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wagtail/admin/tests/test_page_chooser.py
9
6
def test_type_eventpage_two_indexes(self): self.make_event_section("Other events") self.assertEqual( self.get_best_root({"page_type": "tests.EventPage"}), se
Reformat with black
test_type_eventpage_two_indexes
d10f15e55806c6944827d801cd9c2d53f5da4186
wagtail
test_page_chooser.py
12
5
https://github.com/wagtail/wagtail.git
1
31
0
9
58
Python
{ "docstring": "\n The chooser should start at the home page, as there are two\n EventIndexes with EventPages.\n ", "language": "en", "n_whitespaces": 37, "n_words": 15, "vocab_size": 15 }
def test_type_eventpage_two_indexes(self): self.make_event_section("Other events") self.assertEqual( self.get_best_root({"page_type": "tests.EventPage"}), self.home_page )
12,368
60,979
35
.venv/lib/python3.8/site-packages/pip/_internal/req/req_file.py
14
5
def parse(self, filename, constraint): # type: (
upd; format
parse
f638f5d0e6c8ebed0e69a6584bc7f003ec646580
transferlearning
req_file.py
8
2
https://github.com/jindongwang/transferlearning.git
1
20
0
14
33
Python
{ "docstring": "Parse a given file, yielding parsed lines.\n ", "language": "en", "n_whitespaces": 14, "n_words": 7, "vocab_size": 7 }
def parse(self, filename, constraint): # type: (str, bool) -> Iterator[ParsedLine] yield from self._parse_and_recurse(filename, constraint)
81,114
273,358
141
keras/layers/preprocessing/preprocessing_utils.py
44
23
def sparse_bincount(inputs, depth, binary_output, dtype, count_weights=None): result = tf.sparse.bincount( inputs, weights=count_weights, minlength=depth, maxlength=depth, axis=-1, binary_output=binary_output, ) result = tf.cast(result, dtype) if inputs.shape.rank == 1: output_shape = (depth,) else: batch_size = tf.shape(result)[0] output_shape = (batch_size, depth) result = tf
Reformatting the codebase with black. PiperOrigin-RevId: 450093126
sparse_bincount
84afc5193d38057e2e2badf9c889ea87d80d8fbf
keras
preprocessing_utils.py
12
19
https://github.com/keras-team/keras.git
2
117
0
34
172
Python
{ "docstring": "Apply binary or count encoding to an input and return a sparse tensor.", "language": "en", "n_whitespaces": 12, "n_words": 13, "vocab_size": 13 }
def sparse_bincount(inputs, depth, binary_output, dtype, count_weights=None): result = tf.sparse.bincount( inputs, weights=count_weights, minlength=depth, maxlength=depth, axis=-1, binary_output=binary_output, ) result = tf.cast(result, dtype) if inputs.shape.rank == 1: output_shape = (depth,) else: batch_size = tf.shape(result)[0] output_shape = (batch_size, depth) result = tf.SparseTensor( indices=result.indices, values=result.values, dense_shape=output_shape ) return result
20,386
100,939
110
lib/serializer.py
35
13
def unmarshal(self, serialized_data): logger.debug("data type: %s", type(serialized_data)) try: retval = self._unmarshal(serialized_data) except Exception as err: msg
Core updates - Change loss loading mechanism - Autosize tooltips based on content size - Random linting + code modernisation
unmarshal
bad5025aea1adb9126580e14e064e6c99089243d
faceswap
serializer.py
13
9
https://github.com/deepfakes/faceswap.git
2
58
0
30
117
Python
{ "docstring": " Unserialize data to its original object type\n\n Parameters\n ----------\n serialized_data: varies\n Data in serializer format that is to be unmarshalled to its original object\n\n Returns\n -------\n data: varies\n The data in a python object format\n\n Example\n ------\n >>> serializer = get_serializer('json')\n >>> json_data = <json object>\n >>> data = serializer.unmarshal(json_data)\n ", "language": "en", "n_whitespaces": 157, "n_words": 50, "vocab_size": 34 }
def unmarshal(self, serialized_data): logger.debug("data type: %s", type(serialized_data)) try: retval = self._unmarshal(serialized_data) except Exception as err: msg = f"Error unserializing data for type {type(serialized_data)}: {str(err)}" raise FaceswapError(msg) from err logger.debug("returned data type: %s", type(retval)) return retval
42,859
178,914
111
nuitka/freezer/IncludedDataFiles.py
25
7
def copyDataFiles(): for included_datafile in getIncludedDataFiles(): # TODO: directories should be resolved
Plugins: Massive cleanup of data file handling * Move data file handling out of standalone only, allowing support for other modes as well. * Attach logger and tags to data file objects.
copyDataFiles
abfb99b0a05dd76d2ecc6ebc20732a271857c6c8
Nuitka
IncludedDataFiles.py
13
9
https://github.com/Nuitka/Nuitka.git
4
36
0
25
62
Python
{ "docstring": "Copy the data files needed for standalone distribution.\n\n Notes:\n This is for data files only, not DLLs or even extension modules,\n those must be registered as entry points, and would not go through\n necessary handling if provided like this.\n ", "language": "en", "n_whitespaces": 66, "n_words": 39, "vocab_size": 35 }
def copyDataFiles(): for included_datafile in getIncludedDataFiles(): # TODO: directories should be resolved to files. if ( not isinstance(included_datafile, (IncludedDataFile)) or included_datafile.needsCopy() ): _handleDataFile( included_datafile, )
73,733
251,426
81
mitmproxy/optmanager.py
31
9
def toggler(self, attr): if attr not in self._options: raise KeyErr
make it black!
toggler
b3587b52b25077f68116b9852b041d33e7fc6601
mitmproxy
optmanager.py
11
8
https://github.com/mitmproxy/mitmproxy.git
3
47
0
29
73
Python
{ "docstring": "\n Generate a toggler for a boolean attribute. This returns a callable\n that takes no arguments.\n ", "language": "en", "n_whitespaces": 37, "n_words": 15, "vocab_size": 13 }
def toggler(self, attr): if attr not in self._options: raise KeyError("No such option: %s" % attr) o = self._options[attr] if o.typespec != bool: raise ValueError("Toggler can only be used with boolean options")
11,221
55,127
22
src/prefect/testing/cli.py
6
7
def disable_terminal_wrapping(monkeypatch): monkeypatch.setattr( "prefect.cli.profile.console", rich.console.Console(soft_wrap=True) )
Continue moving objects to sensible locations
disable_terminal_wrapping
05b2cf58e0610cedcea27e4d8cb96ad95307a068
prefect
cli.py
10
4
https://github.com/PrefectHQ/prefect.git
1
23
0
6
41
Python
{ "docstring": "\n Sometimes, line wrapping makes it hard to make deterministic assertions about the\n output of a CLI command. Wrapping can be disabled by using this fixture.\n ", "language": "en", "n_whitespaces": 35, "n_words": 25, "vocab_size": 25 }
def disable_terminal_wrapping(monkeypatch): monkeypatch.setattr( "prefect.cli.profile.console", rich.console.Console(soft_wrap=True) )
47,660
196,160
54
sympy/combinatorics/permutations.py
19
9
def __add__(self, other): rank = (self.rank() + other) % self.cardinality rv = self.unrank_lex(self.size, rank) rv._rank = rank
Updated import locations
__add__
498015021131af4dbb07eb110e5badaba8250c7b
sympy
permutations.py
11
5
https://github.com/sympy/sympy.git
1
42
0
15
68
Python
{ "docstring": "Return permutation that is other higher in rank than self.\n\n The rank is the lexicographical rank, with the identity permutation\n having rank of 0.\n\n Examples\n ========\n\n >>> from sympy.combinatorics import Permutation\n >>> I = Permutation([0, 1, 2, 3])\n >>> a = Permutation([2, 1, 3, 0])\n >>> I + a.rank() == a\n True\n\n See Also\n ========\n\n __sub__, inversion_vector\n\n ", "language": "en", "n_whitespaces": 148, "n_words": 57, "vocab_size": 44 }
def __add__(self, other): rank = (self.rank() + other) % self.cardinality rv = self.unrank_lex(self.size, rank) rv._rank = rank return rv
72,059
248,031
167
tests/handlers/test_presence.py
43
16
def test_set_presence_from_syncing_not_set(self): user_id = "@test:server" status_msg = "I'm here!" self._set_presencestate_with
Prevent a sync request from removing a user's busy presence status (#12213) In trying to use the MSC3026 busy presence status, the user's status would be set back to 'online' next time they synced. This change makes it so that syncing does not affect a user's presence status if it is currently set to 'busy': it must be removed through the presence API. The MSC defers to implementations on the behaviour of busy presence, so this ought to remain compatible with the MSC.
test_set_presence_from_syncing_not_set
73d8ded0b030a81e828c07bb134c08db67569e5d
synapse
test_presence.py
12
14
https://github.com/matrix-org/synapse.git
1
85
0
33
139
Python
{ "docstring": "Test that presence is not set by syncing if affect_presence is false", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 11 }
def test_set_presence_from_syncing_not_set(self): user_id = "@test:server" status_msg = "I'm here!" self._set_presencestate_with_status_msg( user_id, PresenceState.UNAVAILABLE, status_msg ) self.get_success( self.presence_handler.user_syncing(user_id, False, PresenceState.ONLINE) ) state = self.get_success( self.presence_handler.get_state(UserID.from_string(user_id)) ) # we should still be unavailable self.assertEqual(state.state, PresenceState.UNAVAILABLE) # and status message should still be the same self.assertEqual(state.status_msg, status_msg)
72,155
248,209
234
tests/events/test_utils.py
37
7
def test_stringy_integers(self): input = { "a": "100", "b": { "foo": 99, "
Convert stringy power levels to integers on room upgrade (#12657)
test_stringy_integers
051a1c3f220938a0ea1a5b328c268bdb3d1ad592
synapse
test_utils.py
11
19
https://github.com/matrix-org/synapse.git
1
71
0
24
131
Python
{ "docstring": "String representations of decimal integers are converted to integers.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }
def test_stringy_integers(self): input = { "a": "100", "b": { "foo": 99, "bar": "-98", }, "d": "0999", } output = copy_and_fixup_power_levels_contents(input) expected_output = { "a": 100, "b": { "foo": 99, "bar": -98, }, "d": 999, } self.assertEqual(output, expected_output)
13,722
64,785
30
erpnext/accounts/doctype/bank_reconciliation_tool/bank_reconciliation_tool.py
41
14
def get_ec_matching_query(bank_account, company, amount_condition): # get matching Expense Claim query mod
style: format code with black
get_ec_matching_query
494bd9ef78313436f0424b918f200dab8fc7c20b
erpnext
bank_reconciliation_tool.py
14
31
https://github.com/frappe/erpnext.git
2
63
0
37
121
Python
{ "docstring": "\n\t\tSELECT\n\t\t\t( CASE WHEN employee = %(party)s THEN 1 ELSE 0 END\n\t\t\t+ 1 ) AS rank,\n\t\t\t'Expense Claim' as doctype,\n\t\t\tname,\n\t\t\ttotal_sanctioned_amount as paid_amount,\n\t\t\t'' as reference_no,\n\t\t\t'' as reference_date,\n\t\t\temployee as party,\n\t\t\t'Employee' as party_type,\n\t\t\tposting_date,\n\t\t\t'{company_currency}' as currency\n\t\tFROM\n\t\t\t`tabExpense Claim`\n\t\tWHERE\n\t\t\ttotal_sanctioned_amount {amount_condition} %(amount)s\n\t\t\tAND docstatus = 1\n\t\t\tAND is_paid = 1\n\t\t\tAND ifnull(clearance_date, '') = \"\"\n\t\t\tAND mode_of_payment in {mode_of_payments}\n\t", "language": "en", "n_whitespaces": 45, "n_words": 65, "vocab_size": 47 }
def get_ec_matching_query(bank_account, company, amount_condition): # get matching Expense Claim query mode_of_payments = [ x["parent"] for x in frappe.db.get_all( "Mode of Payment Account", filters={"default_account": bank_account}, fields=["parent"] ) ] mode_of_payments = "('" + "', '".join(mode_of_payments) + "' )" company_currency = get_company_currency(company) return f
36,108
154,602
453
modin/experimental/core/execution/native/implementations/hdk_on_native/partitioning/partition_manager.py
114
47
def run_exec_plan(cls, plan, index_cols, dtypes, columns): omniSession = DbWorker() # First step is to make sure all partitions are in
FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <[email protected]> Signed-off-by: Andrey Pavlenko <[email protected]>
run_exec_plan
e5b1888cd932909e49194d58035da34b210b91c4
modin
partition_manager.py
17
27
https://github.com/modin-project/modin.git
9
225
0
80
364
Python
{ "docstring": "\n Run execution plan in HDK storage format to materialize frame.\n\n Parameters\n ----------\n plan : DFAlgNode\n A root of an execution plan tree.\n index_cols : list of str\n A list of index columns.\n dtypes : pandas.Index\n Column data types.\n columns : list of str\n A frame column names.\n\n Returns\n -------\n np.array\n Created frame's partitions.\n ", "language": "en", "n_whitespaces": 186, "n_words": 53, "vocab_size": 39 }
def run_exec_plan(cls, plan, index_cols, dtypes, columns): omniSession = DbWorker() # First step is to make sure all partitions are in HDK. frames = plan.collect_frames() for frame in frames: if frame._partitions.size != 1: raise NotImplementedError( "HdkOnNative engine doesn't suport partitioned frames" ) for p in frame._partitions.flatten(): if p.frame_id is None: obj = p.get() if isinstance(obj, (pandas.DataFrame, pandas.Series)): p.frame_id = omniSession.import_pandas_dataframe(obj) else: assert isinstance(obj, pyarrow.Table) p.frame_id = omniSession.import_arrow_table(obj) calcite_plan = CalciteBuilder().build(plan) calcite_json = CalciteSerializer().serialize(calcite_plan) cmd_prefix = "execute relalg " if DoUseCalcite.get(): cmd_prefix = "execute calcite " at = omniSession.executeRA(cmd_prefix + calcite_json) res = np.empty((1, 1), dtype=np.dtype(object)) # workaround for https://github.com/modin-project/modin/issues/1851 if DoUseCalcite.get(): at = at.rename_columns(["F_" + str(c) for c in columns]) res[0][0] = cls._partition_class.put_arrow(at) return res
27,062
121,448
16
jax/_src/dtypes.py
13
3
def to_numeric_dtype(dtype): dtype = np.dt
jax.numpy: improve support for boolean inputs
to_numeric_dtype
3f0619599499fc0751cd6181c04d50245ef5dcce
jax
dtypes.py
10
3
https://github.com/google/jax.git
2
32
0
11
57
Python
{ "docstring": "Promotes a dtype into an numeric dtype, if it is not already one.", "language": "en", "n_whitespaces": 12, "n_words": 13, "vocab_size": 13 }
def to_numeric_dtype(dtype): dtype = np.dtype(dtype) return np.dtype('int32') if dtype == np.dtype('bool') else dtype
9,133
47,501
266
tests/jobs/test_scheduler_job.py
58
43
def test_do_schedule_max_active_runs_task_removed(self, session, dag_maker): with dag_maker( dag_id='test_do_schedule_max_active_runs_task_removed', start_date=DEFAULT_DATE, schedule_interval='@once', max_active_runs=1, session=session, ): # Can't use EmptyOperator as that goes straight to success BashOperator(task_id='dummy1', bash_command='true') run1 = dag_maker.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=DEFAULT_DATE + timedelta(hours=1), state=State.RUNNING, ) self.scheduler_job = Schedule
Replace usage of `DummyOperator` with `EmptyOperator` (#22974) * Replace usage of `DummyOperator` with `EmptyOperator`
test_do_schedule_max_active_runs_task_removed
49e336ae0302b386a2f47269a6d13988382d975f
airflow
test_scheduler_job.py
13
23
https://github.com/apache/airflow.git
1
156
0
50
249
Python
{ "docstring": "Test that tasks in removed state don't count as actively running.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }
def test_do_schedule_max_active_runs_task_removed(self, session, dag_maker): with dag_maker( dag_id='test_do_schedule_max_active_runs_task_removed', start_date=DEFAULT_DATE, schedule_interval='@once', max_active_runs=1, session=session, ): # Can't use EmptyOperator as that goes straight to success BashOperator(task_id='dummy1', bash_command='true') run1 = dag_maker.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=DEFAULT_DATE + timedelta(hours=1), state=State.RUNNING, ) self.scheduler_job = SchedulerJob(subdir=os.devnull) self.scheduler_job.executor = MockExecutor(do_update=False) self.scheduler_job.processor_agent = mock.MagicMock(spec=DagFileProcessorAgent) num_queued = self.scheduler_job._do_scheduling(session) assert num_queued == 1 session.flush() ti = run1.task_instances[0] ti.refresh_from_db(session=session) assert ti.state == State.QUEUED
38,908
161,097
129
ppg_extractor/encoder/encoder.py
36
12
def forward(self, xs, masks): if isinstance(self.embed, (Conv2dSubsampling, VGG2L)): xs, masks = self.embed(xs, masks) else: xs = self.embed(xs) xs, masks = self.encoders(xs, masks) if isinstance(xs, tuple): xs = xs[0] if self.normalize_before:
Init ppg extractor and ppg2mel (#375) * Init ppg extractor and ppg2mel * add preprocess and training * FIx known issues * Update __init__.py Allow to gen audio * Fix length issue * Fix bug of preparing fid * Fix sample issues * Add UI usage of PPG-vc
forward
b617a87ee40ab384767a27335313c2c65ee094ec
MockingBird
encoder.py
11
11
https://github.com/babysor/MockingBird.git
4
89
0
22
138
Python
{ "docstring": "Encode input sequence.\n\n :param torch.Tensor xs: input tensor\n :param torch.Tensor masks: input mask\n :return: position embedded tensor and mask\n :rtype Tuple[torch.Tensor, torch.Tensor]:\n ", "language": "en", "n_whitespaces": 57, "n_words": 22, "vocab_size": 16 }
def forward(self, xs, masks): if isinstance(self.embed, (Conv2dSubsampling, VGG2L)): xs, masks = self.embed(xs, masks) else: xs = self.embed(xs) xs, masks = self.encoders(xs, masks) if isinstance(xs, tuple): xs = xs[0] if self.normalize_before: xs = self.after_norm(xs) return xs, masks
83,921
281,630
32
gamestonk_terminal/parent_classes.py
7
6
def save_class(self): if gtff.REMEMBER_CONTEXTS:
Remember Contexts (#1187) * Refacotred classes * Handling for new instance desired * Added feature flag * Converted all menu calls
save_class
9e671aeba98dacc69ecbbfec1f087aca3b139ee7
OpenBBTerminal
parent_classes.py
10
3
https://github.com/OpenBB-finance/OpenBBTerminal.git
2
19
0
7
33
Python
{ "docstring": "Saves the current instance of the class to be loaded later", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 10 }
def save_class(self): if gtff.REMEMBER_CONTEXTS: controllers[self.PATH] = self
53,912
215,291
416
salt/transport/zeromq.py
119
23
def _decode_messages(self, messages): messages_len = len(messages) # if it was one message, then its old style if messages_len == 1: payload = salt.payload.loads(messages[0]) # 2 includes a header which says who should do it elif messages_len == 2: message_target = salt.utils.stringutils.to_str(messages[0]) if ( self.opts.get("__role") != "syndic" and message_target not in ("broadcast", self.hexid) ) or ( self.opts.get("__role") == "syndic" and message_target not in ("broadcast", "syndic") ): log.debug("Publish received for not this minion: %s", message_target) raise salt.ext.tornado.gen.Return(None) payload = salt.payload.loads(messages[1]) else: raise Exception(
Refactor into transports and channels
_decode_messages
d4e6111086ff713eb6609dc6c98cec98aded2564
salt
zeromq.py
15
23
https://github.com/saltstack/salt.git
7
161
0
84
272
Python
{ "docstring": "\n Take the zmq messages, decrypt/decode them into a payload\n\n :param list messages: A list of messages to be decoded\n ", "language": "en", "n_whitespaces": 41, "n_words": 19, "vocab_size": 18 }
def _decode_messages(self, messages): messages_len = len(messages) # if it was one message, then its old style if messages_len == 1: payload = salt.payload.loads(messages[0]) # 2 includes a header which says who should do it elif messages_len == 2: message_target = salt.utils.stringutils.to_str(messages[0]) if ( self.opts.get("__role") != "syndic" and message_target not in ("broadcast", self.hexid) ) or ( self.opts.get("__role") == "syndic" and message_target not in ("broadcast", "syndic") ): log.debug("Publish received for not this minion: %s", message_target) raise salt.ext.tornado.gen.Return(None) payload = salt.payload.loads(messages[1]) else: raise Exception( "Invalid number of messages ({}) in zeromq pubmessage from master".format( len(messages_len) ) ) # Yield control back to the caller. When the payload has been decoded, assign # the decoded payload to 'ret' and resume operation raise salt.ext.tornado.gen.Return(payload)
31,197
137,593
193
python/ray/tests/test_runtime_env.py
74
14
def test_get_release_wheel_url(): # This should be a commit for which wheels have al
[runtime env] Support python 3.10 for runtime_env conda (#30970) Signed-off-by: Archit Kulkarni <[email protected]> conda environments are isolated, so when runtime_env sets up a conda environment it must download the Ray wheel into the conda environment. It must download the wheel that matches the current Python and Ray version running, otherwise there will be incompatibility issues between the workers that use this runtime_env and the other workers and Ray processes. This PR updates the wheel name format logic to support Python 3.10.
test_get_release_wheel_url
98fef7732852cdb3e9377cd87c1ee1085b894928
ray
test_runtime_env.py
15
9
https://github.com/ray-project/ray.git
6
80
0
53
136
Python
{ "docstring": "Test the code that generates the filenames of the `release` branch wheels.", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 10 }
def test_get_release_wheel_url(): # This should be a commit for which wheels have already been built for # all platforms and python versions at # `s3://ray-wheels/releases/2.2.0/<commit>/`. test_commits = {"2.2.0": "b6af0887ee5f2e460202133791ad941a41f15beb"} for sys_platform in ["darwin", "linux", "win32"]: for py_version in ray_constants.RUNTIME_ENV_CONDA_PY_VERSIONS: for version, commit in test_commits.items(): if sys_platform == "win32" and py_version == (3, 6): # Windows wheels are not built for py3.6 anymore continue url = get_release_wheel_url(commit, sys_platform, version, py_version) assert requests.head(url).status_code == 200, url
72,618
249,111
282
tests/rest/admin/test_media.py
61
19
def test_keep_media_by_date(self) -> None: # timestamp before upload now_ms = self.clock.time_msec() server_and_media_id = self._create_media() self._access_media(server_and_media_id) channel = self.make_request( "POST", self.url + "?before_ts=" + str(now_ms), access_token=self.admin_user_tok, ) self.assertEqual(200, channel.code, msg=channel.json_body) self.assertEqual(0, channel.json_body["total"]) self._access_media(server_and_media_id) # timestamp after upload now_ms = sel
Use literals in place of `HTTPStatus` constants in tests (#13469)
test_keep_media_by_date
c97042f7eef3748e17c90e48a4122389a89c4735
synapse
test_media.py
11
28
https://github.com/matrix-org/synapse.git
1
188
0
35
304
Python
{ "docstring": "\n Tests that media is not deleted if it is newer than `before_ts`\n ", "language": "en", "n_whitespaces": 27, "n_words": 12, "vocab_size": 11 }
def test_keep_media_by_date(self) -> None: # timestamp before upload now_ms = self.clock.time_msec() server_and_media_id = self._create_media() self._access_media(server_and_media_id) channel = self.make_request( "POST", self.url + "?before_ts=" + str(now_ms), access_token=self.admin_user_tok, ) self.assertEqual(200, channel.code, msg=channel.json_body) self.assertEqual(0, channel.json_body["total"]) self._access_media(server_and_media_id) # timestamp after upload now_ms = self.clock.time_msec() channel = self.make_request( "POST", self.url + "?before_ts=" + str(now_ms), access_token=self.admin_user_tok, ) self.assertEqual(200, channel.code, msg=channel.json_body) self.assertEqual(1, channel.json_body["total"]) self.assertEqual( server_and_media_id.split("/")[1], channel.json_body["deleted_media"][0], ) self._access_media(server_and_media_id, False)
@pytest.mark.filterwarnings("ignore:Version 1 of dataset Australian is inactive") # TODO(1.4): remove this filterwarning decorator for `parser` @pytest.mark.filterwarnings("ignore:The default value of `parser` will change") @pytest.mark.parametrize( "params, err_msg", [ ( {"parser": "pandas"}, "Sparse ARFF datasets cannot be loaded with parser='pandas'", ), ( {"as_frame": True}, "Sparse ARFF datasets cannot be loaded with as_frame=True.", ), ( {"parser": "pandas", "as_frame": True}, "Sparse ARFF datasets cannot be loaded with as_frame=True.", ), ], )
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259,885
306
sklearn/datasets/tests/test_openml.py
112
17
def test_fetch_openml_requires_pandas_in_future(monkeypatch): params = {"as_frame": False, "parser": "auto"} data_id = 1119 try: check_pandas_support("test_fetch_openml_requires_pandas") except ImportError: _monkey_patch_webbased_functions(monk
ENH improve ARFF parser using pandas (#21938) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Adrin Jalali <[email protected]>
test_fetch_openml_requires_pandas_in_future
a47d569e670fd4102af37c3165c9b1ddf6fd3005
scikit-learn
test_openml.py
13
14
https://github.com/scikit-learn/scikit-learn.git
2
70
1
80
247
Python
{ "docstring": "Check that we raise a warning that pandas will be required in the future.", "language": "en", "n_whitespaces": 13, "n_words": 14, "vocab_size": 13 }
def test_fetch_openml_requires_pandas_in_future(monkeypatch): params = {"as_frame": False, "parser": "auto"} data_id = 1119 try: check_pandas_support("test_fetch_openml_requires_pandas") except ImportError: _monkey_patch_webbased_functions(monkeypatch, data_id, True) warn_msg = ( "From version 1.4, `parser='auto'` with `as_frame=False` will use pandas" ) with pytest.warns(FutureWarning, match=warn_msg): fetch_openml(data_id=data_id, **params) else: raise SkipTest("This test requires pandas to not be installed.") @pytest.mark.filterwarnings("ignore:Version 1 of dataset Australian is inactive") # TODO(1.4): remove this filterwarning decorator for `parser` @pytest.mark.filterwarnings("ignore:The default value of `parser` will change") @pytest.mark.parametrize( "params, err_msg", [ ( {"parser": "pandas"}, "Sparse ARFF datasets cannot be loaded with parser='pandas'", ), ( {"as_frame": True}, "Sparse ARFF datasets cannot be loaded with as_frame=True.", ), ( {"parser": "pandas", "as_frame": True}, "Sparse ARFF datasets cannot be loaded with as_frame=True.", ), ], )
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22,041
264
pipenv/patched/pip/_vendor/requests/adapters.py
62
19
def get_connection(self, url, proxies=None): proxy = select_proxy(url, proxies) if proxy: proxy = prepend_scheme_if_needed(proxy, "http") proxy_url = parse_url(proxy) if not proxy_url.host: raise InvalidProxyURL( "Please check proxy URL. It is malformed " "and could be missing the host."
Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir.
get_connection
cd5a9683be69c86c8f3adcd13385a9bc5db198ec
pipenv
adapters.py
13
17
https://github.com/pypa/pipenv.git
3
92
0
49
157
Python
{ "docstring": "Returns a urllib3 connection for the given URL. This should not be\n called from user code, and is only exposed for use when subclassing the\n :class:`HTTPAdapter <requests.adapters.HTTPAdapter>`.\n\n :param url: The URL to connect to.\n :param proxies: (optional) A Requests-style dictionary of proxies used on this request.\n :rtype: urllib3.ConnectionPool\n ", "language": "en", "n_whitespaces": 90, "n_words": 48, "vocab_size": 45 }
def get_connection(self, url, proxies=None): proxy = select_proxy(url, proxies) if proxy: proxy = prepend_scheme_if_needed(proxy, "http") proxy_url = parse_url(proxy) if not proxy_url.host: raise InvalidProxyURL( "Please check proxy URL. It is malformed " "and could be missing the host." ) proxy_manager = self.proxy_manager_for(proxy) conn = proxy_manager.connection_from_url(url) else: # Only scheme should be lower case parsed = urlparse(url) url = parsed.geturl() conn = self.poolmanager.connection_from_url(url) return conn
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151,923
258
freqtrade/templates/FreqaiExampleStrategy.py
70
21
def feature_engineering_expand_all(self, dataframe, period, **kwargs): dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period) dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] dataframe["%-close-bb_lower-period"] = ( dataframe["close"] / dataframe["bb_lowerband-period"] ) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-relative_volume-period"] = ( dataframe["volume"] / dataframe["volume"].rolling(period).mean() ) return dataframe
improve doc, update test strats, change function names
feature_engineering_expand_all
c2936d551b8ad6ccf7b57e2ac6cb55d8550622cf
freqtrade
FreqaiExampleStrategy.py
14
24
https://github.com/freqtrade/freqtrade.git
1
217
0
42
361
Python
{ "docstring": "\n *Only functional with FreqAI enabled strategies*\n This function will automatically expand the defined features on the config defined\n `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and\n `include_corr_pairs`. In other words, a single feature defined in this function\n will automatically expand to a total of\n `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *\n `include_corr_pairs` numbers of features added to the model.\n\n All features must be prepended with `%` to be recognized by FreqAI internals.\n\n More details on how these config defined parameters accelerate feature engineering\n in the documentation at:\n\n https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters\n\n https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features\n\n :param df: strategy dataframe which will receive the features\n :param period: period of the indicator - usage example:\n dataframe[\"%-ema-period\"] = ta.EMA(dataframe, timeperiod=period)\n ", "language": "en", "n_whitespaces": 219, "n_words": 106, "vocab_size": 75 }
def feature_engineering_expand_all(self, dataframe, period, **kwargs): dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period) dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] dataframe["%-close-bb_lower-period"] = ( dataframe["close"] / dataframe["bb_lowerband-period"] ) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-relative_volume-period"] = ( dataframe["volume"] / dataframe["volume"].rolling(period).mean() ) return dataframe
50,767
204,521
44
django/core/handlers/asgi.py
12
6
def get_script_prefix(self, scope): if settings
Refs #33476 -- Reformatted code with Black.
get_script_prefix
9c19aff7c7561e3a82978a272ecdaad40dda5c00
django
asgi.py
9
4
https://github.com/django/django.git
3
28
0
11
51
Python
{ "docstring": "\n Return the script prefix to use from either the scope or a setting.\n ", "language": "en", "n_whitespaces": 28, "n_words": 13, "vocab_size": 12 }
def get_script_prefix(self, scope): if settings.FORCE_SCRIPT_NAME: return settings.FORCE_SCRIPT_NAME return scope.get("root_path", "") or ""
11,327
55,476
65
tests/cli/test_storage_cli.py
19
9
def test_get_first_menu_and_fail(): part_one = f part_two = f command = ["storage", "create"] invoke_and_assert_in( command=command, desired_contents=(part_one, part_two), e
Update tests
test_get_first_menu_and_fail
11638691240b7595c0d02542af506a96d344ae8b
prefect
test_storage_cli.py
10
23
https://github.com/PrefectHQ/prefect.git
1
44
0
16
82
Python
{ "docstring": "\n Make sure that our utility function is returning as expected\n \n Found the following storage types:\n 0) Azure Blob Storage\n Store data in an Azure blob storage container.\n 1) File Storage\n Store data as a file on local or remote file systems.\n 2) Google Cloud Storage\n Store data in a GCS bucket.\n 3) Local Storage\n Store data in a run's local file system.\n \n Select a storage type to create: 99999999\n Invalid selection {INVALID_OPTION}\n ", "language": "en", "n_whitespaces": 136, "n_words": 72, "vocab_size": 51 }
def test_get_first_menu_and_fail(): part_one = f part_two = f command = ["storage", "create"] invoke_and_assert_in( command=command, desired_contents=(part_one, part_two), expected_code=1, user_input=f"{INVALID_OPTION}\n", )
41,953
176,544
107
networkx/algorithms/planarity.py
35
8
def check_planarity(G, counterexample=False): planarity_state = LRPlanarity(G) embedding = planarity_state.lr_planarity() if embedding is None: # graph is not planar if counterexample: return False, get_counterexample(G) else: return False, None else:
Improve documentation of PlanarEmbedding class (#5523) * Improve documentation of PlanarEmbedding * Fix type * Make suggested changes * rst formatting nits. * Update networkx/algorithms/planarity.py Co-authored-by: Dan Schult <[email protected]> * Run black for formatting Co-authored-by: Ross Barnowski <[email protected]> Co-authored-by: Dan Schult <[email protected]>
check_planarity
1af7d49d70869081e5cb64d17165652f1b26c57b
networkx
planarity.py
12
10
https://github.com/networkx/networkx.git
3
50
0
22
86
Python
{ "docstring": "Check if a graph is planar and return a counterexample or an embedding.\n\n A graph is planar iff it can be drawn in a plane without\n any edge intersections.\n\n Parameters\n ----------\n G : NetworkX graph\n counterexample : bool\n A Kuratowski subgraph (to proof non planarity) is only returned if set\n to true.\n\n Returns\n -------\n (is_planar, certificate) : (bool, NetworkX graph) tuple\n is_planar is true if the graph is planar.\n If the graph is planar `certificate` is a PlanarEmbedding\n otherwise it is a Kuratowski subgraph.\n\n Examples\n --------\n >>> G = nx.Graph([(0, 1), (0, 2)])\n >>> is_planar, P = nx.check_planarity(G)\n >>> print(is_planar)\n True\n\n When `G` is planar, a `PlanarEmbedding` instance is returned:\n\n >>> P.get_data()\n {0: [1, 2], 1: [0], 2: [0]}\n\n Notes\n -----\n A (combinatorial) embedding consists of cyclic orderings of the incident\n edges at each vertex. Given such an embedding there are multiple approaches\n discussed in literature to drawing the graph (subject to various\n constraints, e.g. integer coordinates), see e.g. [2].\n\n The planarity check algorithm and extraction of the combinatorial embedding\n is based on the Left-Right Planarity Test [1].\n\n A counterexample is only generated if the corresponding parameter is set,\n because the complexity of the counterexample generation is higher.\n\n References\n ----------\n .. [1] Ulrik Brandes:\n The Left-Right Planarity Test\n 2009\n http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.9208\n .. [2] Takao Nishizeki, Md Saidur Rahman:\n Planar graph drawing\n Lecture Notes Series on Computing: Volume 12\n 2004\n ", "language": "en", "n_whitespaces": 404, "n_words": 228, "vocab_size": 154 }
def check_planarity(G, counterexample=False): planarity_state = LRPlanarity(G) embedding = planarity_state.lr_planarity() if embedding is None: # graph is not planar if counterexample: return False, get_counterexample(G) else: return False, None else: # graph is planar return True, embedding
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42,786
81
airflow/providers/cncf/kubernetes/hooks/kubernetes.py
26
8
def _get_bool(val) -> Optional[bool]: if isinstance(val, bool): return val elif isinstance(val, str): if val.strip().lower() == 'true': return True
Use KubernetesHook to create api client in KubernetesPodOperator (#20578) Add support for k8s hook in KPO; use it always (even when no conn id); continue to consider the core k8s settings that KPO already takes into account but emit deprecation warning about them. KPO historically takes into account a few settings from core airflow cfg (e.g. verify ssl, tcp keepalive, context, config file, and in_cluster). So to use the hook to generate the client, somehow the hook has to take these settings into account. But we don't want the hook to consider these settings in general. So we read them in KPO and if necessary patch the hook and warn.
_get_bool
60eb9e106f5915398eafd6aa339ec710c102dc09
airflow
kubernetes.py
14
13
https://github.com/apache/airflow.git
5
61
0
18
104
Python
{ "docstring": "\n Converts val to bool if can be done with certainty.\n If we cannot infer intention we return None.\n ", "language": "en", "n_whitespaces": 28, "n_words": 18, "vocab_size": 17 }
def _get_bool(val) -> Optional[bool]: if isinstance(val, bool): return val elif isinstance(val, str): if val.strip().lower() == 'true': return True elif val.strip().lower() == 'false': return False return None
37,345
158,166
259
d2l/mxnet.py
76
33
def load_data_ptb(batch_size, max_window_size, num_noise_words): sentences = read_ptb() vocab = d2l.Vocab(sentences, min_freq=10) subsampled, counter = subsample(sentences, vocab) corpus = [vocab[line] for line in subsampled] all_centers, all_contexts = get_centers_and_contexts( corpus, max_window_size) all_negatives = get_negatives( all_contexts, vocab, counter, num_noise_words) dataset = gluon.data.ArrayDataset( all_centers, all_contexts, all_negatives) data_iter = gluon.data.DataLoader( dataset, batch_size, shuffle=True,batchify_fn=batc
[PaddlePaddle] Merge master into Paddle branch (#1186) * change 15.2 title in chinese version (#1109) change title ’15.2. 情感分析:使用递归神经网络‘ to ’15.2. 情感分析:使用循环神经网络‘ * 修改部分语义表述 (#1105) * Update r0.17.5 (#1120) * Bump versions in installation * 94行typo: (“bert.mall”)->(“bert.small”) (#1129) * line 313: "bert.mall" -> "bert.small" (#1130) * fix: update language as native reader (#1114) * Fix the translation of "stride" (#1115) * Update index.md (#1118) 修改部分语义表述 * Update self-attention-and-positional-encoding.md (#1133) 依照本书的翻译习惯,将pooling翻译成汇聚 * maybe a comment false (#1149) * maybe a little false * maybe a little false * A minor bug in the rcnn section (Chinese edition) (#1148) * Update bert.md (#1137) 一个笔误 # 假设batch_size=2,num_pred_positions=3 # 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1] * Update calculus.md (#1135) * fix typo in git documentation (#1106) * fix: Update the Chinese translation in lr-scheduler.md (#1136) * Update lr-scheduler.md * Update chapter_optimization/lr-scheduler.md Co-authored-by: goldmermaid <[email protected]> Co-authored-by: goldmermaid <[email protected]> * fix translation for kaggle-house-price.md (#1107) * fix translation for kaggle-house-price.md * fix translation for kaggle-house-price.md Signed-off-by: sunhaizhou <[email protected]> * Update weight-decay.md (#1150) * Update weight-decay.md 关于“k多选d”这一部分,中文读者使用排列组合的方式可能更容易理解 关于“给定k个变量,阶数的个数为...”这句话是有歧义的,不是很像中国话,应该是说“阶数为d的项的个数为...”。 并增加了一句对“因此即使是阶数上的微小变化,比如从$2$到$3$,也会显著增加我们模型的复杂性。”的解释 解释为何会增加复杂性以及为何需要细粒度工具。 * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <[email protected]> * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <[email protected]> Co-authored-by: goldmermaid <[email protected]> * Fix a spelling error (#1161) * Update gru.md (#1152) The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state. 翻译错误 * Unify the function naming (#1113) Unify naming of the function 'init_xavier()'. * Update mlp-concise.md (#1166) * Update mlp-concise.md 语句不通顺 * Update environment.md 语序异常 * Update config.ini * fix the imprecise description (#1168) Co-authored-by: yuande <yuande> * fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175) * Fix some typos. (#1163) * Update batch-norm.md (#1170) fixing typos u->x in article * Update linear-regression.md (#1090) We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that 原译文把who也直接翻译出来了。 * Update mlp.md (#1117) * Update mlp.md 修改部分语义表述 * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: goldmermaid <[email protected]> * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: goldmermaid <[email protected]> * Correct a translation error. (#1091) * Correct a translation error. * Update chapter_computer-vision/image-augmentation.md Co-authored-by: Aston Zhang <[email protected]> * Update aws.md (#1121) * Update aws.md * Update chapter_appendix-tools-for-deep-learning/aws.md Co-authored-by: Aston Zhang <[email protected]> * Update image-augmentation.md (#1093) * Update anchor.md (#1088) fix a minor issue in code * Update anchor.md * Update image-augmentation.md * fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087) * Avoid `torch.meshgrid` user warning (#1174) Avoids the following user warning: ```python ~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] ``` * bump to 2.0.0-beta1 * Update sequence.md * bump beta1 on readme * Add latex code block background to config * BLD: Bump python support version 3.9 (#1183) * BLD: Bump python support version 3.9 * Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4 * BLD: Bump torch and tensorflow * Update Jenkinsfile * Update chapter_installation/index.md * Update chapter_installation/index.md Co-authored-by: Aston Zhang <[email protected]> * Update config.ini * Update INFO.md * Update INFO.md * Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187) * resolve the conflicts * revise from publisher (#1089) * revise from publisher * d2l api * post_latex * revise from publisher * revise ch11 * Delete d2l-Copy1.bib * clear cache * rm d2lbook clear * debug anchor * keep original d2l doc Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: Aston Zhang <[email protected]> * 重复语句 (#1188) Co-authored-by: Aston Zhang <[email protected]> * Improve expression for chapter_preliminaries/pandas.md (#1184) * Update pandas.md * Improve expression * Improve expression * Update chapter_preliminaries/pandas.md Co-authored-by: Aston Zhang <[email protected]> * Improce expression for chapter_preliminaries/linear-algebra.md (#1185) * Improce expression * Improve code comments * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md Co-authored-by: Aston Zhang <[email protected]> * Fix multibox_detection bugs * Update d2l to 0.17.5 version * restore older version * Upgrade pandas * change to python3.8 * Test warning log * relocate warning log * test logs filtering * Update gru.md * Add DeprecationWarning filter * Test warning log * Update attention mechanisms & computational performance * Update multilayer perceptron& linear & convolution networks & computer vision * Update recurrent&optimition&nlp pretraining & nlp applications * ignore warnings * Update index.md * Update linear networks * Update multilayer perceptrons&deep learning computation * Update preliminaries * Check and Add warning filter * Update kaggle-cifar10.md * Update object-detection-dataset.md * Update ssd.md fcn.md * Update hybridize.md * Update hybridize.md Signed-off-by: sunhaizhou <[email protected]> Co-authored-by: zhou201505013 <[email protected]> Co-authored-by: Xinwei Liu <[email protected]> Co-authored-by: Anirudh Dagar <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: hugo_han <[email protected]> Co-authored-by: gyro永不抽风 <[email protected]> Co-authored-by: CanChengZheng <[email protected]> Co-authored-by: linlin <[email protected]> Co-authored-by: iuk <[email protected]> Co-authored-by: yoos <[email protected]> Co-authored-by: Mr. Justice Lawrence John Wargrave <[email protected]> Co-authored-by: Chiyuan Fu <[email protected]> Co-authored-by: Sunhuashan <[email protected]> Co-authored-by: Haiker Sun <[email protected]> Co-authored-by: Ming Liu <[email protected]> Co-authored-by: goldmermaid <[email protected]> Co-authored-by: silenceZheng66 <[email protected]> Co-authored-by: Wenchao Yan <[email protected]> Co-authored-by: Kiki2049 <[email protected]> Co-authored-by: Krahets <[email protected]> Co-authored-by: friedmainfunction <[email protected]> Co-authored-by: Jameson <[email protected]> Co-authored-by: P. Yao <[email protected]> Co-authored-by: Yulv-git <[email protected]> Co-authored-by: Liu,Xiao <[email protected]> Co-authored-by: YIN, Gang <[email protected]> Co-authored-by: Joe-HZ <[email protected]> Co-authored-by: lybloveyou <[email protected]> Co-authored-by: VigourJiang <[email protected]> Co-authored-by: zxhd863943427 <[email protected]> Co-authored-by: LYF <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: xiaotinghe <[email protected]> Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Holly-Max <[email protected]> Co-authored-by: HinGwenWoong <[email protected]> Co-authored-by: Shuai Zhang <[email protected]>
load_data_ptb
b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2
d2l-zh
mxnet.py
11
15
https://github.com/d2l-ai/d2l-zh.git
2
117
0
56
289
Python
{ "docstring": "Download the PTB dataset and then load it into memory.\n\n Defined in :numref:`subsec_word2vec-minibatch-loading`", "language": "en", "n_whitespaces": 15, "n_words": 13, "vocab_size": 13 }
def load_data_ptb(batch_size, max_window_size, num_noise_words): sentences = read_ptb() vocab = d2l.Vocab(sentences, min_freq=10) subsampled, counter = subsample(sentences, vocab) corpus = [vocab[line] for line in subsampled] all_centers, all_contexts = get_centers_and_contexts( corpus, max_window_size) all_negatives = get_negatives( all_contexts, vocab, counter, num_noise_words) dataset = gluon.data.ArrayDataset( all_centers, all_contexts, all_negatives) data_iter = gluon.data.DataLoader( dataset, batch_size, shuffle=True,batchify_fn=batchify, num_workers=d2l.get_dataloader_workers()) return data_iter, vocab d2l.DATA_HUB['glove.6b.50d'] = (d2l.DATA_URL + 'glove.6B.50d.zip', '0b8703943ccdb6eb788e6f091b8946e82231bc4d') d2l.DATA_HUB['glove.6b.100d'] = (d2l.DATA_URL + 'glove.6B.100d.zip', 'cd43bfb07e44e6f27cbcc7bc9ae3d80284fdaf5a') d2l.DATA_HUB['glove.42b.300d'] = (d2l.DATA_URL + 'glove.42B.300d.zip', 'b5116e234e9eb9076672cfeabf5469f3eec904fa') d2l.DATA_HUB['wiki.en'] = (d2l.DATA_URL + 'wiki.en.zip', 'c1816da3821ae9f43899be655002f6c723e91b88')
88,252
289,104
33
homeassistant/components/homekit/__init__.py
12
6
async def async_config_changed(self) -> None: assert self.driver is not None await self.hass.async_add_executor_job(self.driver.config_changed)
Add support for restoring HomeKit IIDs (#79913)
async_config_changed
3b33e0d832b238b40360383099391e2093ea05cb
core
__init__.py
10
4
https://github.com/home-assistant/core.git
1
28
0
12
48
Python
{ "docstring": "Call config changed which writes out the new config to disk.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 10 }
async def async_config_changed(self) -> None: assert self.driver is not None await self.hass.async_add_executor_job(self.driver.config_changed)
13,314
63,461
105
.venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py
21
10
def setDebugActions(self, startAction, successAction, exceptionAction): self.debugActions = (startAction or _defaultStartDebug
upd; format
setDebugActions
f638f5d0e6c8ebed0e69a6584bc7f003ec646580
transferlearning
pyparsing.py
8
6
https://github.com/jindongwang/transferlearning.git
4
36
0
18
54
Python
{ "docstring": "\n Enable display of debugging messages while doing pattern matching.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }
def setDebugActions(self, startAction, successAction, exceptionAction): self.debugActions = (startAction or _defaultStartDebugAction, successAction or _defaultSuccessDebugAction, exceptionAction or _defaultExceptionDebugAction) self.debug = True return self
13,771
64,990
65
erpnext/accounts/doctype/pricing_rule/utils.py
99
34
def get_qty_amount_data_for_cumulative(pr_doc, doc, items=None): if items is None: items = [] sum_qty, sum_amt = [0, 0] doctype = doc.get("parenttype") or doc.doctype date_field = ( "transaction_date" if frappe.get_meta(doctype).has_field("transaction_date") else "posting_date" ) child_doctype = "{0} Item".format(doct
style: format code with black
get_qty_amount_data_for_cumulative
494bd9ef78313436f0424b918f200dab8fc7c20b
erpnext
utils.py
16
42
https://github.com/frappe/erpnext.git
7
245
0
72
406
Python
{ "docstring": " and `tab{child_doc}`.warehouse in ({warehouses})\n\t\t\t SELECT `tab{child_doc}`.stock_qty,\n\t\t\t`tab{child_doc}`.amount\n\t\tFROM `tab{child_doc}`, `tab{parent_doc}`\n\t\tWHERE\n\t\t\t`tab{child_doc}`.parent = `tab{parent_doc}`.name and `tab{parent_doc}`.{date_field}\n\t\t\tbetween %s and %s and `tab{parent_doc}`.docstatus = 1\n\t\t\t{condition} group by `tab{child_doc}`.name\n\t", "language": "en", "n_whitespaces": 22, "n_words": 28, "vocab_size": 23 }
def get_qty_amount_data_for_cumulative(pr_doc, doc, items=None): if items is None: items = [] sum_qty, sum_amt = [0, 0] doctype = doc.get("parenttype") or doc.doctype date_field = ( "transaction_date" if frappe.get_meta(doctype).has_field("transaction_date") else "posting_date" ) child_doctype = "{0} Item".format(doctype) apply_on = frappe.scrub(pr_doc.get("apply_on")) values = [pr_doc.valid_from, pr_doc.valid_upto] condition = "" if pr_doc.warehouse: warehouses = get_child_warehouses(pr_doc.warehouse) condition += .format( child_doc=child_doctype, warehouses=",".join(["%s"] * len(warehouses)) ) values.extend(warehouses) if items: condition = " and `tab{child_doc}`.{apply_on} in ({items})".format( child_doc=child_doctype, apply_on=apply_on, items=",".join(["%s"] * len(items)) ) values.extend(items) data_set = frappe.db.sql( .format( parent_doc=doctype, child_doc=child_doctype, condition=condition, date_field=date_field ), tuple(values), as_dict=1, ) for data in data_set: sum_qty += data.get("stock_qty") sum_amt += data.get("amount") return [sum_qty, sum_amt]
69,699
241,795
132
scipy/sparse/linalg/_isolve/utils.py
62
14
def make_system(A, M, x0, b): A_ = A A = aslinearoperator(A) if A.shape[0] != A.shape[1]: raise ValueError(f'expected square matrix, but got shape={(A.shape,)}') N = A.shape[0] b = asanyarray(b) if not (b.shape == (N,1) or b.shape == (N,)): raise ValueError(f'shapes of A {A.shape} and b {b.shape} are ' 'incompatib
MAINT: sparse.linalg: Remove unnecessary operations
make_system
5628849933f1ba002f34b88b4d3af24f68008b39
scipy
utils.py
13
51
https://github.com/scipy/scipy.git
17
379
0
48
194
Python
{ "docstring": "Make a linear system Ax=b\n\n Parameters\n ----------\n A : LinearOperator\n sparse or dense matrix (or any valid input to aslinearoperator)\n M : {LinearOperator, Nones}\n preconditioner\n sparse or dense matrix (or any valid input to aslinearoperator)\n x0 : {array_like, str, None}\n initial guess to iterative method.\n ``x0 = 'Mb'`` means using the nonzero initial guess ``M @ b``.\n Default is `None`, which means using the zero initial guess.\n b : array_like\n right hand side\n\n Returns\n -------\n (A, M, x, b, postprocess)\n A : LinearOperator\n matrix of the linear system\n M : LinearOperator\n preconditioner\n x : rank 1 ndarray\n initial guess\n b : rank 1 ndarray\n right hand side\n postprocess : function\n converts the solution vector to the appropriate\n type and dimensions (e.g. (N,1) matrix)\n\n ", "language": "en", "n_whitespaces": 303, "n_words": 123, "vocab_size": 77 }
def make_system(A, M, x0, b): A_ = A A = aslinearoperator(A) if A.shape[0] != A.shape[1]: raise ValueError(f'expected square matrix, but got shape={(A.shape,)}') N = A.shape[0] b = asanyarray(b) if not (b.shape == (N,1) or b.shape == (N,)): raise ValueError(f'shapes of A {A.shape} and b {b.shape} are ' 'incompatible') if b.dtype.char not in 'fdFD': b = b.astype('d') # upcast non-FP types to double
41,733
176,163
35
networkx/generators/small.py
23
5
def dodecahedral_graph(create_using=None): G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using) G.name = "Dodecahedral Graph" re
Docstrings for the small.py module (#5240) * added description for the first 5 small graphs * modified descriptions based on comment and added description for two more functions * added doctrings to all the functions * Minor touchups. Co-authored-by: Ross Barnowski <[email protected]>
dodecahedral_graph
dec723f072eb997a497a159dbe8674cd39999ee9
networkx
small.py
10
4
https://github.com/networkx/networkx.git
1
51
0
18
74
Python
{ "docstring": "\n Returns the Platonic Dodecahedral graph.\n\n The dodecahedral graph has 20 nodes and 30 edges. The skeleton of the\n dodecahedron forms a graph. It is one of 5 Platonic graphs [1]_.\n It can be described in LCF notation as:\n ``[10, 7, 4, -4, -7, 10, -4, 7, -7, 4]^2`` [2]_.\n\n Parameters\n ----------\n create_using : NetworkX graph constructor, optional (default=nx.Graph)\n Graph type to create. If graph instance, then cleared before populated.\n\n Returns\n -------\n G : networkx Graph\n Dodecahedral Graph with 20 nodes and 30 edges\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Regular_dodecahedron#Dodecahedral_graph\n .. [2] https://mathworld.wolfram.com/DodecahedralGraph.html\n\n ", "language": "en", "n_whitespaces": 153, "n_words": 91, "vocab_size": 69 }
def dodecahedral_graph(create_using=None): G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using) G.name = "Dodecahedral Graph" return G
99,478
300,618
58
homeassistant/helpers/template.py
17
10
def arc_tangent(value, default=_SENTINEL): try: return math.atan(float(value)) except (ValueError, TypeError): if default is _SENTINEL: ra
Fail template functions when no default specified (#71687)
arc_tangent
4885331509eeffe50f42d76b234996467b06170f
core
template.py
13
7
https://github.com/home-assistant/core.git
3
42
0
15
70
Python
{ "docstring": "Filter and function to get arc tangent of the value.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }
def arc_tangent(value, default=_SENTINEL): try: return math.atan(float(value)) except (ValueError, TypeError): if default is _SENTINEL: raise_no_default("atan", value) return default
81,815
276,983
220
keras/utils/metrics_utils.py
92
22
def sparse_top_k_categorical_matches(y_true, y_pred, k=5): reshape_matches = False y_true = tf.convert_to_tensor(y_true) y_pred = tf.convert_to_tensor(y_pred) y_true_rank = y_true.shape.ndims y_pred_rank = y_pred.shape.ndims y_true_org_shape = tf.shape(y_true) # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,) if (y_true_rank is not None) and (y_pred_rank is not None): if y_pred_rank > 2: y_pred = tf.reshape(y_pred, [-1, y_pred.shape[-1]]) if y_true_rank > 1: reshape_matches = True y_true = tf.reshape(y_true, [-1]) matches = tf.cast( tf.math.in_top_k( predictions=y_pred, ta
Reformatting the codebase with black. PiperOrigin-RevId: 450093126
sparse_top_k_categorical_matches
84afc5193d38057e2e2badf9c889ea87d80d8fbf
keras
metrics_utils.py
15
22
https://github.com/keras-team/keras.git
6
172
0
61
268
Python
{ "docstring": "Creates float Tensor, 1.0 for label-TopK_prediction match, 0.0 for mismatch.\n\n Args:\n y_true: tensor of true targets.\n y_pred: tensor of predicted targets.\n k: (Optional) Number of top elements to look at for computing accuracy.\n Defaults to 5.\n\n Returns:\n Match tensor: 1.0 for label-prediction match, 0.0 for mismatch.\n ", "language": "en", "n_whitespaces": 82, "n_words": 46, "vocab_size": 33 }
def sparse_top_k_categorical_matches(y_true, y_pred, k=5): reshape_matches = False y_true = tf.convert_to_tensor(y_true) y_pred = tf.convert_to_tensor(y_pred) y_true_rank = y_true.shape.ndims y_pred_rank = y_pred.shape.ndims y_true_org_shape = tf.shape(y_true) # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,) if (y_true_rank is not None) and (y_pred_rank is not None): if y_pred_rank > 2: y_pred = tf.reshape(y_pred, [-1, y_pred.shape[-1]]) if y_true_rank > 1: reshape_matches = True y_true = tf.reshape(y_true, [-1]) matches = tf.cast( tf.math.in_top_k( predictions=y_pred, targets=tf.cast(y_true, "int32"), k=k ), dtype=backend.floatx(), ) # returned matches is expected to have same shape as y_true input if reshape_matches: return tf.reshape(matches, shape=y_true_org_shape) return matches
@frappe.whitelist() @frappe.validate_and_sanitize_search_inputs
14,815
68,540
28
erpnext/controllers/queries.py
57
31
def tax_account_query(doctype, txt, searchfield, start, page_len, filters): company_currency = erpnext.get_company_currency(filters.get("company")) def get_accounts(with_account_type_filter): account_type_condition = "" if with_account_type_filter: account_type_condition = "AND account_type in %(account_types)s" accounts = frappe.db.sql( .format( account_type_condition=account_type_condition, searchfield=searchfield, mcond=get_match_cond(doctype), ), dict( account_types=filters.get("account_type"), company=filters.get("company"), disabled=filters.get("disabled", 0), currency=company_currency, txt="%{}%".format(txt), offset=start, limit=page_len, ), ) return accounts tax_accounts = get_accounts(True) if not tax_accounts: tax_accounts = get_accounts(False) return tax_accounts
fix: user can select disabled accounts in taxes table
tax_account_query
a1e3ae8869194a487acccc706a381db74c4aa1ff
erpnext
queries.py
16
7
https://github.com/frappe/erpnext.git
2
48
1
44
249
Python
{ "docstring": "\n\t\t\tSELECT name, parent_account\n\t\t\tFROM `tabAccount`\n\t\t\tWHERE `tabAccount`.docstatus!=2\n\t\t\t\t{account_type_condition}\n\t\t\t\tAND is_group = 0\n\t\t\t\tAND company = %(company)s\n\t\t\t\tAND disabled = %(disabled)s\n\t\t\t\tAND (account_currency = %(currency)s or ifnull(account_currency, '') = '')\n\t\t\t\tAND `{searchfield}` LIKE %(txt)s\n\t\t\t\t{mcond}\n\t\t\tORDER BY idx DESC, name\n\t\t\tLIMIT %(offset)s, %(limit)s\n\t\t", "language": "en", "n_whitespaces": 30, "n_words": 42, "vocab_size": 33 }
def tax_account_query(doctype, txt, searchfield, start, page_len, filters): company_currency = erpnext.get_company_currency(filters.get("company")) def get_accounts(with_account_type_filter): account_type_condition = "" if with_account_type_filter: account_type_condition = "AND account_type in %(account_types)s" accounts = frappe.db.sql( .format( account_type_condition=account_type_condition, searchfield=searchfield, mcond=get_match_cond(doctype), ), dict( account_types=filters.get("account_type"), company=filters.get("company"), disabled=filters.get("disabled", 0), currency=company_currency, txt="%{}%".format(txt), offset=start, limit=page_len, ), ) return accounts tax_accounts = get_accounts(True) if not tax_accounts: tax_accounts = get_accounts(False) return tax_accounts @frappe.whitelist() @frappe.validate_and_sanitize_search_inputs
16,628
77,101
196
wagtail/images/tests/test_admin_views.py
49
25
def test_add_post_duplicate_choose_permission(self): # Create group with access to admin and add permission. bakers_group = Group.objects.create(name="Bakers") access_admin_perm = Permission.objects.get( content_type__app_label="wagtailadmin", codename="access_admin" ) bakers_group.permissions.add(access_admin_perm) # Create the "Bakery" Collection and grant "add" permission to the Bakers group. root = Collection.objects.get(id=get_root_collection_id()) bak
Add duplicate detection to multiple image upload view Add utility function to find an image's potential duplicates Add logic to detect duplicates on multiple images upload view Add template shown when a user is prompted to confirm a duplicate upload Add client-side logic to confirm a duplicate upload Add/update styles Add tests for duplicate image uploads Index Image file_hash field Ensure that a user can choose an image from duplicates returned by find_image_duplicates Use CSS classes instead of HTML elements to hide edit form on duplicate upload Add ImagesPermissionPolicy helper to retrieve the permission policy dynamically This allows test cases that override the base image model to pick up the corresponding permission policy, should they need it. Remove usage of sibling selector Use wagtail image templatetag to generate image Renamed ImagesPermissionPolicy to ImagesPermissionPolicyGetter Fail loudly when setting permission policy and a wromg image model is provided Add decorator to disconnect a signal's receiver during a test execution and use it in get_image_model tests Improve warning message on duplicate upload in multiple upload view Show matching form when confirming a duplicate upload
test_add_post_duplicate_choose_permission
c136f461bc052cef362991458e1bd1fca37a3da9
wagtail
test_admin_views.py
13
31
https://github.com/wagtail/wagtail.git
1
221
0
40
176
Python
{ "docstring": "\n When a duplicate image is added but the user doesn't have permission to choose the original image,\n the add views lets the user upload it as if it weren't a duplicate.\n ", "language": "en", "n_whitespaces": 53, "n_words": 31, "vocab_size": 25 }
def test_add_post_duplicate_choose_permission(self): # Create group with access to admin and add permission. bakers_group = Group.objects.create(name="Bakers") access_admin_perm = Permission.objects.get( content_type__app_label="wagtailadmin", codename="access_admin" ) bakers_group.permissions.add(access_admin_perm) # Create the "Bakery" Collection and grant "add" permission to the Bakers group. root = Collection.objects.get(id=get_root_collection_id()) bakery_collection = root.add_child(instance=Collection(name="Bakery")) GroupCollectionPermission.objects.create( group=bakers_group, collection=bakery_collection, permission=Permission.objects.get( content_type__app_label="wagtailimages", codename="add_image" ), )
@proxy_napalm_wrap
54,638
216,561
38
salt/modules/napalm_mod.py
17
10
def netmiko_commands(*commands, **kwargs): conn = _netmiko_conn(**kwargs) ret = [] for cmd in commands: ret.append(conn.send_command(cmd))
Deprecated netmiko_conn and pyeapi_conn in napalm_mod.py as these function should not be called from the CLI
netmiko_commands
d8305bfaa7b98d898f5963b01ca75f277c266322
salt
napalm_mod.py
11
6
https://github.com/saltstack/salt.git
2
39
1
15
70
Python
{ "docstring": "\n .. versionadded:: 2019.2.0\n\n Invoke one or more commands to be executed on the remote device, via Netmiko.\n Returns a list of strings, with the output from each command.\n\n commands\n A list of commands to be executed.\n\n expect_string\n Regular expression pattern to use for determining end of output.\n If left blank will default to being based on router prompt.\n\n delay_factor: ``1``\n Multiplying factor used to adjust delays (default: ``1``).\n\n max_loops: ``500``\n Controls wait time in conjunction with delay_factor. Will default to be\n based upon self.timeout.\n\n auto_find_prompt: ``True``\n Whether it should try to auto-detect the prompt (default: ``True``).\n\n strip_prompt: ``True``\n Remove the trailing router prompt from the output (default: ``True``).\n\n strip_command: ``True``\n Remove the echo of the command from the output (default: ``True``).\n\n normalize: ``True``\n Ensure the proper enter is sent at end of command (default: ``True``).\n\n use_textfsm: ``False``\n Process command output through TextFSM template (default: ``False``).\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' napalm.netmiko_commands 'show version' 'show interfaces'\n ", "language": "en", "n_whitespaces": 287, "n_words": 157, "vocab_size": 106 }
def netmiko_commands(*commands, **kwargs): conn = _netmiko_conn(**kwargs) ret = [] for cmd in commands: ret.append(conn.send_command(cmd)) return ret @proxy_napalm_wrap
19,938
100,464
194
plugins/train/model/original.py
58
16
def decoder(self, side): input_ = Input(shape=(8, 8, 512)) var_x = input_ var_x = UpscaleBlock(256, activation="leakyrelu")(var_x) var_x = UpscaleBlock(128, activation="leakyrelu")(var_x) var_x = UpscaleBlock(64, activation="leakyrelu")(var_x) var_x = Conv2DOutput(3, 5, name=f"face_out_{side}")(var_x) outputs = [var_x] if self.learn_mask: var_y = input_ var_y = UpscaleBlock(256, activation="leakyrelu")(var_y) var_y = UpscaleBlock(128, activation="leakyrelu")(var_y) var_y = UpscaleBlock(64, activation="leakyrelu")(var_y) var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y) outputs.append(var_y) return KerasModel(input_, outputs=outputs, name=f"dec
Update all Keras Imports to be conditional (#1214) * Remove custom keras importer * first round keras imports fix * launcher.py: Remove KerasFinder references * 2nd round keras imports update (lib and extract) * 3rd round keras imports update (train) * remove KerasFinder from tests * 4th round keras imports update (tests)
decoder
aa39234538a8f83e6aa2b60b8275a570e8876ac2
faceswap
original.py
14
16
https://github.com/deepfakes/faceswap.git
2
168
0
29
283
Python
{ "docstring": " The original Faceswap Decoder Network.\r\n\r\n The decoders for the original model have separate weights for each side \"A\" and \"B\", so two\r\n instances are created in :func:`build_model`, one for each side.\r\n\r\n Parameters\r\n ----------\r\n side: str\r\n Either `\"a` or `\"b\"`. This is used for naming the decoder model.\r\n\r\n Returns\r\n -------\r\n :class:`keras.models.Model`\r\n The Keras decoder model. This will be called twice, once for each side.\r\n ", "language": "en", "n_whitespaces": 149, "n_words": 63, "vocab_size": 49 }
def decoder(self, side): input_ = Input(shape=(8, 8, 512)) var_x = input_ var_x = UpscaleBlock(256, activation="leakyrelu")(var_x) var_x = UpscaleBlock(128, activation="leakyrelu")(var_x) var_x = UpscaleBlock(64, activation="leakyrelu")(var_x) var_x = Conv2DOutput(3, 5, name=f"face_out_{side}")(var_x) outputs = [var_x] if self.learn_mask: var_y = input_ var_y = UpscaleBlock(256, activation="leakyrelu")(var_y) var_y = UpscaleBlock(128, activation="leakyrelu")(var_y) var_y = UpscaleBlock(64, activation="leakyrelu")(var_y) var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y) outputs.append(var_y) return KerasModel(input_, outputs=outputs, name=f"decoder_{side}")
17,720
83,758
382
zerver/tests/test_subs.py
101
34
def test_users_getting_add_peer_event(self) -> None: streams_to_sub = ["multi_user_stream"] othello = self.example_user("othello") cordelia = self.example_user("cordelia") iago = self.example_user("iago") orig_user_ids_to_subscribe = [self.test_user.id, othello.id] self.common_subscribe_to_streams( self.test_user, streams_to_sub, dict(principals=orjson.dumps(orig_user_ids_to_subscribe).decode()), ) new_user_ids_to_subscribe = [iago.id, cordelia.id] events: List[Mapping[str, Any]] = [] with self.tornado_redirected_to_list(events, expected_num_events=5): self.common_subscribe_to_streams( self.test_user, streams_to_sub, dict(principals=orjson.dumps(new_user_ids_to_subscribe).decode()), ) add_peer_events = [event for event in events if event["event"].get("op") == "peer_add"] (add_peer_event,) = add_peer_events self.assertEqual(add_peer_event["event"]["type"], "subscription") self.a
Correctly hyphenate “non-”. Signed-off-by: Anders Kaseorg <[email protected]>
test_users_getting_add_peer_event
6331a314d464f9c49a612023a5969e5d7b8e00a0
zulip
test_subs.py
16
31
https://github.com/zulip/zulip.git
5
228
0
73
378
Python
{ "docstring": "\n Check users getting add_peer_event is correct\n ", "language": "en", "n_whitespaces": 21, "n_words": 6, "vocab_size": 6 }
def test_users_getting_add_peer_event(self) -> None: streams_to_sub = ["multi_user_stream"] othello = self.example_user("othello") cordelia = self.example_user("cordelia") iago = self.example_user("iago") orig_user_ids_to_subscribe = [self.test_user.id, othello.id] self.common_subscribe_to_streams( self.test_user, streams_to_sub, dict(principals=orjson.dumps(orig_user_ids_to_subscribe).decode()), ) new_user_ids_to_subscribe = [iago.id, cordelia.id] events: List[Mapping[str, Any]] = [] with self.tornado_redirected_to_list(events, expected_num_events=5): self.common_subscribe_to_streams( self.test_user, streams_to_sub, dict(principals=orjson.dumps(new_user_ids_to_subscribe).decode()), ) add_peer_events = [event for event in events if event["event"].get("op") == "peer_add"] (add_peer_event,) = add_peer_events self.assertEqual(add_peer_event["event"]["type"], "subscription") self.assertEqual(add_peer_event["event"]["op"], "peer_add") event_sent_to_ids = add_peer_event["users"] for user_id in new_user_ids_to_subscribe: # Make sure new users subscribed to stream is not in # peer_add event recipient list self.assertNotIn(user_id, event_sent_to_ids) for old_user in orig_user_ids_to_subscribe: # Check non-new users are in peer_add event recipient list. self.assertIn(old_user, event_sent_to_ids)
37,343
158,164
100
d2l/mxnet.py
29
19
def show_trace_2d(f, results): d2l.set_figsize() d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e') x1, x2 = d2
[PaddlePaddle] Merge master into Paddle branch (#1186) * change 15.2 title in chinese version (#1109) change title ’15.2. 情感分析:使用递归神经网络‘ to ’15.2. 情感分析:使用循环神经网络‘ * 修改部分语义表述 (#1105) * Update r0.17.5 (#1120) * Bump versions in installation * 94行typo: (“bert.mall”)->(“bert.small”) (#1129) * line 313: "bert.mall" -> "bert.small" (#1130) * fix: update language as native reader (#1114) * Fix the translation of "stride" (#1115) * Update index.md (#1118) 修改部分语义表述 * Update self-attention-and-positional-encoding.md (#1133) 依照本书的翻译习惯,将pooling翻译成汇聚 * maybe a comment false (#1149) * maybe a little false * maybe a little false * A minor bug in the rcnn section (Chinese edition) (#1148) * Update bert.md (#1137) 一个笔误 # 假设batch_size=2,num_pred_positions=3 # 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1] * Update calculus.md (#1135) * fix typo in git documentation (#1106) * fix: Update the Chinese translation in lr-scheduler.md (#1136) * Update lr-scheduler.md * Update chapter_optimization/lr-scheduler.md Co-authored-by: goldmermaid <[email protected]> Co-authored-by: goldmermaid <[email protected]> * fix translation for kaggle-house-price.md (#1107) * fix translation for kaggle-house-price.md * fix translation for kaggle-house-price.md Signed-off-by: sunhaizhou <[email protected]> * Update weight-decay.md (#1150) * Update weight-decay.md 关于“k多选d”这一部分,中文读者使用排列组合的方式可能更容易理解 关于“给定k个变量,阶数的个数为...”这句话是有歧义的,不是很像中国话,应该是说“阶数为d的项的个数为...”。 并增加了一句对“因此即使是阶数上的微小变化,比如从$2$到$3$,也会显著增加我们模型的复杂性。”的解释 解释为何会增加复杂性以及为何需要细粒度工具。 * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <[email protected]> * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <[email protected]> Co-authored-by: goldmermaid <[email protected]> * Fix a spelling error (#1161) * Update gru.md (#1152) The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state. 翻译错误 * Unify the function naming (#1113) Unify naming of the function 'init_xavier()'. * Update mlp-concise.md (#1166) * Update mlp-concise.md 语句不通顺 * Update environment.md 语序异常 * Update config.ini * fix the imprecise description (#1168) Co-authored-by: yuande <yuande> * fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175) * Fix some typos. (#1163) * Update batch-norm.md (#1170) fixing typos u->x in article * Update linear-regression.md (#1090) We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that 原译文把who也直接翻译出来了。 * Update mlp.md (#1117) * Update mlp.md 修改部分语义表述 * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: goldmermaid <[email protected]> * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: goldmermaid <[email protected]> * Correct a translation error. (#1091) * Correct a translation error. * Update chapter_computer-vision/image-augmentation.md Co-authored-by: Aston Zhang <[email protected]> * Update aws.md (#1121) * Update aws.md * Update chapter_appendix-tools-for-deep-learning/aws.md Co-authored-by: Aston Zhang <[email protected]> * Update image-augmentation.md (#1093) * Update anchor.md (#1088) fix a minor issue in code * Update anchor.md * Update image-augmentation.md * fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087) * Avoid `torch.meshgrid` user warning (#1174) Avoids the following user warning: ```python ~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] ``` * bump to 2.0.0-beta1 * Update sequence.md * bump beta1 on readme * Add latex code block background to config * BLD: Bump python support version 3.9 (#1183) * BLD: Bump python support version 3.9 * Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4 * BLD: Bump torch and tensorflow * Update Jenkinsfile * Update chapter_installation/index.md * Update chapter_installation/index.md Co-authored-by: Aston Zhang <[email protected]> * Update config.ini * Update INFO.md * Update INFO.md * Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187) * resolve the conflicts * revise from publisher (#1089) * revise from publisher * d2l api * post_latex * revise from publisher * revise ch11 * Delete d2l-Copy1.bib * clear cache * rm d2lbook clear * debug anchor * keep original d2l doc Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: Aston Zhang <[email protected]> * 重复语句 (#1188) Co-authored-by: Aston Zhang <[email protected]> * Improve expression for chapter_preliminaries/pandas.md (#1184) * Update pandas.md * Improve expression * Improve expression * Update chapter_preliminaries/pandas.md Co-authored-by: Aston Zhang <[email protected]> * Improce expression for chapter_preliminaries/linear-algebra.md (#1185) * Improce expression * Improve code comments * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md Co-authored-by: Aston Zhang <[email protected]> * Fix multibox_detection bugs * Update d2l to 0.17.5 version * restore older version * Upgrade pandas * change to python3.8 * Test warning log * relocate warning log * test logs filtering * Update gru.md * Add DeprecationWarning filter * Test warning log * Update attention mechanisms & computational performance * Update multilayer perceptron& linear & convolution networks & computer vision * Update recurrent&optimition&nlp pretraining & nlp applications * ignore warnings * Update index.md * Update linear networks * Update multilayer perceptrons&deep learning computation * Update preliminaries * Check and Add warning filter * Update kaggle-cifar10.md * Update object-detection-dataset.md * Update ssd.md fcn.md * Update hybridize.md * Update hybridize.md Signed-off-by: sunhaizhou <[email protected]> Co-authored-by: zhou201505013 <[email protected]> Co-authored-by: Xinwei Liu <[email protected]> Co-authored-by: Anirudh Dagar <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: hugo_han <[email protected]> Co-authored-by: gyro永不抽风 <[email protected]> Co-authored-by: CanChengZheng <[email protected]> Co-authored-by: linlin <[email protected]> Co-authored-by: iuk <[email protected]> Co-authored-by: yoos <[email protected]> Co-authored-by: Mr. Justice Lawrence John Wargrave <[email protected]> Co-authored-by: Chiyuan Fu <[email protected]> Co-authored-by: Sunhuashan <[email protected]> Co-authored-by: Haiker Sun <[email protected]> Co-authored-by: Ming Liu <[email protected]> Co-authored-by: goldmermaid <[email protected]> Co-authored-by: silenceZheng66 <[email protected]> Co-authored-by: Wenchao Yan <[email protected]> Co-authored-by: Kiki2049 <[email protected]> Co-authored-by: Krahets <[email protected]> Co-authored-by: friedmainfunction <[email protected]> Co-authored-by: Jameson <[email protected]> Co-authored-by: P. Yao <[email protected]> Co-authored-by: Yulv-git <[email protected]> Co-authored-by: Liu,Xiao <[email protected]> Co-authored-by: YIN, Gang <[email protected]> Co-authored-by: Joe-HZ <[email protected]> Co-authored-by: lybloveyou <[email protected]> Co-authored-by: VigourJiang <[email protected]> Co-authored-by: zxhd863943427 <[email protected]> Co-authored-by: LYF <[email protected]> Co-authored-by: Aston Zhang <[email protected]> Co-authored-by: xiaotinghe <[email protected]> Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Holly-Max <[email protected]> Co-authored-by: HinGwenWoong <[email protected]> Co-authored-by: Shuai Zhang <[email protected]>
show_trace_2d
b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2
d2l-zh
mxnet.py
11
8
https://github.com/d2l-ai/d2l-zh.git
1
113
0
27
193
Python
{ "docstring": "Show the trace of 2D variables during optimization.\n\n Defined in :numref:`subsec_gd-learningrate`", "language": "en", "n_whitespaces": 13, "n_words": 11, "vocab_size": 11 }
def show_trace_2d(f, results): d2l.set_figsize() d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e') x1, x2 = d2l.meshgrid(d2l.arange(-5.5, 1.0, 0.1), d2l.arange(-3.0, 1.0, 0.1)) d2l.plt.contour(x1, x2, f(x1, x2), colors='#1f77b4') d2l.plt.xlabel('x1') d2l.plt.ylabel('x2') d2l.DATA_HUB['airfoil'] = (d2l.DATA_URL + 'airfoil_self_noise.dat', '76e5be1548fd8222e5074cf0faae75edff8cf93f')
@pytest.mark.parametrize("use_local", [True, False])
29,963
133,242
141
python/ray/util/sgd/tests/test_torch_2.py
51
31
def test_dataset(ray_start_4_cpus, use_local): model_creator = mlp_identity.model_creator optimizer_creator = mlp_identity.optimizer_creator dataset_creator = mlp_identity.dataset_creator DatasetOperator = TrainingOperator.from_creators( model_creator=model_creator, optimizer_creator=optimizer_creator, loss_creator=nn.MSELoss, ) trainer = TorchTrainer( training_operator_cls=DatasetOperator, use_local=use_local, num_workers=2, ) dataset = dataset_creator() for i in range(5): trainer.train(dataset=dataset, num_steps=100) x = mlp_identity.to_mat(0.5) prediction = float(trainer.get_model()(x)[0][0]) assert 0.4 <= prediction <= 0.6 trainer.shutdown(
[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.
test_dataset
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
ray
test_torch_2.py
13
21
https://github.com/ray-project/ray.git
2
130
1
41
216
Python
{ "docstring": "\n This test tries training the mlp_identity example. We check the accuracy of\n the model as an all inclusive way of ensuring that we are properly sharding\n and iterating over the entire dataset (instead of repeating the first set\n of points for example).\n ", "language": "en", "n_whitespaces": 58, "n_words": 42, "vocab_size": 35 }
def test_dataset(ray_start_4_cpus, use_local): model_creator = mlp_identity.model_creator optimizer_creator = mlp_identity.optimizer_creator dataset_creator = mlp_identity.dataset_creator DatasetOperator = TrainingOperator.from_creators( model_creator=model_creator, optimizer_creator=optimizer_creator, loss_creator=nn.MSELoss, ) trainer = TorchTrainer( training_operator_cls=DatasetOperator, use_local=use_local, num_workers=2, ) dataset = dataset_creator() for i in range(5): trainer.train(dataset=dataset, num_steps=100) x = mlp_identity.to_mat(0.5) prediction = float(trainer.get_model()(x)[0][0]) assert 0.4 <= prediction <= 0.6 trainer.shutdown() @pytest.mark.parametrize("use_local", [True, False])
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9,114
302
parsing/dml_csr/loss/lovasz_softmax.py
115
33
def lovasz_softmax_flat(probas, labels, classes='present', weighted=None): if probas.numel() == 0: # only void pixels, the gradients should be 0 return probas * 0. C = probas.size(1) losses = [] class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes for c in class_to_sum: fg = (labels == c).float() # foreground for class c if (classes is 'present' and fg.sum() == 0): continue if C == 1: if len(classes) > 1: raise ValueError('Sigmoid output possible only with 1 class') class_pred = probas[:, 0] else: class_pred = probas[:, c] errors = (Variable(fg) - class_pred).abs() errors_sorted, perm = torch.sort(errors, 0, descending=True) perm = perm.data fg_sorted = fg[perm] if weighted is not None: losses.append(wei
Create lovasz_softmax.py
lovasz_softmax_flat
db307ffb12d6ba1f8eaeeafd29ee6d4a3fd6fa97
insightface
lovasz_softmax.py
18
25
https://github.com/deepinsight/insightface.git
9
226
0
83
365
Python
{ "docstring": "\n Multi-class Lovasz-Softmax loss\n probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)\n labels: [P] Tensor, ground truth labels (between 0 and C - 1)\n classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.\n ", "language": "en", "n_whitespaces": 67, "n_words": 45, "vocab_size": 39 }
def lovasz_softmax_flat(probas, labels, classes='present', weighted=None): if probas.numel() == 0: # only void pixels, the gradients should be 0 return probas * 0. C = probas.size(1) losses = [] class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes for c in class_to_sum: fg = (labels == c).float() # foreground for class c if (classes is 'present' and fg.sum() == 0): continue if C == 1: if len(classes) > 1: raise ValueError('Sigmoid output possible only with 1 class') class_pred = probas[:, 0] else: class_pred = probas[:, c] errors = (Variable(fg) - class_pred).abs() errors_sorted, perm = torch.sort(errors, 0, descending=True) perm = perm.data fg_sorted = fg[perm] if weighted is not None: losses.append(weighted[c]*torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted)))) else: losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted)))) return mean(losses)
1,999
10,924
182
jina/parsers/orchestrate/runtimes/distributed.py
53
16
def mixin_distributed_feature_parser(parser): gp = add_arg_group(parser, title='Distributed') gp.add_argument( '--quiet-remote-logs', action='store_true', default=False, help='Do not display the streaming of remote logs on local console', ) gp.add_argument( '--upload-files', type=str, nargs='*', metavar='FILE', help=, ) gp.add_argument(
refactor: rename pod to deployment (#4230) * refactor: rename pod to deployment * style: fix overload and cli autocomplete * fix: undo daemon mistake * refactor: leftover cleanup * fix: more test fixes * fix: more fixes * fix: more fixes * fix: more fixes * fix: more tests * fix: fix more tests * refactor: fix more tests * refactor: more tests fixes * refactor: rename pea to pod * refactor: adjust docs * refactor: complete pea renaming * refactor: more fixes * fix: pea_type in k8s yamls * fix: adjust pod args name * refactor: rename peapods parser folder * fix: da init Co-authored-by: Jina Dev Bot <[email protected]>
mixin_distributed_feature_parser
13edc16d806fb5d77a6849551178ccc75937f25f
jina
distributed.py
10
33
https://github.com/jina-ai/jina.git
2
83
0
44
141
Python
{ "docstring": "Mixing in arguments required by :class:`BaseDeployment` into the given parser.\n :param parser: the parser instance to which we add arguments\n \nThe files on the host to be uploaded to the remote\nworkspace. This can be useful when your Deployment has more\nfile dependencies beyond a single YAML file, e.g.\nPython files, data files.\n\nNote,\n- currently only flatten structure is supported, which means if you upload `[./foo/a.py, ./foo/b.pp, ./bar/c.yml]`, then they will be put under the _same_ workspace on the remote, losing all hierarchies.\n- by default, `--uses` YAML file is always uploaded.\n- uploaded files are by default isolated across the runs. To ensure files are submitted to the same workspace across different runs, use `--workspace-id` to specify the workspace.\n", "language": "en", "n_whitespaces": 119, "n_words": 121, "vocab_size": 90 }
def mixin_distributed_feature_parser(parser): gp = add_arg_group(parser, title='Distributed') gp.add_argument( '--quiet-remote-logs', action='store_true', default=False, help='Do not display the streaming of remote logs on local console', ) gp.add_argument( '--upload-files', type=str, nargs='*', metavar='FILE', help=, ) gp.add_argument( '--disable-remote', action='store_true', default=False, help='If set, remote pod invocation is avoided. This is used by pods created by JinaD' if _SHOW_ALL_ARGS else argparse.SUPPRESS, )
77,473
263,858
317
PyInstaller/depend/analysis.py
155
19
def get_bootstrap_modules(): # Import 'struct' modules to get real paths to module file names. mod_struct = __import__('struct') # Basic modules necessary for the bootstrap process. loader_mods = TOC() loaderpath = os.path.join(HOMEPATH, 'PyInstaller', 'loader') # On some platforms (Windows, Debian/Ubuntu) '_struct' and zlib modules are built-in modules (linked statically) # and thus does not have attribute __file__. 'struct' module is required for reading Python bytecode from # executable. 'zlib' is required to decompress this bytecode. for mod_name in ['_struct', 'zlib']: mod = __import__(mod_name) # C extension. if hasattr(mod, '__file__'): mod_file = os.path.abspath(mod.__file__) if os.path.basename(os.path.dirname(mod_file)) == 'lib-dynload': # Divert extensions originating from python's lib-dynload directory, to match behavior of #5604. mod_name = os.path.join('lib-dynload', mod_name) loader_mods.append((mod_name, mod_file, 'EXTEN
utils: remove compile_py_files helper The only remaining use is in `PYZ.__init__`, and that can be replaced with a loop that uses the new `compile_pymodule` helper. This change, however, requires `get_boostrap_modules()` helper from `PyInstaller.depend˙ to return paths to source `.py` files instead of non-existing `.pyc` files (the old `compile_py_files` helper went to great lengths to convert these back to source file names...).
get_bootstrap_modules
83193a1897232e133966d15e30758a149de50407
pyinstaller
analysis.py
15
20
https://github.com/pyinstaller/pyinstaller.git
4
216
0
116
372
Python
{ "docstring": "\n Get TOC with the bootstrapping modules and their dependencies.\n :return: TOC with modules\n ", "language": "en", "n_whitespaces": 23, "n_words": 13, "vocab_size": 10 }
def get_bootstrap_modules(): # Import 'struct' modules to get real paths to module file names. mod_struct = __import__('struct') # Basic modules necessary for the bootstrap process. loader_mods = TOC() loaderpath = os.path.join(HOMEPATH, 'PyInstaller', 'loader') # On some platforms (Windows, Debian/Ubuntu) '_struct' and zlib modules are built-in modules (linked statically) # and thus does not have attribute __file__. 'struct' module is required for reading Python bytecode from # executable. 'zlib' is required to decompress this bytecode. for mod_name in ['_struct', 'zlib']: mod = __import__(mod_name) # C extension. if hasattr(mod, '__file__'): mod_file = os.path.abspath(mod.__file__) if os.path.basename(os.path.dirname(mod_file)) == 'lib-dynload': # Divert extensions originating from python's lib-dynload directory, to match behavior of #5604. mod_name = os.path.join('lib-dynload', mod_name) loader_mods.append((mod_name, mod_file, 'EXTENSION')) # NOTE:These modules should be kept simple without any complicated dependencies. loader_mods += [ ('struct', os.path.abspath(mod_struct.__file__), 'PYMODULE'), ('pyimod01_os_path', os.path.join(loaderpath, 'pyimod01_os_path.py'), 'PYMODULE'), ('pyimod02_archive', os.path.join(loaderpath, 'pyimod02_archive.py'), 'PYMODULE'), ('pyimod03_importers', os.path.join(loaderpath, 'pyimod03_importers.py'), 'PYMODULE'), ('pyimod04_ctypes', os.path.join(loaderpath, 'pyimod04_ctypes.py'), 'PYMODULE'), ('pyiboot01_bootstrap', os.path.join(loaderpath, 'pyiboot01_bootstrap.py'), 'PYSOURCE'), ] return loader_mods
50,544
203,823
147
django/contrib/gis/db/backends/postgis/adapter.py
41
7
def getquoted(self):
Refs #33476 -- Reformatted code with Black.
getquoted
9c19aff7c7561e3a82978a272ecdaad40dda5c00
django
adapter.py
12
8
https://github.com/django/django.git
3
48
0
36
81
Python
{ "docstring": "\n Return a properly quoted string for use in PostgreSQL/PostGIS.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }
def getquoted(self): if self.is_geometry: # Psycopg will figure out whether to use E'\\000' or '\000'. return b"%s(%s)" % ( b"ST_GeogFromWKB" if self.geography else b"ST_GeomFromEWKB", self._adapter.getquoted(), ) else: # For rasters, add explicit type cast to WKB string. return b"'%s'::raster" % self.ewkb.encode()
20,112
100,650
331
scripts/extract.py
90
21
def _set_skip_list(self) -> None: if self._skip_num == 1 and not self._alignments.data: logger.debug("No frames to be skipped") return skip_list = [] for idx, filename in enumerate(self._images.file_list): if idx % self._skip_num != 0: logger.trace("Adding image '%s' to skip list due to extract_every_n = %s", filename, self._skip_num) skip_list.append
bugfix: extract - stop progress bar from going over max value
_set_skip_list
0d23714875f81ddabdbe8f4e40bef6e5f29eeb19
faceswap
extract.py
13
25
https://github.com/deepfakes/faceswap.git
7
142
0
66
236
Python
{ "docstring": " Add the skip list to the image loader\n\n Checks against `extract_every_n` and the existence of alignments data (can exist if\n `skip_existing` or `skip_existing_faces` has been provided) and compiles a list of frame\n indices that should not be processed, providing these to :class:`lib.image.ImagesLoader`.\n ", "language": "en", "n_whitespaces": 71, "n_words": 42, "vocab_size": 36 }
def _set_skip_list(self) -> None: if self._skip_num == 1 and not self._alignments.data: logger.debug("No frames to be skipped") return skip_list = [] for idx, filename in enumerate(self._images.file_list): if idx % self._skip_num != 0: logger.trace("Adding image '%s' to skip list due to extract_every_n = %s", filename, self._skip_num) skip_list.append(idx) # Items may be in the alignments file if skip-existing[-faces] is selected elif os.path.basename(filename) in self._alignments.data: self._existing_count += 1 logger.trace("Removing image: '%s' due to previously existing", filename) skip_list.append(idx) if self._existing_count != 0: logger.info("Skipping %s frames due to skip_existing/skip_existing_faces.", self._existing_count) logger.debug("Adding skip list: %s", skip_list) self._images.add_skip_list(skip_list)
11,947
59,781
61
tests/conftest.py
20
12
def caplog(caplog): config = setup_logging() for name, logg
Update logging setup to support incremental configuration (#7569)
caplog
8ac2498a0203d3ccb9070d30d7b3a0c475afab92
prefect
conftest.py
12
7
https://github.com/PrefectHQ/prefect.git
3
54
0
19
94
Python
{ "docstring": "\n Overrides caplog to apply to all of our loggers that do not propagate and\n consequently would not be captured by caplog.\n ", "language": "en", "n_whitespaces": 31, "n_words": 21, "vocab_size": 19 }
def caplog(caplog): config = setup_logging() for name, logger_config in config["loggers"].items(): if not logger_config.get("propagate", True): logger = get_logger(name) logger.handlers.append(caplog.handler) yield caplog
20,685
101,266
413
tools/manual/faceviewer/viewport.py
57
34
def _show_mesh(self, mesh_ids, face_index, detected_face, top_left): state = "normal" if (self._tk_vars["selected_editor"].get() != "Mask" or self._optional_annotations["mesh"]) else "hidden" kwargs = dict(polygon=dict(fill="", width=2, outline=self._canvas.control_colors["Mesh"]), line=dict(fill=self._canvas.control_colors["Mesh"], width=2)) edi
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
_show_mesh
5e73437be47f2410439a3c6716de96354e6a0c94
faceswap
viewport.py
16
17
https://github.com/deepfakes/faceswap.git
6
212
0
49
340
Python
{ "docstring": " Display the mesh annotation for the given face, at the given location.\n\n Parameters\n ----------\n mesh_ids: dict\n Dictionary containing the `polygon` and `line` tkinter canvas identifiers that make up\n the mesh for the given face\n face_index: int\n The face index within the frame for the given face\n detected_face: :class:`~lib.align.DetectedFace`\n The detected face object that contains the landmarks for generating the mesh\n top_left: tuple\n The (x, y) top left co-ordinates of the mesh's bounding box\n ", "language": "en", "n_whitespaces": 178, "n_words": 73, "vocab_size": 49 }
def _show_mesh(self, mesh_ids, face_index, detected_face, top_left): state = "normal" if (self._tk_vars["selected_editor"].get() != "Mask" or self._optional_annotations["mesh"]) else "hidden" kwargs = dict(polygon=dict(fill="", width=2, outline=self._canvas.control_colors["Mesh"]), line=dict(fill=self._canvas.control_colors["Mesh"], width=2)) edited = (self._tk_vars["edited"].get() and self._tk_vars["selected_editor"].get() not in ("Mask", "View")) landmarks = self._viewport.get_landmarks(self.frame_index, face_index, detected_face, top_left, edited) for key, kwarg in kwargs.items(): for idx, mesh_id in enumerate(mesh_ids[key]): self._canvas.coords(mesh_id, *landmarks[key][idx].flatten()) self._canvas.itemconfig(mesh_id, state=state, **kwarg) self._canvas.addtag_withtag(f"active_mesh_{key}", mesh_id)
33,279
144,666
1,143
python/ray/serve/deployment_state.py
248
30
def _get_curr_status(self) -> Tuple[DeploymentStatusInfo, bool]: # TODO(edoakes): we could make this more efficient in steady-state by # having a "healthy" flag that gets flipped if an update or replica # failure happens. target_version = self._target_version target_replica_count = self._target_replicas all_running_replica_cnt = self._replicas.count(states=[ReplicaState.RUNNING]) running_at_target_version_replica_cnt = self._replicas.count( states=[ReplicaState.RUNNING], version=target_version ) failed_to_start_count = self._replica_constructor_retry_counter failed_to_start_threshold = min( MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_CO
[serve] Introduce DeploymentStatus, poll for statuses instead of using async goals (#22121)
_get_curr_status
48adb6f7bb335b28fb0fb0d1190bd6c5dfc8ddfa
ray
deployment_state.py
18
66
https://github.com/ray-project/ray.git
8
216
0
151
356
Python
{ "docstring": "Get the current deployment status.\n\n Checks the difference between the target vs. running replica count for\n the target version.\n\n TODO(edoakes): we should report the status as FAILED if replicas are\n repeatedly failing health checks. Need a reasonable heuristic here.\n\n Returns:\n (DeploymentStatusInfo, was_deleted)\n ", "language": "en", "n_whitespaces": 95, "n_words": 42, "vocab_size": 37 }
def _get_curr_status(self) -> Tuple[DeploymentStatusInfo, bool]: # TODO(edoakes): we could make this more efficient in steady-state by # having a "healthy" flag that gets flipped if an update or replica # failure happens. target_version = self._target_version target_replica_count = self._target_replicas all_running_replica_cnt = self._replicas.count(states=[ReplicaState.RUNNING]) running_at_target_version_replica_cnt = self._replicas.count( states=[ReplicaState.RUNNING], version=target_version ) failed_to_start_count = self._replica_constructor_retry_counter failed_to_start_threshold = min( MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_COUNT, target_replica_count * 3 ) # Got to make a call to complete current deploy() goal after # start failure threshold reached, while we might still have # pending replicas in current goal. if ( failed_to_start_count >= failed_to_start_threshold and failed_to_start_threshold != 0 ): if running_at_target_version_replica_cnt > 0: # At least one RUNNING replica at target state, partial # success; We can stop tracking constructor failures and # leave it to the controller to fully scale to target # number of replicas and only return as completed once # reached target replica count self._replica_constructor_retry_counter = -1 else: return ( DeploymentStatusInfo( status=DeploymentStatus.FAILED, message=( "The Deployment constructor failed " f"{failed_to_start_count} times in a row. See " "logs for details." ), ), False, ) # If we have pending ops, the current goal is *not* ready. if ( self._replicas.count( states=[ ReplicaState.STARTING, ReplicaState.UPDATING, ReplicaState.RECOVERING, ReplicaState.STOPPING, ] ) == 0 ): # Check for deleting. if target_replica_count == 0 and all_running_replica_cnt == 0: return DeploymentStatusInfo(status=DeploymentStatus.UPDATING), True # Check for a non-zero number of deployments. elif target_replica_count == running_at_target_version_replica_cnt: return DeploymentStatusInfo(status=DeploymentStatus.RUNNING), False return ( DeploymentStatusInfo( status=DeploymentStatus.UPDATING, message=( f"Running replicas of target version: " f"{running_at_target_version_replica_cnt}, target " "replicas: {target_replica_count}" ), ), False, )
118,325
322,996
63
examples/model_interpretation/task/transformer.py
12
11
def generate_square_subsequent_mask(self, length): return paddle.tensor.triu( (paddle.ones( (lengt
Add NLP model interpretation (#1752) * upload NLP interpretation * fix problems and relocate project * remove abandoned picture * remove abandoned picture * fix dead link in README * fix dead link in README * fix code style problems * fix CR round 1 * remove .gitkeep files * fix code style * fix file encoding problem * fix code style * delete duplicated files due to directory rebuild * fix CR round 2 * fix code style * fix ernie tokenizer * fix code style * fix problem from CR round 1 * fix bugs * fix README * remove duplicated files * deal with diff of old and new tokenizer results * fix CR round 4 * fix code style * add missing dependence * fix broken import path * move some data file to cloud * MRC upper case to lower case Co-authored-by: Zeyu Chen <[email protected]> Co-authored-by: binlinquge <xxx> Co-authored-by: Guo Sheng <[email protected]>
generate_square_subsequent_mask
93cae49c0c572b5c1ac972759140fbe924b0374d
PaddleNLP
transformer.py
14
5
https://github.com/PaddlePaddle/PaddleNLP.git
1
43
0
12
67
Python
{ "docstring": "\n Generate a square mask for the sequence. The mask ensures that the\n predictions for position i can depend only on the known outputs at\n positions less than i.\n\n Parameters:\n length (int|Tensor): The length of sequence.\n\n Returns:\n Tensor: Generated square mask according to the given length.\n\n Examples:\n .. code-block:: python\n\n import paddle\n from paddle.nn.layer.transformer import Transformer\n length = 5\n d_model, n_head, dim_feedforward = 8, 4, 64\n transformer_paddle = Transformer(\n d_model, n_head, dim_feedforward=dim_feedforward)\n mask = transformer_paddle.generate_square_subsequent_mask(length)\n print(mask)\n\n # [[ 0. -inf -inf -inf -inf]\n # [ 0. 0. -inf -inf -inf]\n # [ 0. 0. 0. -inf -inf]\n # [ 0. 0. 0. 0. -inf]\n # [ 0. 0. 0. 0. 0.]]\n\n ", "language": "en", "n_whitespaces": 417, "n_words": 110, "vocab_size": 64 }
def generate_square_subsequent_mask(self, length): return paddle.tensor.triu( (paddle.ones( (length, length), dtype=paddle.get_default_dtype()) * -np.inf), 1)
12,265
60,728
118
.venv/lib/python3.8/site-packages/pip/_internal/index/package_finder.py
37
15
def find_requirement(self, req, upgrade): # type: (InstallRequirement, bool) -> Optional[InstallationCandidate] hashes = req.hashes(trust_internet=False) best_candidate_result = self.find_best_candidate( req.name, specifier=req.specifier, hashes=hashes, ) best_candidate = best_candidate_result.best_candidate
upd; format
find_requirement
f638f5d0e6c8ebed0e69a6584bc7f003ec646580
transferlearning
package_finder.py
12
55
https://github.com/jindongwang/transferlearning.git
11
214
0
30
106
Python
{ "docstring": "Try to find a Link matching req\n\n Expects req, an InstallRequirement and upgrade, a boolean\n Returns a InstallationCandidate if found,\n Raises DistributionNotFound or BestVersionAlreadyInstalled otherwise\n ", "language": "en", "n_whitespaces": 53, "n_words": 25, "vocab_size": 23 }
def find_requirement(self, req, upgrade): # type: (InstallRequirement, bool) -> Optional[InstallationCandidate] hashes = req.hashes(trust_internet=False) best_candidate_result = self.find_best_candidate( req.name, specifier=req.specifier, hashes=hashes, ) best_candidate = best_candidate_result.best_candidate installed_version = None # type: Optional[_BaseVersion] if req.satisfied_by is not None: installed_version = parse_version(req.satisfied_by.version)
3,317
20,307
54
pipenv/patched/notpip/_vendor/pygments/formatters/html.py
11
8
def wrap(self, source, outfile): if s
check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4
wrap
f3166e673fe8d40277b804d35d77dcdb760fc3b3
pipenv
html.py
13
5
https://github.com/pypa/pipenv.git
2
46
0
10
75
Python
{ "docstring": "\n Wrap the ``source``, which is a generator yielding\n individual lines, in custom generators. See docstring\n for `format`. Can be overridden.\n ", "language": "en", "n_whitespaces": 49, "n_words": 20, "vocab_size": 20 }
def wrap(self, source, outfile): if self.wrapcode: return self._wrap_div(self._wrap_pre(self._wrap_code(source))) else: return self._wrap_div(self._wrap_pre(source))
@frappe.whitelist()
14,035
65,846
6
erpnext/education/api.py
14
8
def get_assessment_criteria(course): return frappe.get_all( "Course Assessment Criteria", fields=["assessment_criteria", "weightage"], filt
style: format code with black
get_assessment_criteria
494bd9ef78313436f0424b918f200dab8fc7c20b
erpnext
api.py
11
7
https://github.com/frappe/erpnext.git
1
34
1
14
72
Python
{ "docstring": "Returns Assessmemt Criteria and their Weightage from Course Master.\n\n\t:param Course: Course\n\t", "language": "en", "n_whitespaces": 10, "n_words": 12, "vocab_size": 11 }
def get_assessment_criteria(course): return frappe.get_all( "Course Assessment Criteria", fields=["assessment_criteria", "weightage"], filters={"parent": course}, order_by="idx", ) @frappe.whitelist()
54,316
216,002
379
salt/modules/mount.py
93
33
def rm_filesystems(name, device, config="/etc/filesystems"): modified = False view_lines = [] if "AIX" not in __grains__["kernel"]: return modified criteria = _FileSystemsEntry(name=name, dev=device) try: fsys_filedict = _filesystems(config, False) for fsys_view in fsys_filedict.items(): try: if criteria.match(fsys_view): modified = True else: view_lines.append(fsys_view) except _FileSystemsEntry.ParseError: view_lines.append(fsys_view) except OSError as exc: raise CommandExecutionError("Couldn't read from {}: {}".format(config, exc)) if modified: try: with salt.utils.files.fopen(config, "wb") as ofile: for fsys_view in view_lines: entry = fsys_view[1] list_strgs = _FileSystemsEntry.dict_to_list_lines(entry) ofile.writelines(salt.utils.data.encode(list_strgs)) except OSError as exc: raise CommandExecutionError("Couldn't write to {}: {}".format(config, exc)) except Exception as exc: raise CommandExecutionError("rm_filesystems error exception {exc}") return modified
Convert Py 2'isms to Python 3, and add tests for set_filesystems on AIX
rm_filesystems
9354c15e0818715d055242d14b1308643a6918d7
salt
mount.py
19
30
https://github.com/saltstack/salt.git
10
194
0
59
327
Python
{ "docstring": "\n .. versionadded:: 2018.3.3\n\n Remove the mount point from the filesystems\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' mount.rm_filesystems /mnt/foo /dev/sdg\n ", "language": "en", "n_whitespaces": 43, "n_words": 20, "vocab_size": 18 }
def rm_filesystems(name, device, config="/etc/filesystems"): modified = False view_lines = [] if "AIX" not in __grains__["kernel"]: return modified criteria = _FileSystemsEntry(name=name, dev=device) try: fsys_filedict = _filesystems(config, False) for fsys_view in fsys_filedict.items(): try: if criteria.match(fsys_view): modified = True else: view_lines.append(fsys_view) except _FileSystemsEntry.ParseError: view_lines.append(fsys_view) except OSError as exc: raise CommandExecutionError("Couldn't read from {}: {}".format(config, exc)) if modified: try: with salt.utils.files.fopen(config, "wb") as ofile: for fsys_view in view_lines: entry = fsys_view[1] list_strgs = _FileSystemsEntry.dict_to_list_lines(entry) ofile.writelines(salt.utils.data.encode(list_strgs)) except OSError as exc: raise CommandExecutionError("Couldn't write to {}: {}".format(config, exc)) except Exception as exc: raise CommandExecutionError("rm_filesystems error exception {exc}") return modified
42,004
176,622
87
networkx/generators/classic.py
29
14
def complete_graph(n, create_using=None): _, nodes = n G = empty_graph(nodes, create_using) if len(nodes) > 1: if G.is_directed(): edges = itertools.permutations(nodes, 2) else: edges = itertools.combinations(nodes, 2) G.add_edges_from(edges) return G
Adjust the usage of nodes_or_number decorator (#5599) * recorrect typo in decorators.py * Update tests to show troubles in current code * fix troubles with usage of nodes_or_number * fix typo * remove nodes_or_number where that makes sense * Reinclude nodes_or_numbers and add some tests for nonstandard usage * fix typowq * hopefully final tweaks (no behavior changes * Update test_classic.py Co-authored-by: Jarrod Millman <[email protected]>
complete_graph
de1d00f20e0bc14f1cc911b3486e50225a8fa168
networkx
classic.py
13
10
https://github.com/networkx/networkx.git
3
68
0
22
110
Python
{ "docstring": "Return the complete graph `K_n` with n nodes.\n\n A complete graph on `n` nodes means that all pairs\n of distinct nodes have an edge connecting them.\n\n Parameters\n ----------\n n : int or iterable container of nodes\n If n is an integer, nodes are from range(n).\n If n is a container of nodes, those nodes appear in the graph.\n create_using : NetworkX graph constructor, optional (default=nx.Graph)\n Graph type to create. If graph instance, then cleared before populated.\n\n Examples\n --------\n >>> G = nx.complete_graph(9)\n >>> len(G)\n 9\n >>> G.size()\n 36\n >>> G = nx.complete_graph(range(11, 14))\n >>> list(G.nodes())\n [11, 12, 13]\n >>> G = nx.complete_graph(4, nx.DiGraph())\n >>> G.is_directed()\n True\n\n ", "language": "en", "n_whitespaces": 186, "n_words": 106, "vocab_size": 76 }
def complete_graph(n, create_using=None): _, nodes = n G = empty_graph(nodes, create_using) if len(nodes) > 1: if G.is_directed(): edges = itertools.permutations(nodes, 2) else: edges = itertools.combinations(nodes, 2) G.add_edges_from(edges) return G
76,967
261,735
89
sklearn/pipeline.py
29
16
def fit_predict(self, X, y=None, **fit_params): self._validate_params() fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) fit_params_last_step = fit_params_steps[self.steps[-1][0]] with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): y_pred = self.steps[-1][1].fit_predict(Xt, y, **fit_params_last_step) r
MAINT validate parameters of Pipeline (#25133)
fit_predict
754bd5245aa46b89a1d686a3326c2b853012ff3e
scikit-learn
pipeline.py
15
8
https://github.com/scikit-learn/scikit-learn.git
1
101
0
24
159
Python
{ "docstring": "Transform the data, and apply `fit_predict` with the final estimator.\n\n Call `fit_transform` of each transformer in the pipeline. The\n transformed data are finally passed to the final estimator that calls\n `fit_predict` method. Only valid if the final estimator implements\n `fit_predict`.\n\n Parameters\n ----------\n X : iterable\n Training data. Must fulfill input requirements of first step of\n the pipeline.\n\n y : iterable, default=None\n Training targets. Must fulfill label requirements for all steps\n of the pipeline.\n\n **fit_params : dict of string -> object\n Parameters passed to the ``fit`` method of each step, where\n each parameter name is prefixed such that parameter ``p`` for step\n ``s`` has key ``s__p``.\n\n Returns\n -------\n y_pred : ndarray\n Result of calling `fit_predict` on the final estimator.\n ", "language": "en", "n_whitespaces": 297, "n_words": 118, "vocab_size": 79 }
def fit_predict(self, X, y=None, **fit_params): self._validate_params() fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) fit_params_last_step = fit_params_steps[self.steps[-1][0]] with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): y_pred = self.steps[-1][1].fit_predict(Xt, y, **fit_params_last_step) return y_pred
@pytest.mark.issue(5918) @pytest.mark.parametrize("entity_ruler_factory", ENTITY_RULERS)
24,386
111,358
205
spacy/tests/pipeline/test_entity_ruler.py
94
27
def test_issue4849(entity_ruler_factory): nlp = English() patterns = [ {"label": "PERSON", "pattern": "joe biden", "id": "joe-biden"}, {"label": "PERSON", "pattern": "bernie sanders", "id": "bernie-sanders"}, ] ruler = nlp.add_pipe( entity_ruler_factory, name="entity_ruler", config={"phrase_matcher_attr": "LOWER"}, ) ruler.add_patterns(patterns) text = # USING 1 PROCESS count_ents = 0 for doc in nlp.pipe([text], n_process=1): count_ents += len([ent for ent in doc.ents if ent.ent_id > 0]) asser
Add SpanRuler component (#9880) * Add SpanRuler component Add a `SpanRuler` component similar to `EntityRuler` that saves a list of matched spans to `Doc.spans[spans_key]`. The matches from the token and phrase matchers are deduplicated and sorted before assignment but are not otherwise filtered. * Update spacy/pipeline/span_ruler.py Co-authored-by: Sofie Van Landeghem <[email protected]> * Fix cast * Add self.key property * Use number of patterns as length * Remove patterns kwarg from init * Update spacy/tests/pipeline/test_span_ruler.py Co-authored-by: Sofie Van Landeghem <[email protected]> * Add options for spans filter and setting to ents * Add `spans_filter` option as a registered function' * Make `spans_key` optional and if `None`, set to `doc.ents` instead of `doc.spans[spans_key]`. * Update and generalize tests * Add test for setting doc.ents, fix key property type * Fix typing * Allow independent doc.spans and doc.ents * If `spans_key` is set, set `doc.spans` with `spans_filter`. * If `annotate_ents` is set, set `doc.ents` with `ents_fitler`. * Use `util.filter_spans` by default as `ents_filter`. * Use a custom warning if the filter does not work for `doc.ents`. * Enable use of SpanC.id in Span * Support id in SpanRuler as Span.id * Update types * `id` can only be provided as string (already by `PatternType` definition) * Update all uses of Span.id/ent_id in Doc * Rename Span id kwarg to span_id * Update types and docs * Add ents filter to mimic EntityRuler overwrite_ents * Refactor `ents_filter` to take `entities, spans` args for more filtering options * Give registered filters more descriptive names * Allow registered `filter_spans` filter (`spacy.first_longest_spans_filter.v1`) to take any number of `Iterable[Span]` objects as args so it can be used for spans filter or ents filter * Implement future entity ruler as span ruler Implement a compatible `entity_ruler` as `future_entity_ruler` using `SpanRuler` as the underlying component: * Add `sort_key` and `sort_reverse` to allow the sorting behavior to be customized. (Necessary for the same sorting/filtering as in `EntityRuler`.) * Implement `overwrite_overlapping_ents_filter` and `preserve_existing_ents_filter` to support `EntityRuler.overwrite_ents` settings. * Add `remove_by_id` to support `EntityRuler.remove` functionality. * Refactor `entity_ruler` tests to parametrize all tests to test both `entity_ruler` and `future_entity_ruler` * Implement `SpanRuler.token_patterns` and `SpanRuler.phrase_patterns` properties. Additional changes: * Move all config settings to top-level attributes to avoid duplicating settings in the config vs. `span_ruler/cfg`. (Also avoids a lot of casting.) * Format * Fix filter make method name * Refactor to use same error for removing by label or ID * Also provide existing spans to spans filter * Support ids property * Remove token_patterns and phrase_patterns * Update docstrings * Add span ruler docs * Fix types * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem <[email protected]> * Move sorting into filters * Check for all tokens in seen tokens in entity ruler filters * Remove registered sort key * Set Token.ent_id in a backwards-compatible way in Doc.set_ents * Remove sort options from API docs * Update docstrings * Rename entity ruler filters * Fix and parameterize scoring * Add id to Span API docs * Fix typo in API docs * Include explicit labeled=True for scorer Co-authored-by: Sofie Van Landeghem <[email protected]>
test_issue4849
a322d6d5f2f85c2da6cded4fcd6143d41b5a9e96
spaCy
test_entity_ruler.py
16
25
https://github.com/explosion/spaCy.git
8
166
1
56
313
Python
{ "docstring": "\n The left is starting to take aim at Democratic front-runner Joe Biden.\n Sen. Bernie Sanders joined in her criticism: \"There is no 'middle ground' when it comes to climate policy.\"\n ", "language": "en", "n_whitespaces": 40, "n_words": 30, "vocab_size": 28 }
def test_issue4849(entity_ruler_factory): nlp = English() patterns = [ {"label": "PERSON", "pattern": "joe biden", "id": "joe-biden"}, {"label": "PERSON", "pattern": "bernie sanders", "id": "bernie-sanders"}, ] ruler = nlp.add_pipe( entity_ruler_factory, name="entity_ruler", config={"phrase_matcher_attr": "LOWER"}, ) ruler.add_patterns(patterns) text = # USING 1 PROCESS count_ents = 0 for doc in nlp.pipe([text], n_process=1): count_ents += len([ent for ent in doc.ents if ent.ent_id > 0]) assert count_ents == 2 # USING 2 PROCESSES if isinstance(get_current_ops, NumpyOps): count_ents = 0 for doc in nlp.pipe([text], n_process=2): count_ents += len([ent for ent in doc.ents if ent.ent_id > 0]) assert count_ents == 2 @pytest.mark.issue(5918) @pytest.mark.parametrize("entity_ruler_factory", ENTITY_RULERS)
77,757
264,576
93
netbox/netbox/api/viewsets/__init__.py
17
14
def get_serializer_context(self): context = super().get_serializer_context() if hasattr(self.queryset.model, 'custom_fields'): content_type = ContentType.objects.get_for_model(self.queryset.model) context.update({ 'custom_fields': content_type.custom_fields.all(),
Move CustomFieldModelViewSet functionality into NetBoxModelViewSet
get_serializer_context
bbdeae0ed9bcc06fb96ffa2970272e1a3447448c
netbox
__init__.py
14
8
https://github.com/netbox-community/netbox.git
2
60
0
15
103
Python
{ "docstring": "\n For models which support custom fields, populate the `custom_fields` context.\n ", "language": "en", "n_whitespaces": 25, "n_words": 10, "vocab_size": 10 }
def get_serializer_context(self): context = super().get_serializer_context() if hasattr(self.queryset.model, 'custom_fields'): content_type = ContentType.objects.get_for_model(self.queryset.model) context.update({ 'custom_fields': content_type.custom_fields.all(), }) return context
56,885
223,398
87
python3.10.4/Lib/distutils/util.py
36
9
def execute (func, args, msg=None, verbose=0, dry_run=0): if msg is None: msg = "%s%r" % (func.__name__, args) if msg[-2:] == ',)': # correct for singleton tuple msg = msg[0:-2] + ')' log.info(msg) if not dry_run:
add python 3.10.4 for windows
execute
8198943edd73a363c266633e1aa5b2a9e9c9f526
XX-Net
util.py
14
8
https://github.com/XX-net/XX-Net.git
4
72
0
31
120
Python
{ "docstring": "Perform some action that affects the outside world (eg. by\n writing to the filesystem). Such actions are special because they\n are disabled by the 'dry_run' flag. This method takes care of all\n that bureaucracy for you; all you have to do is supply the\n function to call and an argument tuple for it (to embody the\n \"external action\" being performed), and an optional message to\n print.\n ", "language": "en", "n_whitespaces": 90, "n_words": 66, "vocab_size": 52 }
def execute (func, args, msg=None, verbose=0, dry_run=0): if msg is None: msg = "%s%r" % (func.__name__, args) if msg[-2:] == ',)': # correct for singleton tuple msg = msg[0:-2] + ')' log.info(msg) if not dry_run: func(*args)
80,946
272,035
462
keras/feature_column/dense_features.py
74
28
def call(self, features, cols_to_output_tensors=None, training=None): if training is None: training = backend.learning_phase() if not isinstance(features, dict): raise ValueError( "We expected a dictionary here. Instead we got: ", features ) transformation_cache = ( tf.__internal__.feature_column.FeatureTransformationCache(features) ) output_tens
Reformatting the codebase with black. PiperOrigin-RevId: 450093126
call
84afc5193d38057e2e2badf9c889ea87d80d8fbf
keras
dense_features.py
16
28
https://github.com/keras-team/keras.git
6
148
0
55
233
Python
{ "docstring": "Returns a dense tensor corresponding to the `feature_columns`.\n\n Example usage:\n\n >>> t1 = tf.feature_column.embedding_column(\n ... tf.feature_column.categorical_column_with_hash_bucket(\"t1\", 2),\n ... dimension=8)\n >>> t2 = tf.feature_column.numeric_column('t2')\n >>> feature_layer = tf.compat.v1.keras.layers.DenseFeatures([t1, t2])\n >>> features = {\"t1\": tf.constant([\"a\", \"b\"]), \"t2\": tf.constant([1, 2])}\n >>> dense_tensor = feature_layer(features, training=True)\n\n Args:\n features: A mapping from key to tensors. `FeatureColumn`s look up via\n these keys. For example `numeric_column('price')` will look at 'price'\n key in this dict. Values can be a `SparseTensor` or a `Tensor` depends\n on corresponding `FeatureColumn`.\n cols_to_output_tensors: If not `None`, this will be filled with a dict\n mapping feature columns to output tensors created.\n training: Python boolean or None, indicating whether to the layer is being\n run in training mode. This argument is passed to the call method of any\n `FeatureColumn` that takes a `training` argument. For example, if a\n `FeatureColumn` performed dropout, the column could expose a `training`\n argument to control whether the dropout should be applied. If `None`,\n defaults to `tf.keras.backend.learning_phase()`.\n\n\n Returns:\n A `Tensor` which represents input layer of a model. Its shape\n is (batch_size, first_layer_dimension) and its dtype is `float32`.\n first_layer_dimension is determined based on given `feature_columns`.\n\n Raises:\n ValueError: If features are not a dictionary.\n ", "language": "en", "n_whitespaces": 443, "n_words": 191, "vocab_size": 134 }
def call(self, features, cols_to_output_tensors=None, training=None): if training is None: training = backend.learning_phase() if not isinstance(features, dict): raise ValueError( "We expected a dictionary here. Instead we got: ", features ) transformation_cache = ( tf.__internal__.feature_column.FeatureTransformationCache(features) ) output_tensors = [] for column in self._feature_columns: with backend.name_scope(column.name): try: tensor = column.get_dense_tensor( transformation_cache, self._state_manager, training=training, ) except TypeError: tensor = column.get_dense_tensor( transformation_cache, self._state_manager ) processed_tensors = self._process_dense_tensor(column, tensor) if cols_to_output_tensors is not None: cols_to_output_tensors[column] = processed_tensors output_tensors.append(processed_tensors) return self._verify_and_concat_tensors(output_tensors)
40,053
167,600
49
pandas/compat/pickle_compat.py
16
6
def patch_pickle() -> Iterator[None]: orig_loads = pkl.loads try: setattr(pkl, "loads", loads) yield finally: setattr(pkl, "loads", orig_loads)
TYP: misc return type annotations (#47558)
patch_pickle
f538568afc2c76c2d738d32e3544cf9fe6742960
pandas
pickle_compat.py
11
10
https://github.com/pandas-dev/pandas.git
2
36
0
14
64
Python
{ "docstring": "\n Temporarily patch pickle to use our unpickler.\n ", "language": "en", "n_whitespaces": 14, "n_words": 7, "vocab_size": 7 }
def patch_pickle() -> Iterator[None]: orig_loads = pkl.loads try: setattr(pkl, "loads", loads) yield finally: setattr(pkl, "loads", orig_loads)
8,995
46,791
100
dev/breeze/src/airflow_breeze/utils/run_utils.py
35
9
def get_filesystem_type(filepath): # We import it locally so that click autocomplete wor
Prepare Breeze2 for prime time :) (#22713) This is a review and clean-up for all the parameters and commands for Breeze2 in order to prepare it for being used by the contribugors. There are various small fixes here and there, removal of duplicated code, refactoring and moving code around as well as cleanup and review all the parameters used for all implemented commands. The parameters, default values and their behaviours were updated to match "new" life of Breeze rather than old one. Some improvements are made to the autocomplete and click help messages printed. Full list of choices is always displayed, parameters are groups according to their target audience, and they were sorted according to importance and frequency of use. Various messages have been colourised according to their meaning - warnings as yellow, errors as red and informational messages as bright_blue. The `dry-run` option has been added to just show what would have been run without actually running some potentially "write" commands (read commands are still executed) so that you can easily verify and manually copy and execute the commands with option to modify them before. The `dry_run` and `verbose` options are now used for all commands. The "main" command now runs "shell" by default similarly as the original Breeze. All "shortcut" parameters have been standardized - i.e common options (verbose/dry run/help) have one and all common flags that are likely to be used often have an assigned shortcute. The "stop" and "cleanup" command have been added as they are necessary for average user to complete the regular usage cycle. Documentation for all the important methods have been updated.
get_filesystem_type
4ffd4f09532fceb67675fce4c1f5cd383eff992e
airflow
run_utils.py
11
10
https://github.com/apache/airflow.git
4
49
0
28
87
Python
{ "docstring": "\n Determine the type of filesystem used - we might want to use different parameters if tmpfs is used.\n :param filepath: path to check\n :return: type of filesystem\n ", "language": "en", "n_whitespaces": 40, "n_words": 27, "vocab_size": 23 }
def get_filesystem_type(filepath): # We import it locally so that click autocomplete works import psutil root_type = "unknown" for part in psutil.disk_partitions(): if part.mountpoint == '/': root_type = part.fstype continue if filepath.startswith(part.mountpoint): return part.fstype return root_type
41,928
176,480
447
networkx/algorithms/similarity.py
240
56
def panther_similarity(G, source, k=5, path_length=5, c=0.5, delta=0.1, eps=None): r import numpy as np num_nodes = G.number_of_nodes() if num_nodes < k: warnings.warn( f"Number of nodes is {num_nodes}, but requested k is {k}. " "Setting k to number of nodes." ) k = num_nodes # According to [1], they empirically determined # a good value for ``eps`` to be sqrt( 1 / |E| ) if eps is None: eps = np.sqrt(1.0 / G.number_of_edges()) inv_node_map = {name: index for index, name in enumerate(G.nodes)} node_map = np.array(G) # Calculate the sample size ``R`` for how many paths # to randomly generate t_choose_2 = math.comb(path_length, 2) sample_size = int((c / eps**2) * (np.log2(t_choose_2) + 1 + np.log(1 / delta))) index_map = {} _ = list( generate_random_paths( G, sample_size, path_length=path_length, index_map=index_map ) ) S = np.zeros(num_nodes) inv_sample_size = 1 / sample_size source_paths = set(index_map[source]) # Calculate the path similarities # between ``source`` (v) and ``node`` (v_j) # using our inverted index mapping of # vertices to paths for node, paths in index_map.items(): # Only consider paths where both # ``node`` and ``source`` are present common_paths = source_paths.intersection(paths) S[inv_node_map[node]] = len(common_paths) * inv_sample_size # Retrieve top ``k`` similar # Note: the below performed anywhere from 4-10x faster # (depending on input sizes) vs the equivalent ``np.argsort(S)[::-1]`` top_k_unsorted = np.argpartition(S, -k)[-k:] top_k_sorte
Update black (#5438) * CI: sync up black dev requirements version with precommit * Run black Co-authored-by: Jarrod Millman <[email protected]>
panther_similarity
f6755ffa00211b523c6c0bec5398bc6c3c43c8b1
networkx
similarity.py
14
79
https://github.com/networkx/networkx.git
5
300
0
164
479
Python
{ "docstring": "Returns the Panther similarity of nodes in the graph `G` to node ``v``.\n\n Panther is a similarity metric that says \"two objects are considered\n to be similar if they frequently appear on the same paths.\" [1]_.\n\n Parameters\n ----------\n G : NetworkX graph\n A NetworkX graph\n source : node\n Source node for which to find the top `k` similar other nodes\n k : int (default = 5)\n The number of most similar nodes to return\n path_length : int (default = 5)\n How long the randomly generated paths should be (``T`` in [1]_)\n c : float (default = 0.5)\n A universal positive constant used to scale the number\n of sample random paths to generate.\n delta : float (default = 0.1)\n The probability that the similarity $S$ is not an epsilon-approximation to (R, phi),\n where $R$ is the number of random paths and $\\phi$ is the probability\n that an element sampled from a set $A \\subseteq D$, where $D$ is the domain.\n eps : float or None (default = None)\n The error bound. Per [1]_, a good value is ``sqrt(1/|E|)``. Therefore,\n if no value is provided, the recommended computed value will be used.\n\n Returns\n -------\n similarity : dictionary\n Dictionary of nodes to similarity scores (as floats). Note:\n the self-similarity (i.e., ``v``) will not be included in\n the returned dictionary.\n\n Examples\n --------\n >>> G = nx.star_graph(10)\n >>> sim = nx.panther_similarity(G, 0)\n\n References\n ----------\n .. [1] Zhang, J., Tang, J., Ma, C., Tong, H., Jing, Y., & Li, J.\n Panther: Fast top-k similarity search on large networks.\n In Proceedings of the ACM SIGKDD International Conference\n on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 1445–1454).\n Association for Computing Machinery. https://doi.org/10.1145/2783258.2783267.\n ", "language": "en", "n_whitespaces": 479, "n_words": 275, "vocab_size": 173 }
def panther_similarity(G, source, k=5, path_length=5, c=0.5, delta=0.1, eps=None): r import numpy as np num_nodes = G.number_of_nodes() if num_nodes < k: warnings.warn( f"Number of nodes is {num_nodes}, but requested k is {k}. " "Setting k to number of nodes." ) k = num_nodes # According to [1], they empirically determined # a good value for ``eps`` to be sqrt( 1 / |E| ) if eps is None: eps = np.sqrt(1.0 / G.number_of_edges()) inv_node_map = {name: index for index, name in enumerate(G.nodes)} node_map = np.array(G) # Calculate the sample size ``R`` for how many paths # to randomly generate t_choose_2 = math.comb(path_length, 2) sample_size = int((c / eps**2) * (np.log2(t_choose_2) + 1 + np.log(1 / delta))) index_map = {} _ = list( generate_random_paths( G, sample_size, path_length=path_length, index_map=index_map ) ) S = np.zeros(num_nodes) inv_sample_size = 1 / sample_size source_paths = set(index_map[source]) # Calculate the path similarities # between ``source`` (v) and ``node`` (v_j) # using our inverted index mapping of # vertices to paths for node, paths in index_map.items(): # Only consider paths where both # ``node`` and ``source`` are present common_paths = source_paths.intersection(paths) S[inv_node_map[node]] = len(common_paths) * inv_sample_size # Retrieve top ``k`` similar # Note: the below performed anywhere from 4-10x faster # (depending on input sizes) vs the equivalent ``np.argsort(S)[::-1]`` top_k_unsorted = np.argpartition(S, -k)[-k:] top_k_sorted = top_k_unsorted[np.argsort(S[top_k_unsorted])][::-1] # Add back the similarity scores top_k_sorted_names = map(lambda n: node_map[n], top_k_sorted) top_k_with_val = dict(zip(top_k_sorted_names, S[top_k_sorted])) # Remove the self-similarity top_k_with_val.pop(source, None) return top_k_with_val
77,103
262,017
72
TTS/tts/utils/text/phonemizers/base.py
22
10
def _phonemize_preprocess(self, text) -> Tuple[List[str], List]: text = text.strip() if self._keep_puncs: # a tuple (text, punctuation marks) return self._punctuator.strip_to_restore(text) return [self._punctuator.strip(text)], []
Fix BasePhonemizer
_phonemize_preprocess
ff7c3858389ba250f761d76592cb060ac6be05c0
TTS
base.py
10
12
https://github.com/coqui-ai/TTS.git
2
53
0
21
85
Python
{ "docstring": "Preprocess the text before phonemization\n\n 1. remove spaces\n 2. remove punctuation\n\n Override this if you need a different behaviour\n ", "language": "en", "n_whitespaces": 47, "n_words": 19, "vocab_size": 18 }
def _phonemize_preprocess(self, text) -> Tuple[List[str], List]: text = text.strip() if self._keep_puncs: # a tuple (text, punctuation marks) return self._punctuator.strip_to_restore(text) return [self._punctuator.strip(text)], []
24,448
111,596
21
spacy/cli/_util.py
11
5
def ensure_pathy(path): from pathy import Pathy # noqa: F811 return Pathy.fluid(path)
Support local filesystem remotes for projects (#11762) * Support local filesystem remotes for projects * Fix support for local filesystem remotes for projects * Use `FluidPath` instead of `Pathy` to support both filesystem and remote paths * Create missing parent directories if required for local filesystem * Add a more general `_file_exists` method to support both `Pathy`, `Path`, and `smart_open`-compatible URLs * Add explicit `smart_open` dependency starting with support for `compression` flag * Update `pathy` dependency to exclude older versions that aren't compatible with required `smart_open` version * Update docs to refer to `Pathy` instead of `smart_open` for project remotes (technically you can still push to any `smart_open`-compatible path but you can't pull from them) * Add tests for local filesystem remotes * Update pathy for general BlobStat sorting * Add import * Remove _file_exists since only Pathy remotes are supported * Format CLI docs * Clean up merge
ensure_pathy
1ebe7db07c8dbb1a55dafb09131b1d08242b79c5
spaCy
_util.py
7
3
https://github.com/explosion/spaCy.git
1
17
0
11
32
Python
{ "docstring": "Temporary helper to prevent importing Pathy globally (which can cause\n slow and annoying Google Cloud warning).", "language": "en", "n_whitespaces": 18, "n_words": 16, "vocab_size": 16 }
def ensure_pathy(path): from pathy import Pathy # noqa: F811 return Pathy.fluid(path)
50,400
203,480
147
django/contrib/admin/sites.py
32
13
def index(self, request, extra_context=None): app_list = self.get_app_list(request) context = { **self.each_context(request), "title": self.index_title, "subtitle": None, "app_list": app_list, **(extra_context or {}), }
Refs #33476 -- Reformatted code with Black.
index
9c19aff7c7561e3a82978a272ecdaad40dda5c00
django
sites.py
11
13
https://github.com/django/django.git
3
74
0
27
119
Python
{ "docstring": "\n Display the main admin index page, which lists all of the installed\n apps that have been registered in this site.\n ", "language": "en", "n_whitespaces": 42, "n_words": 20, "vocab_size": 19 }
def index(self, request, extra_context=None): app_list = self.get_app_list(request) context = { **self.each_context(request), "title": self.index_title, "subtitle": None, "app_list": app_list, **(extra_context or {}), } request.current_app = self.name return TemplateResponse( request, self.index_template or "admin/index.html", context )
72,022
247,955
858
docker/start.py
279
39
def generate_config_from_template(config_dir, config_path, environ, ownership): for v in ("SYNAPSE_SERVER_NAME", "SYNAPSE_REPORT_STATS"): if v not in environ: error( "Environment variable '%s' is mandatory when generating a config file." % (v,) ) # populate some params from data files (if they exist, else create new ones) environ = environ.copy() secrets = { "registration": "SYNAPSE_REGISTRATION_SHARED_SECRET", "macaroon": "SYNAPSE_MACAROON_SECRET_KEY", } for name, secret in secrets.items(): if secret not in environ: filename = "/data/%s.%s.key" % (environ["SYNAPSE_SERVER_NAME"], name) # if the file already exists, load in the existing value; otherwise, # generate a new secret and write
Poetry: use locked environment in Docker images (#12385)
generate_config_from_template
3a7e97c7ade17a47517aadc0e9e305a1894119ac
synapse
start.py
19
63
https://github.com/matrix-org/synapse.git
12
375
0
174
674
Python
{ "docstring": "Generate a homeserver.yaml from environment variables\n\n Args:\n config_dir (str): where to put generated config files\n config_path (str): where to put the main config file\n environ (dict): environment dictionary\n ownership (str|None): \"<user>:<group>\" string which will be used to set\n ownership of the generated configs. If None, ownership will not change.\n ", "language": "en", "n_whitespaces": 94, "n_words": 49, "vocab_size": 37 }
def generate_config_from_template(config_dir, config_path, environ, ownership): for v in ("SYNAPSE_SERVER_NAME", "SYNAPSE_REPORT_STATS"): if v not in environ: error( "Environment variable '%s' is mandatory when generating a config file." % (v,) ) # populate some params from data files (if they exist, else create new ones) environ = environ.copy() secrets = { "registration": "SYNAPSE_REGISTRATION_SHARED_SECRET", "macaroon": "SYNAPSE_MACAROON_SECRET_KEY", } for name, secret in secrets.items(): if secret not in environ: filename = "/data/%s.%s.key" % (environ["SYNAPSE_SERVER_NAME"], name) # if the file already exists, load in the existing value; otherwise, # generate a new secret and write it to a file if os.path.exists(filename): log("Reading %s from %s" % (secret, filename)) with open(filename) as handle: value = handle.read() else: log("Generating a random secret for {}".format(secret)) value = codecs.encode(os.urandom(32), "hex").decode() with open(filename, "w") as handle: handle.write(value) environ[secret] = value environ["SYNAPSE_APPSERVICES"] = glob.glob("/data/appservices/*.yaml") if not os.path.exists(config_dir): os.mkdir(config_dir) # Convert SYNAPSE_NO_TLS to boolean if exists if "SYNAPSE_NO_TLS" in environ: tlsanswerstring = str.lower(environ["SYNAPSE_NO_TLS"]) if tlsanswerstring in ("true", "on", "1", "yes"): environ["SYNAPSE_NO_TLS"] = True else: if tlsanswerstring in ("false", "off", "0", "no"): environ["SYNAPSE_NO_TLS"] = False else: error( 'Environment variable "SYNAPSE_NO_TLS" found but value "' + tlsanswerstring + '" unrecognized; exiting.' ) if "SYNAPSE_LOG_CONFIG" not in environ: environ["SYNAPSE_LOG_CONFIG"] = config_dir + "/log.config" log("Generating synapse config file " + config_path) convert("/conf/homeserver.yaml", config_path, environ) log_config_file = environ["SYNAPSE_LOG_CONFIG"] log("Generating log config file " + log_config_file) convert("/conf/log.config", log_config_file, environ) # Hopefully we already have a signing key, but generate one if not. args = [ sys.executable, "-m", "synapse.app.homeserver", "--config-path", config_path, # tell synapse to put generated keys in /data rather than /compiled "--keys-directory", config_dir, "--generate-keys", ] if ownership is not None: log(f"Setting ownership on /data to {ownership}") subprocess.check_output(["chown", "-R", ownership, "/data"]) args = ["gosu", ownership] + args subprocess.check_output(args)
27,173
122,389
40
jax/_src/api_util.py
29
16
def donation_vector(donate_argnums, args, kwargs) -> Tuple[bool, ...]: res: List[bool] = [] for i, arg in enumerate(args): donate = bool(i in donate_argnums) res.extend((donate,) * tree_structur
Annotate tree_util
donation_vector
b742b04380ebe2e824403e603924ca505173bf7a
jax
api_util.py
13
8
https://github.com/google/jax.git
2
81
0
26
126
Python
{ "docstring": "Returns a tuple with a boolean value for each leaf in args.", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 11 }
def donation_vector(donate_argnums, args, kwargs) -> Tuple[bool, ...]: res: List[bool] = [] for i, arg in enumerate(args): donate = bool(i in donate_argnums) res.extend((donate,) * tree_structure(arg).num_leaves) res.extend((False,) * tree_structure(kwargs).num_leaves) return tuple(res)
56,024
220,514
160
python3.10.4/Lib/asyncio/futures.py
44
15
def set_exception(self, exception): if self._state != _PENDING: raise exceptions.InvalidStateError(f'{self._state}: {self!r}') if isinstance(exception, type): exception = exception() if type(exception) is StopIteration: raise TypeError("StopIteration interacts badly with generators " "and cannot be raised into a Future") self._exception = exception
add python 3.10.4 for windows
set_exception
8198943edd73a363c266633e1aa5b2a9e9c9f526
XX-Net
futures.py
12
12
https://github.com/XX-net/XX-Net.git
4
70
0
36
132
Python
{ "docstring": "Mark the future done and set an exception.\n\n If the future is already done when this method is called, raises\n InvalidStateError.\n ", "language": "en", "n_whitespaces": 42, "n_words": 21, "vocab_size": 17 }
def set_exception(self, exception): if self._state != _PENDING: raise exceptions.InvalidStateError(f'{self._state}: {self!r}') if isinstance(exception, type): exception = exception() if type(exception) is StopIteration: raise TypeError("StopIteration interacts badly with generators " "and cannot be raised into a Future") self._exception = exception self._state = _FINISHED self.__schedule_callbacks() self.__log_traceback = True
50,606
204,002
369
django/contrib/gis/gdal/raster/band.py
98
24
def statistics(self, refresh=False, approximate=False): # Prepare array with arguments for capi function smin, smax
Refs #33476 -- Reformatted code with Black.
statistics
9c19aff7c7561e3a82978a272ecdaad40dda5c00
django
band.py
12
25
https://github.com/django/django.git
4
156
0
77
241
Python
{ "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 }
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
71,179
246,367
387
tests/storage/databases/test_state_store.py
116
22
def test_smaller_request_deduplicated(self) -> None: req1 = ensureDeferred( self.state_datastore._get_state_for_group_using_inflight_cache( 42, StateFilter.from_types((("test.type", None),)) ) ) self.pump(by=0.1) # This should have gone to the database self.assertEqual(len(self.get_state_group_calls), 1) self.assertFalse(req1.called) req2 = ensureDeferred( self.state_datastore._get_state_for_group_using_inflight_cache( 42, StateFilter.from_types((("test.type", "b"),)) ) ) self.pump(by=0.1) # No more calls should have gone to the database, because the second # request was already in the i
Add more tests for in-flight state query duplication. (#12033)
test_smaller_request_deduplicated
546b9c9e648f5e2b25bb7c8350570787ff9befae
synapse
test_state_store.py
15
37
https://github.com/matrix-org/synapse.git
1
224
0
80
363
Python
{ "docstring": "\n Tests that duplicate requests for state are deduplicated.\n\n This test:\n - requests some state (state group 42, 'all' state filter)\n - requests a subset of that state, before the first request finishes\n - checks to see that only one database query was made\n - completes the database query\n - checks that both requests see the correct retrieved state\n ", "language": "en", "n_whitespaces": 115, "n_words": 58, "vocab_size": 39 }
def test_smaller_request_deduplicated(self) -> None: req1 = ensureDeferred( self.state_datastore._get_state_for_group_using_inflight_cache( 42, StateFilter.from_types((("test.type", None),)) ) ) self.pump(by=0.1) # This should have gone to the database self.assertEqual(len(self.get_state_group_calls), 1) self.assertFalse(req1.called) req2 = ensureDeferred( self.state_datastore._get_state_for_group_using_inflight_cache( 42, StateFilter.from_types((("test.type", "b"),)) ) ) self.pump(by=0.1) # No more calls should have gone to the database, because the second # request was already in the in-flight cache! self.assertEqual(len(self.get_state_group_calls), 1) self.assertFalse(req1.called) self.assertFalse(req2.called) groups, sf, d = self.get_state_group_calls[0] self.assertEqual(groups, (42,)) # The state filter is expanded internally for increased cache hit rate, # so we the database sees a wider state filter than requested. self.assertEqual(sf, ALL_NON_MEMBERS_STATE_FILTER) # Now we can complete the request self._complete_request_fake(groups, sf, d) self.assertEqual( self.get_success(req1), {("test.type", "a"): "AAA", ("test.type", "b"): "BBB"}, ) self.assertEqual(self.get_success(req2), {("test.type", "b"): "BBB"})
33,038
143,733
232
rllib/examples/simulators/sumo/marlenvironment.py
55
20
def get_observation(self, agent): speed = 0 distance = self._config["scenari
[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.
get_observation
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
ray
marlenvironment.py
15
15
https://github.com/ray-project/ray.git
4
108
0
40
176
Python
{ "docstring": "\n Returns the observation of a given agent.\n See http://sumo.sourceforge.net/pydoc/traci._simulation.html\n ", "language": "en", "n_whitespaces": 31, "n_words": 9, "vocab_size": 9 }
def get_observation(self, agent): speed = 0 distance = self._config["scenario_config"]["misc"]["max_distance"] if agent in self.simulation.veh_subscriptions: speed = round( self.simulation.veh_subscriptions[agent][tc.VAR_SPEED] * MS_TO_KMH ) leader = self.simulation.veh_subscriptions[agent][tc.VAR_LEADER] if leader: # compatible with traci veh, dist = leader if veh: # compatible with libsumo distance = round(dist) ret = [speed, distance] logger.debug("Agent %s --> Obs: %s", agent, pformat(ret)) return ret
23,144
108,332
197
lib/matplotlib/colors.py
51
10
def register(self, name, color_list): if name in self._BUILTIN_COLOR_SEQUENCES: raise ValueError(f"{name!r} is a reserved name for a builtin " "color sequence") color_list = list(color_list) # force copy and coerce type to list for color in color_list: try: to_rgba(color) except ValueError: raise ValueError( f"{color!r} is not a valid color specification") self._color_sequences[name] = color_list
Add a registry for color sequences Color sequences are simply lists of colors, that we store by name in a registry. The registry is modelled similar to the ColormapRegistry to 1) support immutable builtin color sequences and 2) to return copies so that one cannot mess with the global definition of the color sequence through an obtained instance. For now, I've made the sequences used for `ListedColormap`s available as builtin sequences, but that's open for discussion. More usage documentation should be added in the color examples and/or tutorials, but I'll wait with that till after the general approval of the structure and API. One common use case will be ``` plt.rc_params['axes.prop_cycle'] = plt.cycler(color=plt.color_sequences['Pastel1') ``` Co-authored-by: Elliott Sales de Andrade <[email protected]>
register
0abe0ce2f2748d1d0383154d045da3609a4b871b
matplotlib
colors.py
14
12
https://github.com/matplotlib/matplotlib.git
4
58
0
41
108
Python
{ "docstring": "\n Register a new color sequence.\n\n The color sequence registry stores a copy of the given *color_list*, so\n that future changes to the original list do not affect the registered\n color sequence. Think of this as the registry taking a snapshot\n of *color_list* at registration.\n\n Parameters\n ----------\n name : str\n The name for the color sequence.\n\n color_list : list of colors\n An iterable returning valid Matplotlib colors when iterating over.\n Note however that the returned color sequence will always be a\n list regardless of the input type.\n\n ", "language": "en", "n_whitespaces": 201, "n_words": 86, "vocab_size": 58 }
def register(self, name, color_list): if name in self._BUILTIN_COLOR_SEQUENCES: raise ValueError(f"{name!r} is a reserved name for a builtin " "color sequence") color_list = list(color_list) # force copy and coerce type to list for color in color_list: try: to_rgba(color) except ValueError: raise ValueError( f"{color!r} is not a valid color specification") self._color_sequences[name] = color_list
@pytest.mark.parametrize( "attributes, no_attributes, limit", [ ({"attr": True}, False, 5000), ({}, True, 5000), ({"attr": True}, False, 3), ({}, True, 3), ], )
92,957
293,911
362
tests/components/recorder/test_history.py
145
28
def test_get_states_no_attributes(hass_recorder): hass = hass_recorder() now, future, states = _setup_get_states(hass) for state in states: state.attributes = {} # Get states returns everything before POINT for all entities for state1, state2 in zip( states, sorted( history.get_states(hass, future, no_attributes=True), key=lambda state: state.entity_id, ), ): assert state1 == state2 # Get states returns everything before POINT for tested entities entities = [f"test.point_in_time_{i % 5}
Avoid selecting attributes in the history api when `no_attributes` is passed (#68352)
test_get_states_no_attributes
816695cc96c19110ccda10431d92160ea6064d32
core
test_history.py
12
31
https://github.com/home-assistant/core.git
5
199
1
85
381
Python
{ "docstring": "Test getting states without attributes at a specific point in time.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }
def test_get_states_no_attributes(hass_recorder): hass = hass_recorder() now, future, states = _setup_get_states(hass) for state in states: state.attributes = {} # Get states returns everything before POINT for all entities for state1, state2 in zip( states, sorted( history.get_states(hass, future, no_attributes=True), key=lambda state: state.entity_id, ), ): assert state1 == state2 # Get states returns everything before POINT for tested entities entities = [f"test.point_in_time_{i % 5}" for i in range(5)] for state1, state2 in zip( states, sorted( history.get_states(hass, future, entities, no_attributes=True), key=lambda state: state.entity_id, ), ): assert state1 == state2 # Test get_state here because we have a DB setup assert states[0] == history.get_state( hass, future, states[0].entity_id, no_attributes=True ) time_before_recorder_ran = now - timedelta(days=1000) assert history.get_states(hass, time_before_recorder_ran, no_attributes=True) == [] assert ( history.get_state(hass, time_before_recorder_ran, "demo.id", no_attributes=True) is None ) @pytest.mark.parametrize( "attributes, no_attributes, limit", [ ({"attr": True}, False, 5000), ({}, True, 5000), ({"attr": True}, False, 3), ({}, True, 3), ], )
56,203
221,098
63
python3.10.4/Lib/bdb.py
24
6
def set_until(self, frame, lineno=None): # the name "until" is borrowed from gdb if lineno is None: lineno = frame.f_lineno + 1 self._set_stopinfo(frame, frame, lineno)
add python 3.10.4 for windows
set_until
8198943edd73a363c266633e1aa5b2a9e9c9f526
XX-Net
bdb.py
10
4
https://github.com/XX-net/XX-Net.git
2
34
0
21
54
Python
{ "docstring": "Stop when the line with the lineno greater than the current one is\n reached or when returning from current frame.", "language": "en", "n_whitespaces": 26, "n_words": 20, "vocab_size": 16 }
def set_until(self, frame, lineno=None): # the name "until" is borrowed from gdb if lineno is None: lineno = frame.f_lineno + 1 self._set_stopinfo(frame, frame, lineno)
89,205
290,079
43
homeassistant/components/rest/switch.py
15
13
async def get_device_state(self, hass): websession = async_get_clientsession(hass, self._verify_ssl) rendered_headers = template.render_complex(self._headers, parse_res
Use _attr_is_on in rest (#81305)
get_device_state
fee3898f648d4fffdf9dbec748aab2410a0bd227
core
switch.py
9
31
https://github.com/home-assistant/core.git
6
180
0
13
68
Python
{ "docstring": "Get the latest data from REST API and update the state.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 10 }
async def get_device_state(self, hass): websession = async_get_clientsession(hass, self._verify_ssl) rendered_headers = template.render_complex(self._headers, parse_result=False) rendered_params = template.render_complex(self._params)
29,525
131,429
75
python/ray/tests/test_client_reconnect.py
15
14
def reset_channel(self) -> None: if self.channel: self.
[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.
reset_channel
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
ray
test_client_reconnect.py
10
12
https://github.com/ray-project/ray.git
2
74
0
15
121
Python
{ "docstring": "\n Manually close and reopen the channel to the real ray server. This\n simulates a disconnection between the client and the server.\n ", "language": "en", "n_whitespaces": 43, "n_words": 21, "vocab_size": 16 }
def reset_channel(self) -> None: if self.channel: self.channel.close() self.channel = grpc.insecure_channel(self.real_addr, options=GRPC_OPTIONS) grpc.channel_ready_future(self.channel) self.task_servicer.set_channel(self.channel) self.data_servicer.set_channel(self.channel) self.logs_servicer.set_channel(self.channel)
117,805
321,575
221
tests/end2end/fixtures/quteprocess.py
78
15
def wait_scroll_pos_changed(self, x=None, y=None): __tracebackhide__ = (lambda e: e.errisinstance(testprocess.WaitForTimeout)) if (x is None and y is not None) or (y is None and x is not None): raise ValueError("Either both x/y or neither must be given!") if x is None and y is None: point = 'Py*.QtCore.QPoint(*, *)' # not counting 0/0 here elif x == '0' and y == '0':
qt6 tests: Fix remaining PyQt5 references
wait_scroll_pos_changed
deb21acdebd77c6dc6d5fe4d8cad75e4ca074138
qutebrowser
quteprocess.py
12
13
https://github.com/qutebrowser/qutebrowser.git
9
107
0
54
184
Python
{ "docstring": "Wait until a \"Scroll position changed\" message was found.\n\n With QtWebEngine, on older Qt versions which lack\n QWebEnginePage.scrollPositionChanged, this also skips the test.\n ", "language": "en", "n_whitespaces": 44, "n_words": 23, "vocab_size": 23 }
def wait_scroll_pos_changed(self, x=None, y=None): __tracebackhide__ = (lambda e: e.errisinstance(testprocess.WaitForTimeout)) if (x is None and y is not None) or (y is None and x is not None): raise ValueError("Either both x/y or neither must be given!") if x is None and y is None: point = 'Py*.QtCore.QPoint(*, *)' # not counting 0/0 here elif x == '0' and y == '0': point = 'Py*.QtCore.QPoint()' else: point = 'Py*.QtCore.QPoint({}, {})'.format(x, y) self.wait_for(category='webview', message='Scroll position changed to ' + point)
30,665
135,586
1,453
python/ray/data/tests/test_dataset_tfrecords.py
231
40
def test_write_tfrecords(ray_start_regular_shared, tmp_path): import tensorflow as tf # The dataset we will write to a .tfrecords file. ds = ray.data.from_items( [ # Row one. { "int_item": 1, "int_list": [2, 2, 3], "float_item": 1.0, "float_list": [2.0, 3.0, 4.0], "bytes_item": b"abc", "bytes_list": [b"abc", b"1234"], }, # Row two. { "int_item": 2, "int_list": [3, 3, 4], "float_item": 2.0, "float_list": [2.0, 2.0, 3.0], "bytes_item": b"def", "bytes_list": [b"def", b"1234"], }, ] ) # The corresponding tf.train.Example that we would expect to read # from this dataset. expected_records = [ # Record one (corresponding to row one). tf.train.Example( features=tf.train.Features( feature={ "int_item": tf.train.Feature( int64_list=tf.train.Int64List(value=[1]) ), "int_list": tf.train.Feature( int64_list=tf.train.Int64List(value=[2, 2, 3]) ), "float_item": tf.train.Feature( float_list=tf.train.FloatList(value=[1.0]) ), "float_l
[Datasets] Add writer for TFRecords. (#29448) This PR enables users to write TFRecords from datasets. In particular, the master branch already includes an API for reading TFRecords from datasets. Users have requested the ability to write these datasets back to TFRecords.
test_write_tfrecords
9fab504fe776f96fecf85e12ea006264cbe92f4a
ray
test_dataset_tfrecords.py
23
82
https://github.com/ray-project/ray.git
3
590
0
127
885
Python
{ "docstring": "Test that write_tfrecords writes TFRecords correctly.\n\n Test this by writing a Dataset to a TFRecord (function under test),\n reading it back out into a tf.train.Example,\n and checking that the result is analogous to the original Dataset.\n ", "language": "en", "n_whitespaces": 48, "n_words": 36, "vocab_size": 30 }
def test_write_tfrecords(ray_start_regular_shared, tmp_path): import tensorflow as tf # The dataset we will write to a .tfrecords file. ds = ray.data.from_items( [ # Row one. { "int_item": 1, "int_list": [2, 2, 3], "float_item": 1.0, "float_list": [2.0, 3.0, 4.0], "bytes_item": b"abc", "bytes_list": [b"abc", b"1234"], }, # Row two. { "int_item": 2, "int_list": [3, 3, 4], "float_item": 2.0, "float_list": [2.0, 2.0, 3.0], "bytes_item": b"def", "bytes_list": [b"def", b"1234"], }, ] ) # The corresponding tf.train.Example that we would expect to read # from this dataset. expected_records = [ # Record one (corresponding to row one). tf.train.Example( features=tf.train.Features( feature={ "int_item": tf.train.Feature( int64_list=tf.train.Int64List(value=[1]) ), "int_list": tf.train.Feature( int64_list=tf.train.Int64List(value=[2, 2, 3]) ), "float_item": tf.train.Feature( float_list=tf.train.FloatList(value=[1.0]) ), "float_list": tf.train.Feature( float_list=tf.train.FloatList(value=[2.0, 3.0, 4.0]) ), "bytes_item": tf.train.Feature( bytes_list=tf.train.BytesList(value=[b"abc"]) ), "bytes_list": tf.train.Feature( bytes_list=tf.train.BytesList(value=[b"abc", b"1234"]) ), } ) ), # Record two (corresponding to row two). tf.train.Example( features=tf.train.Features( feature={ "int_item": tf.train.Feature( int64_list=tf.train.Int64List(value=[2]) ), "int_list": tf.train.Feature( int64_list=tf.train.Int64List(value=[3, 3, 4]) ), "float_item": tf.train.Feature( float_list=tf.train.FloatList(value=[2.0]) ), "float_list": tf.train.Feature( float_list=tf.train.FloatList(value=[2.0, 2.0, 3.0]) ), "bytes_item": tf.train.Feature( bytes_list=tf.train.BytesList(value=[b"def"]) ), "bytes_list": tf.train.Feature( bytes_list=tf.train.BytesList(value=[b"def", b"1234"]) ), } ) ), ] # Perform the test. # Write the dataset to a .tfrecords file. ds.write_tfrecords(tmp_path) # Read the Examples back out from the .tfrecords file. # This follows the offical TFRecords tutorial: # https://www.tensorflow.org/tutorials/load_data/tfrecord#reading_a_tfrecord_file_2 filenames = sorted(os.listdir(tmp_path)) filepaths = [os.path.join(tmp_path, filename) for filename in filenames] raw_dataset = tf.data.TFRecordDataset(filepaths) tfrecords = [] for raw_record in raw_dataset: example = tf.train.Example() example.ParseFromString(raw_record.numpy()) tfrecords.append(example) assert tfrecords == expected_records
42,946
179,373
165
test/test_processing_utils.py
38
19
def test_float_conversion_dtype(self): x = np.a
Format The Codebase - black formatting - isort formatting
test_float_conversion_dtype
cc0cff893f9d7d472788adc2510c123967b384fe
gradio
test_processing_utils.py
14
12
https://github.com/gradio-app/gradio.git
2
87
0
33
137
Python
{ "docstring": "Test any convertion from a float dtype to an other.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }
def test_float_conversion_dtype(self): x = np.array([-1, 1]) # Test all combinations of dtypes conversions dtype_combin = np.array( np.meshgrid( OutputPreprocessing.float_dtype_list, OutputPreprocessing.float_dtype_list, ) ).T.reshape(-1, 2) for dtype_in, dtype_out in dtype_combin: x = x.astype(dtype_in) y = gr.processing_utils._convert(x, dtype_out) assert y.dtype == np.dtype(dtype_out)
@app.server.websocket("/api/ws")
5,544
30,395
61
spotdl/console/web.py
35
6
def fix_mime_types(): # Known to be problematic when Visual Studio is installed: # <https://github.com/tensorflow/tensorboard/issues/3120> # https://github.com/spotDL/spotify-downloader/issues/1540 mimetypes.add_type("application/javascript", ".js") # Not known to be problematic, but used by spotDL: mimetypes.add_type("text/css", ".css") mimetypes.add_type("image/
fix: broken mimetypes #1540
fix_mime_types
de31601550e5b6b243f7a00b2bc82300f43f2d9d
spotify-downloader
web.py
8
5
https://github.com/spotDL/spotify-downloader.git
1
37
1
30
97
Python
{ "docstring": "Fix incorrect entries in the `mimetypes` registry.\n On Windows, the Python standard library's `mimetypes` reads in\n mappings from file extension to MIME type from the Windows\n registry. Other applications can and do write incorrect values\n to this registry, which causes `mimetypes.guess_type` to return\n incorrect values, which causes spotDL to fail to render on\n the frontend.\n This method hard-codes the correct mappings for certain MIME\n types that are known to be either used by TensorBoard or\n problematic in general.\n ", "language": "en", "n_whitespaces": 108, "n_words": 78, "vocab_size": 58 }
def fix_mime_types(): # Known to be problematic when Visual Studio is installed: # <https://github.com/tensorflow/tensorboard/issues/3120> # https://github.com/spotDL/spotify-downloader/issues/1540 mimetypes.add_type("application/javascript", ".js") # Not known to be problematic, but used by spotDL: mimetypes.add_type("text/css", ".css") mimetypes.add_type("image/svg+xml", ".svg") mimetypes.add_type("text/html", ".html") @app.server.websocket("/api/ws")
20,797
101,382
87
scripts/convert.py
26
12
def _get_threads(self) -> MultiThread: # TODO Check if multiple threads actually speeds anything up save_queue = queue_manager.get_queue("convert_out") patch_queue = queue_manager.get_queue("patch") return MultiThread(self._converter.process, patch_queue, save_queue, thread_count=self._pool_p
Bugfix: convert - Gif Writer - Fix non-launch error on Gif Writer - convert plugins - linting - convert/fs_media/preview/queue_manager - typing - Change convert items from dict to Dataclass
_get_threads
1022651eb8a7741014f5d2ec7cbfe882120dfa5f
faceswap
convert.py
9
11
https://github.com/deepfakes/faceswap.git
1
47
0
25
80
Python
{ "docstring": " Get the threads for patching the converted faces onto the frames.\n\n Returns\n :class:`lib.multithreading.MultiThread`\n The threads that perform the patching of swapped faces onto the output frames\n ", "language": "en", "n_whitespaces": 59, "n_words": 26, "vocab_size": 18 }
def _get_threads(self) -> MultiThread: # TODO Check if multiple threads actually speeds anything up save_queue = queue_manager.get_queue("convert_out") patch_queue = queue_manager.get_queue("patch") return MultiThread(self._converter.process, patch_queue, save_queue, thread_count=self._pool_processes, name="patch")
56,740
222,788
51
python3.10.4/Lib/distutils/command/register.py
23
8
def verify_metadata(self): # send the info to the server and report the result (code, result) = self.post_to_server(self.build_post_da
add python 3.10.4 for windows
verify_metadata
8198943edd73a363c266633e1aa5b2a9e9c9f526
XX-Net
register.py
11
3
https://github.com/XX-net/XX-Net.git
1
33
0
20
58
Python
{ "docstring": " Send the metadata to the package index server to be checked.\n ", "language": "en", "n_whitespaces": 19, "n_words": 11, "vocab_size": 9 }
def verify_metadata(self): # send the info to the server and report the result (code, result) = self.post_to_server(self.build_post_data('verify')) log.info('Server response (%s): %s', code, result)
17,668
83,381
119
zerver/tests/test_subs.py
22
13
def test_subscriptions_add_for_principal_invite_only(self) -> None: invitee = self.example_user("iago") current_streams = self.get_streams(invitee) invite_streams = self.make_random_stream_names(current_streams) self.assert_adding_subscriptions_for_principal( invitee.id, invitee.realm, invite_streams, invite_only=True,
stream_settings: Show stream privacy & description in stream events. Provide stream privacy and description in stream notification events when stream is created. In function "send_messages_for_new_subscribers" for when stream is created, put policy name and description of the stream. Fixes #21004
test_subscriptions_add_for_principal_invite_only
4b9770e270823b7ed2bbbeda0e4450f0ba6a288b
zulip
test_subs.py
9
14
https://github.com/zulip/zulip.git
1
55
0
20
90
Python
{ "docstring": "\n You can subscribe other people to invite only streams.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }
def test_subscriptions_add_for_principal_invite_only(self) -> None: invitee = self.example_user("iago") current_streams = self.get_streams(invitee) invite_streams = self.make_random_stream_names(current_streams) self.assert_adding_subscriptions_for_principal( invitee.id, invitee.realm, invite_streams, invite_only=True, policy_name="Private, protected history", )
960
6,328
19
ludwig/features/feature_utils.py
10
5
def get_module_dict_key_from_name(name): key = name.replace(".", "__ludwig_punct_peri
Enable feature names with periods in them. (#1787) * Enable feature names with periods in them. * Simplify new unit test.
get_module_dict_key_from_name
c9b6f4dfa32631c320d122ad07f09013769d9d5d
ludwig
feature_utils.py
9
3
https://github.com/ludwig-ai/ludwig.git
1
20
0
9
38
Python
{ "docstring": "Returns a key that's guaranteed to be compatible with torch.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 10 }
def get_module_dict_key_from_name(name): key = name.replace(".", "__ludwig_punct_period__") return key + FEATURE_NAME_SUFFIX
@pytest.mark.parametrize( "import_strategy, expected_to_fail", [ pytest.param( """ from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker import pyarrow.gandiva """, True, id="import_pydbe_first-pyarrow_gandiva_second", ), pytest.param( """ import pyarrow.gandiva from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker """, False, id="import_pyarrow_gandiva_first-pydbe_second", ), ], )
36,121
154,641
196
modin/test/storage_formats/hdk/test_internals.py
41
20
def test_hdk_import(import_strategy, has_other_engines): remove_other_engines = if not has_other_engines: import_strategy = f"{remove_oth
FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <[email protected]> Signed-off-by: Andrey Pavlenko <[email protected]>
test_hdk_import
e5b1888cd932909e49194d58035da34b210b91c4
modin
test_internals.py
12
15
https://github.com/modin-project/modin.git
3
66
1
34
182
Python
{ "docstring": "\n Test import of HDK engine.\n\n The import of DbWorker requires to set special dlopen flags which make it then\n incompatible to import some other libraries further (like ``pyarrow.gandiva``).\n This test verifies that it's not the case when a user naturally imports Modin\n with HDK engine.\n\n Parameters\n ----------\n import_strategy : str\n There are several scenarios of how a user can import Modin with HDK engine:\n configure Modin first to use HDK engine and then import ``modin.pandas`` or vice versa.\n This parameters holds a python code, implementing one of these scenarios.\n has_other_engines : bool\n The problem with import may appear depending on whether other engines are\n installed. This parameter indicates whether to remove modules for\n non-hdk engines before the test.\n\n Notes\n -----\n The failed import flow may cause segfault, which causes to crash the pytest itself.\n This makes us to run the test in a separate process and check its exit-code to\n decide the success of the test.\n \nimport sys\nsys.modules['ray'] = None\nsys.modules['dask'] = None\n\nfrom modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker\nimport pyarrow.gandiva\n\nimport pyarrow.gandiva\nfrom modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker\n", "language": "en", "n_whitespaces": 257, "n_words": 176, "vocab_size": 115 }
def test_hdk_import(import_strategy, has_other_engines): remove_other_engines = if not has_other_engines: import_strategy = f"{remove_other_engines}\n{import_strategy}" res = subprocess.run( [sys.executable, "-c", import_strategy], stderr=subprocess.PIPE, stdout=subprocess.PIPE, ) if res.returncode != 0: pytest.fail(str(res.stderr)) @pytest.mark.parametrize( "import_strategy, expected_to_fail", [ pytest.param( , True, id="import_pydbe_first-pyarrow_gandiva_second", ), pytest.param( , False, id="import_pyarrow_gandiva_first-pydbe_second", ), ], )
29,214
130,289
665
python/ray/_private/thirdparty/pathspec/util.py
240
33
def _iter_tree_entries_next(root_full, dir_rel, memo, on_error, follow_links): dir_full = os.path.join(root_full, dir_rel) dir_real = os.path.realpath(dir_full) # Remember each encountered ancestor directory and its canonical # (real) path. If a canonical path is encountered more than once, # recursion has occurred. if dir_real not in memo:
[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.
_iter_tree_entries_next
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
ray
util.py
16
38
https://github.com/ray-project/ray.git
14
249
0
140
396
Python
{ "docstring": "\n Scan the directory for all descendant files.\n\n *root_full* (:class:`str`) the absolute path to the root directory.\n\n *dir_rel* (:class:`str`) the path to the directory to scan relative to\n *root_full*.\n\n *memo* (:class:`dict`) keeps track of ancestor directories\n encountered. Maps each ancestor real path (:class:`str`) to relative\n path (:class:`str`).\n\n *on_error* (:class:`~collections.abc.Callable` or :data:`None`)\n optionally is the error handler for file-system exceptions.\n\n *follow_links* (:class:`bool`) is whether to walk symbolic links that\n resolve to directories.\n\n Yields each entry (:class:`.TreeEntry`).\n ", "language": "en", "n_whitespaces": 114, "n_words": 74, "vocab_size": 52 }
def _iter_tree_entries_next(root_full, dir_rel, memo, on_error, follow_links): dir_full = os.path.join(root_full, dir_rel) dir_real = os.path.realpath(dir_full) # Remember each encountered ancestor directory and its canonical # (real) path. If a canonical path is encountered more than once, # recursion has occurred. if dir_real not in memo: memo[dir_real] = dir_rel else: raise RecursionError( real_path=dir_real, first_path=memo[dir_real], second_path=dir_rel ) for node_name in os.listdir(dir_full): node_rel = os.path.join(dir_rel, node_name) node_full = os.path.join(root_full, node_rel) # Inspect child node. try: node_lstat = os.lstat(node_full) except OSError as e: if on_error is not None: on_error(e) continue if stat.S_ISLNK(node_lstat.st_mode): # Child node is a link, inspect the target node. is_link = True try: node_stat = os.stat(node_full) except OSError as e: if on_error is not None: on_error(e) continue else: is_link = False node_stat = node_lstat if stat.S_ISDIR(node_stat.st_mode) and (follow_links or not is_link): # Child node is a directory, recurse into it and yield its # descendant files. yield TreeEntry(node_name, node_rel, node_lstat, node_stat) for entry in _iter_tree_entries_next( root_full, node_rel, memo, on_error, follow_links ): yield entry elif stat.S_ISREG(node_stat.st_mode) or is_link: # Child node is either a file or an unfollowed link, yield it. yield TreeEntry(node_name, node_rel, node_lstat, node_stat) # NOTE: Make sure to remove the canonical (real) path of the directory # from the ancestors memo once we are done with it. This allows the # same directory to appear multiple times. If this is not done, the # second occurrence of the directory will be incorrectly interpreted # as a recursion. See <https://github.com/cpburnz/python-path-specification/pull/7>. del memo[dir_real]
47,723
196,223
318
sympy/combinatorics/util.py
88
24
def _remove_gens(base, strong_gens, basic_orbits=None, strong_gens_distr=None): from sympy.combinatorics.perm_groups import _orbit base_len = len(base) degree = strong_gens[0].size if strong_gens_distr is None: strong_gens_distr = _distribute_gens_by_base(base, strong_gens) if basic_orbits is None: basic_orbits = [] for i in range(base_len): basic_orbit = _orbit(degree, strong_gens_distr[i], base[i]) basic_orbits.append(basic_orbit) strong_gens_distr.append([]) res = strong_gens[:] for i in range(base_len - 1, -1, -1): gens_copy = strong_gens_distr[i][:] for gen in strong_gens_distr[i]: if gen not in strong_gens_distr[i + 1]: temp_gens = gens_copy[:] temp_gens.remove(gen) if temp_gens == []: continue temp_orbit = _orbit(degree, temp_gens,
Updated import locations
_remove_gens
498015021131af4dbb07eb110e5badaba8250c7b
sympy
util.py
15
26
https://github.com/sympy/sympy.git
9
201
0
59
308
Python
{ "docstring": "\n Remove redundant generators from a strong generating set.\n\n Parameters\n ==========\n\n ``base`` - a base\n ``strong_gens`` - a strong generating set relative to ``base``\n ``basic_orbits`` - basic orbits\n ``strong_gens_distr`` - strong generators distributed by membership in basic\n stabilizers\n\n Returns\n =======\n\n A strong generating set with respect to ``base`` which is a subset of\n ``strong_gens``.\n\n Examples\n ========\n\n >>> from sympy.combinatorics import SymmetricGroup\n >>> from sympy.combinatorics.util import _remove_gens\n >>> from sympy.combinatorics.testutil import _verify_bsgs\n >>> S = SymmetricGroup(15)\n >>> base, strong_gens = S.schreier_sims_incremental()\n >>> new_gens = _remove_gens(base, strong_gens)\n >>> len(new_gens)\n 14\n >>> _verify_bsgs(S, base, new_gens)\n True\n\n Notes\n =====\n\n This procedure is outlined in [1],p.95.\n\n References\n ==========\n\n .. [1] Holt, D., Eick, B., O'Brien, E.\n \"Handbook of computational group theory\"\n\n ", "language": "en", "n_whitespaces": 219, "n_words": 115, "vocab_size": 79 }
def _remove_gens(base, strong_gens, basic_orbits=None, strong_gens_distr=None): from sympy.combinatorics.perm_groups import _orbit base_len = len(base) degree = strong_gens[0].size if strong_gens_distr is None: strong_gens_distr = _distribute_gens_by_base(base, strong_gens) if basic_orbits is None: basic_orbits = [] for i in range(base_len): basic_orbit = _orbit(degree, strong_gens_distr[i], base[i]) basic_orbits.append(basic_orbit) strong_gens_distr.append([]) res = strong_gens[:] for i in range(base_len - 1, -1, -1): gens_copy = strong_gens_distr[i][:] for gen in strong_gens_distr[i]: if gen not in strong_gens_distr[i + 1]: temp_gens = gens_copy[:] temp_gens.remove(gen) if temp_gens == []: continue temp_orbit = _orbit(degree, temp_gens, base[i]) if temp_orbit == basic_orbits[i]: gens_copy.remove(gen) res.remove(gen) return res
50,758
204,505
64
django/core/files/uploadedfile.py
10
4
def from_dict(cls, file_dict): return cls( file_dict["filename"], file_dict["content"], file_dict.get("con
Refs #33476 -- Reformatted code with Black.
from_dict
9c19aff7c7561e3a82978a272ecdaad40dda5c00
django
uploadedfile.py
10
6
https://github.com/django/django.git
1
31
0
10
54
Python
{ "docstring": "\n Create a SimpleUploadedFile object from a dictionary with keys:\n - filename\n - content-type\n - content\n ", "language": "en", "n_whitespaces": 60, "n_words": 15, "vocab_size": 12 }
def from_dict(cls, file_dict): return cls( file_dict["filename"], file_dict["content"], file_dict.get("content-type", "text/plain"), )
52,073
207,730
189
tests/admin_views/tests.py
41
7
def test_change_list_sorting_callable(self): response = self.client.get( reverse("admin:admin_views_article_changelist"), {"o": 2} ) self.assertContentBefore
Refs #33476 -- Reformatted code with Black.
test_change_list_sorting_callable
9c19aff7c7561e3a82978a272ecdaad40dda5c00
django
tests.py
11
16
https://github.com/django/django.git
1
51
0
23
92
Python
{ "docstring": "\n Ensure we can sort on a list_display field that is a callable\n (column 2 is callable_year in ArticleAdmin)\n ", "language": "en", "n_whitespaces": 40, "n_words": 18, "vocab_size": 16 }
def test_change_list_sorting_callable(self): response = self.client.get( reverse("admin:admin_views_article_changelist"), {"o": 2} ) self.assertContentBefore( response, "Oldest content", "Middle content", "Results of sorting on callable are out of order.", ) self.assertContentBefore( response, "Middle content", "Newest content", "Results of sorting on callable are out of order.", )
20,789
101,374
107
scripts/convert.py
32
16
def pre_encode(self) -> Optional[Callable[[np.ndarray], List[bytes]]]: dummy = np.zeros((20, 20, 3), dtype="uint8") test = self._writer.pre_encode(dummy) retval: O
Bugfix: convert - Gif Writer - Fix non-launch error on Gif Writer - convert plugins - linting - convert/fs_media/preview/queue_manager - typing - Change convert items from dict to Dataclass
pre_encode
1022651eb8a7741014f5d2ec7cbfe882120dfa5f
faceswap
convert.py
11
9
https://github.com/deepfakes/faceswap.git
2
91
0
27
138
Python
{ "docstring": " python function: Selected writer's pre-encode function, if it has one,\n otherwise ``None`` ", "language": "en", "n_whitespaces": 20, "n_words": 12, "vocab_size": 12 }
def pre_encode(self) -> Optional[Callable[[np.ndarray], List[bytes]]]: dummy = np.zeros((20, 20, 3), dtype="uint8") test = self._writer.pre_encode(dummy) retval: Optional[Callable[[np.ndarray], List[bytes]]] = None if test is None else self._writer.pre_encode logger.debug("Writer pre_encode function: %s", retval) return retval
25,459
115,431
59
mindsdb/integrations/handlers/sqlite_handler/sqlite_handler.py
13
5
def disconnect(self):
added connection_args and connection_args_example dicts
disconnect
fc9776d9b342f873cbb3f36fd39955b9e1ea6f76
mindsdb
sqlite_handler.py
8
6
https://github.com/mindsdb/mindsdb.git
2
30
0
10
52
Python
{ "docstring": "\r\n Close any existing connections.\r\n ", "language": "en", "n_whitespaces": 19, "n_words": 4, "vocab_size": 4 }
def disconnect(self): if self.is_connected is False: return self.connection.close() self.is_connected = False return self.is_connected
30,029
133,434
196
python/ray/workflow/step_executor.py
60
27
def resolve(self) -> Tuple[List, Dict]: objects_mapping = [] for obj_ref in self.workflow_outputs: obj, ref = _resolve_object_ref(obj_ref.ref) objects_mapping.append(o
[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.
resolve
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
ray
step_executor.py
11
30
https://github.com/ray-project/ray.git
4
103
0
49
164
Python
{ "docstring": "\n This function resolves the inputs for the code inside\n a workflow step (works on the callee side). For outputs from other\n workflows, we resolve them into object instances inplace.\n\n For each ObjectRef argument, the function returns both the ObjectRef\n and the object instance. If the ObjectRef is a chain of nested\n ObjectRefs, then we resolve it recursively until we get the\n object instance, and we return the *direct* ObjectRef of the\n instance. This function does not resolve ObjectRef\n inside another object (e.g. list of ObjectRefs) to give users some\n flexibility.\n\n Returns:\n Instances of arguments.\n ", "language": "en", "n_whitespaces": 190, "n_words": 94, "vocab_size": 62 }
def resolve(self) -> Tuple[List, Dict]: objects_mapping = [] for obj_ref in self.workflow_outputs: obj, ref = _resolve_object_ref(obj_ref.ref) objects_mapping.append(obj) workflow_ref_mapping = _resolve_dynamic_workflow_refs(self.workflow_refs) with serialization_context.workflow_args_resolving_context( objects_mapping, workflow_ref_mapping ): # reconstruct input arguments under correct serialization context flattened_args: List[Any] = ray.get(self.args) # dereference arguments like Ray remote functions flattened_args = [ ray.get(a) if isinstance(a, ObjectRef) else a for a in flattened_args ] return signature.recover_args(flattened_args)
80,573
270,768
35
keras/engine/base_layer.py
10
6
def _maybe_create_attribute(self, name, default_value):
Reformatting the codebase with black. PiperOrigin-RevId: 450093126
_maybe_create_attribute
84afc5193d38057e2e2badf9c889ea87d80d8fbf
keras
base_layer.py
9
3
https://github.com/keras-team/keras.git
2
27
0
10
43
Python
{ "docstring": "Create the attribute with the default value if it hasn't been created.\n\n This is useful for fields that is used for tracking purpose,\n _trainable_weights, or _layers. Note that user could create a layer subclass\n and assign an internal field before invoking the Layer.__init__(), the\n __setattr__() need to create the tracking fields and __init__() need to not\n override them.\n\n Args:\n name: String, the name of the attribute.\n default_value: Object, the default value of the attribute.\n ", "language": "en", "n_whitespaces": 141, "n_words": 74, "vocab_size": 53 }
def _maybe_create_attribute(self, name, default_value): if not hasattr(self, name): self.__setattr__(name, default_value)
42,421
177,527
283
networkx/classes/digraph.py
56
17
def add_nodes_from(self, nodes_for_adding, **attr): for n in nodes_for_adding: try: newnode = n not in self._node newdict = attr except Type
doc: update documentation when providing an iterator over current graph to add/remove_edges_from. (#6268) * doc for add_edges_from * doc for digraph * doc for multigraph * multigraph.add_nodes_from returns keylist * update docs for graph - edges * doc update: graph.add_nodes_from * doc update: graph.remove_nodes_from * doc update: graph.add_edges_from * doc update: rewording for graph.add_edges_from * doc update: graph.add_weighted_edges_from rewording * doc update: digraph updated as graph * doc update: digraph minor sync * doc update: multigraph same as graph * Update graph.py * Update digraph.py * Update multigraph.py
add_nodes_from
979d54acba7c3d372c93d44c6c149700608ce8b0
networkx
digraph.py
14
17
https://github.com/networkx/networkx.git
5
118
0
37
191
Python
{ "docstring": "Add multiple nodes.\n\n Parameters\n ----------\n nodes_for_adding : iterable container\n A container of nodes (list, dict, set, etc.).\n OR\n A container of (node, attribute dict) tuples.\n Node attributes are updated using the attribute dict.\n attr : keyword arguments, optional (default= no attributes)\n Update attributes for all nodes in nodes.\n Node attributes specified in nodes as a tuple take\n precedence over attributes specified via keyword arguments.\n\n See Also\n --------\n add_node\n\n Notes\n -------\n When adding nodes from an iterator over the graph you are changing,\n a `RuntimeError` can be raised with message:\n `RuntimeError: dictionary changed size during iteration`. This\n happens when the graph's underlying dictionary is modified during\n iteration. To avoid this error, evaluate the iterator into a separate\n object, e.g. by using `list(iterator_of_nodes)`, and pass this\n object to `G.add_nodes_from`.\n\n Examples\n --------\n >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc\n >>> G.add_nodes_from(\"Hello\")\n >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])\n >>> G.add_nodes_from(K3)\n >>> sorted(G.nodes(), key=str)\n [0, 1, 2, 'H', 'e', 'l', 'o']\n\n Use keywords to update specific node attributes for every node.\n\n >>> G.add_nodes_from([1, 2], size=10)\n >>> G.add_nodes_from([3, 4], weight=0.4)\n\n Use (node, attrdict) tuples to update attributes for specific nodes.\n\n >>> G.add_nodes_from([(1, dict(size=11)), (2, {\"color\": \"blue\"})])\n >>> G.nodes[1][\"size\"]\n 11\n >>> H = nx.Graph()\n >>> H.add_nodes_from(G.nodes(data=True))\n >>> H.nodes[1][\"size\"]\n 11\n\n Evaluate an iterator over a graph if using it to modify the same graph\n\n >>> G = nx.DiGraph([(0, 1), (1, 2), (3, 4)])\n >>> # wrong way - will raise RuntimeError\n >>> # G.add_nodes_from(n + 1 for n in G.nodes)\n >>> # correct way\n >>> G.add_nodes_from(list(n + 1 for n in G.nodes))\n ", "language": "en", "n_whitespaces": 632, "n_words": 260, "vocab_size": 173 }
def add_nodes_from(self, nodes_for_adding, **attr): for n in nodes_for_adding: try: newnode = n not in self._node newdict = attr except TypeError: n, ndict = n newnode = n not in self._node newdict = attr.copy() newdict.update(ndict) if newnode: if n is None: raise ValueError("None cannot be a node") self._succ[n] = self.adjlist_inner_dict_factory() self._pred[n] = self.adjlist_inner_dict_factory() self._node[n] = self.node_attr_dict_factory() self._node[n].update(newdict)
15,860
72,246
393
wagtail/admin/tests/test_workflows.py
153
22
def test_submitted_email_notifications_sent(self): self.login(self.submitter) self.submit() self.assertEqual(len(mail.outbox), 4) task_submission_emails = [ email for email in mail.outbox if "task" in email.subject ] task_submission_emailed_addresses = [ address for email in task_submission_emails for address in email.to ] workflow_submission_emails = [ email for email in mail.outbox if "workflow" in email.subject ] workflow_submission_emailed_addresses = [ address for email in workflow_submission_emails for address in email.t
Reformat with black
test_submitted_email_notifications_sent
d10f15e55806c6944827d801cd9c2d53f5da4186
wagtail
test_workflows.py
10
26
https://github.com/wagtail/wagtail.git
9
214
0
64
335
Python
{ "docstring": "Test that 'submitted' notifications for WorkflowState and TaskState are both sent correctly", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 12 }
def test_submitted_email_notifications_sent(self): self.login(self.submitter) self.submit() self.assertEqual(len(mail.outbox), 4) task_submission_emails = [ email for email in mail.outbox if "task" in email.subject ] task_submission_emailed_addresses = [ address for email in task_submission_emails for address in email.to ] workflow_submission_emails = [ email for email in mail.outbox if "workflow" in email.subject ] workflow_submission_emailed_addresses = [ address for email in workflow_submission_emails for address in email.to ] self.assertEqual(len(task_submission_emails), 3) # the moderator is in the Group assigned to the GroupApproval task, so should get an email self.assertIn(self.moderator.email, task_submission_emailed_addresses) self.assertIn(self.moderator2.email, task_submission_emailed_addresses) # with `WAGTAILADMIN_NOTIFICATION_INCLUDE_SUPERUSERS`, the superuser should get a task email self.assertIn(self.superuser.email, task_submission_emailed_addresses) # the submitter triggered this workflow update, so should not get an email self.assertNotIn(self.submitter.email, task_submission_emailed_addresses) self.assertEqual(len(workflow_submission_emails), 1) # the moderator should not get a workflow email self.assertNotIn(self.moderator.email, workflow_submission_emailed_addresses) self.assertNotIn(self.moderator2.email, workflow_submission_emailed_addresses) # with `WAGTAILADMIN_NOTIFICATION_INCLUDE_SUPERUSERS`, the superuser should get a workflow email self.assertIn(self.superuser.email, workflow_submission_emailed_addresses) # as the submitter was the triggering user, the submitter should not get an email notification self.assertNotIn(self.submitter.email, workflow_submission_emailed_addresses)
6,543
35,810
535
src/transformers/models/maskformer/modeling_maskformer.py
229
51
def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> List[Tuple[Tensor]]: indices: List[Tuple[np.array]] = [] preds_masks = masks_queries_logits preds_probs = class_queries_logits.softmax(dim=-1) # downsample all masks in one go -> save memory mask_labels = nn.functional.interpolate(mask_labels, size=preds_masks.shape[-2:], mode="nearest") # iterate through batch size for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels): # Compute the classification cost. Contrary to the loss, we don't use the NLL, # but approximate it in 1 - proba[target class]. # The 1 is a constant that doesn't change the matching, it can be ommitted. cost_class = -pred_probs[:, labels] # flatten spatial dimension "q h w -> q (h w)" num_queries, height, width = pred_mask.shape pred_mask_flat = pred_mask.view(num_queries, height * width) # [num_queries, H*W] # same for target_mask "c h w -> c (h w)" num_channels, height, width = target_mask.shape target_mask_flat = target_mask.view(num_channels, height * width) # [num_total_labels, H*W] # compute the focal loss between each mask pairs -> shape [NUM_QUERIES, CLASSES] cost_mask = pair_wise_sigmoid_focal_loss(pred_mask_flat, target_mask_flat) # Compute the dice loss betwen each mask pairs -> shape [NUM_QUERIES, CLASSES] cost_dice = pair_wise_dice_loss(pred_mask_flat, target_mask_flat) # final cost matrix cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice # do the assigmented using the hungarian algorithm in scipy assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.cpu()) indices.append(assigned_indices)
Maskformer (#15682) * maskformer * conflicts * conflicts * minor fixes * feature extractor test fix refactor MaskFormerLoss following conversation MaskFormer related types should not trigger a module time import error missed one removed all the types that are not used update config mapping minor updates in the doc resolved conversation that doesn't need a discussion minor changes resolved conversations fixed DetrDecoder * minor changes minor changes fixed mdx file test feature_extractor return types functional losses -> classes removed the return type test for the feature extractor minor changes + style + quality * conflicts? * rebase master * readme * added missing files * deleded poolformers test that where in the wrong palce * CI * minor changes * Apply suggestions from code review Co-authored-by: NielsRogge <[email protected]> * resolved conversations * minor changes * conversations [Unispeech] Fix slow tests (#15818) * remove soundfile old way of loading audio * Adapt slow test [Barthez Tokenizer] Fix saving (#15815) [TFXLNet] Correct tf xlnet generate (#15822) * [TFXLNet] Correct tf xlnet * adapt test comment Fix the push run (#15807) Fix semantic segmentation pipeline test (#15826) Fix dummy_inputs() to dummy_inputs in symbolic_trace doc (#15776) Add model specific output classes to PoolFormer model docs (#15746) * Added model specific output classes to poolformer docs * Fixed Segformer typo in Poolformer docs Adding the option to return_timestamps on pure CTC ASR models. (#15792) * Adding the option to return_timestamps on pure CTC ASR models. * Remove `math.prod` which was introduced in Python 3.8 * int are not floats. * Reworking the PR to support "char" vs "word" output. * Fixup! * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <[email protected]> * Quality. Co-authored-by: Patrick von Platen <[email protected]> HFTracer.trace should use/return self.graph to be compatible with torch.fx.Tracer (#15824) Fix tf.concatenate + test past_key_values for TF models (#15774) * fix wrong method name tf.concatenate * add tests related to causal LM / decoder * make style and quality * clean-up * Fix TFBertModel's extended_attention_mask when past_key_values is provided * Fix tests * fix copies * More tf.int8 -> tf.int32 in TF test template * clean-up * Update TF test template * revert the previous commit + update the TF test template * Fix TF template extended_attention_mask when past_key_values is provided * Fix some styles manually * clean-up * Fix ValueError: too many values to unpack in the test * Fix more: too many values to unpack in the test * Add a comment for extended_attention_mask when there is past_key_values * Fix TFElectra extended_attention_mask when past_key_values is provided * Add tests to other TF models * Fix for TF Electra test: add prepare_config_and_inputs_for_decoder * Fix not passing training arg to lm_head in TFRobertaForCausalLM * Fix tests (with past) for TF Roberta * add testing for pask_key_values for TFElectra model Co-authored-by: ydshieh <[email protected]> [examples/summarization and translation] fix readme (#15833) Add ONNX Runtime quantization for text classification notebook (#15817) Re-enable doctests for the quicktour (#15828) * Re-enable doctests for the quicktour * Re-enable doctests for task_summary (#15830) * Remove & Framework split model report (#15825) Add TFConvNextModel (#15750) * feat: initial implementation of convnext in tensorflow. * fix: sample code for the classification model. * chore: added checked for from the classification model. * chore: set bias initializer in the classification head. * chore: updated license terms. * chore: removed ununsed imports * feat: enabled argument during using drop_path. * chore: replaced tf.identity with layers.Activation(linear). * chore: edited default checkpoint. * fix: minor bugs in the initializations. * partial-fix: tf model errors for loading pretrained pt weights. * partial-fix: call method updated * partial-fix: cross loading of weights (4x3 variables to be matched) * chore: removed unneeded comment. * removed playground.py * rebasing * rebasing and removing playground.py. * fix: renaming TFConvNextStage conv and layer norm layers * chore: added initializers and other minor additions. * chore: added initializers and other minor additions. * add: tests for convnext. * fix: integration tester class. * fix: issues mentioned in pr feedback (round 1). * fix: how output_hidden_states arg is propoagated inside the network. * feat: handling of arg for pure cnn models. * chore: added a note on equal contribution in model docs. * rebasing * rebasing and removing playground.py. * feat: encapsulation for the convnext trunk. * Fix variable naming; Test-related corrections; Run make fixup * chore: added Joao as a contributor to convnext. * rebasing * rebasing and removing playground.py. * rebasing * rebasing and removing playground.py. * chore: corrected copyright year and added comment on NHWC. * chore: fixed the black version and ran formatting. * chore: ran make style. * chore: removed from_pt argument from test, ran make style. * rebasing * rebasing and removing playground.py. * rebasing * rebasing and removing playground.py. * fix: tests in the convnext subclass, ran make style. * rebasing * rebasing and removing playground.py. * rebasing * rebasing and removing playground.py. * chore: moved convnext test to the correct location * fix: locations for the test file of convnext. * fix: convnext tests. * chore: applied sgugger's suggestion for dealing w/ output_attentions. * chore: added comments. * chore: applied updated quality enviornment style. * chore: applied formatting with quality enviornment. * chore: revert to the previous tests/test_modeling_common.py. * chore: revert to the original test_modeling_common.py * chore: revert to previous states for test_modeling_tf_common.py and modeling_tf_utils.py * fix: tests for convnext. * chore: removed output_attentions argument from convnext config. * chore: revert to the earlier tf utils. * fix: output shapes of the hidden states * chore: removed unnecessary comment * chore: reverting to the right test_modeling_tf_common.py. * Styling nits Co-authored-by: ariG23498 <[email protected]> Co-authored-by: Joao Gante <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]> * minor changes * doc fix in feature extractor * doc * typose * removed detr logic from config * removed detr logic from config * removed num_labels * small fix in the config * auxilary -> auxiliary * make style * some test is failing * fix a weird char in config prevending doc-builder * retry to fix the doc-builder issue * make style * new try to fix the doc builder * CI * change weights to facebook Co-authored-by: NielsRogge <[email protected]> Co-authored-by: ariG23498 <[email protected]> Co-authored-by: Joao Gante <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]>
forward
d83d22f578276e9f201b0b3b0f8f9bd68e86c133
transformers
modeling_maskformer.py
12
44
https://github.com/huggingface/transformers.git
3
243
0
152
376
Python
{ "docstring": "Performs the matching\n\n Params:\n masks_queries_logits (`torch.Tensor`):\n A tensor` of dim `batch_size, num_queries, num_classes` with the\n classification logits.\n class_queries_logits (`torch.Tensor`):\n A tensor` of dim `batch_size, num_queries, height, width` with the\n predicted masks.\n\n class_labels (`torch.Tensor`):\n A tensor` of dim `num_target_boxes` (where num_target_boxes is the number\n of ground-truth objects in the target) containing the class labels.\n mask_labels (`torch.Tensor`):\n A tensor` of dim `num_target_boxes, height, width` containing the target\n masks.\n\n Returns:\n `List[Tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where:\n - index_i is the indices of the selected predictions (in order)\n - index_j is the indices of the corresponding selected labels (in order)\n For each batch element, it holds:\n len(index_i) = len(index_j) = min(num_queries, num_target_boxes).\n ", "language": "en", "n_whitespaces": 374, "n_words": 114, "vocab_size": 67 }
def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> List[Tuple[Tensor]]: indices: List[Tuple[np.array]] = [] preds_masks = masks_queries_logits preds_probs = class_queries_logits.softmax(dim=-1) # downsample all masks in one go -> save memory mask_labels = nn.functional.interpolate(mask_labels, size=preds_masks.shape[-2:], mode="nearest") # iterate through batch size for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels): # Compute the classification cost. Contrary to the loss, we don't use the NLL, # but approximate it in 1 - proba[target class]. # The 1 is a constant that doesn't change the matching, it can be ommitted. cost_class = -pred_probs[:, labels] # flatten spatial dimension "q h w -> q (h w)" num_queries, height, width = pred_mask.shape pred_mask_flat = pred_mask.view(num_queries, height * width) # [num_queries, H*W] # same for target_mask "c h w -> c (h w)" num_channels, height, width = target_mask.shape target_mask_flat = target_mask.view(num_channels, height * width) # [num_total_labels, H*W] # compute the focal loss between each mask pairs -> shape [NUM_QUERIES, CLASSES] cost_mask = pair_wise_sigmoid_focal_loss(pred_mask_flat, target_mask_flat) # Compute the dice loss betwen each mask pairs -> shape [NUM_QUERIES, CLASSES] cost_dice = pair_wise_dice_loss(pred_mask_flat, target_mask_flat) # final cost matrix cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice # do the assigmented using the hungarian algorithm in scipy assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.cpu()) indices.append(assigned_indices) # It could be stacked in one tensor matched_indices = [ (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices ] return matched_indices
17,245
81,681
174
awx/main/models/unified_jobs.py
58
14
def cancel_dispatcher_process(self): if not self.celery_task_id: return canceled = [] try: # Use control and reply mechanism to cancel and obtain confirmation timeout = 5 canceled = ControlDispatcher('dispatcher', self.controller_node).cancel([self.celery_task_id]) except socket.timeout: logger.error(f'could not reach dispatcher on {self.controller_node} within {timeou
Refactor canceling to work through messaging and signals, not database If canceled attempted before, still allow attempting another cancel in this case, attempt to send the sigterm signal again. Keep clicking, you might help! Replace other cancel_callbacks with sigterm watcher adapt special inventory mechanism for this too Get rid of the cancel_watcher method with exception in main thread Handle academic case of sigterm race condition Process cancelation as control signal Fully connect cancel method and run_dispatcher to control Never transition workflows directly to canceled, add logs
cancel_dispatcher_process
c59bbdecdbdd920c5d3d298d691129c6bbc94c5e
awx
unified_jobs.py
13
12
https://github.com/ansible/awx.git
4
71
0
49
136
Python
{ "docstring": "Returns True if dispatcher running this job acknowledged request and sent SIGTERM", "language": "en", "n_whitespaces": 11, "n_words": 12, "vocab_size": 12 }
def cancel_dispatcher_process(self): if not self.celery_task_id: return canceled = [] try: # Use control and reply mechanism to cancel and obtain confirmation timeout = 5 canceled = ControlDispatcher('dispatcher', self.controller_node).cancel([self.celery_task_id]) except socket.timeout: logger.error(f'could not reach dispatcher on {self.controller_node} within {timeout}s') except Exception: logger.exception("error encountered when checking task status") return bool(self.celery_task_id in canceled) # True or False, whether confirmation was obtained
36,083
154,573
61
modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe/dataframe.py
18
6
def _mangle_index_names(cls, names): return [ f"__index__{i
FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <[email protected]> Signed-off-by: Andrey Pavlenko <[email protected]>
_mangle_index_names
e5b1888cd932909e49194d58035da34b210b91c4
modin
dataframe.py
10
5
https://github.com/modin-project/modin.git
2
22
0
17
54
Python
{ "docstring": "\n Return mangled index names for index labels.\n\n Mangled names are used for index columns because index\n labels cannot always be used as HDK table column\n names. E.e. label can be a non-string value or an\n unallowed string (empty strings, etc.) for a table column\n name.\n\n Parameters\n ----------\n names : list of str\n Index labels.\n\n Returns\n -------\n list of str\n Mangled names.\n ", "language": "en", "n_whitespaces": 175, "n_words": 61, "vocab_size": 43 }
def _mangle_index_names(cls, names): return [ f"__index__{i}_{'__None__' if n is None else n}" for i, n in enumerate(names) ]
14,444
67,221
69
erpnext/regional/report/gstr_1/gstr_1.py
101
21
def get_b2cs_json(data, gstin): company_state_number = gstin[0:2] out = [] for d in data: if not d.get("place_of_supply"): frappe.throw( _( ).format(frappe.bold("Place Of Supply")) ) pos = d.get("place_of_supply").split("-")[0] tax_details = {} rate = d.get("rate", 0) tax = flt((d["taxable_value"] * rate) / 100.0, 2) if company_state_number == pos: tax_details.update({"camt": flt(tax / 2.0, 2), "samt": flt(tax / 2.0, 2)}) else: tax_details.update({"iamt": tax}) inv = { "sply_ty": "INTRA" if company_state_number == pos else "INTER", "pos": pos, "typ": d.get("type"), "txval": flt(d.get("taxable_value"), 2), "rt": rate, "iamt": flt(tax_details.get("iamt"), 2), "camt": flt(tax_details.get("camt"), 2), "samt": flt(tax_details.get("samt"), 2), "csamt": flt(d.get("cess_amount"), 2), } if d.get("type") == "E" and d.get("ecommerce_gstin"): inv.upda
style: format code with black
get_b2cs_json
494bd9ef78313436f0424b918f200dab8fc7c20b
erpnext
gstr_1.py
16
34
https://github.com/frappe/erpnext.git
7
291
0
76
495
Python
{ "docstring": "{0} not entered in some invoices.\n\t\t\t\tPlease update and try again", "language": "en", "n_whitespaces": 9, "n_words": 11, "vocab_size": 11 }
def get_b2cs_json(data, gstin): company_state_number = gstin[0:2] out = [] for d in data: if not d.get("place_of_supply"): frappe.throw( _( ).format(frappe.bold("Place Of Supply")) ) pos = d.get("place_of_supply").split("-")[0] tax_details = {} rate = d.get("rate", 0) tax = flt((d["taxable_value"] * rate) / 100.0, 2) if company_state_number == pos: tax_details.update({"camt": flt(tax / 2.0, 2), "samt": flt(tax / 2.0, 2)}) else: tax_details.update({"iamt": tax}) inv = { "sply_ty": "INTRA" if company_state_number == pos else "INTER", "pos": pos, "typ": d.get("type"), "txval": flt(d.get("taxable_value"), 2), "rt": rate, "iamt": flt(tax_details.get("iamt"), 2), "camt": flt(tax_details.get("camt"), 2), "samt": flt(tax_details.get("samt"), 2), "csamt": flt(d.get("cess_amount"), 2), } if d.get("type") == "E" and d.get("ecommerce_gstin"): inv.update({"etin": d.get("ecommerce_gstin")}) out.append(inv) return out