semeru/code-text-galeras-code-summarization-3k-deduped

9

save_model

def save_model(model, filepath, weights_format="h5"): if not filepath.endswith(".keras"): raise ValueError( "Invalid filename: expected a `.keras` extension. " f"Received: filepath={filepath}" ) if weights_format == "h5" and h5py is None: raise ImportError("h5py must be installed in order to save a model.") if not model.built: warnings.warn( "You are saving a model that has not yet been built. " "It might not contain any weights yet. " "Consider building the model first by calling it " "on some data.", stacklevel=2, ) saving_v3_enabled_value = getattr(_SAVING_V3_ENABLED, "value", False) _SAVING_V3_ENABLED.value = True serialized_model_dict = serialize_keras_object(model) config_json = json.dumps(serialized_model_dict) metadata_json = json.dumps( { "keras_version": keras.__version__, "date_saved": datetime.datetime.now().strftime("%Y-%m-%d@%H:%M:%S"), } ) try: with zipfile.ZipFile(filepath, "w") as zf: with zf.open(_METADATA_FILENAME, "w") as f: f.write(metadata_json.encode()) with zf.open(_CONFIG_FILENAME, "w") as f: f.write(config_json.encode()) if weights_format == "h5": weights_store = H5IOStore( _VARS_FNAME + ".h5", archive=zf, mode="w" ) elif weights_format == "npz": weights_store = NpzIOStore( _VARS_FNAME + ".npz", archive=zf, mode="w" ) else: raise ValueError( "Unknown weights_format. Expected 'h5' or 'npz'. " f"Received: {weights_format}" ) asset_store = DiskIOStore(_ASSETS_DIRNAME, archive=zf, mode="w") _save_state( model, weights_handler=weights_store, assets_handler=asset_store, inner_path="", visited_trackables=set(), ) weights_store.close() asset_store.close() except Exception as e: raise e finally: _SAVING_V3_ENABLED.value = saving_v3_enabled_value

e6f739a31247c43a86c37c33b0b8b2ba6be6a5f6

17

saving_lib.py

521

- Add standalone weights file saving/loading functionality. - Switch to in-memory, single write / single read archive saving for better performance. - Remove ability to pick between zipping or not zipping a Keras saved artifact: it's always a zip archive now. PiperOrigin-RevId: 483705728

83,285

0

770

291

129

280,200

181

keras

54

keras/saving/experimental/saving_lib.py

Python

59

{ "docstring": "Save a zip-archive representing a Keras model to the given filepath.\n\n The zip-based archive contains the following structure:\n\n - JSON-based configuration file (config.json): Records of model, layer, and\n other trackables' configuration.\n - NPZ-based trackable state files, found in respective directories, such as\n model/states.npz, model/dense_layer/states.npz, etc.\n - Metadata file.\n\n The states of Keras trackables (layers, optimizers, loss, and metrics) are\n automatically saved as long as they can be discovered through the attributes\n returned by `dir(Model)`. Typically, the state includes the variables\n associated with the trackable, but some specially purposed layers may\n contain more such as the vocabularies stored in the hashmaps. The trackables\n define how their states are saved by exposing `save_state()` and\n `load_state()` APIs.\n\n For the case of layer states, the variables will be visited as long as\n they are either 1) referenced via layer attributes, or 2) referenced via a\n container (list, tuple, or dict), and the container is referenced via a\n layer attribute.\n ", "language": "en", "n_whitespaces": 217, "n_words": 155, "vocab_size": 106 }

https://github.com/keras-team/keras.git

3

update_dtype

def update_dtype(self, dtype) -> SparseDtype: cls = type(self) dtype = pandas_dtype(dtype) if not isinstance(dtype, cls): if not isinstance(dtype, np.dtype): raise TypeError("sparse arrays of extension dtypes not supported") fvarr = astype_nansafe(np.array(self.fill_value), dtype) # NB: not fv_0d.item(), as that casts dt64->int fill_value = fvarr[0] dtype = cls(dtype, fill_value=fill_value) return dtype

eb2abb86616978ef6e4971b600849ccabc686de4

13

dtype.py

130

CLN: address xfails (#46287)

39,677

0

153

80

36

165,545

48

pandas

14

pandas/core/arrays/sparse/dtype.py

Python

47

{ "docstring": "\n Convert the SparseDtype to a new dtype.\n\n This takes care of converting the ``fill_value``.\n\n Parameters\n ----------\n dtype : Union[str, numpy.dtype, SparseDtype]\n The new dtype to use.\n\n * For a SparseDtype, it is simply returned\n * For a NumPy dtype (or str), the current fill value\n is converted to the new dtype, and a SparseDtype\n with `dtype` and the new fill value is returned.\n\n Returns\n -------\n SparseDtype\n A new SparseDtype with the correct `dtype` and fill value\n for that `dtype`.\n\n Raises\n ------\n ValueError\n When the current fill value cannot be converted to the\n new `dtype` (e.g. trying to convert ``np.nan`` to an\n integer dtype).\n\n\n Examples\n --------\n >>> SparseDtype(int, 0).update_dtype(float)\n Sparse[float64, 0.0]\n\n >>> SparseDtype(int, 1).update_dtype(SparseDtype(float, np.nan))\n Sparse[float64, nan]\n ", "language": "en", "n_whitespaces": 357, "n_words": 116, "vocab_size": 71 }

https://github.com/pandas-dev/pandas.git

4

convert_to_legacy_optimizer

def convert_to_legacy_optimizer(optimizer): if not isinstance(optimizer, base_optimizer.Optimizer): raise ValueError( "`convert_to_legacy_optimizer` should only be called " "on instances of `tf.keras.optimizers.Optimizer`, but " f"received {optimizer} of type {type(optimizer)}." ) optimizer_name = optimizer.__class__.__name__.lower() config = optimizer.get_config() # Remove fields that only exist in experimental optimizer. keys_to_remove = [ "weight_decay", "use_ema", "ema_momentum", "ema_overwrite_frequency", "jit_compile", "is_legacy_optimizer", ] for key in keys_to_remove: config.pop(key, None) # Learning rate can be a custom LearningRateSchedule, which is stored as # a dict in config, and cannot be deserialized. if isinstance( optimizer._learning_rate, learning_rate_schedule.LearningRateSchedule ): config["learning_rate"] = optimizer._learning_rate legacy_optimizer_config = { "class_name": optimizer_name, "config": config, } return deserialize(legacy_optimizer_config, use_legacy_optimizer=True) @keras_export("keras.optimizers.get")

5a105aadbdc6fde2c2529280c4789864adbb81c7

@keras_export("keras.optimizers.get")

14

__init__.py

220

Move new optimizer out of optimizer_experimental/ directory. PiperOrigin-RevId: 488998585

83,358

1

266

113

82

280,501

98

keras

23

keras/optimizers/__init__.py

Python

28

{ "docstring": "Convert experimental optimizer to legacy optimizer.\n\n This function takes in a `tf.keras.optimizers.experimental.Optimizer`\n instance and converts it to the corresponding\n `tf.keras.optimizers.legacy.Optimizer` instance.\n For example, `tf.keras.optimizers.experimental.Adam(...)` to\n `tf.keras.optimizers.legacy.Adam(...)`.\n\n Args:\n optimizer: An instance of `tf.keras.optimizers.experimental.Optimizer`.\n ", "language": "en", "n_whitespaces": 60, "n_words": 32, "vocab_size": 29 }

https://github.com/keras-team/keras.git

7

is_symbolic_tensor

def is_symbolic_tensor(tensor): if isinstance(tensor, tf.Tensor): return hasattr(tensor, "graph") elif is_extension_type(tensor): component_tensors = tf.nest.flatten(tensor, expand_composites=True) return any(hasattr(t, "graph") for t in component_tensors) elif isinstance(tensor, tf.Variable): # Variables that are output of a Keras Layer in Functional API mode # should be considered symbolic. # TODO(omalleyt): We need a better way to check this in order to # enable `run_eagerly=True` for Models containing Layers that # return Variables as outputs. return ( getattr(tensor, "_keras_history", False) or not tf.executing_eagerly() ) elif isinstance(tensor, tuple(_user_convertible_tensor_types)): tensor = ops.convert_to_tensor_or_composite(tensor) return is_symbolic_tensor(tensor) else: return False @keras_export("keras.__internal__.utils.register_symbolic_tensor_type", v1=[])

84afc5193d38057e2e2badf9c889ea87d80d8fbf

@keras_export("keras.__internal__.utils.register_symbolic_tensor_type", v1=[])

13

tf_utils.py

205

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

81,861

1

220

113

68

277,089

90

keras

22

keras/utils/tf_utils.py

Python

16

{ "docstring": "Returns whether a tensor is symbolic (from a TF graph) or an eager tensor.\n\n A Variable can be seen as either: it is considered symbolic\n when we are in a graph scope, and eager when we are in an eager scope.\n\n Args:\n tensor: A tensor instance to test.\n\n Returns:\n True for symbolic tensors, False for eager tensors.\n ", "language": "en", "n_whitespaces": 82, "n_words": 57, "vocab_size": 41 }

https://github.com/keras-team/keras.git

1

sys_info

def sys_info(self) -> GPUInfo: return GPUInfo(vram=self._vram, driver=self._driver, devices=self._device_names, devices_active=self._active_devices)

bdbbad4d310fb606b6f412aa81e9f57ccd994e97

9

_base.py

54

Refactor lib.gpu_stats (#1218) * inital gpu_stats refactor * Add dummy CPU Backend * Update Sphinx documentation

19,996

0

89

35

9

100,532

9

faceswap

11

lib/gpu_stats/_base.py

Python

20

{ "docstring": " dict: GPU Stats that are required for system information logging.\n\n The dictionary contains the following data:\n\n **vram** (`list`): the total amount of VRAM in Megabytes for each GPU as pertaining to\n :attr:`_handles`\n\n **driver** (`str`): The GPU driver version that is installed on the OS\n\n **devices** (`list`): The device name of each GPU on the system as pertaining\n to :attr:`_handles`\n\n **devices_active** (`list`): The device name of each active GPU on the system as\n pertaining to :attr:`_handles`\n ", "language": "en", "n_whitespaces": 167, "n_words": 75, "vocab_size": 42 }

https://github.com/deepfakes/faceswap.git

9

_normalize_feature_columns

def _normalize_feature_columns(feature_columns): if isinstance( feature_columns, tf.__internal__.feature_column.FeatureColumn ): feature_columns = [feature_columns] if isinstance(feature_columns, collections.abc.Iterator): feature_columns = list(feature_columns) if isinstance(feature_columns, dict): raise ValueError("Expected feature_columns to be iterable, found dict.") for column in feature_columns: if not isinstance(column, tf.__internal__.feature_column.FeatureColumn): raise ValueError( "Items of feature_columns must be a FeatureColumn. " "Given (type {}): {}.".format(type(column), column) ) if not feature_columns: raise ValueError("feature_columns must not be empty.") name_to_column = {} for column in feature_columns: if column.name in name_to_column: raise ValueError( "Duplicate feature column name found for columns: {} " "and {}. This usually means that these columns refer to " "same base feature. Either one must be discarded or a " "duplicated but renamed item must be inserted in " "features dict.".format(column, name_to_column[column.name]) ) name_to_column[column.name] = column return sorted(feature_columns, key=lambda x: x.name)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

15

base_feature_layer.py

266

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

80,944

0

360

160

81

272,032

125

keras

21

keras/feature_column/base_feature_layer.py

Python

29

{ "docstring": "Normalizes the `feature_columns` input.\n\n This method converts the `feature_columns` to list type as best as it can. In\n addition, verifies the type and other parts of feature_columns, required by\n downstream library.\n\n Args:\n feature_columns: The raw feature columns, usually passed by users.\n\n Returns:\n The normalized feature column list.\n\n Raises:\n ValueError: for any invalid inputs, such as empty, duplicated names, etc.\n ", "language": "en", "n_whitespaces": 95, "n_words": 59, "vocab_size": 50 }

https://github.com/keras-team/keras.git

1

test_loadtxt_converter_with_unicode_dtype

def test_loadtxt_converter_with_unicode_dtype(): txt = StringIO('abc,def\nrst,xyz') conv = bytes.upper res = np.loadtxt(txt, dtype=np.dtype("U3"), converters=conv, delimiter=",") expected = np.array([['ABC', 'DEF'], ['RST', 'XYZ']]) assert_equal(res, expected)

66a61b03658f3c9f312505dcf7eab07e4cf91ac6

12

test_io.py

118

Port over tests from npreadtext test suite - Add test for parsing scientific notation. - Add multiple-char comment test. - Port over tests for structured dtypes. - Add tests for exceptions on skiprows/max_rows. - port over ndmin tests. - Make structured data reusable, add unpack tests. - Port over delimiter tests. - Port over maxrows test w/ various dtypes. - Port over test of exception msg on parse failure. - Port over test for converters w/neg indices. - Port over usecols tests - Port over unicode tests. - Port over more converter tests. - Port over test for large rows. - Port over test for string-len discovery. - Port over float conversion accuracy test. - Port over bool test. - Add test for implicit float->int conversion. - Port over complex parsing tests. - Port over tests for reading from generator. - Port over object cleanup test. - Port over bytes incompat test. - Port over converters tests. Co-authored-by: Warren Weckesser <warren.weckesser@gmail.com> Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>

38,421

0

40

67

19

159,776

22

numpy

15

numpy/lib/tests/test_io.py

Python

6

{ "docstring": "\n With the default 'bytes' encoding, tokens are encoded prior to being passed\n to the converter. This means that the output of the converter may be bytes\n instead of unicode as expected by `read_rows`.\n\n This test checks that outputs from the above scenario are properly decoded\n prior to parsing by `read_rows`.\n ", "language": "en", "n_whitespaces": 69, "n_words": 50, "vocab_size": 37 }

https://github.com/numpy/numpy.git

1

required_resources

def required_resources(self) -> Dict[str, float]: return _sum_bundles(self._bundles)

96cceb08e8bf73df990437002e25883c5a72d30c

8

placement_groups.py

33

[tune] Raise error in PGF if head and worker bundles are empty (#28445) Scheduling empty placement groups is not supported by Ray core (see e.g. #28443), so we shouldn't allow them to be created in the first place. If we need fully empty resource requests, we can include this in the upcoming execution/resource refactor. Signed-off-by: Kai Fricke <kai@anyscale.com>

28,458

0

21

20

7

127,516

7

ray

7

python/ray/tune/execution/placement_groups.py

Python

3

{ "docstring": "Returns a dict containing the sums of all resources", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

https://github.com/ray-project/ray.git

1

test_supported_features_ignore_cache

async def test_supported_features_ignore_cache(hass, client): mock_restore_cache( hass, [ State( ENTITY_ID, STATE_OFF, attributes={ ATTR_SUPPORTED_FEATURES: SUPPORT_WEBOSTV | SUPPORT_WEBOSTV_VOLUME, }, ) ], ) await setup_webostv(hass) supported = ( SUPPORT_WEBOSTV | SUPPORT_WEBOSTV_VOLUME | MediaPlayerEntityFeature.VOLUME_SET ) attrs = hass.states.get(ENTITY_ID).attributes assert attrs[ATTR_SUPPORTED_FEATURES] == supported

0ac581a0b1fa438a53f048adfab9b787884a63f6

14

test_media_player.py

107

Cleanup EntityFeature in tests (#78859)

106,957

0

190

69

30

308,196

37

core

18

tests/components/webostv/test_media_player.py

Python

19

{ "docstring": "Test ignore cached supported features if device is on at startup.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

https://github.com/home-assistant/core.git

6

search_next

def search_next(self, count=1): tab = self._current_widget() window_text = self._tabbed_browser.search_text window_options = self._tabbed_browser.search_options if window_text is None: raise cmdutils.CommandError("No search done yet.") tab.scroller.before_jump_requested.emit() if window_text is not None and window_text != tab.search.text: tab.search.clear() tab.search.search(window_text, **window_options) count -= 1 if count == 0: return cb = functools.partial(self._search_cb, tab=tab, old_match=tab.search.match, options=window_options, text=window_text, prev=False) for _ in range(count - 1): tab.search.next_result() tab.search.next_result(result_cb=cb)

265b018c172f8c1f6d9e7f8850256363f0629f82

11

commands.py

243

Add a SearchMatch helper class

117,401

0

291

154

48

320,860

58

qutebrowser

30

qutebrowser/browser/commands.py

Python

20

{ "docstring": "Continue the search to the ([count]th) next term.\n\n Args:\n count: How many elements to ignore.\n ", "language": "en", "n_whitespaces": 40, "n_words": 15, "vocab_size": 13 }

https://github.com/qutebrowser/qutebrowser.git

1

test_logentry_change_message_localized_datetime_input

def test_logentry_change_message_localized_datetime_input(self): post_data = { "site": self.site.pk, "title": "Changed", "hist": "Some content", "created_0": "12/03/2008", "created_1": "11:54", } with translation.override("fr"): change_url = reverse( "admin:admin_utils_article_change", args=[quote(self.a1.pk)] ) response = self.client.post(change_url, post_data) self.assertRedirects( response, reverse("admin:admin_utils_article_changelist") ) logentry = LogEntry.objects.filter( content_type__model__iexact="article" ).latest("id") self.assertEqual(logentry.get_change_message(), "Changed Title and History.")

9c19aff7c7561e3a82978a272ecdaad40dda5c00

16

test_logentry.py

206

Refs #33476 -- Reformatted code with Black.

51,961

0

243

113

39

207,420

43

django

24

tests/admin_utils/test_logentry.py

Python

20

{ "docstring": "\n Localized date/time inputs shouldn't affect changed form data detection.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }

https://github.com/django/django.git

1

test_find_next_time_expression_microseconds

def test_find_next_time_expression_microseconds(): hour_minute_second = (None, "5", "10") test_time = datetime(2022, 5, 13, 0, 5, 9, tzinfo=dt_util.UTC) matching_hours, matching_minutes, matching_seconds = _get_matches( *hour_minute_second ) next_time = dt_util.find_next_time_expression_time( test_time, matching_seconds, matching_minutes, matching_hours ) assert next_time == datetime(2022, 5, 13, 0, 5, 10, tzinfo=dt_util.UTC) next_time_last_microsecond_plus_one = next_time.replace( microsecond=999999 ) + timedelta(seconds=1) time_after = dt_util.find_next_time_expression_time( next_time_last_microsecond_plus_one, matching_seconds, matching_minutes, matching_hours, ) assert time_after == datetime(2022, 5, 13, 1, 5, 10, tzinfo=dt_util.UTC)

4e9bc9eaffd464f192d187a01771a86699b2f932

10

test_dt.py

197

Small cleanups to find_next_time_expression and addition of tests (#71845)

99,553

0

154

139

36

300,693

66

core

19

tests/util/test_dt.py

Python

20

{ "docstring": "Test finding next time expression with microsecond clock drift.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

https://github.com/home-assistant/core.git

2

test_float32_float64_equivalence

def test_float32_float64_equivalence(is_sparse): rng = np.random.RandomState(0) X = rng.rand(10, 2) if is_sparse: X[X < 0.8] = 0 X = sp.csr_matrix(X) km64 = BisectingKMeans(n_clusters=3, random_state=0).fit(X) km32 = BisectingKMeans(n_clusters=3, random_state=0).fit(X.astype(np.float32)) assert_allclose(km32.cluster_centers_, km64.cluster_centers_) assert_array_equal(km32.labels_, km64.labels_)

0822851f5cb17827939a7d7b4f8c84f43184ae89

11

test_bisect_k_means.py

167

FEA Bisecting K-Means (#20031) Co-authored-by: Gael Varoquaux <gael.varoquaux@normalesup.org> Co-authored-by: Tom Dupré la Tour <tom.dupre-la-tour@m4x.org> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

75,910

0

69

108

24

259,765

31

scikit-learn

22

sklearn/cluster/tests/test_bisect_k_means.py

Python

10

{ "docstring": "Check that the results are the same between float32 and float64.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 10 }

https://github.com/scikit-learn/scikit-learn.git

1

test_all_day_reader_access

async def test_all_day_reader_access(hass, mock_events_list_items, component_setup): week_from_today = dt_util.now().date() + datetime.timedelta(days=7) end_event = week_from_today + datetime.timedelta(days=1) event = { **TEST_EVENT, "start": {"date": week_from_today.isoformat()}, "end": {"date": end_event.isoformat()}, } mock_events_list_items([event]) assert await component_setup() state = hass.states.get(TEST_ENTITY) assert state.name == TEST_ENTITY_NAME assert state.state == STATE_OFF assert dict(state.attributes) == { "friendly_name": TEST_ENTITY_NAME, "message": event["summary"], "all_day": True, "offset_reached": False, "start_time": week_from_today.strftime(DATE_STR_FORMAT), "end_time": end_event.strftime(DATE_STR_FORMAT), "location": event["location"], "description": event["description"], } @pytest.mark.parametrize("calendar_access_role", ["reader", "freeBusyReader"])

5d1ca73a3491f0abf5925e01465c4525a49dafef

@pytest.mark.parametrize("calendar_access_role", ["reader", "freeBusyReader"])

12

test_calendar.py

312

Add create and delete for Google Calendar events (#83034) * Add Google Calendar create/delete support Includes editing for recurring events * Fix default calendar access role * Formatting improvements * Address other details that have changed due to local sync * Update tests/components/google/test_calendar.py Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Update tests/components/google/test_calendar.py Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Update tests/components/google/test_calendar.py Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Increase test coverage Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

95,868

1

177

167

52

296,896

65

core

29

tests/components/google/test_calendar.py

Python

23

{ "docstring": "Test that reader / freebusy reader access can load properly.", "language": "en", "n_whitespaces": 9, "n_words": 10, "vocab_size": 9 }

https://github.com/home-assistant/core.git

4

_set_resize_callback

def _set_resize_callback(self): if self._full_size: logger.debug("Setting resize callback for actual size display") for fig, size in self._images.values(): self._resize_ids.append((fig, fig.canvas.mpl_connect("resize_event", self._on_resize))) fig.set_size_inches(size) else: logger.debug("Removing resize callback for screen-fit display") for fig, cid in self._resize_ids: fig.canvas.mpl_disconnect(cid) self._resize_ids = []

7b9fc0454d982a2425ec44e90e5b05a87d149953

15

train.py

151

Live Preview - Replace cv2 with matplotlib viewer

20,476

0

225

90

27

101,037

36

faceswap

17

scripts/train.py

Python

12

{ "docstring": " Sets the resize callback if displaying preview at actual size or removes it if\n displaying at screen-fit size. ", "language": "en", "n_whitespaces": 26, "n_words": 18, "vocab_size": 15 }

https://github.com/deepfakes/faceswap.git

2

block

def block(self, *, extra = None): self.write(":") if extra: self.write(extra) self._indent += 1 yield self._indent -= 1

8198943edd73a363c266633e1aa5b2a9e9c9f526

9

ast.py

67

add python 3.10.4 for windows

55,949

0

70

38

15

220,242

17

XX-Net

5

python3.10.4/Lib/ast.py

Python

7

{ "docstring": "A context manager for preparing the source for blocks. It adds\n the character':', increases the indentation on enter and decreases\n the indentation on exit. If *extra* is given, it will be directly\n appended after the colon character.\n ", "language": "en", "n_whitespaces": 65, "n_words": 37, "vocab_size": 30 }

https://github.com/XX-net/XX-Net.git

2

get_avail_mem_per_ray_worker_node

def get_avail_mem_per_ray_worker_node(spark, object_store_memory_per_node): num_cpus_per_spark_task = int( spark.sparkContext.getConf().get("spark.task.cpus", "1") )

e76ccee69aaa7583be1a9d81cf7b2aa72cf25647

13

utils.py

49

Ray on spark implementation (#28771) REP: ray-project/enhancements#14

31,216

0

25

83

9

137,681

9

ray

8

python/ray/util/spark/utils.py

Python

20

{ "docstring": "\n Return the available heap memory and object store memory for each ray worker.\n NB: We have one ray node per spark task.\n ", "language": "en", "n_whitespaces": 32, "n_words": 22, "vocab_size": 20 }

https://github.com/ray-project/ray.git

2

get_total_accepted_amount

def get_total_accepted_amount(scorecard): supplier = frappe.get_doc("Supplier", scorecard.supplier) # Look up all PO Items with delivery dates between our dates data = frappe.db.sql( , {"supplier": supplier.name, "start_date": scorecard.start_date, "end_date": scorecard.end_date}, as_dict=0, )[0][0] if not data: data = 0 return data

494bd9ef78313436f0424b918f200dab8fc7c20b

13

supplier_scorecard_variable.py

114

style: format code with black

13,930

0

27

68

33

65,553

38

erpnext

12

erpnext/buying/doctype/supplier_scorecard_variable/supplier_scorecard_variable.py

Python

20

{ "docstring": "Gets the total amount (in company currency) accepted in the period (based on Purchase Receipts)\n\t\t\tSELECT\n\t\t\t\tSUM(pr_item.qty * pr_item.base_rate)\n\t\t\tFROM\n\t\t\t\t`tabPurchase Receipt Item` pr_item,\n\t\t\t\t`tabPurchase Receipt` pr\n\t\t\tWHERE\n\t\t\t\tpr.supplier = %(supplier)s\n\t\t\t\tAND pr.posting_date BETWEEN %(start_date)s AND %(end_date)s\n\t\t\t\tAND pr_item.docstatus = 1\n\t\t\t\tAND pr_item.parent = pr.name", "language": "en", "n_whitespaces": 34, "n_words": 45, "vocab_size": 38 }

https://github.com/frappe/erpnext.git

4

icon

def icon(name=None, classname=None, title=None, wrapped=False, class_name=None): if not name: raise ValueError("You must supply an icon name") return { "name": name, # supporting class_name for backwards compatibility "classname": classname or class_name or "icon", "title": title, "wrapped": wrapped, } @register.filter()

3d484e133dbf59ebc36da9a40172a454315b95b7

@register.filter()

10

wagtailadmin_tags.py

106

Update icon template to allow `classname` - Preserve the existing `class_name` behaviour in most other cases - Update only docs reference to use `classname` - Relates to #6107 & #6028

17,004

1

91

56

36

80,081

38

wagtail

9

wagtail/admin/templatetags/wagtailadmin_tags.py

Python

9

{ "docstring": "\n Abstracts away the actual icon implementation.\n\n Usage:\n {% load wagtailadmin_tags %}\n ...\n {% icon name=\"cogs\" classname=\"icon--red\" title=\"Settings\" %}\n\n :param name: the icon name/id, required (string)\n :param classname: defaults to 'icon' if not provided (string)\n :param title: accessible label intended for screen readers (string)\n :return: Rendered template snippet (string)\n ", "language": "en", "n_whitespaces": 91, "n_words": 48, "vocab_size": 38 }

https://github.com/wagtail/wagtail.git

1

test_querysets

def test_querysets(self): self.assertQuerysetEqual( Employee.objects.filter(pk=123), [ "Dan Jones", ], str, ) self.assertQuerysetEqual( Employee.objects.filter(employee_code=123), [ "Dan Jones", ], str, ) self.assertQuerysetEqual( Employee.objects.filter(pk__in=[123, 456]), [ "Fran Bones", "Dan Jones", ], str, ) self.assertQuerysetEqual( Employee.objects.all(), [ "Fran Bones", "Dan Jones", ], str, ) self.assertQuerysetEqual( Business.objects.filter(name="Sears"), ["Sears"], lambda b: b.name ) self.assertQuerysetEqual( Business.objects.filter(pk="Sears"), [ "Sears", ], lambda b: b.name, )

9c19aff7c7561e3a82978a272ecdaad40dda5c00

11

tests.py

250

Refs #33476 -- Reformatted code with Black.

50,144

0

482

157

24

202,523

55

django

14

tests/custom_pk/tests.py

Python

41

{ "docstring": "\n Both pk and custom attribute_name can be used in filter and friends\n ", "language": "en", "n_whitespaces": 27, "n_words": 12, "vocab_size": 11 }

https://github.com/django/django.git

4

_set_session_summary

def _set_session_summary(self, message): if self._thread is None: logger.debug("Setting session summary. (message: '%s')", message) self._thread = LongRunningTask(target=self._summarise_data, args=(Session, ), widget=self) self._thread.start() self.after(1000, lambda msg=message: self._set_session_summary(msg)) elif not self._thread.complete.is_set(): logger.debug("Data not yet available") self.after(1000, lambda msg=message: self._set_session_summary(msg)) else: logger.debug("Retrieving data from thread") result = self._thread.get_result() if result is None: logger.debug("No result from session summary. Clearing analysis view") self._clear_session() return self._summary = result self._thread = None self.set_info(f"Session: {message}") self._stats.tree_insert_data(self._summary)

adb5975c94f0fb10296ef7f0c8d087d03a436e3c

13

display_analysis.py

283

Graph popup - Always open in same position

19,968

0

366

168

47

100,497

66

faceswap

24

lib/gui/display_analysis.py

Python

22

{ "docstring": " Set the summary data and info message.\n\n Parameters\n ----------\n message: str\n The information message to set\n ", "language": "en", "n_whitespaces": 56, "n_words": 16, "vocab_size": 16 }

https://github.com/deepfakes/faceswap.git

4

slice_filter

def slice_filter(value, arg): try: bits = [] for x in str(arg).split(":"): if not x: bits.append(None) else: bits.append(int(x)) return value[slice(*bits)] except (ValueError, TypeError): return value # Fail silently. @register.filter(is_safe=True, needs_autoescape=True)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

@register.filter(is_safe=True, needs_autoescape=True)

16

defaultfilters.py

132

Refs #33476 -- Reformatted code with Black.

51,440

1

118

66

28

206,249

29

django

16

django/template/defaultfilters.py

Python

11

{ "docstring": "\n Return a slice of the list using the same syntax as Python's list slicing.\n ", "language": "en", "n_whitespaces": 21, "n_words": 14, "vocab_size": 12 }

https://github.com/django/django.git

1

clear

def clear(self) -> AwaitRemove: await_remove = self.query("ListView > ListItem").remove() self.index = None return await_remove

853d05631d69044c17dfbc568bb887128d704a1a

11

_list_view.py

49

PR feedback

45,298

0

42

27

12

186,022

14

textual

7

src/textual/widgets/_list_view.py

Python

10

{ "docstring": "Clear all items from the ListView.\n\n Returns:\n AwaitRemove: An awaitable that yields control to the event loop until\n the DOM has been updated to reflect all children being removed.\n ", "language": "en", "n_whitespaces": 69, "n_words": 29, "vocab_size": 25 }

https://github.com/Textualize/textual.git

8

_get_images

def _get_images(self): logger.debug("Getting image paths") images = {} for side in ("a", "b"): image_dir = getattr(self._args, f"input_{side}") if not os.path.isdir(image_dir): logger.error("Error: '%s' does not exist", image_dir) sys.exit(1) images[side] = get_image_paths(image_dir, ".png") if not images[side]: logger.error("Error: '%s' contains no images", image_dir) sys.exit(1) # Validate the first image is a detected face test_image = next(img for img in images[side]) meta = read_image_meta(test_image) logger.debug("Test file: (filename: %s, metadata: %s)", test_image, meta) if "itxt" not in meta or "alignments" not in meta["itxt"]: logger.error("The input folder '%s' contains images that are not extracted faces.", image_dir) logger.error("You can only train a model on faces generated from Faceswap's " "extract process. Please check your sources and try again.") sys.exit(1) logger.info("Model %s Directory: '%s' (%s images)", side.upper(), image_dir, len(images[side])) logger.debug("Got image paths: %s", [(key, str(len(val)) + " images") for key, val in images.items()]) self._validate_image_counts(images) return images

0f7ee1603f093e70496da1585f137f268c0c5f87

14

train.py

375

training - Enable resize in popup preview image

20,070

0

525

219

105

100,607

138

faceswap

29

scripts/train.py

Python

27

{ "docstring": " Check the image folders exist and contains valid extracted faces. Obtain image paths.\n\n Returns\n -------\n dict\n The image paths for each side. The key is the side, the value is the list of paths\n for that side.\n ", "language": "en", "n_whitespaces": 88, "n_words": 37, "vocab_size": 27 }

https://github.com/deepfakes/faceswap.git