
|
4 | 17 | def _move_model_to_meta(model, loaded_state_dict_keys, start_prefix):
# meta device was added in pt=1.9
require_version_core("torch>=1.9")
# dematerialize param storage for keys that are going to be replaced by state_dict, by
# putting those on the meta device
for k in loaded_state_dict_keys:
submodule, param_name = find_submodule_and_param_name(model, k, start_prefix)
if submodule is not None:
# selectively switch to the meta device only those params/buffers that will
# be next replaced from state_dict. This a complex way to do p.to_("meta")
# since we have no in-place to_ for tensors.
new_val = getattr(submodule, param_name)
if isinstance(new_val, torch.nn.Parameter):
# isinstance returns False for Params on meta device, so switch after the check
new_val = torch.nn.Parameter(new_val.to("meta"))
else:
new_val = new_val.to("meta")
setattr(submodule, param_name, new_val)
| src/transformers/modeling_utils.py | 152 | transformers | {
"docstring": "\n Moves `loaded_state_dict_keys` in model to meta device which frees up the memory taken by those params.\n\n `start_prefix` is used for models which insert their name into model keys, e.g. `bert` in\n `bert.pooler.dense.weight`\n\n ",
"language": "en",
"n_whitespaces": 45,
"n_words": 32,
"vocab_size": 29
} | 114 | Python | 82 | 5da33f872913255d64717efe745a053975bbc28e | modeling_utils.py | 37,154 | 11 | 90 | _move_model_to_meta | https://github.com/huggingface/transformers.git | [modeling utils] revamp `from_pretrained(..., low_cpu_mem_usage=True)` + tests (#16657)
* add low_cpu_mem_usage tests
* wip: revamping
* wip
* install /usr/bin/time
* wip
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* fix assert
* put the wrapper back
* cleanup; switch to bert-base-cased
* Trigger CI
* Trigger CI | 268 | 0 | 6,745 | 17 |
|
7 | 31 | def _get_source_sum(source_hash, file_path, saltenv):
ret = dict()
schemes = ("salt", "http", "https", "ftp", "swift", "s3", "file")
invalid_hash_msg = (
"Source hash '{}' format is invalid. It must be in "
"the format <hash type>=<hash>".format(source_hash)
)
source_hash = str(source_hash)
source_hash_scheme = urllib.parse.urlparse(source_hash).scheme
if source_hash_scheme in schemes:
# The source_hash is a file on a server
try:
cached_hash_file = __salt__["cp.cache_file"](source_hash, saltenv)
except MinionError as exc:
log.exception("Failed to cache %s", source_hash, exc_info=exc)
raise
if not cached_hash_file:
raise CommandExecutionError(
"Source hash file {} not found".format(source_hash)
)
ret = __salt__["file.extract_hash"](cached_hash_file, "", file_path)
if ret is None:
raise SaltInvocationError(invalid_hash_msg)
else:
# The source_hash is a hash string
items = source_hash.split("=", 1)
if len(items) != 2:
invalid_hash_msg = "{}, or it must be a supported protocol: {}".format(
invalid_hash_msg, ", ".join(schemes)
)
raise SaltInvocationError(invalid_hash_msg)
ret["hash_type"], ret["hsum"] = (item.strip().lower() for item in items)
return ret
| salt/modules/win_pkg.py | 350 | salt | {
"docstring": "\n Extract the hash sum, whether it is in a remote hash file, or just a string.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 16,
"vocab_size": 14
} | 136 | Python | 93 | f2a783643de61cac1ff3288b40241e5ce6e1ddc8 | win_pkg.py | 215,965 | 31 | 201 | _get_source_sum | https://github.com/saltstack/salt.git | Update to latest ``pyupgrade`` hook. Stop skipping it on CI.
Signed-off-by: Pedro Algarvio <[email protected]> | 379 | 0 | 54,287 | 16 |
|
4 | 24 | def generate_rgbas_array(self, color, opacity):
colors = list(tuplify(color))
opacities = list(tuplify(opacity))
rgbas = np.array(
[color_to_rgba(c, o) for c, o in zip(*make_even(colors, opacities))],
)
sheen_factor = self.get_sheen_factor()
if sheen_factor != 0 and len(rgbas) == 1:
light_rgbas = np.array(rgbas)
light_rgbas[:, :3] += sheen_factor
np.clip(light_rgbas, 0, 1, out=light_rgbas)
rgbas = np.append(rgbas, light_rgbas, axis=0)
return rgbas
| manim/mobject/types/vectorized_mobject.py | 193 | manim | {
"docstring": "\n First arg can be either a color, or a tuple/list of colors.\n Likewise, opacity can either be a float, or a tuple of floats.\n If self.sheen_factor is not zero, and only\n one color was passed in, a second slightly light color\n will automatically be added for the gradient\n ",
"language": "en",
"n_whitespaces": 91,
"n_words": 48,
"vocab_size": 37
} | 51 | Python | 42 | d8dc0b462d973f0c1ddd62e557d2da89e45f6265 | vectorized_mobject.py | 189,410 | 13 | 125 | generate_rgbas_array | https://github.com/ManimCommunity/manim.git | Cleanup `simple_functions.py` (#2437)
* Remove fdiv
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* actually remove fdiv
* Use lru cache and scipy's func
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* set maxsize
should be enough for how it's used
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Remove get_num_args
* Remove one instance of clip_in_place
* Readd clip_in_place, it has a use
* rm unnecessary line
* Properly clip color
* Revert "Properly clip color"
This reverts commit 0591c7833457930b399f4125958f81d038c96e69.
* remove clip in place
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* actually remove
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> | 162 | 0 | 46,046 | 15 |
|
1 | 2 | def getExtraIncludeDirectories(self):
# Virtual method, pylint: disable=no-self-use
return None
| nuitka/plugins/PluginBase.py | 17 | Nuitka | {
"docstring": "Decide which extra directories to use for C includes in compilation.\n\n Returns:\n List of directories or None by default\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 19,
"vocab_size": 18
} | 9 | Python | 9 | 5251e9561d7d1527fb99068e7b3e33592394cc16 | PluginBase.py | 178,812 | 2 | 8 | getExtraIncludeDirectories | https://github.com/Nuitka/Nuitka.git | Plugins: Add interface for adding include directories for C | 30 | 0 | 42,830 | 6 |
|
1 | 6 | def list_datasets() -> List[str]:
return sorted(_get_dataset_configs().keys())
| ludwig/datasets/__init__.py | 38 | ludwig | {
"docstring": "Returns a list of the names of all available datasets.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 6 | Python | 6 | e4fc06f986e03919d9aef3ab55c05fee5a6b9d3a | __init__.py | 8,063 | 3 | 21 | list_datasets | https://github.com/ludwig-ai/ludwig.git | Config-first Datasets API (ludwig.datasets refactor) (#2479)
* Adds README and stub for reading dataset configs.
* Adds __init__.py for configs, moves circular import into function scope in ludwig/datasets/__init__.py
* Print config files in datasets folder.
* First pass at automatic archive extraction.
* Implemented downloading and extract.
* Refactor DatasetConfig into its own file.
* Fixed bugs downloading kaggle dataset.
* Makes registry store dataset instances, not classes. Also comments out import_submodules for testing.
* Typo fix.
* Only pass data files on to load_unprocessed_dataframe, symlink directories.
* Downloading dataset files into existing directory if exists.
* Refactor: make datasets fully config-first, lazy load dataset loaders.
* Implemented agnews custom loader.
* Implements train/validation/test split by files, and globbing support
* Adds _glob_multiple
* Adds adult_census_income, agnews, allstate_claims_severity.
* Implements sha256 verification, adds more datasets up to creditcard_fraud.
* Adds checksums, dbpedia, electricity
* Fixes gzip file name returned as string not list, adds up to forest_cover dataset.
* Adds datasets up to reuters_r8
* Adds all datasets which don't require a custom class.
* Restore dataset import behavior by implementing module __getattr__
* Adds KDD datasets.
* Adds ieee_fraud.
* Adds imbalanced_insurance, insurance_lite.
* Adds mnist.
* Completes implementation of all of the built-in datasets.
* Made cache_dir optional, read from environment variable if set.
* Upgrades datasets tests.
* Adds test for new dataset config API. Also adds scripts for dataset link checking.
* Fixes loading allstate claims severity dataset.
* Use @lru_cache(1), @cache not supported in python < 3.9
* Deletes dataset registry, updates automl test utils
* Fix imports of datasets API.
* Adds more detail to sha256: docstring and basic README
* Copy-paste link oops.
* Fixes handling of nested archive types like .tar.bz Also adds a LUDWIG_CACHE and export to the README
* Adds link for twitter bots.
* Fix order of splits in README.md
* typo
* Adds verify as a phase in doc string.
* Support .pqt, .pq extensions for parquet.
* Handle nested archives with longer file extensions like .csv.zip
* Handle nested .gz types properly too. Check all extensions with .endswith
* Handle all archive types with .endswith
* Update ludwig/datasets/loaders/split_loaders.py
Co-authored-by: Joppe Geluykens <[email protected]>
* Adds explanation for export, fixes preserve_paths (should be relative to processed_dataset_dir)
* Resolve preserved paths relative to raw dataset dir before move.
* Catch runtime exception from extracting sub-archives.
Co-authored-by: Daniel Treiman <[email protected]>
Co-authored-by: Joppe Geluykens <[email protected]> | 12 | 0 | 1,318 | 11 |
|
1 | 11 | def test_cable_cannot_terminate_to_an_existing_connection(self):
# Try to create a cable with the same interface terminations
cable = Cable(a_terminations=[self.interface2], b_terminations=[self.interface1])
with self.assertRaises(ValidationError):
cable.clean()
| netbox/dcim/tests/test_models.py | 68 | netbox | {
"docstring": "\n Either side of a cable cannot be terminated when that side already has a connection\n ",
"language": "en",
"n_whitespaces": 30,
"n_words": 15,
"vocab_size": 13
} | 20 | Python | 18 | 3a461d02793e6f9d41c2b1a92647e691de1abaac | test_models.py | 264,887 | 4 | 39 | test_cable_cannot_terminate_to_an_existing_connection | https://github.com/netbox-community/netbox.git | Update Cable instantiations to match new signature | 59 | 0 | 77,898 | 11 |
|
5 | 15 | def _get_pyqt_webengine_qt_version() -> Optional[str]:
try:
import importlib.metadata as importlib_metadata # type: ignore[import]
except ImportError:
try:
import importlib_metadata # type: ignore[no-redef]
except ImportError:
log.misc.debug("Neither importlib.metadata nor backport available")
return None
for suffix in ['Qt5', 'Qt']:
try:
return importlib_metadata.version(f'PyQtWebEngine-{suffix}')
except importlib_metadata.PackageNotFoundError:
log.misc.debug(f"PyQtWebEngine-{suffix} not found")
return None
@dataclasses.dataclass | qutebrowser/utils/version.py | 144 | @dataclasses.dataclass | qutebrowser | {
"docstring": "Get the version of the PyQtWebEngine-Qt package.\n\n With PyQtWebEngine 5.15.3, the QtWebEngine binary got split into its own\n PyQtWebEngine-Qt PyPI package:\n\n https://www.riverbankcomputing.com/pipermail/pyqt/2021-February/043591.html\n https://www.riverbankcomputing.com/pipermail/pyqt/2021-February/043638.html\n\n PyQtWebEngine 5.15.4 renamed it to PyQtWebEngine-Qt5...:\n https://www.riverbankcomputing.com/pipermail/pyqt/2021-March/043699.html\n\n Here, we try to use importlib.metadata or its backport (optional dependency) to\n figure out that version number. If PyQtWebEngine is installed via pip, this will\n give us an accurate answer.\n ",
"language": "en",
"n_whitespaces": 90,
"n_words": 60,
"vocab_size": 51
} | 45 | Python | 32 | 4094e15bcbe71311685cb8c57abb6bfb4deadbdc | version.py | 320,660 | 30 | 73 | _get_pyqt_webengine_qt_version | https://github.com/qutebrowser/qutebrowser.git | version: Always prefer builtin importlib.metadata
If we have a builtin importlib.metadata (Python 3.8+) and the importlib_metadata
backport installed, we preferred the backport. However, the version.py tests do
the opposite: They only mock the builtin if it is available. This did lead to
failing tests if the backport was installed in an environment where the builtin
was available too.
Since we don't need any specialized functionality (only reading the version), we
can prefer the builtin no matter whether a backport is available or not. | 151 | 1 | 117,258 | 14 |
3 | 32 | def trustworthiness(X, X_embedded, *, n_neighbors=5, metric="euclidean"):
r
n_samples = X.shape[0]
if n_neighbors >= n_samples / 2:
raise ValueError(
f"n_neighbors ({n_neighbors}) should be less than n_samples / 2"
f" ({n_samples / 2})"
)
dist_X = pairwise_distances(X, metric=metric)
if metric == "precomputed":
dist_X = dist_X.copy()
# we set the diagonal to np.inf to exclude the points themselves from
# their own neighborhood
np.fill_diagonal(dist_X, np.inf)
ind_X = np.argsort(dist_X, axis=1)
# `ind_X[i]` is the index of sorted distances between i and other samples
ind_X_embedded = (
NearestNeighbors(n_neighbors=n_neighbors)
.fit(X_embedded)
.kneighbors(return_distance=False)
)
# We build an inverted index of neighbors in the input space: For sample i,
# we define `inverted_index[i]` as the inverted index of sorted distances:
# inverted_index[i][ind_X[i]] = np.arange(1, n_sample + 1)
inverted_index = np.zeros((n_samples, n_samples), dtype=int)
ordered_indices = np.arange(n_samples + 1)
inverted_index[ordered_indices[:-1, np.newaxis], ind_X] = ordered_indices[1:]
ranks = (
inverted_index[ordered_indices[:-1, np.newaxis], ind_X_embedded] - n_neighbors
)
t = np.sum(ranks[ranks > 0])
t = 1.0 - t * (
2.0 / (n_samples * n_neighbors * (2.0 * n_samples - 3.0 * n_neighbors - 1.0))
)
return t
| sklearn/manifold/_t_sne.py | 352 | scikit-learn | {
"docstring": "Expresses to what extent the local structure is retained.\n\n The trustworthiness is within [0, 1]. It is defined as\n\n .. math::\n\n T(k) = 1 - \\frac{2}{nk (2n - 3k - 1)} \\sum^n_{i=1}\n \\sum_{j \\in \\mathcal{N}_{i}^{k}} \\max(0, (r(i, j) - k))\n\n where for each sample i, :math:`\\mathcal{N}_{i}^{k}` are its k nearest\n neighbors in the output space, and every sample j is its :math:`r(i, j)`-th\n nearest neighbor in the input space. In other words, any unexpected nearest\n neighbors in the output space are penalised in proportion to their rank in\n the input space.\n\n Parameters\n ----------\n X : ndarray of shape (n_samples, n_features) or (n_samples, n_samples)\n If the metric is 'precomputed' X must be a square distance\n matrix. Otherwise it contains a sample per row.\n\n X_embedded : ndarray of shape (n_samples, n_components)\n Embedding of the training data in low-dimensional space.\n\n n_neighbors : int, default=5\n The number of neighbors that will be considered. Should be fewer than\n `n_samples / 2` to ensure the trustworthiness to lies within [0, 1], as\n mentioned in [1]_. An error will be raised otherwise.\n\n metric : str or callable, default='euclidean'\n Which metric to use for computing pairwise distances between samples\n from the original input space. If metric is 'precomputed', X must be a\n matrix of pairwise distances or squared distances. Otherwise, for a list\n of available metrics, see the documentation of argument metric in\n `sklearn.pairwise.pairwise_distances` and metrics listed in\n `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the\n \"cosine\" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.\n\n .. versionadded:: 0.20\n\n Returns\n -------\n trustworthiness : float\n Trustworthiness of the low-dimensional embedding.\n\n References\n ----------\n .. [1] Jarkko Venna and Samuel Kaski. 2001. Neighborhood\n Preservation in Nonlinear Projection Methods: An Experimental Study.\n In Proceedings of the International Conference on Artificial Neural Networks\n (ICANN '01). Springer-Verlag, Berlin, Heidelberg, 485-491.\n\n .. [2] Laurens van der Maaten. Learning a Parametric Embedding by Preserving\n Local Structure. Proceedings of the Twelth International Conference on\n Artificial Intelligence and Statistics, PMLR 5:384-391, 2009.\n ",
"language": "en",
"n_whitespaces": 550,
"n_words": 314,
"vocab_size": 202
} | 173 | Python | 115 | ade90145c9c660a1a7baf2315185995899b0f356 | _t_sne.py | 259,640 | 84 | 228 | trustworthiness | https://github.com/scikit-learn/scikit-learn.git | FIX Raise error when n_neighbors >= n_samples / 2 in manifold.trustworthiness (#23033)
Co-authored-by: Shao Yang Hong <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]> | 322 | 0 | 75,842 | 16 |
|
1 | 5 | def writelines(self, seq):
return _compression.BaseStream.writelines(self, seq)
| python3.10.4/Lib/bz2.py | 31 | XX-Net | {
"docstring": "Write a sequence of byte strings to the file.\n\n Returns the number of uncompressed bytes written.\n seq can be any iterable yielding byte strings.\n\n Line separators are not added between the written byte strings.\n ",
"language": "en",
"n_whitespaces": 62,
"n_words": 34,
"vocab_size": 28
} | 6 | Python | 6 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | bz2.py | 221,197 | 2 | 19 | writelines | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 20 | 0 | 56,267 | 8 |
|
1 | 3 | def test_generic_errors(self, constructor):
| pandas/tests/indexes/interval/test_constructors.py | 15 | pandas | {
"docstring": "\n override the base class implementation since errors are handled\n differently; checks unnecessary since caught at the Interval level\n ",
"language": "en",
"n_whitespaces": 40,
"n_words": 18,
"vocab_size": 16
} | 3 | Python | 3 | 76923d7b58d8f25329e779a40b87e2b6959f9cea | test_constructors.py | 170,653 | 2 | 9 | test_generic_errors | https://github.com/pandas-dev/pandas.git | issue 48855 enable pylint unnecessary-pass (#49418)
issue 48855 enable unnecessary-pass | 10 | 0 | 40,592 | 6 |
|
1 | 6 | def get_actual_gle_dict(name):
return dict(
frappe.db.sql(
,
name,
)
)
| erpnext/assets/doctype/asset_capitalization/test_asset_capitalization.py | 33 | erpnext | {
"docstring": "\n\t\tselect account, sum(debit-credit) as diff\n\t\tfrom `tabGL Entry`\n\t\twhere voucher_type = 'Asset Capitalization' and voucher_no = %s\n\t\tgroup by account\n\t\thaving diff != 0\n\t",
"language": "en",
"n_whitespaces": 19,
"n_words": 24,
"vocab_size": 22
} | 9 | Python | 8 | 58d430fe3ee62e93ad8d16a08bb42156a25b7d41 | test_asset_capitalization.py | 69,222 | 13 | 20 | get_actual_gle_dict | https://github.com/frappe/erpnext.git | feat: Asset Capitalization
- manual selection of entry type
- GLE cleanup with smaller functions
- GLE considering periodical inventory
- test cases | 2 | 0 | 14,997 | 10 |
|
1 | 1 | def doctor_output_no_config():
return
| tests/conftest.py | 13 | thumbor | {
"docstring": "\nThumbor doctor will analyze your install and verify if everything is working as expected.\n\nVerifying libraries support...\n\n✅ pycurl is installed correctly.\n✅ cairosvg is installed correctly.\n\nVerifying thumbor compiled extensions...\n\n✅ _alpha\n✅ _bounding_box\n✅ _brightness\n✅ _colorize\n✅ _composite\n✅ _contrast\n✅ _convolution\n✅ _curve\n✅ _equalize\n✅ _fill\n✅ _nine_patch\n✅ _noise\n✅ _rgb\n✅ _round_corner\n✅ _saturation\n✅ _sharpen\n\nVerifying extension programs...\n\n✅ jpegtran is installed correctly.\n✅ ffmpeg is installed correctly.\n✅ gifsicle is installed correctly.\nVerifying security...\n\n\n🎉 Congratulations! No errors found! 🎉\n",
"language": "en",
"n_whitespaces": 62,
"n_words": 89,
"vocab_size": 52
} | 3 | Python | 3 | 0e845259cd3d49b39889ae15df19922af0ef7269 | conftest.py | 191,181 | 38 | 6 | doctor_output_no_config | https://github.com/thumbor/thumbor.git | Remove snapshottest to reduce number of dependencies (#1433)
Having an extra package that can be replaced with something already
included makes packaging easier. For instance, in Debian, one would have
to either be fortunate to find an existing package or go over the
trouble of creating such package and all its dependencies.
I believe this CL is a good small compromise considering the benefit it
brings. | 6 | 0 | 46,464 | 6 |
|
5 | 18 | def draw(G, pos=None, ax=None, **kwds):
import matplotlib.pyplot as plt
if ax is None:
cf = plt.gcf()
else:
cf = ax.get_figure()
cf.set_facecolor("w")
if ax is None:
if cf.axes:
ax = cf.gca()
else:
ax = cf.add_axes((0, 0, 1, 1))
if "with_labels" not in kwds:
kwds["with_labels"] = "labels" in kwds
draw_networkx(G, pos=pos, ax=ax, **kwds)
ax.set_axis_off()
plt.draw_if_interactive()
return
| networkx/drawing/nx_pylab.py | 206 | networkx | {
"docstring": "Draw the graph G with Matplotlib.\n\n Draw the graph as a simple representation with no node\n labels or edge labels and using the full Matplotlib figure area\n and no axis labels by default. See draw_networkx() for more\n full-featured drawing that allows title, axis labels etc.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary, optional\n A dictionary with nodes as keys and positions as values.\n If not specified a spring layout positioning will be computed.\n See :py:mod:`networkx.drawing.layout` for functions that\n compute node positions.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in specified Matplotlib axes.\n\n kwds : optional keywords\n See networkx.draw_networkx() for a description of optional keywords.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> nx.draw(G)\n >>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout\n\n See Also\n --------\n draw_networkx\n draw_networkx_nodes\n draw_networkx_edges\n draw_networkx_labels\n draw_networkx_edge_labels\n\n Notes\n -----\n This function has the same name as pylab.draw and pyplot.draw\n so beware when using `from networkx import *`\n\n since you might overwrite the pylab.draw function.\n\n With pyplot use\n\n >>> import matplotlib.pyplot as plt\n >>> G = nx.dodecahedral_graph()\n >>> nx.draw(G) # networkx draw()\n >>> plt.draw() # pyplot draw()\n\n Also see the NetworkX drawing examples at\n https://networkx.org/documentation/latest/auto_examples/index.html\n ",
"language": "en",
"n_whitespaces": 348,
"n_words": 190,
"vocab_size": 118
} | 54 | Python | 39 | 7f3ec2c5906b709733a5c26285032bf24134bcf0 | nx_pylab.py | 177,150 | 18 | 125 | draw | https://github.com/networkx/networkx.git | See matplotlb 3.6rc1 failure (#5937)
* See matplotlb 3.6rc1 failure
* replace use of private class method to allow mpl v3.6 to work.
* ensure ax exists before calling colorbar
* Undo matplotlib pin
Co-authored-by: Dan Schult <[email protected]> | 144 | 0 | 42,290 | 14 |
|
1 | 11 | def adjacency_matrix(G, nodelist=None, dtype=None, weight="weight"):
import warnings
warnings.warn(
"adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.",
FutureWarning,
stacklevel=2,
)
# TODO: Change to `to_scipy_sparse_array` for networkx 3.0
return nx.to_scipy_sparse_matrix(G, nodelist=nodelist, dtype=dtype, weight=weight)
| networkx/linalg/graphmatrix.py | 81 | networkx | {
"docstring": "Returns adjacency matrix of G.\n\n Parameters\n ----------\n G : graph\n A NetworkX graph\n\n nodelist : list, optional\n The rows and columns are ordered according to the nodes in nodelist.\n If nodelist is None, then the ordering is produced by G.nodes().\n\n dtype : NumPy data-type, optional\n The desired data-type for the array.\n If None, then the NumPy default is used.\n\n weight : string or None, optional (default='weight')\n The edge data key used to provide each value in the matrix.\n If None, then each edge has weight 1.\n\n Returns\n -------\n A : SciPy sparse matrix\n Adjacency matrix representation of G.\n\n Notes\n -----\n For directed graphs, entry i,j corresponds to an edge from i to j.\n\n If you want a pure Python adjacency matrix representation try\n networkx.convert.to_dict_of_dicts which will return a\n dictionary-of-dictionaries format that can be addressed as a\n sparse matrix.\n\n For MultiGraph/MultiDiGraph with parallel edges the weights are summed.\n See `to_numpy_array` for other options.\n\n The convention used for self-loop edges in graphs is to assign the\n diagonal matrix entry value to the edge weight attribute\n (or the number 1 if the edge has no weight attribute). If the\n alternate convention of doubling the edge weight is desired the\n resulting Scipy sparse matrix can be modified as follows:\n\n >>> G = nx.Graph([(1, 1)])\n >>> A = nx.adjacency_matrix(G)\n >>> print(A.todense())\n [[1]]\n >>> A.setdiag(A.diagonal() * 2)\n >>> print(A.todense())\n [[2]]\n\n See Also\n --------\n to_numpy_array\n to_scipy_sparse_array\n to_dict_of_dicts\n adjacency_spectrum\n ",
"language": "en",
"n_whitespaces": 392,
"n_words": 231,
"vocab_size": 137
} | 37 | Python | 35 | 5dfd57af2a141a013ae3753e160180b82bec9469 | graphmatrix.py | 176,190 | 8 | 52 | adjacency_matrix | https://github.com/networkx/networkx.git | Use scipy.sparse array datastructure (#5139)
* Step 1: use sparse arrays in nx.to_scipy_sparse_matrix.
Seems like a reasonable place to start.
nx.to_scipy_sparse_matrix is one of the primary interfaces to
scipy.sparse from within NetworkX.
* 1: Use np.outer instead of mult col/row vectors
Fix two instances in modularitymatrix where a new 2D array was being
created via an outer product of two \"vectors\".
In the matrix case, this was a row vector \* a column vector. In the
array case this can be disambiguated by being explicit with np.outer.
* Update _transition_matrix in laplacianmatrix module
- A few instances of matrix multiplication operator
- Add np.newaxis + transpose to get shape right for broadcasting
- Explicitly convert e.g. sp.sparse.spdiags to a csr_array.
* Update directed_combinitorial_laplacian w/ sparse array.
- Wrap spdiags in csr_array and update matmul operators.
* Rm matrix-specific code from lgc and hmn modules
- Replace .A call with appropriate array semantics
- wrap sparse.diags in csr_array.
* Change hits to use sparse array semantics.
- Replace * with @
- Remove superfluous calls to flatten.
* Update sparse matrix usage in layout module.
- Simplify lil.getrowview call
- Wrap spdiags in csr_array.
* lil_matrix -> lil_array in graphmatrix.py.
* WIP: Start working on algebraic connectivity module.
* Incorporate auth mat varname feedback.
* Revert 1D slice and comment for 1D sparse future.
* Add TODOs: rm csr_array wrapper around spdiags etc.
* WIP: cleanup algebraicconn: tracemin_fiedler.
* Typo.
* Finish reviewing algebraicconnectivity.
* Convert bethe_hessian matrix to use sparse arrays.
* WIP: update laplacian.
Update undirected laplacian functions.
* WIP: laplacian - add comment about _transition_matrix return types.
* Finish laplacianmatrix review.
* Update attrmatrix.
* Switch to official laplacian function.
* Update pagerank to use sparse array.
* Switch bipartite matrix to sparse arrays.
* Check from_scipy_sparse_matrix works with arrays.
Modifies test suite.
* Apply changes from review.
* Fix failing docstring tests.
* Fix missing axis for in-place multiplication.
* Use scipy==1.8rc2
* Use matrix multiplication
* Fix PyPy CI
* [MRG] Create plot_subgraphs.py example (#5165)
* Create plot_subgraphs.py
https://github.com/networkx/networkx/issues/4220
* Update plot_subgraphs.py
black
* Update plot_subgraphs.py
lint plus font_size
* Update plot_subgraphs.py
added more plots
* Update plot_subgraphs.py
removed plots from the unit test and added comments
* Update plot_subgraphs.py
lint
* Update plot_subgraphs.py
typos fixed
* Update plot_subgraphs.py
added nodes to the plot of the edges removed that was commented out for whatever reason
* Update plot_subgraphs.py
revert the latest commit - the line was commented out for a reason - it's broken
* Update plot_subgraphs.py
fixed node color issue
* Update plot_subgraphs.py
format fix
* Update plot_subgraphs.py
forgot to draw the nodes... now fixed
* Fix sphinx warnings about heading length.
* Update examples/algorithms/plot_subgraphs.py
* Update examples/algorithms/plot_subgraphs.py
Co-authored-by: Ross Barnowski <[email protected]>
Co-authored-by: Dan Schult <[email protected]>
* Add traveling salesman problem to example gallery (#4874)
Adds an example of the using Christofides to solve the TSP problem to the example galery.
Co-authored-by: Ross Barnowski <[email protected]>
* Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() (#5037)
* Fixed inconsistent documentation for nbunch parameter in DiGraph.edges()
* Resolved Requested Changes
* Revert changes to degree docstrings.
* Update comments in example.
* Apply wording to edges method in all graph classes.
Co-authored-by: Ross Barnowski <[email protected]>
* Compatibility updates from testing with numpy/scipy/pytest rc's (#5226)
* Rm deprecated scipy subpkg access.
* Use recwarn fixture in place of deprecated pytest pattern.
* Rm unnecessary try/except from tests.
* Replace internal `close` fn with `math.isclose`. (#5224)
* Replace internal close fn with math.isclose.
* Fix lines in docstring examples.
* Fix Python 3.10 deprecation warning w/ int div. (#5231)
* Touchups and suggestions for subgraph gallery example (#5225)
* Simplify construction of G with edges rm'd
* Rm unused graph attribute.
* Shorten categorization by node type.
* Simplify node coloring.
* Simplify isomorphism check.
* Rm unit test.
* Rm redundant plotting of each subgraph.
* Use new package name (#5234)
* Allowing None edges in weight function of bidirectional Dijkstra (#5232)
* added following feature also to bidirectional dijkstra: The weight function can be used to hide edges by returning None.
* changed syntax for better readability and code duplicate avoidance
Co-authored-by: Hohmann, Nikolas <[email protected]>
* Add an FAQ about assigning issues. (#5182)
* Add FAQ about assigning issues.
* Add note about linking issues from new PRs.
* Update dev deps (#5243)
* Update minor doc issues with tex notation (#5244)
* Add FutureWarnings to fns that return sparse matrices
- biadjacency_matrix.
- bethe_hessian_matrix.
- incidence_matrix.
- laplacian functions.
- modularity_matrix functions.
- adjacency_matrix.
* Add to_scipy_sparse_array and use it everywhere.
Add a new conversion function to preserve array semantics internally
while not altering behavior for users.
Also adds FutureWarning to to_scipy_sparse_matrix.
* Add from_scipy_sparse_array. Supercedes from_scipy_sparse_matrix.
* Handle deprecations in separate PR.
* Fix docstring examples.
Co-authored-by: Mridul Seth <[email protected]>
Co-authored-by: Jarrod Millman <[email protected]>
Co-authored-by: Andrew Knyazev <[email protected]>
Co-authored-by: Dan Schult <[email protected]>
Co-authored-by: eskountis <[email protected]>
Co-authored-by: Anutosh Bhat <[email protected]>
Co-authored-by: NikHoh <[email protected]>
Co-authored-by: Hohmann, Nikolas <[email protected]>
Co-authored-by: Sultan Orazbayev <[email protected]>
Co-authored-by: Mridul Seth <[email protected]> | 76 | 0 | 41,756 | 8 |
|
1 | 18 | def test_cancellation_while_holding_read_lock(self):
rwlock = ReadWriteLock()
key = "key"
# 1. A reader takes the lock and blocks.
reader_d, _, _ = self._start_blocking_reader(rwlock, key, "read completed")
# 2. A writer waits for the reader to complete.
writer_d, _ = self._start_nonblocking_writer(rwlock, key, "write completed")
self.assertFalse(writer_d.called)
# 3. The reader is cancelled.
reader_d.cancel()
self.failureResultOf(reader_d, CancelledError)
# 4. The writer should take the lock and complete.
self.assertTrue(
writer_d.called, "Writer is stuck waiting for a cancelled reader"
)
self.assertEqual("write completed", self.successResultOf(writer_d))
| tests/util/test_rwlock.py | 152 | synapse | {
"docstring": "Test cancellation while holding a read lock.\n\n A waiting writer should be given the lock when the reader holding the lock is\n cancelled.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 23,
"vocab_size": 19
} | 76 | Python | 55 | 605d161d7d585847fd1bb98d14d5281daeac8e86 | test_rwlock.py | 247,584 | 12 | 88 | test_cancellation_while_holding_read_lock | https://github.com/matrix-org/synapse.git | Add cancellation support to `ReadWriteLock` (#12120)
Also convert `ReadWriteLock` to use async context managers.
Signed-off-by: Sean Quah <[email protected]> | 192 | 0 | 71,759 | 9 |
|
1 | 5 | def is_re(obj) -> bool:
return isinstance(obj, Pattern)
| pandas/core/dtypes/inference.py | 26 | pandas | {
"docstring": "\n Check if the object is a regex pattern instance.\n\n Parameters\n ----------\n obj : The object to check\n\n Returns\n -------\n bool\n Whether `obj` is a regex pattern.\n\n Examples\n --------\n >>> is_re(re.compile(\".*\"))\n True\n >>> is_re(\"foo\")\n False\n ",
"language": "en",
"n_whitespaces": 84,
"n_words": 34,
"vocab_size": 29
} | 7 | Python | 7 | bce995817caf00ab5e82cb4cf1b540f1530cf4ea | inference.py | 172,101 | 21 | 15 | is_re | https://github.com/pandas-dev/pandas.git | Fix some dosctring RT02 error (#50197) | 13 | 0 | 40,755 | 7 |
|
12 | 25 | def partial_fit(self, X, y, classes=None, sample_weight=None):
first_time = not hasattr(self, "estimators_")
if first_time:
self._validate_params()
y = self._validate_data(X="no_validation", y=y, multi_output=True)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_partial_fit_estimator)(
self.estimators_[i] if not first_time else self.estimator,
X,
y[:, i],
classes[i] if classes is not None else None,
sample_weight,
first_time,
)
for i in range(y.shape[1])
)
if first_time and hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if first_time and hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self
| sklearn/multioutput.py | 332 | scikit-learn | {
"docstring": "Incrementally fit a separate model for each class output.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input data.\n\n y : {array-like, sparse matrix} of shape (n_samples, n_outputs)\n Multi-output targets.\n\n classes : list of ndarray of shape (n_outputs,), default=None\n Each array is unique classes for one output in str/int.\n Can be obtained via\n ``[np.unique(y[:, i]) for i in range(y.shape[1])]``, where `y`\n is the target matrix of the entire dataset.\n This argument is required for the first call to partial_fit\n and can be omitted in the subsequent calls.\n Note that `y` doesn't need to contain all labels in `classes`.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights. If `None`, then samples are equally weighted.\n Only supported if the underlying regressor supports sample\n weights.\n\n Returns\n -------\n self : object\n Returns a fitted instance.\n ",
"language": "en",
"n_whitespaces": 349,
"n_words": 136,
"vocab_size": 100
} | 107 | Python | 77 | d942600e1f1979c431c24f59933a95155789f324 | multioutput.py | 260,558 | 30 | 214 | partial_fit | https://github.com/scikit-learn/scikit-learn.git | MAINT add parameter_constraints for MultiOutputClassifier and MultiOutputRegressor (#23902)
Co-authored-by: jeremiedbb <[email protected]> | 421 | 0 | 76,339 | 13 |
|
1 | 8 | def print_help(self):
help_text = f
console.print(text=help_text, menu="Stocks - Due Diligence")
| gamestonk_terminal/stocks/due_diligence/dd_controller.py | 47 | OpenBBTerminal | {
"docstring": "Print help\n[param]Ticker: [/param]{self.ticker}[cmds]\n\n[src][Finviz][/src]\n analyst analyst prices and ratings of the company\n[src][FMP][/src]\n rating rating over time (daily)\n[src][Finnhub][/src]\n rot number of analysts ratings over time (monthly)\n[src][Business Insider][/src]\n pt price targets over time\n est quarter and year analysts earnings estimates\n[src][Market Watch][/src]\n sec SEC filings\n[src][Csimarket][/src]\n supplier list of suppliers\n customer list of customers\n[src][Cathiesark.com][/src]\n arktrades get ARK trades for ticker[/cmds]\n ",
"language": "en",
"n_whitespaces": 157,
"n_words": 63,
"vocab_size": 50
} | 10 | Python | 10 | 82747072c511beb1b2672846ae2ee4aec53eb562 | dd_controller.py | 281,541 | 22 | 22 | print_help | https://github.com/OpenBB-finance/OpenBBTerminal.git | Terminal Wide Rich (#1161)
* My idea for how we handle Rich moving forward
* remove independent consoles
* FIxed pylint issues
* add a few vars
* Switched print to console
* More transitions
* Changed more prints
* Replaced all prints
* Fixing tabulate
* Finished replace tabulate
* Finished removing rich from Tabulate
* add Panel around menu
* add GST watermark under feature flag
* Fixed 46 tests
* Delete test_screener[False].yaml
* Delete test_screener[True].yaml
* Fixed the rest of the tests
* add help and source color vars and use rgb
* rich on stocks/options
* update rich on disc, dps, sia
* rich in gov, ins and scr menus
* ba and ca menus with rich
* Fixed import issue
* Fixed some tests
* removed termcolor
* Removed prettytable
* add rich to remaining stocks menus
* FIxed linting issue
* Added James' changes
* Updated dependencies
* Add rich to cryptocurrency menu
* refactor economy and forex
* refactor etf with rich
* refactor mfunds
* refactor rich rest
* not specify style so default color works well on any background
* Fixing mypy issues
* Updated tests
* More test fixes
* James' test fixes
* Updating tests : stocks/screener - fix cassettes using BR
* Updating tests : crypto
* Updating tests : disable DEBUG_MODE
* Updating tests : stocks/fa/yfinance
* minor fixes that escape
* Improve the rich table function (that replaces tabulate :D )
* Fixed bad code
* delete rogue file + dcf fix + NoConsole
* sia mypy
* fuck you linter
* fuck you linter pt 2
* skip hehe
* i hate the black linter
* ubuntu mypy attempt
* Update : rich_config + gtff
* Updating tests : conftest
* Updating tests : stocks
* Update : rich_config
* Updating : rich_config
* make panel configurable for Theodore :b
* colors update
* Merged
* Updating : rich_config + feature_flags
* Updating : rich_config
* Updating tests : stocks
* Updating : feature_flags
Co-authored-by: DidierRLopes <[email protected]>
Co-authored-by: Chavithra PARANA <[email protected]>
Co-authored-by: james <[email protected]>
Co-authored-by: jose-donato <[email protected]> | 31 | 0 | 83,839 | 9 |
|
5 | 13 | def get_preprocess_function(self, field, value, export_format):
# Try to find a field specific function and return it
format_dict = self.custom_field_preprocess.get(field, {})
if export_format in format_dict:
return format_dict[export_format]
# Otherwise check for a value class specific function
for value_classes, format_dict in self.custom_value_preprocess.items():
if isinstance(value, value_classes) and export_format in format_dict:
return format_dict[export_format]
# Finally resort to force_str to prevent encoding errors
return force_str
| wagtail/admin/views/mixins.py | 105 | wagtail | {
"docstring": "Returns the preprocessing function for a given field name, field value, and export format",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 13
} | 60 | Python | 40 | d10f15e55806c6944827d801cd9c2d53f5da4186 | mixins.py | 72,424 | 8 | 67 | get_preprocess_function | https://github.com/wagtail/wagtail.git | Reformat with black | 153 | 0 | 15,891 | 10 |
|
3 | 11 | def get_corrected_cpu(cpu_count): # formerlly get_cpu_capacity
from django.conf import settings
settings_abscpu = getattr(settings, 'SYSTEM_TASK_ABS_CPU', None)
env_abscpu = os.getenv('SYSTEM_TASK_ABS_CPU', None)
if env_abscpu is not None:
return convert_cpu_str_to_decimal_cpu(env_abscpu)
elif settings_abscpu is not None:
return convert_cpu_str_to_decimal_cpu(settings_abscpu)
return cpu_count # no correction
| awx/main/utils/common.py | 94 | awx | {
"docstring": "Some environments will do a correction to the reported CPU number\n because the given OpenShift value is a lie\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 19,
"vocab_size": 17
} | 37 | Python | 27 | 799968460d4794bcd9959f57a2b97846b9a00bb7 | common.py | 80,659 | 9 | 56 | get_corrected_cpu | https://github.com/ansible/awx.git | Fixup conversion of memory and cpu settings to support k8s resource request format (#11725)
fix memory and cpu settings to suport k8s resource request format
* fix conversion of memory setting to bytes
This setting has not been getting set by default, and needed some fixing
up to be compatible with setting the memory in the same way as we set it
in the operator, as well as with other changes from last year which
assume that ansible runner is returning memory in bytes.
This way we can start setting this setting in the operator, and get a
more accurate reflection of how much memory is available to the control
pod in k8s.
On platforms where services are all sharing memory, we deduct a
penalty from the memory available. On k8s we don't need to do this
because the web, redis, and task containers each have memory
allocated to them.
* Support CPU setting expressed in units used by k8s
This setting has not been getting set by default, and needed some fixing
up to be compatible with setting the CPU resource request/limits in the
same way as we set it in the resource requests/limits.
This way we can start setting this setting in the
operator, and get a more accurate reflection of how much cpu is
available to the control pod in k8s.
Because cpu on k8s can be partial cores, migrate cpu field to decimal.
k8s does not allow granularity of less than 100m (equivalent to 0.1 cores), so only
store up to 1 decimal place.
fix analytics to deal with decimal cpu
need to use DjangoJSONEncoder when Decimal fields in data passed to
json.dumps | 74 | 0 | 17,088 | 10 |
|
1 | 3 | def clear(self):
raise NotImplementedError
| python/ray/air/execution/resources/resource_manager.py | 16 | ray | {
"docstring": "Reset internal state and clear all resources.\n\n Calling this method will reset the resource manager to its initialization state.\n All resources will be removed.\n\n Clearing the state will remove tracked resources from the manager, but there are\n no guarantees about the tasks and actors scheduled on the resources. The caller\n should make sure that any references to tasks or actors scheduled on the\n resources have been removed before calling ``clear()``.\n ",
"language": "en",
"n_whitespaces": 119,
"n_words": 70,
"vocab_size": 53
} | 4 | Python | 4 | edb17fd2069844f12237c85ba6607afae536401d | resource_manager.py | 138,043 | 2 | 8 | clear | https://github.com/ray-project/ray.git | [air/tune] Internal resource management 1 - Ray AIR resource manager implementation (#30777)
Prerequisite to #30016
This PR adds a new Ray AIR resource manager to replace the PlacementGroupManager of Ray Tune. Details can be found in #30016.
Specifically, this PR
- Adds the main resource manager abstractions
- Renames (and moves) PlacementGroupFactory to ResourceRequest
- Adds implementations and tests for a placement group based manager and a budget based manager
Signed-off-by: Kai Fricke <[email protected]>
Signed-off-by: Kai Fricke <[email protected]>
Co-authored-by: matthewdeng <[email protected]> | 18 | 0 | 31,286 | 6 |
|
2 | 4 | def _supports_universal_builds():
# As an approximation, we assume that if we are running on 10.4 or above,
# then we are running with an Xcode environment that supports universal
# builds, in particular -isysroot and -arch arguments to the compiler. This
# is in support of allowing 10.4 universal builds to run on 10.3.x systems.
osx_version = _get_system_version_tuple()
return bool(osx_version >= (10, 4)) if osx_version else False
| python3.10.4/Lib/_osx_support.py | 46 | XX-Net | {
"docstring": "Returns True if universal builds are supported on this system",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 67 | Python | 51 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | _osx_support.py | 219,598 | 3 | 25 | _supports_universal_builds | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 88 | 0 | 55,636 | 10 |
|
7 | 18 | def symmetric_poly(n, *gens, **args):
# TODO: use an explicit keyword argument
gens = _analyze_gens(gens)
if n < 0 or n > len(gens) or not gens:
raise ValueError("Cannot generate symmetric polynomial of order %s for %s" % (n, gens))
elif not n:
poly = S.One
else:
poly = Add(*[Mul(*s) for s in subsets(gens, int(n))])
if not args.get('polys', False):
return poly
else:
return Poly(poly, *gens)
@public | sympy/polys/specialpolys.py | 174 | @public | sympy | {
"docstring": "Generates symmetric polynomial of order `n`.\n\n Returns a Poly object when ``polys=True``, otherwise\n (default) returns an expression.\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 17,
"vocab_size": 17
} | 64 | Python | 52 | 337e5c51b1ae7e202b7d7c62107fab6d5ea58d93 | specialpolys.py | 195,838 | 12 | 103 | symmetric_poly | https://github.com/sympy/sympy.git | Removed even more Python 2-support | 122 | 1 | 47,432 | 18 |
1 | 23 | def test_ensure_print_span_characteristics_wont_fail():
nlp = English()
spans_key = "sc"
pred = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."])
pred.spans[spans_key] = [Span(pred, 3, 6, "ORG"), Span(pred, 5, 6, "GPE")]
ref = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."])
ref.spans[spans_key] = [Span(ref, 3, 6, "ORG"), Span(ref, 5, 6, "GPE")]
eg = Example(pred, ref)
examples = [eg]
data = _compile_gold(examples, ["spancat"], nlp, True)
span_characteristics = _get_span_characteristics(
examples=examples, compiled_gold=data, spans_key=spans_key
)
_print_span_characteristics(span_characteristics)
@pytest.mark.parametrize("threshold", [70, 80, 85, 90, 95]) | spacy/tests/test_cli.py | 309 | @pytest.mark.parametrize("threshold", [70, 80, 85, 90, 95]) | spaCy | {
"docstring": "Test if interface between two methods aren't destroyed if refactored",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 75 | Python | 51 | 1d34aa2b3dd1ba0931dcb1863dfbeba6ae5b912d | test_cli.py | 111,324 | 14 | 172 | test_ensure_print_span_characteristics_wont_fail | https://github.com/explosion/spaCy.git | Add spacy-span-analyzer to debug data (#10668)
* Rename to spans_key for consistency
* Implement spans length in debug data
* Implement how span bounds and spans are obtained
In this commit, I implemented how span boundaries (the tokens) around a
given span and spans are obtained. I've put them in the compile_gold()
function so that it's accessible later on. I will do the actual
computation of the span and boundary distinctiveness in the main
function above.
* Compute for p_spans and p_bounds
* Add computation for SD and BD
* Fix mypy issues
* Add weighted average computation
* Fix compile_gold conditional logic
* Add test for frequency distribution computation
* Add tests for kl-divergence computation
* Fix weighted average computation
* Make tables more compact by rounding them
* Add more descriptive checks for spans
* Modularize span computation methods
In this commit, I added the _get_span_characteristics and
_print_span_characteristics functions so that they can be reusable
anywhere.
* Remove unnecessary arguments and make fxs more compact
* Update a few parameter arguments
* Add tests for print_span and get_span methods
* Update API to talk about span characteristics in brief
* Add better reporting of spans_length
* Add test for span length reporting
* Update formatting of span length report
Removed '' to indicate that it's not a string, then
sort the n-grams by their length, not by their frequency.
* Apply suggestions from code review
Co-authored-by: Adriane Boyd <[email protected]>
* Show all frequency distribution when -V
In this commit, I displayed the full frequency distribution of the
span lengths when --verbose is passed. To make things simpler, I
rewrote some of the formatter functions so that I can call them
whenever.
Another notable change is that instead of showing percentages as
Integers, I showed them as floats (max 2-decimal places). I did this
because it looks weird when it displays (0%).
* Update logic on how total is computed
The way the 90% thresholding is computed now is that we keep
adding the percentages until we reach >= 90%. I also updated the wording
and used the term "At least" to denote that >= 90% of your spans have
these distributions.
* Fix display when showing the threshold percentage
* Apply suggestions from code review
Co-authored-by: Adriane Boyd <[email protected]>
* Add better phrasing for span information
* Update spacy/cli/debug_data.py
Co-authored-by: Adriane Boyd <[email protected]>
* Add minor edits for whitespaces etc.
Co-authored-by: Adriane Boyd <[email protected]>
Co-authored-by: Adriane Boyd <[email protected]> | 120 | 1 | 24,375 | 11 |
4 | 14 | def find_path_to_setup_from_repo_root(location, repo_root):
# type: (str, str) -> Optional[str]
# find setup.py
orig_location = location
while not os.path.exists(os.path.join(location, 'setup.py')):
last_location = location
location = os.path.dirname(location)
if location == last_location:
# We've traversed up to the root of the filesystem without
# finding setup.py
logger.warning(
"Could not find setup.py for directory %s (tried all "
"parent directories)",
orig_location,
)
return None
if os.path.samefile(repo_root, location):
return None
return os.path.relpath(location, repo_root)
| .venv/lib/python3.8/site-packages/pip/_internal/vcs/versioncontrol.py | 145 | transferlearning | {
"docstring": "\n Find the path to `setup.py` by searching up the filesystem from `location`.\n Return the path to `setup.py` relative to `repo_root`.\n Return None if `setup.py` is in `repo_root` or cannot be found.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 31,
"vocab_size": 23
} | 68 | Python | 51 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | versioncontrol.py | 61,395 | 15 | 86 | find_path_to_setup_from_repo_root | https://github.com/jindongwang/transferlearning.git | upd; format | 217 | 0 | 12,543 | 13 |
|
1 | 2 | def isomin(self):
return self["isomin"]
| packages/python/plotly/plotly/graph_objs/_isosurface.py | 22 | plotly.py | {
"docstring": "\n Sets the minimum boundary for iso-surface plot.\n\n The 'isomin' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n ",
"language": "en",
"n_whitespaces": 78,
"n_words": 26,
"vocab_size": 26
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _isosurface.py | 227,305 | 2 | 11 | isomin | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 58,978 | 7 |
|
7 | 4 | def _configure_matplotlib(cls):
rcParams["keymap.fullscreen"] = [k for k in rcParams["keymap.fullscreen"] if k != "f"]
rcParams["keymap.save"] = [k for k in rcParams["keymap.save"] if k != "s"]
rcParams["keymap.home"] = [k for k in rcParams["keymap.home"] if k != "r"]
rcParams["figure.raise_window"] = False
| scripts/train.py | 123 | faceswap | {
"docstring": " Remove `F`, 'S' and 'R' from their default bindings and stop Matplotlib from stealing\n focus ",
"language": "en",
"n_whitespaces": 23,
"n_words": 15,
"vocab_size": 13
} | 38 | Python | 17 | c8122bc499afba4fcb99030e42e08bfb8d3a75e1 | train.py | 101,053 | 5 | 69 | _configure_matplotlib | https://github.com/deepfakes/faceswap.git | bugfix: Stop preview window from stealing focus | 73 | 0 | 20,490 | 10 |
|
4 | 20 | def _per_replica_aggregate_batch(strategy, batch_outs, model, mode):
if strategy is not None and mode == ModeKeys.PREDICT:
total_batch_outs = []
for i in range(len(model.outputs)):
num_replicas = strategy.num_replicas_in_sync
nested_outs = batch_outs[
i * num_replicas : i * num_replicas + num_replicas
]
total_batch_outs.append(
concat_along_batch_dimension(tf.nest.flatten(nested_outs))
)
return total_batch_outs
return batch_outs
| keras/distribute/distributed_training_utils_v1.py | 125 | keras | {
"docstring": "Aggregates the per-replica batch-level outputs from a distributed step.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 44 | Python | 34 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | distributed_training_utils_v1.py | 270,352 | 13 | 80 | _per_replica_aggregate_batch | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 159 | 0 | 80,449 | 16 |
|
1 | 16 | def test_get_bad_image(self):
# Get
response = self.client.get(
reverse(
"wagtailimages:generate_url", args=(self.image.id + 1, "fill-800x600")
)
)
# Check response
self.assertEqual(response.status_code, 404)
self.assertEqual(response["Content-Type"], "application/json")
# Check JSON
self.assertJSONEqual(
response.content.decode(),
json.dumps(
{
"error": "Cannot find image.",
}
),
)
| wagtail/images/tests/test_admin_views.py | 137 | wagtail | {
"docstring": "\n This tests that the view gives a 404 response if a user attempts to use it with an image which doesn't exist\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 22,
"vocab_size": 21
} | 36 | Python | 30 | d10f15e55806c6944827d801cd9c2d53f5da4186 | test_admin_views.py | 75,173 | 16 | 79 | test_get_bad_image | https://github.com/wagtail/wagtail.git | Reformat with black | 225 | 0 | 16,373 | 15 |
|
4 | 18 | def to_dict(self, is_png=False) -> MaskAlignmentsFileDict:
assert self._mask is not None
affine_matrix = self.affine_matrix.tolist() if is_png else self.affine_matrix
retval = MaskAlignmentsFileDict(mask=self._mask,
affine_matrix=affine_matrix,
interpolator=self.interpolator,
stored_size=self.stored_size,
stored_centering=self.stored_centering)
logger.trace({k: v if k != "mask" else type(v) for k, v in retval.items()}) # type: ignore
return retval
| lib/align/detected_face.py | 149 | faceswap | {
"docstring": " Convert the mask to a dictionary for saving to an alignments file\n\n Parameters\n ----------\n is_png: bool\n ``True`` if the dictionary is being created for storage in a png header otherwise\n ``False``. Default: ``False``\n\n Returns\n -------\n dict:\n The :class:`Mask` for saving to an alignments file. Contains the keys ``mask``,\n ``affine_matrix``, ``interpolator``, ``stored_size``, ``stored_centering``\n ",
"language": "en",
"n_whitespaces": 146,
"n_words": 52,
"vocab_size": 41
} | 42 | Python | 37 | 5e73437be47f2410439a3c6716de96354e6a0c94 | detected_face.py | 101,226 | 24 | 97 | to_dict | https://github.com/deepfakes/faceswap.git | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | 241 | 0 | 20,646 | 12 |
|
3 | 13 | def getgeneratorlocals(generator):
if not isgenerator(generator):
raise TypeError("{!r} is not a Python generator".format(generator))
frame = getattr(generator, "gi_frame", None)
if frame is not None:
return generator.gi_frame.f_locals
else:
return {}
# ------------------------------------------------ coroutine introspection
CORO_CREATED = 'CORO_CREATED'
CORO_RUNNING = 'CORO_RUNNING'
CORO_SUSPENDED = 'CORO_SUSPENDED'
CORO_CLOSED = 'CORO_CLOSED'
| python3.10.4/Lib/inspect.py | 115 | XX-Net | {
"docstring": "\n Get the mapping of generator local variables to their current values.\n\n A dict is returned, with the keys the local variable names and values the\n bound values.",
"language": "en",
"n_whitespaces": 36,
"n_words": 27,
"vocab_size": 22
} | 43 | Python | 33 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | inspect.py | 218,447 | 8 | 50 | getgeneratorlocals | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 74 | 0 | 55,315 | 12 |
|
1 | 8 | async def setup(self) -> None:
await self._update_gauges()
self._clock.looping_call(
run_as_background_process,
5 * 60 * 1000,
desc="common_usage_metrics_update_gauges",
func=self._update_gauges,
)
| synapse/metrics/common_usage_metrics.py | 65 | synapse | {
"docstring": "Keep the gauges for common usage metrics up to date.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 17 | Python | 16 | 898fef2789c9b1a20ef53c7d588f536f51f0fe2f | common_usage_metrics.py | 249,451 | 9 | 39 | setup | https://github.com/matrix-org/synapse.git | Share some metrics between the Prometheus exporter and the phone home stats (#13671) | 89 | 0 | 72,923 | 9 |
|
2 | 23 | def query_trial(request):
trial_id = request.GET.get("trial_id")
trials = TrialRecord.objects.filter(trial_id=trial_id).order_by("-start_time")
if len(trials) == 0:
resp = "Unkonwn trial id %s.\n" % trials
else:
trial = trials[0]
result = {
"trial_id": trial.trial_id,
"job_id": trial.job_id,
"trial_status": trial.trial_status,
"start_time": trial.start_time,
"end_time": trial.end_time,
"params": trial.params,
}
resp = json.dumps(result)
return HttpResponse(resp, content_type="application/json;charset=utf-8")
| python/ray/tune/automlboard/frontend/query.py | 192 | ray | {
"docstring": "Rest API to query the trial info, with the given trial_id.\n\n The url pattern should be like this:\n\n curl http://<server>:<port>/query_trial?trial_id=<trial_id>\n\n The response may be:\n\n {\n \"app_url\": \"None\",\n \"trial_status\": \"TERMINATED\",\n \"params\": {'a': 1, 'b': 2},\n \"job_id\": \"asynchyperband_test\",\n \"end_time\": \"2018-07-19 20:49:44\",\n \"start_time\": \"2018-07-19 20:49:40\",\n \"trial_id\": \"2067R2ZD\",\n }\n ",
"language": "en",
"n_whitespaces": 112,
"n_words": 45,
"vocab_size": 42
} | 46 | Python | 38 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | query.py | 132,073 | 17 | 111 | query_trial | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 165 | 0 | 29,666 | 12 |
|
1 | 31 | def test_transaction_outcome_accepted(self):
manager = EventManager(
make_event(
transaction="wait",
contexts={
"trace": {
"parent_span_id": "bce14471e0e9654d",
"op": "foobar",
"trace_id": "a0fa8803753e40fd8124b21eeb2986b5",
"span_id": "bf5be759039ede9a",
}
},
spans=[],
timestamp=iso_format(before_now(minutes=5)),
start_timestamp=iso_format(before_now(minutes=5)),
type="transaction",
platform="python",
)
)
manager.normalize()
mock_track_outcome = mock.Mock()
with mock.patch("sentry.event_manager.track_outcome", mock_track_outcome):
with self.feature({"organizations:transaction-metrics-extraction": False}):
manager.save(self.project.id)
assert_mock_called_once_with_partial(
mock_track_outcome, outcome=Outcome.ACCEPTED, category=DataCategory.TRANSACTION
)
| tests/sentry/event_manager/test_event_manager.py | 244 | sentry | {
"docstring": "\n Without metrics extraction, we count the number of accepted transaction\n events in the TRANSACTION data category. This maintains compatibility\n with Sentry installations that do not have a metrics pipeline.\n ",
"language": "en",
"n_whitespaces": 58,
"n_words": 29,
"vocab_size": 27
} | 43 | Python | 39 | abcccb3fe46fb8479687b77e8bce07dc5df13c90 | test_event_manager.py | 88,863 | 27 | 140 | test_transaction_outcome_accepted | https://github.com/getsentry/sentry.git | fix(event_manager): Emit TRANSACTION outcomes if metrics are disabled (#41607)
In #40507 we started to count transaction metrics in the `transaction`
data category and transaction events in the `transaction_indexed` data
category. That PR missed that metrics extraction can be disabled, in
which case the old behavior of counting events as `transaction` should
remain. Relay already implemented this logic since getsentry/relay#1537
based on the metrics extraction flag.
This PR adds a feature check to the
`organizations:transaction-metrics-extraction` feature, which is the
same feature flag used to control Relay's behavior. We also remove the
previously used option to sample a percentage of organizations into
metrics extraction.
The default for this feature remains off (`false`) until metrics
components have been added to all deployment targets including
self-hosted.
Co-authored-by: Matej Minar <[email protected]> | 408 | 0 | 18,459 | 16 |
|
5 | 19 | def set_weights(self, weights):
params = self.weights
if len(params) != len(weights):
raise ValueError(
f"You called `set_weights(weights)` on optimizer {self._name} "
f"with a weight list of length {str(len(weights))}, "
f"but the optimizer was expecting {str(len(params))} "
f"weights. Provided weights: {str(weights)[:50]}..."
)
if not params:
return
weight_value_tuples = []
param_values = backend.batch_get_value(params)
for pv, p, w in zip(param_values, params, weights):
if pv.shape != w.shape:
raise ValueError(
f"Optimizer weight shape {str(pv.shape)} "
"not compatible with "
f"provided weight shape {str(w.shape)}."
)
weight_value_tuples.append((p, w))
backend.batch_set_value(weight_value_tuples)
| keras/optimizers/optimizer_v2/optimizer_v2.py | 241 | keras | {
"docstring": "Set the weights of the optimizer.\n\n The weights of an optimizer are its state (ie, variables).\n This function takes the weight values associated with this\n optimizer as a list of Numpy arrays. The first value is always the\n iterations count of the optimizer, followed by the optimizer's state\n variables in the order they are created. The passed values are used to set\n the new state of the optimizer.\n\n For example, the RMSprop optimizer for this simple model takes a list of\n three values-- the iteration count, followed by the root-mean-square value\n of the kernel and bias of the single Dense layer:\n\n >>> opt = tf.keras.optimizers.RMSprop()\n >>> m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])\n >>> m.compile(opt, loss='mse')\n >>> data = np.arange(100).reshape(5, 20)\n >>> labels = np.zeros(5)\n >>> results = m.fit(data, labels) # Training.\n >>> new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])]\n >>> opt.set_weights(new_weights)\n >>> opt.iterations\n <tf.Variable 'RMSprop/iter:0' shape=() dtype=int64, numpy=10>\n\n Args:\n weights: weight values as a list of numpy arrays.\n ",
"language": "en",
"n_whitespaces": 313,
"n_words": 154,
"vocab_size": 96
} | 80 | Python | 63 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | optimizer_v2.py | 275,511 | 22 | 103 | set_weights | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 339 | 0 | 81,406 | 17 |
|
4 | 17 | def delete(self) -> None:
try:
if hasattr(self.object, 'close'):
self.object.close()
self._logger.info(self.item.arguments)
if self.item.arguments.get('identity'):
self._logger.success(
f'{colored(self.item.arguments["identity"], "cyan")} is removed!'
)
else:
self._logger.success('object is removed!')
else:
self._logger.warning(f'nothing to close. exiting')
except Exception as e:
self._logger.error(f'{e!r}')
raise
else:
self.item = PartialStoreItem()
| daemon/stores/partial.py | 214 | jina | {
"docstring": "Terminates the object in the store & stops the server",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 8
} | 37 | Python | 33 | 933415bfa1f9eb89f935037014dfed816eb9815d | partial.py | 9,815 | 19 | 105 | delete | https://github.com/jina-ai/jina.git | feat: star routing (#3900)
* feat(proto): adjust proto for star routing (#3844)
* feat(proto): adjust proto for star routing
* feat(proto): generate proto files
* feat(grpc): refactor grpclet interface (#3846)
* feat: refactor connection pool for star routing (#3872)
* feat(k8s): add more labels to k8s deployments
* feat(network): refactor connection pool
* feat(network): refactor k8s pool
* feat: star routing graph gateway (#3877)
* feat: star routing - refactor grpc data runtime (#3887)
* feat(runtimes): refactor grpc dataruntime
* fix(tests): adapt worker runtime tests
* fix(import): fix import
* feat(proto): enable sending multiple lists (#3891)
* feat: star routing gateway (#3893)
* feat: star routing gateway all protocols (#3897)
* test: add streaming and prefetch tests (#3901)
* feat(head): new head runtime for star routing (#3899)
* feat(head): new head runtime
* feat(head): new head runtime
* style: fix overload and cli autocomplete
* feat(network): improve proto comments
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(worker): merge docs in worker runtime (#3905)
* feat(worker): merge docs in worker runtime
* feat(tests): assert after clean up
* feat(tests): star routing runtime integration tests (#3908)
* fix(tests): fix integration tests
* test: test runtimes fast slow request (#3910)
* feat(zmq): purge zmq, zed, routing_table (#3915)
* feat(zmq): purge zmq, zed, routing_table
* style: fix overload and cli autocomplete
* feat(zmq): adapt comment in dependency list
* style: fix overload and cli autocomplete
* fix(tests): fix type tests
Co-authored-by: Jina Dev Bot <[email protected]>
* test: add test gateway to worker connection (#3921)
* feat(pea): adapt peas for star routing (#3918)
* feat(pea): adapt peas for star routing
* style: fix overload and cli autocomplete
* feat(pea): add tests
* feat(tests): add failing head pea test
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(tests): integration tests for peas (#3923)
* feat(tests): integration tests for peas
* feat(pea): remove _inner_pea function
* feat: star routing container pea (#3922)
* test: rescue tests (#3942)
* fix: fix streaming tests (#3945)
* refactor: move docker run to run (#3948)
* feat: star routing pods (#3940)
* feat(pod): adapt pods for star routing
* feat(pods): adapt basepod to star routing
* feat(pod): merge pod and compound pod
* feat(tests): fix tests
* style: fix overload and cli autocomplete
* feat(test): add container pea int test
* feat(ci): remove more unnecessary tests
* fix(tests): remove jinad runtime
* feat(ci): remove latency tracking
* fix(ci): fix ci def
* fix(runtime): enable runtime to be exited
* fix(tests): wrap runtime test in process
* fix(runtimes): remove unused runtimes
* feat(runtimes): improve cancel wait
* fix(ci): build test pip again in ci
* fix(tests): fix a test
* fix(test): run async in its own process
* feat(pod): include shard in activate msg
* fix(pea): dont join
* feat(pod): more debug out
* feat(grpc): manage channels properly
* feat(pods): remove exitfifo
* feat(network): add simple send retry mechanism
* fix(network): await pool close
* fix(test): always close grpc server in worker
* fix(tests): remove container pea from tests
* fix(tests): reorder tests
* fix(ci): split tests
* fix(ci): allow alias setting
* fix(test): skip a test
* feat(pods): address comments
Co-authored-by: Jina Dev Bot <[email protected]>
* test: unblock skipped test (#3957)
* feat: jinad pea (#3949)
* feat: jinad pea
* feat: jinad pea
* test: remote peas
* test: toplogy tests with jinad
* ci: parallel jobs
* feat(tests): add pod integration tests (#3958)
* feat(tests): add pod integration tests
* fix(tests): make tests less flaky
* fix(test): fix test
* test(pea): remote pea topologies (#3961)
* test(pea): remote pea simple topology
* test: remote pea topologies
* refactor: refactor streamer result handling (#3960)
* feat(k8s): adapt K8s Pod for StarRouting (#3964)
* test: optimize k8s test
* test: increase timeout and use different namespace
* test: optimize k8s test
* test: build and load image when needed
* test: refactor k8s test
* test: fix image name error
* test: fix k8s image load
* test: fix typoe port expose
* test: update tests in connection pool and handling
* test: remove unused fixture
* test: parameterize docker images
* test: parameterize docker images
* test: parameterize docker images
* feat(k8s): adapt k8s pod for star routing
* fix(k8s): dont overwrite add/remove function in pool
* fix(k8s): some fixes
* fix(k8s): some more fixes
* fix(k8s): linting
* fix(tests): fix tests
* fix(tests): fix k8s unit tests
* feat(k8s): complete k8s integration test
* feat(k8s): finish k8s tests
* feat(k8s): fix test
* fix(tests): fix test with no name
* feat(k8s): unify create/replace interface
* feat(k8s): extract k8s port constants
* fix(tests): fix tests
* fix(tests): wait for runtime being ready in tests
* feat(k8s): address comments
Co-authored-by: bwanglzu <[email protected]>
* feat(flow): adapt Flow for StarRouting (#3986)
* feat(flow): add routes
* feat(flow): adapt flow to star routing
* style: fix overload and cli autocomplete
* feat(flow): handle empty topologies
* feat(k8s): allow k8s pool disabling
* style: fix overload and cli autocomplete
* fix(test): fix test with mock
* fix(tests): fix more tests
* feat(flow): clean up tests
* style: fix overload and cli autocomplete
* fix(tests): fix more tests
* feat: add plot function (#3994)
* fix(tests): avoid hanging tests
* feat(flow): add type hinting
* fix(test): fix duplicate exec name in test
* fix(tests): fix more tests
* fix(tests): enable jinad test again
* fix(tests): random port fixture
* fix(style): replace quotes
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* feat(ci): bring back ci (#3997)
* feat(ci): enable ci again
* style: fix overload and cli autocomplete
* feat(ci): add latency tracking
* feat(ci): bring back some tests
* fix(tests): remove invalid port test
* feat(ci): disable daemon and distributed tests
* fix(tests): fix entrypoint in hub test
* fix(tests): wait for gateway to be ready
* fix(test): fix more tests
* feat(flow): do rolling update and scale sequentially
* fix(tests): fix more tests
* style: fix overload and cli autocomplete
* feat: star routing hanging pods (#4011)
* fix: try to handle hanging pods better
* test: hanging pods test work
* fix: fix topology graph problem
* test: add unit test to graph
* fix(tests): fix k8s tests
* fix(test): fix k8s test
* fix(test): fix k8s pool test
* fix(test): fix k8s test
* fix(test): fix k8s connection pool setting
* fix(tests): make runtime test more reliable
* fix(test): fix routes test
* fix(tests): make rolling update test less flaky
* feat(network): gurantee unique ports
* feat(network): do round robin for shards
* fix(ci): increase pytest timeout to 10 min
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* fix(ci): fix ci file
* feat(daemon): jinad pod for star routing
* Revert "feat(daemon): jinad pod for star routing"
This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92.
* feat(daemon): remote jinad pod support (#4042)
* feat(daemon): add pod tests for star routing
* feat(daemon): add remote pod test
* test(daemon): add remote pod arguments test
* test(daemon): add async scale test
* test(daemon): add rolling update test
* test(daemon): fix host
* feat(proto): remove message proto (#4051)
* feat(proto): remove message proto
* fix(tests): fix tests
* fix(tests): fix some more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* feat(proto): put docs back in data
* fix(proto): clean up
* feat(proto): clean up
* fix(tests): skip latency tracking
* fix(test): fix hub test
* fix(tests): fix k8s test
* fix(test): some test clean up
* fix(style): clean up style issues
* feat(proto): adjust for rebase
* fix(tests): bring back latency tracking
* fix(tests): fix merge accident
* feat(proto): skip request serialization (#4074)
* feat: add reduce to star routing (#4070)
* feat: add reduce on shards to head runtime
* test: add reduce integration tests with fixed order
* feat: add reduce on needs
* chore: get_docs_matrix_from_request becomes public
* style: fix overload and cli autocomplete
* docs: remove undeterministic results warning
* fix: fix uses_after
* test: assert correct num docs after reducing in test_external_pod
* test: correct asserts after reduce in test_rolling_update
* fix: no reduce if uses_after_address is set
* fix: get_docs_from_request only if needed
* fix: fix tests after merge
* refactor: move reduce from data_request_handler to head
* style: fix overload and cli autocomplete
* chore: apply suggestions
* fix: fix asserts
* chore: minor test fix
* chore: apply suggestions
* test: remove flow tests with external executor (pea)
* fix: fix test_expected_messages_routing
* fix: fix test_func_joiner
* test: adapt k8s test
Co-authored-by: Jina Dev Bot <[email protected]>
* fix(k8s): fix static pool config
* fix: use custom protoc doc generator image (#4088)
* fix: use custom protoc doc generator image
* fix(docs): minor doc improvement
* fix(docs): use custom image
* fix(docs): copy docarray
* fix: doc building local only
* fix: timeout doc building
* fix: use updated args when building ContainerPea
* test: add container PeaFactory test
* fix: force pea close on windows (#4098)
* fix: dont reduce if uses exist (#4099)
* fix: dont use reduce if uses exist
* fix: adjust reduce tests
* fix: adjust more reduce tests
* fix: fix more tests
* fix: adjust more tests
* fix: ignore non jina resources (#4101)
* feat(executor): enable async executors (#4102)
* feat(daemon): daemon flow on star routing (#4096)
* test(daemon): add remote flow test
* feat(daemon): call scale in daemon
* feat(daemon): remove tail args and identity
* test(daemon): rename scalable executor
* test(daemon): add a small delay in async test
* feat(daemon): scale partial flow only
* feat(daemon): call scale directly in partial flow store
* test(daemon): use asyncio sleep
* feat(daemon): enable flow level distributed tests
* test(daemon): fix jinad env workspace config
* test(daemon): fix pod test use new port rolling update
* feat(daemon): enable distribuetd tests
* test(daemon): remove duplicate tests and zed runtime test
* test(daemon): fix stores unit test
* feat(daemon): enable part of distributed tests
* feat(daemon): enable part of distributed tests
* test: correct test paths
* test(daemon): add client test for remote flows
* test(daemon): send a request with jina client
* test(daemon): assert async generator
* test(daemon): small interval between tests
* test(daemon): add flow test for container runtime
* test(daemon): add flow test for container runtime
* test(daemon): fix executor name
* test(daemon): fix executor name
* test(daemon): use async client fetch result
* test(daemon): finish container flow test
* test(daemon): enable distributed in ci
* test(daemon): enable distributed in ci
* test(daemon): decare flows and pods
* test(daemon): debug ci if else
* test(daemon): debug ci if else
* test(daemon): decare flows and pods
* test(daemon): correct test paths
* test(daemon): add small delay for async tests
* fix: star routing fixes (#4100)
* docs: update docs
* fix: fix Request.__repr__
* docs: update flow remarks
* docs: fix typo
* test: add non_empty_fields test
* chore: remove non_empty_fields test
* feat: polling per endpoint (#4111)
* feat(polling): polling per endpoint configurable
* fix: adjust tests
* feat(polling): extend documentation
* style: fix overload and cli autocomplete
* fix: clean up
* fix: adjust more tests
* fix: remove repeat from flaky test
* fix: k8s test
* feat(polling): address pr feedback
* feat: improve docs
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(grpc): support connect grpc server via ssl tunnel (#4092)
* feat(grpc): support ssl grpc connect if port is 443
* fix(grpc): use https option instead of detect port automatically
* chore: fix typo
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* test(networking): add test for peapods networking
* fix: address comments
Co-authored-by: Joan Fontanals <[email protected]>
* feat(polling): unify polling args (#4113)
* fix: several issues for jinad pods (#4119)
* fix: activate for jinad pods
* fix: dont expose worker pod in partial daemon
* fix: workspace setting
* fix: containerized flows
* fix: hub test
* feat(daemon): remote peas on star routing (#4112)
* test(daemon): fix request in peas
* test(daemon): fix request in peas
* test(daemon): fix sync async client test
* test(daemon): enable remote peas test
* test(daemon): replace send message to send request
* test(daemon): declare pea tests in ci
* test(daemon): use pea args fixture
* test(daemon): head pea use default host
* test(daemon): fix peas topologies
* test(daemon): fix pseudo naming
* test(daemon): use default host as host
* test(daemon): fix executor path
* test(daemon): add remote worker back
* test(daemon): skip local remote remote topology
* fix: jinad pea test setup
* fix: jinad pea tests
* fix: remove invalid assertion
Co-authored-by: jacobowitz <[email protected]>
* feat: enable daemon tests again (#4132)
* feat: enable daemon tests again
* fix: remove bogy empty script file
* fix: more jinad test fixes
* style: fix overload and cli autocomplete
* fix: scale and ru in jinad
* fix: fix more jinad tests
Co-authored-by: Jina Dev Bot <[email protected]>
* fix: fix flow test
* fix: improve pea tests reliability (#4136)
Co-authored-by: Joan Fontanals <[email protected]>
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Deepankar Mahapatro <[email protected]>
Co-authored-by: bwanglzu <[email protected]>
Co-authored-by: AlaeddineAbdessalem <[email protected]>
Co-authored-by: Zhaofeng Miao <[email protected]> | 275 | 0 | 1,707 | 20 |
|
2 | 9 | def markInputline(self, markerString=">!<"):
line_str = self.line
line_column = self.column - 1
if markerString:
line_str = "".join((line_str[:line_column],
markerString, line_str[line_column:]))
return line_str.strip() | .venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py | 88 | transferlearning | {
"docstring": "Extracts the exception line from the input string, and marks\n the location of the exception with a special symbol.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 19,
"vocab_size": 15
} | 20 | Python | 17 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | pyparsing.py | 63,453 | 7 | 53 | markInputline | https://github.com/jindongwang/transferlearning.git | upd; format | 97 | 0 | 13,311 | 13 |
|
15 | 12 | def convert_indexed_to_array(expr, first_indices=None):
r
result, indices = _convert_indexed_to_array(expr)
if any(isinstance(i, (int, Integer)) for i in indices):
result = ArrayElement(result, indices)
indices = []
if not first_indices:
return result
| sympy/tensor/array/expressions/conv_indexed_to_array.py | 87 | sympy | {
"docstring": "\n Parse indexed expression into a form useful for code generation.\n\n Examples\n ========\n\n >>> from sympy.tensor.array.expressions.conv_indexed_to_array import convert_indexed_to_array\n >>> from sympy import MatrixSymbol, Sum, symbols\n\n >>> i, j, k, d = symbols(\"i j k d\")\n >>> M = MatrixSymbol(\"M\", d, d)\n >>> N = MatrixSymbol(\"N\", d, d)\n\n Recognize the trace in summation form:\n\n >>> expr = Sum(M[i, i], (i, 0, d-1))\n >>> convert_indexed_to_array(expr)\n ArrayContraction(M, (0, 1))\n\n Recognize the extraction of the diagonal by using the same index `i` on\n both axes of the matrix:\n\n >>> expr = M[i, i]\n >>> convert_indexed_to_array(expr)\n ArrayDiagonal(M, (0, 1))\n\n This function can help perform the transformation expressed in two\n different mathematical notations as:\n\n `\\sum_{j=0}^{N-1} A_{i,j} B_{j,k} \\Longrightarrow \\mathbf{A}\\cdot \\mathbf{B}`\n\n Recognize the matrix multiplication in summation form:\n\n >>> expr = Sum(M[i, j]*N[j, k], (j, 0, d-1))\n >>> convert_indexed_to_array(expr)\n ArrayContraction(ArrayTensorProduct(M, N), (1, 2))\n\n Specify that ``k`` has to be the starting index:\n\n >>> convert_indexed_to_array(expr, first_indices=[k])\n ArrayContraction(ArrayTensorProduct(N, M), (0, 3))\n ",
"language": "en",
"n_whitespaces": 236,
"n_words": 151,
"vocab_size": 107
} | 28 | Python | 23 | 0aabd1d7b8c3cb521f713ea925a0bf019ba1f3ca | conv_indexed_to_array.py | 196,010 | 62 | 191 | convert_indexed_to_array | https://github.com/sympy/sympy.git | Extend conversion function of indexed expression to arrays to support broadcasting and addition of different indices | 60 | 0 | 47,511 | 10 |
|
7 | 22 | def is_symbolic_tensor(tensor):
if isinstance(tensor, tf.Tensor):
return hasattr(tensor, "graph")
elif is_extension_type(tensor):
component_tensors = tf.nest.flatten(tensor, expand_composites=True)
return any(hasattr(t, "graph") for t in component_tensors)
elif isinstance(tensor, tf.Variable):
# Variables that are output of a Keras Layer in Functional API mode
# should be considered symbolic.
# TODO(omalleyt): We need a better way to check this in order to
# enable `run_eagerly=True` for Models containing Layers that
# return Variables as outputs.
return (
getattr(tensor, "_keras_history", False)
or not tf.executing_eagerly()
)
elif isinstance(tensor, tuple(_user_convertible_tensor_types)):
tensor = ops.convert_to_tensor_or_composite(tensor)
return is_symbolic_tensor(tensor)
else:
return False
@keras_export("keras.__internal__.utils.register_symbolic_tensor_type", v1=[]) | keras/utils/tf_utils.py | 205 | @keras_export("keras.__internal__.utils.register_symbolic_tensor_type", v1=[]) | keras | {
"docstring": "Returns whether a tensor is symbolic (from a TF graph) or an eager tensor.\n\n A Variable can be seen as either: it is considered symbolic\n when we are in a graph scope, and eager when we are in an eager scope.\n\n Args:\n tensor: A tensor instance to test.\n\n Returns:\n True for symbolic tensors, False for eager tensors.\n ",
"language": "en",
"n_whitespaces": 82,
"n_words": 57,
"vocab_size": 41
} | 90 | Python | 68 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | tf_utils.py | 277,089 | 16 | 113 | is_symbolic_tensor | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 220 | 1 | 81,861 | 13 |
10 | 36 | def _on_size(self, event):
_log.debug("%s - _on_size()", type(self))
sz = self.GetParent().GetSizer()
if sz:
si = sz.GetItem(self)
if sz and si and not si.Proportion and not si.Flag & wx.EXPAND:
# managed by a sizer, but with a fixed size
size = self.GetMinSize()
else:
# variable size
size = self.GetClientSize()
# Do not allow size to become smaller than MinSize
size.IncTo(self.GetMinSize())
if getattr(self, "_width", None):
if size == (self._width, self._height):
# no change in size
return
self._width, self._height = size
self._isDrawn = False
if self._width <= 1 or self._height <= 1:
return # Empty figure
# Create a new, correctly sized bitmap
self.bitmap = wx.Bitmap(self._width, self._height)
dpival = self.figure.dpi
winch = self._width / dpival
hinch = self._height / dpival
self.figure.set_size_inches(winch, hinch, forward=False)
# Rendering will happen on the associated paint event
# so no need to do anything here except to make sure
# the whole background is repainted.
self.Refresh(eraseBackground=False)
ResizeEvent("resize_event", self)._process()
| lib/matplotlib/backends/backend_wx.py | 344 | matplotlib | {
"docstring": "\n Called when wxEventSize is generated.\n\n In this application we attempt to resize to fit the window, so it\n is better to take the performance hit and redraw the whole window.\n ",
"language": "en",
"n_whitespaces": 59,
"n_words": 30,
"vocab_size": 25
} | 149 | Python | 101 | 4e21912d2938b0e8812c4d1f7cd902c080062ff2 | backend_wx.py | 108,945 | 24 | 207 | _on_size | https://github.com/matplotlib/matplotlib.git | Make it easier to improve UI event metadata.
Currently, UI events (MouseEvent, KeyEvent, etc.) are generated by
letting the GUI-specific backends massage the native event objects into
a list of args/kwargs and then call
`FigureCanvasBase.motion_notify_event`/`.key_press_event`/etc. This
makes it a bit tricky to improve the metadata on the events, because one
needs to change the signature on both the `FigureCanvasBase` method and
the event class. Moreover, the `motion_notify_event`/etc. methods are
directly bound as event handlers in the gtk3 and tk backends, and thus
have incompatible signatures there.
Instead, the native GUI handlers can directly construct the relevant
event objects and trigger the events themselves; a new `Event._process`
helper method makes this even shorter (and allows to keep factoring some
common functionality e.g. for tracking the last pressed button or key).
As an example, this PR also updates figure_leave_event to always
correctly set the event location based on the *current* cursor position,
instead of the last triggered location event (which may be outdated);
this can now easily be done on a backend-by-backend basis, instead of
coordinating the change with FigureCanvasBase.figure_leave_event.
This also exposed another (minor) issue, in that resize events
often trigger *two* calls to draw_idle -- one in the GUI-specific
handler, and one in FigureCanvasBase.draw_idle (now moved to
ResizeEvent._process, but should perhaps instead be a callback
autoconnected to "resize_event") -- could probably be fixed later. | 426 | 0 | 23,396 | 12 |
|
1 | 26 | def test_subdag_pools_no_possible_conflict(self):
dag = DAG('parent', default_args=default_args)
subdag = DAG('parent.child', default_args=default_args)
session = airflow.settings.Session()
pool_1 = airflow.models.Pool(pool='test_pool_1', slots=1)
pool_10 = airflow.models.Pool(pool='test_pool_10', slots=10)
session.add(pool_1)
session.add(pool_10)
session.commit()
EmptyOperator(task_id='dummy', dag=subdag, pool='test_pool_10')
mock_session = Mock()
SubDagOperator(task_id='child', dag=dag, subdag=subdag, pool='test_pool_1', session=mock_session)
assert not mock_session.query.called
session.delete(pool_1)
session.delete(pool_10)
session.commit()
| tests/operators/test_subdag_operator.py | 250 | airflow | {
"docstring": "\n Subdags and subdag tasks with no pool overlap, should not to query\n pools\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 13,
"vocab_size": 13
} | 41 | Python | 34 | 49e336ae0302b386a2f47269a6d13988382d975f | test_subdag_operator.py | 47,649 | 16 | 149 | test_subdag_pools_no_possible_conflict | https://github.com/apache/airflow.git | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | 153 | 0 | 9,190 | 10 |
|
24 | 35 | def tree_all_pairs_lowest_common_ancestor(G, root=None, pairs=None):
r
if len(G) == 0:
raise nx.NetworkXPointlessConcept("LCA meaningless on null graphs.")
elif None in G:
raise nx.NetworkXError("None is not a valid node.")
# Index pairs of interest for efficient lookup from either side.
if pairs is not None:
pair_dict = defaultdict(set)
# See note on all_pairs_lowest_common_ancestor.
if not isinstance(pairs, (Mapping, Set)):
pairs = set(pairs)
for u, v in pairs:
for n in (u, v):
if n not in G:
msg = f"The node {str(n)} is not in the digraph."
raise nx.NodeNotFound(msg)
pair_dict[u].add(v)
pair_dict[v].add(u)
# If root is not specified, find the exactly one node with in degree 0 and
# use it. Raise an error if none are found, or more than one is. Also check
# for any nodes with in degree larger than 1, which would imply G is not a
# tree.
if root is None:
for n, deg in G.in_degree:
if deg == 0:
if root is not None:
msg = "No root specified and tree has multiple sources."
raise nx.NetworkXError(msg)
root = n
elif deg > 1:
msg = "Tree LCA only defined on trees; use DAG routine."
raise nx.NetworkXError(msg)
if root is None:
raise nx.NetworkXError("Graph contains a cycle.")
# Iterative implementation of Tarjan's offline lca algorithm
# as described in CLRS on page 521 (2nd edition)/page 584 (3rd edition)
uf = UnionFind()
ancestors = {}
for node in G:
ancestors[node] = uf[node]
colors = defaultdict(bool)
for node in nx.dfs_postorder_nodes(G, root):
colors[node] = True
for v in pair_dict[node] if pairs is not None else G:
if colors[v]:
# If the user requested both directions of a pair, give it.
# Otherwise, just give one.
if pairs is not None and (node, v) in pairs:
yield (node, v), ancestors[uf[v]]
if pairs is None or (v, node) in pairs:
yield (v, node), ancestors[uf[v]]
if node != root:
parent = arbitrary_element(G.pred[node])
uf.union(parent, node)
ancestors[uf[parent]] = parent
@not_implemented_for("undirected")
@not_implemented_for("multigraph") | networkx/algorithms/lowest_common_ancestors.py | 573 | @not_implemented_for("undirected")
@not_implemented_for("multigraph") | networkx | {
"docstring": "Yield the lowest common ancestor for sets of pairs in a tree.\n\n Parameters\n ----------\n G : NetworkX directed graph (must be a tree)\n\n root : node, optional (default: None)\n The root of the subtree to operate on.\n If None, assume the entire graph has exactly one source and use that.\n\n pairs : iterable or iterator of pairs of nodes, optional (default: None)\n The pairs of interest. If None, Defaults to all pairs of nodes\n under `root` that have a lowest common ancestor.\n\n Returns\n -------\n lcas : generator of tuples `((u, v), lca)` where `u` and `v` are nodes\n in `pairs` and `lca` is their lowest common ancestor.\n\n Examples\n --------\n >>> import pprint\n >>> G = nx.DiGraph([(1, 3), (2, 4), (1, 2)])\n >>> pprint.pprint(dict(nx.tree_all_pairs_lowest_common_ancestor(G)))\n {(1, 1): 1,\n (2, 1): 1,\n (2, 2): 2,\n (3, 1): 1,\n (3, 2): 1,\n (3, 3): 3,\n (3, 4): 1,\n (4, 1): 1,\n (4, 2): 2,\n (4, 4): 4}\n\n We can also use `pairs` argument to specify the pairs of nodes for which we\n want to compute lowest common ancestors. Here is an example:\n\n >>> dict(nx.tree_all_pairs_lowest_common_ancestor(G, pairs=[(1, 4), (2, 3)]))\n {(2, 3): 1, (1, 4): 1}\n\n Notes\n -----\n Only defined on non-null trees represented with directed edges from\n parents to children. Uses Tarjan's off-line lowest-common-ancestors\n algorithm. Runs in time $O(4 \\times (V + E + P))$ time, where 4 is the largest\n value of the inverse Ackermann function likely to ever come up in actual\n use, and $P$ is the number of pairs requested (or $V^2$ if all are needed).\n\n Tarjan, R. E. (1979), \"Applications of path compression on balanced trees\",\n Journal of the ACM 26 (4): 690-715, doi:10.1145/322154.322161.\n\n See Also\n --------\n all_pairs_lowest_common_ancestor: similar routine for general DAGs\n lowest_common_ancestor: just a single pair for general DAGs\n ",
"language": "en",
"n_whitespaces": 457,
"n_words": 290,
"vocab_size": 186
} | 314 | Python | 173 | abaa68779ccb4cce8d1a5ecade622ab96d01edeb | lowest_common_ancestors.py | 176,977 | 102 | 345 | tree_all_pairs_lowest_common_ancestor | https://github.com/networkx/networkx.git | Add examples to lowest common ancestors algorithms (#5531)
* Add examples to lowest common ancestors documentation
* Fix output style of examples
* Fix output style of example
* Update pre-commit
* Update networkx/algorithms/lowest_common_ancestors.py
Co-authored-by: Ross Barnowski <[email protected]>
* Update networkx/algorithms/lowest_common_ancestors.py
Co-authored-by: Ross Barnowski <[email protected]>
* Indentation fix & pprint dictionary
* Update networkx/algorithms/lowest_common_ancestors.py
Co-authored-by: Ross Barnowski <[email protected]>
* Update networkx/algorithms/lowest_common_ancestors.py
Co-authored-by: Ross Barnowski <[email protected]>
* Update networkx/algorithms/lowest_common_ancestors.py
Co-authored-by: Ross Barnowski <[email protected]>
* Move "import pprint" to the example
Co-authored-by: dtuncturk <[email protected]>
Co-authored-by: Ross Barnowski <[email protected]> | 808 | 1 | 42,205 | 18 |
1 | 11 | def view_transformation(E, R, V, roll):
u, v, w = _view_axes(E, R, V, roll)
M = _view_transformation_uvw(u, v, w, E)
return M
| lib/mpl_toolkits/mplot3d/proj3d.py | 59 | matplotlib | {
"docstring": "\n Return the view transformation matrix.\n\n Parameters\n ----------\n E : 3-element numpy array\n The coordinates of the eye/camera.\n R : 3-element numpy array\n The coordinates of the center of the view box.\n V : 3-element numpy array\n Unit vector in the direction of the vertical axis.\n roll : float\n The roll angle in radians.\n ",
"language": "en",
"n_whitespaces": 106,
"n_words": 53,
"vocab_size": 30
} | 21 | Python | 16 | 4896ec1a2cfb8c454e385632d8df213c915ced52 | proj3d.py | 109,756 | 4 | 42 | view_transformation | https://github.com/matplotlib/matplotlib.git | Add pan and zoom toolbar handling to 3D Axes (Replaces PR#22614) (#23449)
* ENH: Add pan and zoom toolbar handling to 3D Axes
1) This moves the pan logic that was already in the mouse move handler
into the "drag_pan" method to make it available from the toolbar.
2) This expands upon the panning logic to enable a zoom-to-box feature.
The zoom-to-box is done relative to the Axes, so it shrinks/expands
the box as a fraction of each delta, from lower-left Axes to lower-left
zoom-box. Thus, it tries to handle non-centered zooms, which adds more
cases to handle versus the current right-click zoom only scaling from
the center of the projection.
* Rewrite zooming with bounding box
* Rewrite 3d panning to work with a roll angle
* Whats new for zoom and pan buttons
* Make pan button configurable
* Do not jump when zooming and mouse goes over other subplot
* Rework zooming for 3d plots
* Handle x/y lock when zooming and panning
* Update tests
* Docstrings
* Dont assume a scale_z
* Limit zoom box
* Test zoom pan key modifiers
* Save some calculation by saving view axes
* Deprecation warnings for Axes3D.eye, .vvec
* Remove Axes3D._prepare_view_from_bbox for now
* Comments and docstrings
* Switch from uvn to uvw
* Save aspect to axes
* Constrain zooming with mouse when one of the equal aspect ratios is set
* Cleanup
* Cleanup
* Consolidate finding equal aspect axis indices
* linting
* More intuitive scaling
* Box zoom keeps existing aspect ratios
* Linting
* Code review comments
* Revert parameters for view_transformation
* Fix new 3d pan/zoom view going on view stack twice
* Better clipping
* Test 3d toolbar navigation
* Privatize helper functions
* Deprecations
* Code review changes
* Deprecation note
* Undeprecate proj3d.view_transformation
* Undeprecate proj3d.view_transformation
* Update doc/api/next_api_changes/deprecations/23449-SS.rst
Co-authored-by: Greg Lucas <[email protected]>
Co-authored-by: Scott Shambaugh <[email protected]>
Co-authored-by: Oscar Gustafsson <[email protected]> | 33 | 0 | 23,737 | 8 |
|
4 | 19 | def _get_missing_alignments(self) -> Generator[str, None, None]:
self.output_message = "Frames missing from alignments file"
exclude_filetypes = set(["yaml", "yml", "p", "json", "txt"])
for frame in tqdm(cast(Dict[str, str], self._items),
desc=self.output_message,
leave=False):
frame_name = frame["frame_fullname"]
if (frame["frame_extension"] not in exclude_filetypes
and not self._alignments.frame_exists(frame_name)):
logger.debug("Returning: '%s'", frame_name)
yield frame_name
| tools/alignments/jobs.py | 169 | faceswap | {
"docstring": " yield each frame that does not exist in alignments file\n\n Yields\n ------\n str\n The frame name of any frames missing alignments\n ",
"language": "en",
"n_whitespaces": 61,
"n_words": 21,
"vocab_size": 19
} | 44 | Python | 38 | e2a77e7c6e84e81f642cb22f528e25e3f2d2dbc1 | jobs.py | 101,717 | 18 | 103 | _get_missing_alignments | https://github.com/deepfakes/faceswap.git | Alignments Tool - Typing, Documentation + Re-org | 193 | 0 | 21,121 | 13 |
|
1 | 3 | def rebalance_partitions(cls, partitions):
return partitions
| modin/core/dataframe/pandas/partitioning/partition_manager.py | 18 | modin | {
"docstring": "\n Return the provided array of partitions without rebalancing it.\n\n Parameters\n ----------\n partitions : np.ndarray\n The 2-d array of partitions to rebalance.\n\n Returns\n -------\n np.ndarray\n The same 2-d array.\n ",
"language": "en",
"n_whitespaces": 107,
"n_words": 28,
"vocab_size": 21
} | 5 | Python | 5 | 8d1004fdbdaa05700613c8e6287641a732acf606 | partition_manager.py | 153,177 | 2 | 10 | rebalance_partitions | https://github.com/modin-project/modin.git | FIX-#3675: Expand virtual partitioning utility (#3886)
Co-authored-by: mvashishtha <[email protected]>
Co-authored-by: jeffreykennethli <[email protected]>
Co-authored-by: Anatoly Myachev <[email protected]>
Co-authored-by: Vasily Litvinov <[email protected]>
Co-authored-by: Alexey Prutskov <[email protected]>
Co-authored-by: Mahesh Vashishtha <[email protected]>
Co-authored-by: Naren Krishna <[email protected]>
Co-authored-by: Yaroslav Igoshev <[email protected]>
Co-authored-by: Dmitry Chigarev <[email protected]>
Co-authored-by: Yaroslav Igoshev <[email protected]>
Co-authored-by: Doris Lee <[email protected]>
Co-authored-by: Aditya Parameswaran <[email protected]>
Co-authored-by: Rehan Sohail Durrani <[email protected]>
Co-authored-by: Susmit Vengurlekar <[email protected]>
Signed-off-by: Devin Petersohn <[email protected]> | 19 | 0 | 35,280 | 6 |
|
2 | 8 | def _handle_deprecations(self) -> None:
if self._args.distributed:
deprecation_warning("`-d`, `--distributed`",
"Please use `-D`, `--distribution-strategy`")
logger.warning("Setting 'distribution-strategy' to 'mirrored'")
setattr(self._args, "distribution_strategy", "mirrored")
del self._args.distributed
| scripts/train.py | 79 | faceswap | {
"docstring": " Handle the update of deprecated arguments and output warnings. ",
"language": "en",
"n_whitespaces": 10,
"n_words": 9,
"vocab_size": 9
} | 21 | Python | 21 | 2ea05623bd684b2d1dd75679ad00441a5c751e7e | train.py | 101,076 | 8 | 43 | _handle_deprecations | https://github.com/deepfakes/faceswap.git | Update Distibution Strategies:
- Add Central Storage Stategy
- Deprecate 'distributed' cli argument | 110 | 0 | 20,513 | 10 |
|
3 | 33 | def to_qa_preds(self, top_preds, no_ans_gaps, baskets):
ret = []
# Iterate over each set of document level prediction
for pred_d, no_ans_gap, basket in zip(top_preds, no_ans_gaps, baskets):
# Unpack document offsets, clear text and id
token_offsets = basket.raw["document_offsets"]
pred_id = basket.id_external if basket.id_external else basket.id_internal
# These options reflect the different input dicts that can be assigned to the basket
# before any kind of normalization or preprocessing can happen
question_names = ["question_text", "qas", "questions"]
doc_names = ["document_text", "context", "text"]
document_text = try_get(doc_names, basket.raw)
question = self.get_question(question_names, basket.raw)
ground_truth = self.get_ground_truth(basket)
curr_doc_pred = QAPred(
id=pred_id,
prediction=pred_d,
context=document_text,
question=question,
token_offsets=token_offsets,
context_window_size=self.context_window_size,
aggregation_level="document",
ground_truth_answer=ground_truth,
no_answer_gap=no_ans_gap,
)
ret.append(curr_doc_pred)
return ret
| haystack/modeling/model/prediction_head.py | 238 | haystack | {
"docstring": "\n Groups Span objects together in a QAPred object\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | 105 | Python | 84 | a59bca366174d9c692fa19750c24d65f47660ef7 | prediction_head.py | 256,248 | 23 | 152 | to_qa_preds | https://github.com/deepset-ai/haystack.git | 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> | 418 | 0 | 74,828 | 12 |
|
2 | 9 | def _deconstruct_messages(snuba_messages):
return [
(json.loads(msg.payload.value.decode("utf-8")), msg.payload.headers)
for msg in snuba_messages
]
| tests/sentry/sentry_metrics/test_batch.py | 59 | sentry | {
"docstring": "\n Convert a list of messages returned by `reconstruct_messages` into python\n primitives, to run assertions on:\n\n assert _deconstruct_messages(batch.reconstruct_messages(...)) == [ ... ]\n\n This is slightly nicer to work with than:\n\n assert batch.reconstruct_messages(...) == _construct_messages([ ... ])\n\n ...because pytest's assertion diffs work better with python primitives.\n ",
"language": "en",
"n_whitespaces": 74,
"n_words": 44,
"vocab_size": 37
} | 11 | Python | 11 | f31b57cbc5ec359c8ef9c6459d3d9d8ffcd6e8d9 | test_batch.py | 93,937 | 5 | 36 | _deconstruct_messages | https://github.com/getsentry/sentry.git | ref(metrics_indexer): Improve typing, introduce more dataclasses, fix org_id namespacing bug in metadata [INGEST-1380] (#37170) | 34 | 0 | 19,028 | 13 |
|
3 | 9 | def from_key_val_list(value):
if value is None:
return None
if isinstance(value, (str, bytes, bool, int)):
raise ValueError("cannot encode objects that are not 2-tuples")
return OrderedDict(value)
| pipenv/patched/pip/_vendor/requests/utils.py | 63 | pipenv | {
"docstring": "Take an object and test to see if it can be represented as a\n dictionary. Unless it can not be represented as such, return an\n OrderedDict, e.g.,\n\n ::\n\n >>> from_key_val_list([('key', 'val')])\n OrderedDict([('key', 'val')])\n >>> from_key_val_list('string')\n Traceback (most recent call last):\n ...\n ValueError: cannot encode objects that are not 2-tuples\n >>> from_key_val_list({'key': 'val'})\n OrderedDict([('key', 'val')])\n\n :rtype: OrderedDict\n ",
"language": "en",
"n_whitespaces": 127,
"n_words": 56,
"vocab_size": 44
} | 24 | Python | 22 | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | utils.py | 22,139 | 6 | 39 | from_key_val_list | https://github.com/pypa/pipenv.git | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | 50 | 0 | 4,211 | 10 |
|
2 | 10 | def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, BloomModel):
module.gradient_checkpointing = value
BLOOM_START_DOCSTRING = r
BLOOM_INPUTS_DOCSTRING = r
@add_start_docstrings(
"The bare Bloom Model transformer outputting raw hidden-states without any specific head on top.",
BLOOM_START_DOCSTRING,
) | src/transformers/models/bloom/modeling_bloom.py | 64 | @add_start_docstrings(
"The bare Bloom Model transformer outputting raw hidden-states without any specific head on top.",
BLOOM_START_DOCSTRING,
) | transformers | {
"docstring": "\n\n This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)\n\n This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n and behavior.\n\n Parameters:\n config ([`BloomConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):\n `input_ids_length` = `sequence_length` if `past_key_values` is `None` else\n `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input\n sequence tokens in the vocabulary.\n\n If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as\n `input_ids`.\n\n Indices can be obtained using [`BloomTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`):\n Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see\n `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have\n their past given to this model should not be passed as `input_ids` as they have already been computed.\n attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n config.max_position_embeddings - 1]`.\n\n [What are position IDs?](../glossary#position-ids)\n head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n - 1 indicates the head is **not masked**,\n - 0 indicates the head is **masked**.\n\n inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n model's internal embedding lookup matrix.\n\n If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see\n `past_key_values`).\n use_cache (`bool`, *optional*):\n If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see\n `past_key_values`).\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n",
"language": "en",
"n_whitespaces": 957,
"n_words": 474,
"vocab_size": 241
} | 33 | Python | 30 | ca2a55e9dfb245527b5e1c954fec6ffbb7aef07b | modeling_bloom.py | 31,156 | 3 | 24 | _set_gradient_checkpointing | https://github.com/huggingface/transformers.git | BLOOM (#17474)
* adding template
* update model
* model update
* update conf for debug model
* update conversion
* update conversion script
* update conversion script
* fix missing keys check
* add tests to test the tokenizer in the local machine
* Change variable name
* add tests on xnli dataset
* add more description
* add descriptions + clearer code
* clearer code
* adding new tests + skipping few tests because of env problems
* change comment
* add dtype on the configuration
* add test embeddings
* add hardcoded test
* fix dtype issue
* adding torch.float16 to config
* adding more metrics (min, max, mean)
* add sum
* now the test passes with almost equal
* add files for conversion - test passes on cpu gpu
* add final changes
* cleaning code
* add new args in the docstring
* fix one liner function
* remove macros
* remove forward attention
* clean up init funtion
* add comments on the issue
* rm scale mask softmax
* do make style
* fix dtype in init
* fixing for loop on att probs
* fix style with black
* fix style + doc error
* fix and debug CI errors (docs + style)
* some updates
- change new operations
- finally add scaled softmax
- added new args in the config
* make use cache working
* add changes
- save sharded models
- final changes on the modeling script
* add changes
- comment on alibi
- add TODO on seq length
* test commit
- added a text to test the commit
Co-authored-by: thomasw21 <[email protected]>
* final changes
- attention mask change
- generation works on BS176b
Co-authored-by: thomasw21 <[email protected]>
* changes - model + conversion
* move to correct dir
* put ,
* fex fixes
* fix tokenizer autodoc
* fix minor CI issues
* fix minor CI issues
* fix minor CI issues
* fix style issue
* fix minor import issues
* fix few issues
* remove def main on the test
* add require torch
* replace decorator with 'with'
* fix style
* change to bloom
* add quick fix tokenizer
* fix tokenizer file
* fix tokenizer
- merge tests
- small fixes
* fix import issue
* add bloom to readme
* fix consistency
* Update docs/source/en/model_doc/bloom.mdx
Co-authored-by: Sylvain Gugger <[email protected]>
* Apply suggestions from code review
fix comment issues on file headers
Co-authored-by: Sylvain Gugger <[email protected]>
* fix doc issue
* small fix - modeling test
* some changes
- refactor some code
- taking into account reviews
- more tests should pass
- removed pruning tests
* remove useless division
* more tests should pass
* more tests should pass
* more tests should pass
* let's try this one
-add alibi offset
- remove all permutes to make the grad operations work
- finger crossed
* refactor
- refactor code
- style changes
- add new threshold for test
* major changes
- change BLOOM to Bloom
- add quick doc on bloom.mdx
- move embeddings test on modeling test
* modify readme
* small fixes
* small fix
- better threshold for a test
* remove old test file from fetcher
* fix small typo
* major change
- change BloomLMHead to BloomForCausalLM
* remove onnx config
* major changes
- refactor the code
- remove asserts
- change tol for test
* make style
* small change
* adding a slow test + commenting old ones for now
* make style
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
* make style
* fix duplicates
* cleaning comments on config
* clean a bit conversion file
* refacor a bit modeling file
* refactor tokenizer file
* fix tokenization test issue
* fix tokenization issue #2
* fix tokenization issue second try
* fix test issue
* make style + add suggestions
* change test fetcher
* try this one
- slow tests should pass
- finger crossed
* possible final changes
* make style
* try fix padding side issue
* fix side
* fix padding issue
* fix ko-readme
* fix config auto
* cleaning modeling file
* keep bloom in caps in ko
* update config docs
* remove pretraining_pp
* remove model parallel
* update config
- add correct config files
* fix duplicates
* fix fetcher
* fix refactor issue
- remove divide function
* try to remove alibi
* small fixes
- fix alibi
- remove seq length
- refactor a bit the code
* put correct values
- fix bos and eos token ids
* fix attention mask loop
Co-authored-by: thomasw21 <[email protected]>
* small fixes:
- remove skip bias add
* small fixes
- fix typo in readme
- fix typos in config
* small changes
- remove a test
- add reconstruction test
- change config
* small changes
- change Scaled Softmax to BloomScaledSoftmax
* small fixes
- fix alibi dtype
* major changes
- removing explicit dtype when loading modules
- fixing test args (torch_dtype=auto)
- add dosctring
* fix readmes
* major changes
- now bloom supports alibi shifting
- refactor a bit the code
- better test tolerance now
* refactor a bit
* refactor a bit
* put correct name on test
* change docstring
* small changes
- fix docstring modeling
- fix test tolerance
* fix small nit
- take dtype from tensors in the conversion script
* minor fix
- fix mdx issue
* minor fix
- change config docstring
* forward contrib credits from PR14084
* Apply suggestions from code review
Co-authored-by: Stas Bekman <[email protected]>
* apply modifications
Co-authored-by: Stas Bekman <[email protected]>
* resolve softmax upcast
* Apply suggestions from code review
Co-authored-by: Stas Bekman <[email protected]>
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Niklas Muennighoff <[email protected]>
* final changes modeling
Co-authored-by: Stas Bekman <[email protected]>
* Merge commit 'd156898f3b9b2c990e5963f5030a7143d57921a2'
* merge commit
* Apply suggestions from code review
Co-authored-by: Stas Bekman <[email protected]>
* apply suggestions
Apply suggestions from Stas comments
Co-authored-by: Stas Bekman <[email protected]>
* Fix gradient checkpointing
Co-authored-by: Stas Bekman <[email protected]>
* add slow but exact
* add accelerate compatibility
Co-authored-by: Nicolas Patry <[email protected]>
* forward contrib credits
Co-authored-by: thomasw21 <[email protected]>
Co-authored-by: sgugger <[email protected]>
Co-authored-by: patrickvonplaten <[email protected]>
Co-authored-by: Niklas Muennighoff <[email protected]>
Co-authored-by: LysandreJik <[email protected]>
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <[email protected]>
* fix torch device on tests
* make style
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <[email protected]>
* fix nits
Co-authored-by: patrickvonplaten<[email protected]>
* remove final nits
* fix doc
- add more details on the doc
- add links to checkpoints
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Sylvain Gugger <[email protected]>
* apply suggestions
Co-authored-by: sgugger <[email protected]>
* put test torchscript to false
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: justheuristic <[email protected]>
* fix alibi
- create alibi only once
* add small doc
* make quality
* replace torch.nn
* remove token type emb
* fix fused op + output bias
* add fused op
- now can control fused operation from config
* remove fused op
* make quality
* small changes
- remove unsed args on config
- removed bias gelu file
- make the model torchscriptable
- add torchscript slow tests
* Update src/transformers/models/bloom/modeling_bloom.py
* fix slow
* make style
* add accelerate support
* add bloom to deepspeed tests
* minor changes
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <[email protected]>
* minor change
* slow tests pass
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
* Update docs/source/en/model_doc/bloom.mdx
Co-authored-by: Sylvain Gugger <[email protected]>
* minor changes:
- change docstring
- add link to paper
Co-authored-by: Thomwolf <[email protected]>
Co-authored-by: Thomas Wolf <[email protected]>
Co-authored-by: thomasw21 <[email protected]>
Co-authored-by: Sylvain Gugger <[email protected]>
Co-authored-by: sIncerass <[email protected]>
Co-authored-by: Stas Bekman <[email protected]>
Co-authored-by: Niklas Muennighoff <[email protected]>
Co-authored-by: Nicolas Patry <[email protected]>
Co-authored-by: thomasw21 <[email protected]>
Co-authored-by: sgugger <[email protected]>
Co-authored-by: patrickvonplaten <[email protected]>
Co-authored-by: LysandreJik <[email protected]>
Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: justheuristic <[email protected]>
Co-authored-by: Stas Bekman <[email protected]> | 52 | 1 | 5,691 | 9 |
3 | 15 | def __getitem__(self, name): # -> EntryPoint:
if isinstance(name, int):
warnings.warn(
"Accessing entry points by index is deprecated. "
"Cast to tuple if needed.",
DeprecationWarning,
stacklevel=2,
)
return super().__getitem__(name)
try:
return next(iter(self.select(name=name)))
except StopIteration:
raise KeyError(name)
| python3.10.4/Lib/importlib/metadata/__init__.py | 108 | XX-Net | {
"docstring": "\n Get the EntryPoint in self matching name.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | 35 | Python | 33 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | __init__.py | 218,271 | 13 | 64 | __getitem__ | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 179 | 0 | 55,236 | 14 |
|
1 | 7 | def isgeneratorfunction(obj):
return _inspect.isgeneratorfunction(
tf.__internal__.decorator.unwrap(obj)[1]
)
| keras/utils/tf_inspect.py | 42 | keras | {
"docstring": "TFDecorator-aware replacement for inspect.isgeneratorfunction.",
"language": "en",
"n_whitespaces": 3,
"n_words": 4,
"vocab_size": 4
} | 6 | Python | 6 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | tf_inspect.py | 277,066 | 4 | 25 | isgeneratorfunction | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 22 | 0 | 81,843 | 12 |
|
4 | 31 | def _update_from_feed(self, feed_entry, last_update, last_update_successful):
self._title = feed_entry.title
# Convert distance if not metric system.
if self._unit_system == CONF_UNIT_SYSTEM_IMPERIAL:
self._distance = round(
DistanceConverter.convert(
feed_entry.distance_to_home, LENGTH_KILOMETERS, LENGTH_MILES
),
1,
)
else:
self._distance = round(feed_entry.distance_to_home, 1)
self._latitude = round(feed_entry.coordinates[0], 5)
self._longitude = round(feed_entry.coordinates[1], 5)
self._attribution = feed_entry.attribution
self._alert_level = feed_entry.alert_level
self._activity = feed_entry.activity
self._hazards = feed_entry.hazards
self._feed_last_update = dt.as_utc(last_update) if last_update else None
self._feed_last_update_successful = (
dt.as_utc(last_update_successful) if last_update_successful else None
)
| homeassistant/components/geonetnz_volcano/sensor.py | 228 | core | {
"docstring": "Update the internal state from the provided feed entry.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 8
} | 70 | Python | 52 | 503434e538af4b708f01cee9ca20bfa8426cec94 | sensor.py | 288,948 | 21 | 150 | _update_from_feed | https://github.com/home-assistant/core.git | Use DistanceConverter in components (#80182)
* Use DistanceConverter in components
* Adjust for METRIC_SYSTEM | 276 | 0 | 88,097 | 13 |
|
7 | 17 | def test_checking_core_page_fields_are_indexed(self):
# first confirm that errors show as EventPage (in test models) has no Page.search_fields
errors = [error for error in checks.run_checks() if error.id == 'wagtailsearch.W001']
# should only ever get this warning on the sub-classes of the page model
self.assertEqual([EventPage, SingleEventPage], [error.obj for error in errors])
for error in errors:
self.assertEqual(error.msg, 'Core Page fields missing in `search_fields`', )
self.assertIn(
'Page model search fields `search_fields = Page.search_fields + [...]`',
error.hint)
# second check that we get no errors when setting up the models correctly
with patch_search_fields(EventPage, Page.search_fields + EventPage.search_fields):
errors = [error for error in checks.run_checks() if error.id == 'wagtailsearch.W001']
self.assertEqual([], errors)
| wagtail/search/tests/test_indexed_class.py | 185 | wagtail | {
"docstring": "Run checks to ensure that when core page fields are missing we get a warning",
"language": "en",
"n_whitespaces": 14,
"n_words": 15,
"vocab_size": 15
} | 103 | Python | 70 | d964675ee8fcb7ea58681ac8869733a86d58e4ec | test_indexed_class.py | 70,443 | 11 | 113 | test_checking_core_page_fields_are_indexed | https://github.com/wagtail/wagtail.git | add check for correct search_fields on pages
- fixes #4940 | 233 | 0 | 15,509 | 12 |
|
1 | 20 | def test_mixed_string_bytes_categoricals():
# data as unicode
X = np.array([["b"], ["a"]], dtype="U")
# predefined categories as bytes
categories = [np.array(["b", "a"], dtype="S")]
ohe = OneHotEncoder(categories=categories, sparse_output=False)
msg = re.escape(
"In column 0, the predefined categories have type 'bytes' which is incompatible"
" with values of type 'str_'."
)
with pytest.raises(ValueError, match=msg):
ohe.fit(X)
@pytest.mark.parametrize("missing_value", [np.nan, None]) | sklearn/preprocessing/tests/test_encoders.py | 175 | @pytest.mark.parametrize("missing_value", [np.nan, None]) | scikit-learn | {
"docstring": "Check that this mixture of predefined categories and X raises an error.\n\n Categories defined as bytes can not easily be compared to data that is\n a string.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 27,
"vocab_size": 26
} | 54 | Python | 44 | ecb9a70e82d4ee352e2958c555536a395b53d2bd | test_encoders.py | 261,789 | 10 | 82 | test_mixed_string_bytes_categoricals | https://github.com/scikit-learn/scikit-learn.git | FIX Ensure dtype of categories is `object` for strings in `OneHotEncoder` (#25174)
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]> | 101 | 1 | 76,996 | 11 |
1 | 5 | def execute():
frappe.reload_doc("HR", "doctype", "Leave Allocation")
frappe.reload_doc("HR", "doctype", "Leave Ledger Entry")
frappe.db.sql(
)
frappe.db.sql(
)
| erpnext/patches/v13_0/set_company_in_leave_ledger_entry.py | 78 | erpnext | {
"docstring": "update `tabLeave Ledger Entry` as lle set company = (select company from `tabEmployee` where employee = lle.employee)update `tabLeave Allocation` as la set company = (select company from `tabEmployee` where employee = la.employee)",
"language": "en",
"n_whitespaces": 31,
"n_words": 32,
"vocab_size": 18
} | 15 | Python | 10 | 494bd9ef78313436f0424b918f200dab8fc7c20b | set_company_in_leave_ledger_entry.py | 66,793 | 9 | 40 | execute | https://github.com/frappe/erpnext.git | style: format code with black | 8 | 0 | 14,336 | 8 |
|
2 | 11 | def collocations(self, num=20, window_size=2):
collocation_strings = [
w1 + " " + w2 for w1, w2 in self.collocation_list(num, window_size)
]
print(tokenwrap(collocation_strings, separator="; "))
| nltk/text.py | 76 | nltk | {
"docstring": "\n Print collocations derived from the text, ignoring stopwords.\n\n >>> from nltk.book import text4\n >>> text4.collocations() # doctest: +NORMALIZE_WHITESPACE\n United States; fellow citizens; years ago; four years; Federal\n Government; General Government; American people; Vice President; God\n bless; Chief Justice; one another; fellow Americans; Old World;\n Almighty God; Fellow citizens; Chief Magistrate; every citizen; Indian\n tribes; public debt; foreign nations\n\n\n :param num: The maximum number of collocations to print.\n :type num: int\n :param window_size: The number of tokens spanned by a collocation (default=2)\n :type window_size: int\n ",
"language": "en",
"n_whitespaces": 204,
"n_words": 84,
"vocab_size": 69
} | 23 | Python | 20 | 8a4cf5d94eb94b6427c5d1d7907ba07b119932c5 | text.py | 42,549 | 5 | 47 | collocations | https://github.com/nltk/nltk.git | Docstring tests (#3050)
* fixed pytests
* fixed more pytests
* fixed more pytest and changed multiline pytest issues fixes for snowball.py and causal.py
* fixed pytests (mainly multiline or rounding issues)
* fixed treebank pytests, removed test for return_string=True (deprecated)
* fixed destructive.py pytests, removed test for return_string=True (deprecated)
* fixed pytest (rounding issues)
* fixed pytest (initialised missing object)
* fixed pytest (formatting issues)
* fixed pytest (formatting issues)
* fixed pytest (formatting issues)
* added pytest +SKIP for deprecated module stanford
* updated AUTHORS.md
* changed docstring corrections by usage of ELLIPSIS and different roundings
* fixed AUTHORS.md to be consistent
* Fix framenet doctest formatting with pprint
* Change docstring on MultiListBox.__init__
I believe the original typo was misinterpreted and changed to something that was not originally intended.
Co-authored-by: Jan Lennartz <[email protected]>
Co-authored-by: Tom Aarsen <[email protected]>
Co-authored-by: Tom Aarsen <[email protected]> | 62 | 0 | 7,611 | 11 |
|
3 | 10 | def get_held_invoices(party_type, party):
held_invoices = None
if party_type == "Supplier":
held_invoices = frappe.db.sql(
"select name from `tabPurchase Invoice` where release_date IS NOT NULL and release_date > CURDATE()",
as_dict=1,
)
held_invoices = set(d["name"] for d in held_invoices)
return held_invoices
| erpnext/accounts/utils.py | 78 | erpnext | {
"docstring": "\n\tReturns a list of names Purchase Invoices for the given party that are on hold\n\t",
"language": "en",
"n_whitespaces": 14,
"n_words": 15,
"vocab_size": 15
} | 38 | Python | 32 | 494bd9ef78313436f0424b918f200dab8fc7c20b | utils.py | 65,407 | 9 | 46 | get_held_invoices | https://github.com/frappe/erpnext.git | style: format code with black | 29 | 0 | 13,888 | 12 |
|
2 | 4 | def is_reserved(self):
return (self.network_address.is_reserved and
self.broadcast_address.is_reserved)
| python3.10.4/Lib/ipaddress.py | 34 | XX-Net | {
"docstring": "Test if the address is otherwise IETF reserved.\n\n Returns:\n A boolean, True if the address is within one of the\n reserved IPv6 Network ranges.\n\n ",
"language": "en",
"n_whitespaces": 60,
"n_words": 24,
"vocab_size": 19
} | 6 | Python | 6 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | ipaddress.py | 218,555 | 3 | 20 | is_reserved | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 35 | 0 | 55,386 | 9 |
|
2 | 13 | def reshape(self, *newshape):
new_total_size = functools.reduce(lambda x,y: x*y, newshape)
if new_total_size != self._loop_size:
raise ValueError("Invalid reshape parameters " + str(newshape))
# there is no `.func` as this class does not subtype `Basic`:
return type(self)(self._array, newshape)
| sympy/tensor/array/dense_ndim_array.py | 90 | sympy | {
"docstring": "\n Returns MutableDenseNDimArray instance with new shape. Elements number\n must be suitable to new shape. The only argument of method sets\n new shape.\n\n Examples\n ========\n\n >>> from sympy import MutableDenseNDimArray\n >>> a = MutableDenseNDimArray([1, 2, 3, 4, 5, 6], (2, 3))\n >>> a.shape\n (2, 3)\n >>> a\n [[1, 2, 3], [4, 5, 6]]\n >>> b = a.reshape(3, 2)\n >>> b.shape\n (3, 2)\n >>> b\n [[1, 2], [3, 4], [5, 6]]\n\n ",
"language": "en",
"n_whitespaces": 196,
"n_words": 69,
"vocab_size": 49
} | 35 | Python | 33 | 645539ed9a65eec4a7bfc4571bdf2135cfb68cfb | dense_ndim_array.py | 199,144 | 5 | 55 | reshape | https://github.com/sympy/sympy.git | Fix bug in error message (cast tuple to str)
```python
from sympy.abc import x, y, z
from sympy import Array
a2 = Array([[[x, y], [z, x*z]], [[1, x*y], [1/x, x/y]]])
a2.reshape(1)
```
Out:
```text
TypeError: can only concatenate str (not "tuple") to str
```
This casts `newshape` to a string to the error message makes sense. | 81 | 0 | 49,163 | 12 |
|
2 | 19 | def _looks_like_red_hat_scheme() -> bool:
from distutils.command.install import install
from distutils.dist import Distribution
cmd: Any = install(Distribution())
cmd.finalize_options()
return (
cmd.exec_prefix == f"{os.path.normpath(sys.exec_prefix)}/local"
and cmd.prefix == f"{os.path.normpath(sys.prefix)}/local"
)
@functools.lru_cache(maxsize=None) | pipenv/patched/notpip/_internal/locations/__init__.py | 137 | @functools.lru_cache(maxsize=None) | pipenv | {
"docstring": "Red Hat patches ``sys.prefix`` and ``sys.exec_prefix``.\n\n Red Hat's ``00251-change-user-install-location.patch`` changes the install\n command's ``prefix`` and ``exec_prefix`` to append ``\"/local\"``. This is\n (fortunately?) done quite unconditionally, so we create a default command\n object without any configuration to detect this.\n ",
"language": "en",
"n_whitespaces": 53,
"n_words": 38,
"vocab_size": 35
} | 28 | Python | 25 | 7e33fcae4384563b4c927fd44318c29dd524a097 | __init__.py | 19,466 | 16 | 52 | _looks_like_red_hat_scheme | https://github.com/pypa/pipenv.git | Vendor in pip 21.2.4 release (from pip 21.2.2 prior). (#5009)
* Vendor in pip 21.2.4 release (from pip 21.2.2 prior).
* Add news fragment for pip 21.2.4 vendor update.
* Add potentially missing LICENSE files | 62 | 1 | 2,983 | 13 |
2 | 5 | def __call__(self, name=None):
if name is not None:
return self._setResultsName(name)
else:
return self.copy()
| .venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py | 52 | transferlearning | {
"docstring": "\n Shortcut for :class:`setResultsName`, with ``listAllMatches=False``.\n\n If ``name`` is given with a trailing ``'*'`` character, then ``listAllMatches`` will be\n passed as ``True``.\n\n If ``name` is omitted, same as calling :class:`copy`.\n\n Example::\n\n # these are equivalent\n userdata = Word(alphas).setResultsName(\"name\") + Word(nums + \"-\").setResultsName(\"socsecno\")\n userdata = Word(alphas)(\"name\") + Word(nums + \"-\")(\"socsecno\")\n ",
"language": "en",
"n_whitespaces": 124,
"n_words": 48,
"vocab_size": 38
} | 13 | Python | 12 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | pyparsing.py | 63,355 | 5 | 31 | __call__ | https://github.com/jindongwang/transferlearning.git | upd; format | 56 | 0 | 13,264 | 10 |
|
1 | 10 | def _num_elements(losses):
with backend.name_scope("num_elements") as scope:
return tf.cast(tf.size(losses, name=scope), dtype=losses.dtype)
| keras/utils/losses_utils.py | 66 | keras | {
"docstring": "Computes the number of elements in `losses` tensor.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 10 | Python | 10 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | losses_utils.py | 276,975 | 3 | 38 | _num_elements | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 23 | 0 | 81,809 | 12 |
|
1 | 4 | def get_input_for_correctness_test(self, **kwargs):
return get_correctness_test_inputs(**kwargs)
| keras/distribute/keras_correctness_test_base.py | 27 | keras | {
"docstring": "Generates inputs that are dictionaries.\n\n We only provide a default implementation of this method here. If you need\n more customized way of providing input to your model, overwrite this method.\n\n Args:\n **kwargs: key word arguments about how to create the input dictionaries\n\n Returns:\n Three dictionaries representing the input for fit(), evaluate() and\n predict()\n ",
"language": "en",
"n_whitespaces": 115,
"n_words": 53,
"vocab_size": 46
} | 5 | Python | 5 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras_correctness_test_base.py | 270,387 | 2 | 15 | get_input_for_correctness_test | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 19 | 0 | 80,460 | 8 |
|
5 | 15 | def __new__(cls, freq=None):
if isinstance(freq, PeriodDtype):
return freq
elif freq is None:
# empty constructor for pickle compat
# -10_000 corresponds to PeriodDtypeCode.UNDEFINED
u = PeriodDtypeBase.__new__(cls, -10_000)
u._freq = None
return u
if not isinstance(freq, BaseOffset):
freq = cls._parse_dtype_strict(freq)
try:
return cls._cache_dtypes[freq.freqstr]
except KeyError:
dtype_code = freq._period_dtype_code
u = PeriodDtypeBase.__new__(cls, dtype_code)
u._freq = freq
cls._cache_dtypes[freq.freqstr] = u
return u
| pandas/core/dtypes/dtypes.py | 169 | pandas | {
"docstring": "\n Parameters\n ----------\n freq : frequency\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 5,
"vocab_size": 5
} | 59 | Python | 37 | c7010a7adec1c47a4642fa068544699fc8e1ea6a | dtypes.py | 171,304 | 17 | 106 | __new__ | https://github.com/pandas-dev/pandas.git | STYLE enable pylint's redefined-outer-name (#49671)
* fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes.py, pandas/core/indexes/base.py
* fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes.py, pandas/core/indexes/base.py
* fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes.py, pandas/core/indexes/base.py
* fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes.py, pandas/core/indexes/base.py
Co-authored-by: bishwas jha <[email protected]> | 244 | 0 | 40,660 | 12 |
|
4 | 21 | def actor_table(self, actor_id):
self._check_connected()
if actor_id is not None:
actor_id = ray.ActorID(hex_to_binary(actor_id))
actor_info = self.global_state_accessor.get_actor_info(actor_id)
if actor_info is None:
return {}
else:
actor_table_data = gcs_utils.ActorTableData.FromString(actor_info)
return self._gen_actor_info(actor_table_data)
else:
actor_table = self.global_state_accessor.get_actor_table()
results = {}
for i in range(len(actor_table)):
actor_table_data = gcs_utils.ActorTableData.FromString(actor_table[i])
results[
binary_to_hex(actor_table_data.actor_id)
] = self._gen_actor_info(actor_table_data)
return results
| python/ray/state.py | 202 | ray | {
"docstring": "Fetch and parse the actor table information for a single actor ID.\n\n Args:\n actor_id: A hex string of the actor ID to fetch information about.\n If this is None, then the actor table is fetched.\n\n Returns:\n Information from the actor table.\n ",
"language": "en",
"n_whitespaces": 99,
"n_words": 41,
"vocab_size": 31
} | 48 | Python | 30 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | state.py | 131,062 | 19 | 124 | actor_table | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 273 | 0 | 29,463 | 15 |
|
1 | 2 | def test_ragged_tensor_output(self):
| keras/engine/compile_utils_test.py | 13 | keras | {
"docstring": "Ensure that ragged tensors can be passed as targets and predictions.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 2 | Python | 2 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | compile_utils_test.py | 271,076 | 15 | 192 | test_ragged_tensor_output | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 9 | 0 | 80,686 | 6 |
|
1 | 22 | def test_xml_element(self):
el = Element("tag")
el.set("key", "value")
el.text = "text"
childA = Element("childA")
childB = Element("childB")
el.append(childA)
el.append(childB)
upper = transform(str.upper)
newelem: Element = validate(xml_element(tag=upper, text=upper, attrib={upper: upper}), el)
assert newelem is not el
assert newelem.tag == "TAG"
assert newelem.text == "TEXT"
assert newelem.attrib == {"KEY": "VALUE"}
assert newelem[0].tag == "childA"
assert newelem[1].tag == "childB"
assert newelem[0] is not childA
assert newelem[1] is not childB
with self.assertRaises(ValueError) as cm:
validate(xml_element(tag="invalid"), el)
assert_validationerror(cm.exception, )
with self.assertRaises(ValueError) as cm:
validate(xml_element(text="invalid"), el)
assert_validationerror(cm.exception, )
with self.assertRaises(ValueError) as cm:
validate(xml_element(attrib={"key": "invalid"}), el)
assert_validationerror(cm.exception, )
| tests/test_api_validate.py | 407 | streamlink | {
"docstring": "\n ValidationError(XmlElementSchema):\n Unable to validate XML tag\n Context(equality):\n 'tag' does not equal 'invalid'\n \n ValidationError(XmlElementSchema):\n Unable to validate XML text\n Context(equality):\n 'text' does not equal 'invalid'\n \n ValidationError(XmlElementSchema):\n Unable to validate XML attributes\n Context(dict):\n Unable to validate value of key 'key'\n Context(equality):\n 'value' does not equal 'invalid'\n ",
"language": "en",
"n_whitespaces": 256,
"n_words": 44,
"vocab_size": 21
} | 90 | Python | 52 | 3d44da082b3ba202b9d0557bfd8ce747a1d7960c | test_api_validate.py | 187,159 | 44 | 235 | test_xml_element | https://github.com/streamlink/streamlink.git | plugin.api.validate: implement ValidationError
- Implement `ValidationError`
- Inherit from `ValueError` to preserve backwards compatiblity
- Allow collecting multiple errors (AnySchema)
- Keep an error stack of parent `ValidationError`s or other exceptions
- Format error stack when converting error to string
- Raise `ValidationError` instead of `ValueError`
- Add error contexts where it makes sense
- Add schema names to error instances
- Add and update tests | 283 | 0 | 45,718 | 15 |
|
1 | 5 | def require_fsdp(test_case):
return unittest.skipUnless(is_torch_version(">=", "1.12.0"), "test requires torch version >= 1.12.0")(test_case)
| src/accelerate/test_utils/testing.py | 44 | accelerate | {
"docstring": "\n Decorator marking a test that requires FSDP installed. These tests are skipped when FSDP isn't installed\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 16,
"vocab_size": 15
} | 11 | Python | 11 | 0c6bdc2c237ac071be99ac6f93ddfbc8bbcb8441 | testing.py | 337,934 | 2 | 23 | require_fsdp | https://github.com/huggingface/accelerate.git | enhancements and fixes for FSDP and DeepSpeed (#532)
* checkpointing enhancements and fixes for FSDP and DeepSpeed
* resolving comments
1. Adding deprecation args and warnings in launcher for FSDP
2. Handling old configs to work with new launcher args wrt FSDP.
3. Reverting changes to public methods in `checkpointing.py` and handling it in `Accelerator`
4. Explicitly writing the defaults of various FSDP options in `dataclasses` for readability.
* fixes
1. FSDP wrapped model being added to the `_models`.
2. Not passing the env variables when args are None.
* resolving comments
* adding FSDP for all the collective operations
* adding deepspeed and fsdp tests
1. Removes mrpc datafiles and directly relies on HF datasets as it was throwing `file not found` error when running from within `tests` folder. Updating `moke_dataloaders` as a result.
2. adding `test_performance.py`, `test_memory.py` and `test_checkpointing.py` for multi-gpu FSDP and DeepSpeed tests
* reverting `mocked_dataloader` changes
* adding FSDP tests
* data files revert
* excluding fsdp tests from `tests_core`
* try 2
* adding time delay to avoid `torchrun` from crashing at times leading which causing flaky behaviour
* reducing the time of tests
* fixes
* fix
* fixes and reduce time further
* reduce time further and minor fixes
* adding a deepspeed basic e2e test for single gpu setup | 17 | 0 | 121,141 | 11 |
|
1 | 4 | def cur_num_workers(self):
# Factor like this for convenient re-use.
return self._cur_num_workers(self.node_data_dict)
| python/ray/autoscaler/batching_node_provider.py | 28 | ray | {
"docstring": "Returns dict mapping node type to the number of nodes of that type.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 12
} | 11 | Python | 11 | c51b0c9a5664e5c6df3d92f9093b56e61b48f514 | batching_node_provider.py | 136,556 | 2 | 15 | cur_num_workers | https://github.com/ray-project/ray.git | [autoscaler][kuberay] Batching node provider (#29933)
Implements the abstract subclass of NodeProvider proposed in
https://docs.google.com/document/d/1JyQINBFirZw7YenA_14zize0R3hIII1_fnfQytIXTPo/
The goal is to simplify the autoscaler's interactions with external cluster managers like the KubeRay operator.
A follow-up PR will implement KuberayNodeProvider as a subclass of the BatchingNodeProvider added here.
Signed-off-by: Dmitri Gekhtman <[email protected]> | 32 | 0 | 30,939 | 8 |
|
2 | 3 | def generate_lscolors(self) -> str:
| kittens/tui/dircolors.py | 16 | kitty | {
"docstring": " Output the database in the format used by the LS_COLORS environment variable. ",
"language": "en",
"n_whitespaces": 13,
"n_words": 12,
"vocab_size": 10
} | 4 | Python | 4 | 4a3ed628092fac5b2552c8554c0482c569d14323 | dircolors.py | 102,926 | 4 | 29 | generate_lscolors | https://github.com/kovidgoyal/kitty.git | Refactor: More f-string for kittens | 11 | 0 | 21,582 | 6 |
|
3 | 7 | def get_keras_custom_objects():
# pylint:disable=no-name-in-module,import-outside-toplevel
if get_backend() == "amd" or get_tf_version() < 2.8:
from keras.utils import get_custom_objects
else:
from keras.utils.generic_utils import get_custom_objects
return get_custom_objects()
| lib/utils.py | 68 | faceswap | {
"docstring": " Wrapper to obtain keras.utils.get_custom_objects from correct location depending on\n backend used and tensorflow version. ",
"language": "en",
"n_whitespaces": 18,
"n_words": 14,
"vocab_size": 14
} | 23 | Python | 20 | c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf | utils.py | 100,363 | 6 | 40 | get_keras_custom_objects | https://github.com/deepfakes/faceswap.git | Update code to support Tensorflow versions up to 2.8 (#1213)
* Update maximum tf version in setup + requirements
* - bump max version of tf version in launcher
- standardise tf version check
* update keras get_custom_objects for tf>2.6
* bugfix: force black text in GUI file dialogs (linux)
* dssim loss - Move to stock tf.ssim function
* Update optimizer imports for compatibility
* fix logging for tf2.8
* Fix GUI graphing for TF2.8
* update tests
* bump requirements.txt versions
* Remove limit on nvidia-ml-py
* Graphing bugfixes
- Prevent live graph from displaying if data not yet available
* bugfix: Live graph. Collect loss labels correctly
* fix: live graph - swallow inconsistent loss errors
* Bugfix: Prevent live graph from clearing during training
* Fix graphing for AMD | 52 | 0 | 19,852 | 9 |
|
3 | 19 | def __call__(self, data_tuple):
# Metaupdate Step.
print("Meta-Update Step")
samples = data_tuple[0]
adapt_metrics_dict = data_tuple[1]
self.postprocess_metrics(
adapt_metrics_dict, prefix="MAMLIter{}".format(self.step_counter)
)
# MAML Meta-update.
fetches = None
for i in range(self.maml_optimizer_steps):
fetches = self.workers.local_worker().learn_on_batch(samples)
learner_stats = get_learner_stats(fetches)
# Update KLs. | rllib/agents/mbmpo/mbmpo.py | 126 | ray | {
"docstring": "Args:\n data_tuple (tuple): 1st element is samples collected from MAML\n Inner adaptation steps and 2nd element is accumulated metrics\n ",
"language": "en",
"n_whitespaces": 40,
"n_words": 19,
"vocab_size": 17
} | 37 | Python | 30 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | mbmpo.py | 133,770 | 34 | 242 | __call__ | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 143 | 0 | 30,102 | 13 |
|
6 | 20 | def hmean(a, axis=0, dtype=None, *, weights=None):
if not isinstance(a, np.ndarray):
a = np.array(a, dtype=dtype)
elif dtype:
# Must change the default dtype allowing array type
if isinstance(a, np.ma.MaskedArray):
a = np.ma.asarray(a, dtype=dtype)
else:
a = np.asarray(a, dtype=dtype)
if np.all(a >= 0):
# Harmonic mean only defined if greater than or equal to zero.
if weights is not None:
weights = np.asanyarray(weights, dtype=dtype)
with np.errstate(divide='ignore'):
return 1.0 / np.average(1.0 / a, axis=axis, weights=weights)
else:
raise ValueError("Harmonic mean only defined if all elements greater "
"than or equal to zero")
ModeResult = namedtuple('ModeResult', ('mode', 'count'))
| scipy/stats/_stats_py.py | 265 | scipy | {
"docstring": "Calculate the harmonic mean along the specified axis.\n\n That is: n / (1/x1 + 1/x2 + ... + 1/xn)\n\n Parameters\n ----------\n a : array_like\n Input array, masked array or object that can be converted to an array.\n axis : int or None, optional\n Axis along which the harmonic mean is computed. Default is 0.\n If None, compute over the whole array `a`.\n dtype : dtype, optional\n Type of the returned array and of the accumulator in which the\n elements are summed. If `dtype` is not specified, it defaults to the\n dtype of `a`, unless `a` has an integer `dtype` with a precision less\n than that of the default platform integer. In that case, the default\n platform integer is used.\n weights : array_like, optional\n The weights array can either be 1-D (in which case its length must be\n the size of `a` along the given `axis`) or of the same shape as `a`.\n Default is None, which gives each value a weight of 1.0.\n\n .. versionadded:: 1.9\n\n Returns\n -------\n hmean : ndarray\n See `dtype` parameter above.\n\n See Also\n --------\n numpy.mean : Arithmetic average\n numpy.average : Weighted average\n gmean : Geometric mean\n\n Notes\n -----\n The harmonic mean is computed over a single dimension of the input\n array, axis=0 by default, or all values in the array if axis=None.\n float64 intermediate and return values are used for integer inputs.\n\n Use masked arrays to ignore any non-finite values in the input or that\n arise in the calculations such as Not a Number and infinity.\n\n References\n ----------\n .. [1] \"Weighted Harmonic Mean\", *Wikipedia*,\n https://en.wikipedia.org/wiki/Harmonic_mean#Weighted_harmonic_mean\n .. [2] Ferger, F., \"The nature and use of the harmonic mean\", Journal of\n the American Statistical Association, vol. 26, pp. 36-40, 1931\n\n Examples\n --------\n >>> from scipy.stats import hmean\n >>> hmean([1, 4])\n 1.6000000000000001\n >>> hmean([1, 2, 3, 4, 5, 6, 7])\n 2.6997245179063363\n\n ",
"language": "en",
"n_whitespaces": 516,
"n_words": 302,
"vocab_size": 188
} | 93 | Python | 66 | a1546047bc146bf3189fa905c3415475b0e47931 | _stats_py.py | 241,810 | 16 | 155 | hmean | https://github.com/scipy/scipy.git | ENH: stats: add weights in harmonic mean (#15347)
Co-authored-by: Pamphile Roy <[email protected]> | 231 | 0 | 69,705 | 15 |
|
3 | 12 | def ordinal(value):
try:
value = int(value)
except (TypeError, ValueError):
return value
if value % 100 in (11, 12, 13):
# Translators: Ordinal format for 11 (11th), 12 (12th), and 13 (13th).
value = pgettext("ordinal 11, 12, 13", "{}th").format(value)
else:
templates = (
# Translators: Ordinal format when value ends with 0, e.g. 80th.
pgettext("ordinal 0", "{}th"),
# Translators: Ordinal format when value ends with 1, e.g. 81st, except 11.
pgettext("ordinal 1", "{}st"),
# Translators: Ordinal format when value ends with 2, e.g. 82nd, except 12.
pgettext("ordinal 2", "{}nd"),
# Translators: Ordinal format when value ends with 3, e.g. 83th, except 13.
pgettext("ordinal 3", "{}rd"),
# Translators: Ordinal format when value ends with 4, e.g. 84th.
pgettext("ordinal 4", "{}th"),
# Translators: Ordinal format when value ends with 5, e.g. 85th.
pgettext("ordinal 5", "{}th"),
# Translators: Ordinal format when value ends with 6, e.g. 86th.
pgettext("ordinal 6", "{}th"),
# Translators: Ordinal format when value ends with 7, e.g. 87th.
pgettext("ordinal 7", "{}th"),
# Translators: Ordinal format when value ends with 8, e.g. 88th.
pgettext("ordinal 8", "{}th"),
# Translators: Ordinal format when value ends with 9, e.g. 89th.
pgettext("ordinal 9", "{}th"),
)
value = templates[value % 10].format(value)
# Mark value safe so i18n does not break with <sup> or <sub> see #19988
return mark_safe(value)
@register.filter(is_safe=True) | django/contrib/humanize/templatetags/humanize.py | 278 | @register.filter(is_safe=True) | django | {
"docstring": "\n Convert an integer to its ordinal as a string. 1 is '1st', 2 is '2nd',\n 3 is '3rd', etc. Works for any integer.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 23,
"vocab_size": 21
} | 212 | Python | 94 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | humanize.py | 204,149 | 22 | 143 | ordinal | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 501 | 1 | 50,649 | 13 |
1 | 11 | def get_granger_causality(dependent_series, independent_series, lags):
granger_set = pd.concat([dependent_series, independent_series], axis=1)
granger = grangercausalitytests(granger_set, [lags], verbose=False)
return granger
| openbb_terminal/econometrics/econometrics_model.py | 63 | OpenBBTerminal | {
"docstring": "Calculate granger tests\n\n Parameters\n ----------\n dependent_series: Series\n The series you want to test Granger Causality for.\n independent_series: Series\n The series that you want to test whether it Granger-causes time_series_y\n lags : int\n The amount of lags for the Granger test. By default, this is set to 3.\n ",
"language": "en",
"n_whitespaces": 86,
"n_words": 47,
"vocab_size": 36
} | 16 | Python | 14 | 9e1a58e2dbedec4e4a9f9c2e32ddf091776c606b | econometrics_model.py | 285,200 | 4 | 42 | get_granger_causality | https://github.com/OpenBB-finance/OpenBBTerminal.git | Here we merge all API Refactor related branches (#2236)
* Update api.py
* Updated forex menu
* refactor ycrv command
* refactor ycrv command black
* refactor ecocal command
* Minh changes
* Adding space to test pushing
* title fix ecocal df
* get economic calendar annotation
* fix investingcom tests
* refactor index command
* refactor overview command
* give defaults to wsj view function args
* rename date args investincom
* refacto bigmac command
* fix ecocal typo
* refactor rtps command
* alphavantage gdp
* alphavantage gdp per capita
* alphavantage cpi
* alphavantage tyld
* alphavantage inf
* refactor macro command
* refactor macro command w helpers
* refactor treasury command
* fix macro on terminal
* treasury labels
* refactor maturities
* update treasury maturities doc strings
* refactor get economic calendar finhub
* refactor map command api
* display map filter choices
* route economy api to performance map
* route economy api to performance map
* display group choices on valuation command
* refactor performance and valuation commands
* refactor spectrum model and view
* add choices to spectrum controller
* delete image after view
* fix model tests finviz
* fix finciz view tests
* refactor futures
* fix some tests
* fix more tests
* fix controller test
* refactor fred series notes
* update fred notes docstring
* refacto fred series ids
* fix pred and qa when empty datasets
* refactor fred
* uncomment stuff
* refacto get series data
* fix some tests
* set defaults on args
* refactor fred yield curve
* black
* fix spell and remove ecocal names
* fix linting
* linting
* pylint fix
* change dangerous defaults
* Working through crypto fixes (#2256)
* Working through crypto fixes
* Continued adding crypto stuff
* Added crypto overview
* Added test fixes
* Added fixtures
* Fixed tests
* Fixed charting issue
* Removed broken APIs
* Final adjustments
* Added test fixes
* map get groups and get ycrv countries into old api
* exposed econdb helper funcs
* remove helpers
* refactor search indices
* linting
* refactor arg currency
* pylint from currency
* Started switching crpyto ascending to ascend
* Merging
* Portfolio model arguements, params, and docstring
* Refactored for etf commands (#2292)
* Refactored for etf commands
* Fixed tests
* Added load command
* Fixed menu
* Portfolio logic fixes
* Added econometrics (#2260)
* Added econometrics
* Fixed tests
* Simplified API
* Added test fixes
* Added test csv
* Allowed examples to be loaded
* Fund refactor (#2291)
* Fund refactor
* Changed fund_name and fund to name
* Changed ascending to ascend
* Stock menu refactoring for easier API usage (#2194)
* Stocks refactoring for easier API usage
* Linting
* Refactor newly added features
* Linting
* Fixing tests
* Refactor common files used by stocks menu
* Fixing flake8
* Fix linting and tests
* Linting
* Fix flake8
* refactor insider_data
* refactor mentions
* refactor watchlist
* refactor sentiment
* refactor sentiment
* fix yahoofinance tests
* refactor load and candle
* refactor get_news and display_news
* refactor stocks.ins.act
* candle default matplotlib
* fix yahoofinance_view tests
* fix ark model tests
* fix ark view tests
* fix business insider model
* fix business insider view
* refactor csimarket model
* fix tests csi market model
* update dd controller
* fix get suppliers tests
* fix dd controller tests
* fix finhub tests
* fix finviz tests
* fix fmp tests
* fix marketwatch tests
* corrected argument keywords in test_bt_model
* corrected argument keywords in test_bt_view
* refactor fa controller
* refactor marketwatch view
* refactor gov controller
* fix tests fa av
* fix tests elect
* fix dcf tests
* fix polygon tests
* fix fmp tests
* fix quiverquant tests
* fix yahoofinance fa tests
* fix more fa tests
* fix insider tests
* fix more tests
* fix more tests
* fix options tests
* fix stock gov tests
* fix tests test_ba_controller
* fix tests for test_finviz_compare_model.py
* fixed 2 tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* fix final tests
* fixed tests
* fixed tests
* Fix tests
* black
* forgot to black tests
* fixed tests
* fixed tests
* fixed tests
* fixed tests
* flakefix
* Tests + code : Stocks / Discovery
* fix tests
* added recorder
* fixed tests
* fixed tests
* black
* black
* remove unused imports
* refactor display raw
* sia dicts fix
* pylint
* linting
* remove dangerous default
* fix tests
* fix beta model test
* black
* skip screener qa test
* change sector path to sectors
* update tests readme
* fix metric defaults
* black
* substitute lost ticker
* defaults cpic
* another round on sia
* refactor cramer
* reduce default tweets on sentiment
* refactor yf hist, corr, volume
* arkorders default
* refactor income, balance, cashflow
* refacto scorr, screener, getfinnhub
* refactor stockgrid
* ibkr refactor
* another round on stockgrid
* add dividens end point
* refactor discovery endpoints
* update docstrings with similar input
* refactor messages
* refactor ba
* refactor regioons
* refactor twitter sentiment
* refactor hist
* refactor regions
* give default to timeframe
* refactor bunch of defaults and arg names
* remove leftover imports
* refactor vwap
* let tests run
* fix tests
* fix stock tests
* fix stockanalysis tests
* flake
* MYPY
* Made important changes
* added fixes
* Fixed big issue
* Added fixes to tests
* fix qa tests
* fix tests
* fix 1 more test
* last stocks failing
* fix crypto test
Co-authored-by: Chavithra PARANA <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: hjoaquim <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: colin99d <[email protected]>
* fix portfolio tests
* change period to window
* update ca docstrings
* refactor get_similar_companies func
* Fixed
* Update CI
* Update CI 2
* Update CI 3
* Update dependencies
Co-authored-by: colin99d <[email protected]>
Co-authored-by: Colin Delahunty <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: James Simmons <[email protected]>
Co-authored-by: Theodore Aptekarev <[email protected]>
Co-authored-by: minhhoang1023 <[email protected]>
Co-authored-by: jose-donato <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: northern-64bit <[email protected]>
Co-authored-by: hjoaquim <[email protected]> | 28 | 0 | 85,240 | 9 |
|
1 | 14 | def fetch_buffered_group_stats(group):
from sentry import buffer
from sentry.models import Group
result = buffer.get(Group, ["times_seen"], {"pk": group.id})
group.times_seen_pending = result["times_seen"]
@instrumented_task(
name="sentry.tasks.post_process.post_process_group",
time_limit=120,
soft_time_limit=110,
) | src/sentry/tasks/post_process.py | 101 | @instrumented_task(
name="sentry.tasks.post_process.post_process_group",
time_limit=120,
soft_time_limit=110,
) | sentry | {
"docstring": "\n Fetches buffered increments to `times_seen` for this group and adds them to the current\n `times_seen`.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 15,
"vocab_size": 14
} | 24 | Python | 21 | 09726d7fc95e53bb516e328fc1811fc9a0704cac | post_process.py | 96,154 | 5 | 44 | fetch_buffered_group_stats | https://github.com/getsentry/sentry.git | fix(post_process): Fetch buffered `times_seen` values and add them to `Group.times_seen` (#31624)
In `post_process_group` we process issue alert rules and also ignored groups. Both of these can have
conditions that read from the `times_seen` value on the `Group`.
The problem here is that updates to `times_seen` are buffered and only written every 45s or so. This
means that most of the time when a `Group` goes through `post_process_group` it has an out of date
`times_seen` value. For infrequently updated groups, this can just mean that the count is -1. But
for high volume groups this could mean that we're considerably below the count.
To improve this, we read the current value from buffers and store it as pending updates on the group.
We then use this pending value when checking rules and snoozes in post process. There's a potential
race condition here where we fetch the `Group`, and before we fetch the value from buffers it is
cleared, and so we miss out on the update. This should be infrequent enough that it's not a problem,
and either way we will be considerably more accurate most of the time. | 46 | 1 | 19,285 | 11 |
3 | 21 | def mixin_base_ppr_parser(parser):
mixin_essential_parser(parser)
gp = add_arg_group(parser, title='Base Deployment')
gp.add_argument(
'--extra-search-paths',
type=str,
default=[],
nargs='*',
help='Extra search paths to be used when loading modules and finding YAML config files.'
if _SHOW_ALL_ARGS
else argparse.SUPPRESS,
)
gp.add_argument(
'--timeout-ctrl',
type=int,
default=int(os.getenv('JINA_DEFAULT_TIMEOUT_CTRL', '60')),
help='The timeout in milliseconds of the control request, -1 for waiting forever',
)
parser.add_argument(
'--k8s-namespace',
type=str,
help='Name of the namespace where Kubernetes deployment should be deployed, to be filled by flow name'
if _SHOW_ALL_ARGS
else argparse.SUPPRESS,
)
gp.add_argument(
'--polling',
type=str,
default=PollingType.ANY.name,
help=,
)
| jina/parsers/orchestrate/base.py | 202 | jina | {
"docstring": "Mixing in arguments required by pod/deployment/runtime module into the given parser.\n :param parser: the parser instance to which we add arguments\n \n The polling strategy of the Deployment and its endpoints (when `shards>1`).\n Can be defined for all endpoints of a Deployment or by endpoint.\n Define per Deployment:\n - ANY: only one (whoever is idle) Pod polls the message\n - ALL: all Pods poll the message (like a broadcast)\n Define per Endpoint:\n JSON dict, {endpoint: PollingType}\n {'/custom': 'ALL', '/search': 'ANY', '*': 'ANY'}\n \n ",
"language": "en",
"n_whitespaces": 119,
"n_words": 81,
"vocab_size": 66
} | 80 | Python | 64 | a3b71c7208b3cd48aa7bc978c3343a074947e3d9 | base.py | 12,207 | 41 | 123 | mixin_base_ppr_parser | https://github.com/jina-ai/jina.git | fix(parsers): clearify flow args (#4701) | 253 | 0 | 2,215 | 13 |
|
4 | 9 | def preprocess(self, x):
if self.type == "value":
return x
elif self.type == "index":
return [self.choices.index(choice) for choice in x]
else:
raise ValueError(
"Unknown type: "
+ str(self.type)
+ ". Please choose from: 'value', 'index'."
)
| gradio/inputs.py | 96 | gradio | {
"docstring": "\n Parameters:\n x (List[str]): list of selected choices\n Returns:\n (Union[List[str], List[int]]): list of selected choices as strings or indices within choice list\n ",
"language": "en",
"n_whitespaces": 57,
"n_words": 21,
"vocab_size": 16
} | 35 | Python | 31 | cc0cff893f9d7d472788adc2510c123967b384fe | inputs.py | 179,244 | 11 | 55 | preprocess | https://github.com/gradio-app/gradio.git | Format The Codebase
- black formatting
- isort formatting | 152 | 0 | 42,924 | 15 |
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