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1 | makeDllEntryPoint | def makeDllEntryPoint(cls, source_path, dest_path, package_name):
return makeDllEntryPoint(
source_path=source_path, dest_path=dest_path, package_name=package_name
)
| 56eb59d93f13815e66d0dea07e7669dfe275fa10 | 8 | PluginBase.py | 40 | Plugins: Massive cleanup and API improvements and Kivy support
* Added method to locate a DLL and to create a DLL entry point
as expected, removing need for imports and making it more
clear as an API.
* The location of modules had already an API, but it wasn'
used where it could be.
* Moved implicit imports and DLL usage for Gi to its plugin,
solving a TODO for it.
* Make sure sure to only yield, and not return, that is just
more error prone.
* Also allow generators for implicit dependencies, such that
generators work in a yield from fashion.
* With this, Kivy apps work on at least Linux. | 42,655 | 0 | 43 | 27 | 11 | 178,303 | 11 | Nuitka | 5 | nuitka/plugins/PluginBase.py | Python | 4 | {
"docstring": "Create an entry point, as expected to be provided by getExtraDlls.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | https://github.com/Nuitka/Nuitka.git |
|
1 | contains_point | def contains_point(self, point):
return self.patch.contains_point(point, radius=1.0)
| f156db08eee54d285ab0fb4e031e48d078ba6aa3 | 8 | _base.py | 34 | DOC: More cleanup axes -> Axes | 22,768 | 0 | 20 | 23 | 6 | 107,479 | 6 | matplotlib | 5 | lib/matplotlib/axes/_base.py | Python | 2 | {
"docstring": "\n Return whether *point* (pair of pixel coordinates) is inside the Axes\n patch.\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 12,
"vocab_size": 12
} | https://github.com/matplotlib/matplotlib.git |
|
13 | to_dict | def to_dict(self) -> dict:
if self._data_dict:
# If the checkpoint data is already a dict, return
checkpoint_data = self._data_dict
elif self._obj_ref:
# If the checkpoint data is an object reference, resolve
checkpoint_data = ray.get(self._obj_ref)
elif self._local_path or self._uri:
# Else, checkpoint is either on FS or external storage
with self.as_directory() as local_path:
checkpoint_data_path = os.path.join(
local_path, _DICT_CHECKPOINT_FILE_NAME
)
if os.path.exists(checkpoint_data_path):
# If we are restoring a dict checkpoint, load the dict
# from the checkpoint file.
with open(checkpoint_data_path, "rb") as f:
checkpoint_data = pickle.load(f)
# If there are additional files in the directory, add them as
# _DICT_CHECKPOINT_ADDITIONAL_FILE_KEY
additional_files = {}
for file_or_dir in os.listdir(local_path):
if file_or_dir in [
".",
"..",
_DICT_CHECKPOINT_FILE_NAME,
_CHECKPOINT_METADATA_FILE_NAME,
]:
continue
additional_files[file_or_dir] = _pack(
os.path.join(local_path, file_or_dir)
)
if additional_files:
checkpoint_data[
_DICT_CHECKPOINT_ADDITIONAL_FILE_KEY
] = additional_files
else:
files = [
f
for f in os.listdir(local_path)
if os.path.isfile(os.path.join(local_path, f))
and f.endswith(_METADATA_CHECKPOINT_SUFFIX)
]
metadata = {}
for file in files:
with open(os.path.join(local_path, file), "rb") as f:
key = file[: -len(_METADATA_CHECKPOINT_SUFFIX)]
value = pickle.load(f)
metadata[key] = value
data = _pack(local_path)
checkpoint_data = {
_FS_CHECKPOINT_KEY: data,
}
checkpoint_data.update(metadata)
else:
raise RuntimeError(f"Empty data for checkpoint {self}")
return _CheckpointDict(checkpoint_data, metadata=self._metadata)
| 5034544d5df5d7d9596b261d7bdffdd28e76fe2b | 23 | checkpoint.py | 465 | [AIR] Maintain checkpoint type information during serialization (#28387)
These changes are needed to fix the errors described in #28134.
Signed-off-by: Balaji Veeramani <[email protected]>
Co-authored-by: Amog Kamsetty <[email protected]> | 28,585 | 0 | 1,257 | 280 | 112 | 127,998 | 186 | ray | 43 | python/ray/air/checkpoint.py | Python | 60 | {
"docstring": "Return checkpoint data as dictionary.\n\n .. note::\n :meth:`~Checkpoint.to_dict` returns a ``dict`` subclass that contains\n information about the checkpoint type. This ``dict`` subclass is\n functionally identical to the built-in ``dict``.\n\n Returns:\n dict: Dictionary containing checkpoint data.\n ",
"language": "en",
"n_whitespaces": 100,
"n_words": 35,
"vocab_size": 30
} | https://github.com/ray-project/ray.git |
|
5 | fill_properties | def fill_properties(self, address, props_dict):
device = self._devices[Address(address)]
operating_flags = props_dict.get("operating_flags", {})
properties = props_dict.get("properties", {})
with patch("pyinsteon.subscriber_base.publish_topic", MagicMock()):
for flag in operating_flags:
value = operating_flags[flag]
if device.operating_flags.get(flag):
device.operating_flags[flag].load(value)
for flag in properties:
value = properties[flag]
if device.properties.get(flag):
device.properties[flag].load(value)
| 781ec87dff674b5f47f4138552d818cc490b5201 | 15 | mock_devices.py | 190 | Fix Insteon tests (#71092) | 98,486 | 0 | 193 | 117 | 28 | 299,558 | 38 | core | 15 | tests/components/insteon/mock_devices.py | Python | 13 | {
"docstring": "Fill the operating flags and extended properties of a device.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/home-assistant/core.git |
|
1 | set_freqai_targets | def set_freqai_targets(self, dataframe, **kwargs):
return dataframe
###
# END - Intended to be overridden by strategy
###
| c2936d551b8ad6ccf7b57e2ac6cb55d8550622cf | 6 | interface.py | 25 | improve doc, update test strats, change function names | 35,164 | 0 | 28 | 13 | 16 | 151,922 | 17 | freqtrade | 4 | freqtrade/strategy/interface.py | Python | 2 | {
"docstring": "\n *Only functional with FreqAI enabled strategies*\n Required function to set the targets for the model.\n All targets must be prepended with `&` to be recognized by the FreqAI internals.\n\n More details about feature engineering available:\n\n https://www.freqtrade.io/en/latest/freqai-feature-engineering\n\n :param df: strategy dataframe which will receive the targets\n usage example: dataframe[\"&-target\"] = dataframe[\"close\"].shift(-1) / dataframe[\"close\"]\n ",
"language": "en",
"n_whitespaces": 109,
"n_words": 52,
"vocab_size": 43
} | https://github.com/freqtrade/freqtrade.git |
|
2 | __exit__ | def __exit__(self, type, value, traceback):
if self.is_locked:
self.release()
| c541f4ba1fcab8304c7ac4efdce3d63a2e478176 | 9 | file_lock.py | 36 | Add model parallel for FasterGPT. (#1755)
* Add model parallel for FasterGPT.
* Make GPT model parallel runable
* Make FT model parallel optional.
* Fix _write_setup_file when kwargs is not empty.
* Fix ext_utils.load
* Add file lock for model parallel.
* Fix model_parallel.flag in CMakeLists.txt.
* Use a separated lib for model parallel.
* Make from_pretrained get partial model.
* Make model parallel support layer group in python.
* Fix fit_partial_model when model having keys state not including.
Add api docs for model parallel.
* Fix the default world_size when mpi is not available.
* Add demo for GPT model parallel.
* Fix default global ft_para_conf.
* Fix GPTModel state_dict wrapper for layer parallel.
* Set seed for tensor parallel.
* Fix location of gpt.h in cmake.
* Fix seed in gpt.h
* Fix NV FT GPT embedding.
* Add more cases in gpt_mp_sample.py
* Fix seed in ker_curand_setupLauncher.
Put build dir of FG in PPNLP_HOME with digest of current path.
* Refine gpt_mp_sample.py | 118,415 | 0 | 33 | 22 | 8 | 323,211 | 8 | PaddleNLP | 7 | paddlenlp/utils/file_lock.py | Python | 3 | {
"docstring": " Activated at the end of the with statement.\n It automatically releases the lock if it isn't locked.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 17,
"vocab_size": 15
} | https://github.com/PaddlePaddle/PaddleNLP.git |
|
2 | pip_imported_during_build | def pip_imported_during_build():
import traceback
return any(
frame.f_globals['__file__'].endswith('setup.py')
for frame, line in traceback.walk_stack(None)
)
DISTUTILS_FINDER = DistutilsMetaFinder()
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 11 | __init__.py | 68 | upd; format | 12,171 | 0 | 65 | 33 | 16 | 60,461 | 16 | transferlearning | 10 | .venv/lib/python3.8/site-packages/_distutils_hack/__init__.py | Python | 6 | {
"docstring": "\n Detect if pip is being imported in a build script. Ref #2355.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 12,
"vocab_size": 12
} | https://github.com/jindongwang/transferlearning.git |
|
3 | compute_loss | def compute_loss(self, batch, return_output=False):
if batch.label_vec is None:
raise ValueError('Cannot compute loss without a label.')
model_output = self.model(*self._model_input(batch), ys=batch.label_vec)
scores, preds, *_ = model_output
score_view = scores.reshape(-1, scores.size(-1))
loss_flattened = self.criterion(score_view, batch.label_vec.view(-1))
loss_per_token = loss_flattened.view(scores.shape[:-1])
notnull = batch.label_vec.ne(self.NULL_IDX)
# save loss to metrics
# cross entropy loss
self.record_local_metric(
'loss', AverageMetric.from_mask(loss_per_token, notnull)
)
# perplexity
self.record_local_metric('ppl', PPLMetric.from_mask(loss_per_token, notnull))
# token-wise accuracy
self.record_local_metric(
'token_acc', AverageMetric.from_mask(batch.label_vec == preds, notnull)
)
# utterance-wise exact match
num_target_tokens = notnull.long().sum(dim=-1)
num_tokens_correct = ((batch.label_vec == preds) * notnull).sum(dim=-1)
self.record_local_metric(
'token_em', AverageMetric.many(num_tokens_correct == num_target_tokens)
)
# actually do backwards loss
loss = loss_per_token.sum(dim=1)
loss = loss.sum()
loss /= num_target_tokens.sum() # average loss per token
if return_output:
return (loss, model_output)
else:
return loss
| 05ff60935043cf39192535eac38e2730212057c7 | 13 | torch_generator_agent.py | 410 | Add Metric.from_mask helper method (#3411) (#4894)
* Add Metric.from_mask helper method (#3411)
* Use cls directly instead of passing in MyMetric | 47,269 | 0 | 377 | 252 | 80 | 195,534 | 114 | ParlAI | 35 | parlai/core/torch_generator_agent.py | Python | 28 | {
"docstring": "\n Compute and return the loss for the given batch.\n\n Easily overridable for customized loss functions.\n\n If return_output is True, the full output from the call to self.model()\n is also returned, via a (loss, model_output) pair.\n ",
"language": "en",
"n_whitespaces": 71,
"n_words": 35,
"vocab_size": 29
} | https://github.com/facebookresearch/ParlAI.git |
|
2 | _relative_position_bucket | def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
# now relative_position is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_position_if_large = torch.min(
relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
)
relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
return relative_buckets
| a72f1c9f5b907f96cbb7de3bbb02a1d431d34071 | 17 | modeling_longt5.py | 240 | Add `LongT5` model (#16792)
* Initial commit
* Make some fixes
* Make PT model full forward pass
* Drop TF & Flax implementation, fix copies etc
* Add Flax model and update some corresponding stuff
* Drop some TF things
* Update config and flax local attn
* Add encoder_attention_type to config
* .
* Update docs
* Do some cleansing
* Fix some issues -> make style; add some docs
* Fix position_bias + mask addition + Update tests
* Fix repo consistency
* Fix model consistency by removing flax operation over attn_mask
* [WIP] Add PT TGlobal LongT5
* .
* [WIP] Add flax tglobal model
* [WIP] Update flax model to use the right attention type in the encoder
* Fix flax tglobal model forward pass
* Make the use of global_relative_attention_bias
* Add test suites for TGlobal model
* Fix minor bugs, clean code
* Fix pt-flax equivalence though not convinced with correctness
* Fix LocalAttn implementation to match the original impl. + update READMEs
* Few updates
* Update: [Flax] improve large model init and loading #16148
* Add ckpt conversion script accoring to #16853 + handle torch device placement
* Minor updates to conversion script.
* Typo: AutoModelForSeq2SeqLM -> FlaxAutoModelForSeq2SeqLM
* gpu support + dtype fix
* Apply some suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
Co-authored-by: Patrick von Platen <[email protected]>
* * Remove (de)parallelize stuff
* Edit shape comments
* Update README.md
* make fix-copies
* Remove caching logic for local & tglobal attention
* Apply another batch of suggestions from code review
* Add missing checkpoints
* Format converting scripts
* Drop (de)parallelize links from longT5 mdx
* Fix converting script + revert config file change
* Revert "Remove caching logic for local & tglobal attention"
This reverts commit 2a619828f6ddc3e65bd9bb1725a12b77fa883a46.
* Stash caching logic in Flax model
* Make side relative bias used always
* Drop caching logic in PT model
* Return side bias as it was
* Drop all remaining model parallel logic
* Remove clamp statements
* Move test files to the proper place
* Update docs with new version of hf-doc-builder
* Fix test imports
* Make some minor improvements
* Add missing checkpoints to docs
* Make TGlobal model compatible with torch.onnx.export
* Replace some np.ndarray with jnp.ndarray
* Fix TGlobal for ONNX conversion + update docs
* fix _make_global_fixed_block_ids and masked neg value
* update flax model
* style and quality
* fix imports
* remove load_tf_weights_in_longt5 from init and fix copies
* add slow test for TGlobal model
* typo fix
* Drop obsolete is_parallelizable and one warning
* Update __init__ files to fix repo-consistency
* fix pipeline test
* Fix some device placements
* [wip]: Update tests -- need to generate summaries to update expected_summary
* Fix quality
* Update LongT5 model card
* Update (slow) summarization tests
* make style
* rename checkpoitns
* finish
* fix flax tests
Co-authored-by: phungvanduy <[email protected]>
Co-authored-by: Sylvain Gugger <[email protected]>
Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: patil-suraj <[email protected]> | 5,721 | 0 | 301 | 150 | 70 | 31,289 | 108 | transformers | 20 | src/transformers/models/longt5/modeling_longt5.py | Python | 20 | {
"docstring": "\n Adapted from Mesh Tensorflow:\n https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593\n\n Translate relative position to a bucket number for relative attention. The relative position is defined as\n memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to\n position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for\n small absolute relative_position and larger buckets for larger absolute relative_positions. All relative\n positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.\n This should allow for more graceful generalization to longer sequences than the model has been trained on\n\n Args:\n relative_position: an int32 Tensor\n bidirectional: a boolean - whether the attention is bidirectional\n num_buckets: an integer\n max_distance: an integer\n\n Returns:\n a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)\n ",
"language": "en",
"n_whitespaces": 265,
"n_words": 132,
"vocab_size": 88
} | https://github.com/huggingface/transformers.git |
|
12 | _file_lists | def _file_lists(load, form):
if "env" in load:
# "env" is not supported; Use "saltenv".
load.pop("env")
saltenv = load["saltenv"]
actual_saltenv = saltenv
if saltenv not in __opts__["file_roots"]:
if "__env__" in __opts__["file_roots"]:
log.debug(
"salt environment '%s' maps to __env__ file_roots directory", saltenv
)
saltenv = "__env__"
else:
return []
list_cachedir = os.path.join(__opts__["cachedir"], "file_lists", "roots")
if not os.path.isdir(list_cachedir):
try:
os.makedirs(list_cachedir)
except OSError:
log.critical("Unable to make cachedir %s", list_cachedir)
return []
list_cache = os.path.join(
list_cachedir, "{}.p".format(salt.utils.files.safe_filename_leaf(saltenv))
)
w_lock = os.path.join(
list_cachedir, ".{}.w".format(salt.utils.files.safe_filename_leaf(saltenv))
)
cache_match, refresh_cache, save_cache = salt.fileserver.check_file_list_cache(
__opts__, form, list_cache, w_lock
)
if cache_match is not None:
return cache_match
if refresh_cache:
ret = {"files": set(), "dirs": set(), "empty_dirs": set(), "links": {}}
| 52e1d0b8116c86777c85cb6c3d940e2c04a518c4 | 14 | roots.py | 377 | add __env__ substitution inside file and pillar root paths | 54,610 | 0 | 322 | 366 | 70 | 216,493 | 108 | salt | 31 | salt/fileserver/roots.py | Python | 51 | {
"docstring": "\n Return a dict containing the file lists for files, dirs, emtydirs and symlinks\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 13,
"vocab_size": 13
} | https://github.com/saltstack/salt.git |
|
4 | _preprocess_conv2d_input | def _preprocess_conv2d_input(x, data_format, force_transpose=False):
tf_data_format = "NHWC"
if data_format == "channels_first":
if not _has_nchw_support() or force_transpose:
x = tf.compat.v1.transpose(x, (0, 2, 3, 1)) # NCHW -> NHWC
else:
tf_data_format = "NCHW"
return x, tf_data_format
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 14 | backend.py | 99 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 80,102 | 0 | 83 | 59 | 29 | 269,467 | 34 | keras | 10 | keras/backend.py | Python | 8 | {
"docstring": "Transpose and cast the input before the conv2d.\n\n Args:\n x: input tensor.\n data_format: string, `\"channels_last\"` or `\"channels_first\"`.\n force_transpose: Boolean. If True, the input will always be transposed\n from NCHW to NHWC if `data_format` is `\"channels_first\"`.\n If False, the transposition only occurs on CPU (GPU ops are\n assumed to support NCHW).\n\n Returns:\n A tensor.\n ",
"language": "en",
"n_whitespaces": 123,
"n_words": 53,
"vocab_size": 44
} | https://github.com/keras-team/keras.git |
|
1 | test_changelist_field_classes | def test_changelist_field_classes(self):
Podcast.objects.create(name="Django Dose", release_date=datetime.date.today())
response = self.client.get(reverse("admin:admin_views_podcast_changelist"))
self.assertContains(response, '<th class="field-name">')
self.assertContains(response, '<td class="field-release_date nowrap">')
self.assertContains(response, '<td class="action-checkbox">')
try:
import docutils
except ImportError:
docutils = None
@unittest.skipUnless(docutils, "no docutils installed.")
@override_settings(ROOT_URLCONF="admin_views.urls")
@modify_settings(
INSTALLED_APPS={"append": ["django.contrib.admindocs", "django.contrib.flatpages"]}
) | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | @unittest.skipUnless(docutils, "no docutils installed.")
@override_settings(ROOT_URLCONF="admin_views.urls")
@modify_settings(
INSTALLED_APPS={"append": ["django.contrib.admindocs", "django.contrib.flatpages"]}
) | 11 | tests.py | 186 | Refs #33476 -- Reformatted code with Black. | 52,031 | 1 | 81 | 63 | 30 | 207,636 | 36 | django | 23 | tests/admin_views/tests.py | Python | 6 | {
"docstring": "\n Cells of the change list table should contain the field name in their class attribute\n Refs #11195.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 17,
"vocab_size": 16
} | https://github.com/django/django.git |
1 | time | def time(self) -> npt.NDArray[np.object_]:
# If the Timestamps have a timezone that is not UTC,
# convert them into their i8 representation while
# keeping their timezone and not using UTC
timestamps = self._local_timestamps()
return ints_to_pydatetime(timestamps, box="time")
| 521259299f7829da667ba39302ec77acedde9e5e | 9 | datetimes.py | 56 | DOC: Improve doc summaries in series.rst (#45237) | 39,396 | 0 | 79 | 31 | 32 | 163,188 | 37 | pandas | 10 | pandas/core/arrays/datetimes.py | Python | 8 | {
"docstring": "\n Returns numpy array of :class:`datetime.time` objects.\n\n The time part of the Timestamps.\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 12,
"vocab_size": 11
} | https://github.com/pandas-dev/pandas.git |
|
10 | putalpha | def putalpha(self, alpha):
self._ensure_mutable()
if self.mode not in ("LA", "PA", "RGBA"):
# attempt to promote self to a matching alpha mode
try:
mode = getmodebase(self.mode) + "A"
try:
self.im.setmode(mode)
except (AttributeError, ValueError) as e:
# do things the hard way
im = self.im.convert(mode)
if im.mode not in ("LA", "PA", "RGBA"):
raise ValueError from e # sanity check
self.im = im
self.pyaccess = None
self.mode = self.im.mode
except KeyError as e:
msg = "illegal image mode"
raise ValueError(msg) from e
if self.mode in ("LA", "PA"):
band = 1
else:
band = 3
if isImageType(alpha):
# alpha layer
if alpha.mode not in ("1", "L"):
msg = "illegal image mode"
raise ValueError(msg)
alpha.load()
if alpha.mode == "1":
alpha = alpha.convert("L")
else:
# constant alpha
try:
self.im.fillband(band, alpha)
except (AttributeError, ValueError):
# do things the hard way
alpha = new("L", self.size, alpha)
else:
return
self.im.putband(alpha.im, band)
| 2ae55ccbdad9c842929fb238ea1eb81d1f999024 | 16 | Image.py | 403 | Improve exception traceback readability | 70,078 | 0 | 670 | 233 | 78 | 243,706 | 142 | Pillow | 22 | src/PIL/Image.py | Python | 36 | {
"docstring": "\n Adds or replaces the alpha layer in this image. If the image\n does not have an alpha layer, it's converted to \"LA\" or \"RGBA\".\n The new layer must be either \"L\" or \"1\".\n\n :param alpha: The new alpha layer. This can either be an \"L\" or \"1\"\n image having the same size as this image, or an integer or\n other color value.\n ",
"language": "en",
"n_whitespaces": 120,
"n_words": 62,
"vocab_size": 43
} | https://github.com/python-pillow/Pillow.git |
|
1 | run_query | def run_query(filters, extra_fields, extra_joins, extra_filters, as_dict=1):
query = .format(
extra_fields=extra_fields,
extra_joins=extra_joins,
extra_filters=extra_filters
)
gl_entries = frappe.db.sql(query, filters, as_dict=as_dict)
return gl_entries
| 363ed9ccba3f848908113e6d728735a1c894aec8 | 9 | datev.py | 79 | revert: BU Schlüssel (a21f76f) | 13,598 | 0 | 12 | 52 | 18 | 64,293 | 20 | erpnext | 12 | erpnext/regional/report/datev/datev.py | Python | 58 | {
"docstring": "\n\tGet a list of accounting entries.\n\n\tSelect GL Entries joined with Account and Party Account in order to get the\n\taccount numbers. Returns a list of accounting entries.\n\n\tArguments:\n\tfilters -- dict of filters to be passed to the sql query\n\tas_dict -- return as list of dicts [0,1]\n\t\n\t\tSELECT\n\n\t\t\t/* either debit or credit amount; always positive */\n\t\t\tcase gl.debit when 0 then gl.credit else gl.debit end as 'Umsatz (ohne Soll/Haben-Kz)',\n\n\t\t\t/* 'H' when credit, 'S' when debit */\n\t\t\tcase gl.debit when 0 then 'H' else 'S' end as 'Soll/Haben-Kennzeichen',\n\n\t\t\t/* account number or, if empty, party account number */\n\t\t\tacc.account_number as 'Konto',\n\n\t\t\t/* against number or, if empty, party against number */\n\t\t\t%(temporary_against_account_number)s as 'Gegenkonto (ohne BU-Schlüssel)',\n\n\t\t\t'' as 'BU-Schlüssel',\n\n\t\t\tgl.posting_date as 'Belegdatum',\n\t\t\tgl.voucher_no as 'Belegfeld 1',\n\t\t\tREPLACE(LEFT(gl.remarks, 60), '\\n', ' ') as 'Buchungstext',\n\t\t\tgl.voucher_type as 'Beleginfo - Art 1',\n\t\t\tgl.voucher_no as 'Beleginfo - Inhalt 1',\n\t\t\tgl.against_voucher_type as 'Beleginfo - Art 2',\n\t\t\tgl.against_voucher as 'Beleginfo - Inhalt 2',\n\t\t\tgl.party_type as 'Beleginfo - Art 3',\n\t\t\tgl.party as 'Beleginfo - Inhalt 3',\n\t\t\tcase gl.party_type when 'Customer' then 'Debitorennummer' when 'Supplier' then 'Kreditorennummer' else NULL end as 'Beleginfo - Art 4',\n\t\t\tpar.debtor_creditor_number as 'Beleginfo - Inhalt 4'\n\n\t\t\t{extra_fields}\n\n\t\tFROM `tabGL Entry` gl\n\n\t\t\t/* Kontonummer */\n\t\t\tLEFT JOIN `tabAccount` acc\n\t\t\tON gl.account = acc.name\n\n\t\t\tLEFT JOIN `tabParty Account` par\n\t\t\tON par.parent = gl.party\n\t\t\tAND par.parenttype = gl.party_type\n\t\t\tAND par.company = %(company)s\n\n\t\t\t{extra_joins}\n\n\t\tWHERE gl.company = %(company)s\n\t\tAND DATE(gl.posting_date) >= %(from_date)s\n\t\tAND DATE(gl.posting_date) <= %(to_date)s\n\n\t\t{extra_filters}\n\n\t\tORDER BY 'Belegdatum', gl.voucher_no",
"language": "en",
"n_whitespaces": 205,
"n_words": 247,
"vocab_size": 136
} | https://github.com/frappe/erpnext.git |
|
5 | get_preset_choices | def get_preset_choices() -> Dict:
PRESETS_PATH = PRESETS_DIRECTORY / "etf" / "screener"
PRESETS_PATH_DEFAULT = Path(__file__).parent / "presets"
preset_choices = {
filepath.name: filepath
for filepath in PRESETS_PATH.iterdir()
if filepath.suffix == ".ini"
}
preset_choices.update(
{
filepath.name: filepath
for filepath in PRESETS_PATH_DEFAULT.iterdir()
if filepath.suffix == ".ini"
}
)
return preset_choices
@log_start_end(log=logger) | 3780cffeced904d41e04e2adfe8c1f2a34337099 | @log_start_end(log=logger) | 11 | screener_model.py | 142 | New path for presets (#2634)
* modify path for etf, options, insider
* fix small
* change path for stocks screener
* fix flake 8
* remove preset_path
* black
* fix
* fix tests
* fix tests
* fix flake8
* change paths_helper load
* put ns parser back
Co-authored-by: minhhoang1023 <[email protected]> | 85,436 | 1 | 138 | 75 | 30 | 285,809 | 47 | OpenBBTerminal | 17 | openbb_terminal/etf/screener/screener_model.py | Python | 20 | {
"docstring": "\n Return a dict containing keys as name of preset and\n filepath as value\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 13,
"vocab_size": 12
} | https://github.com/OpenBB-finance/OpenBBTerminal.git |
2 | tokenize | def tokenize(doc):
return (tok.lower() for tok in re.findall(r"\w+", doc))
list(tokenize("This is a simple example, isn't it?"))
# %%
# We define an additional function that counts the (frequency of) occurrence of
# each token in a given document. It returns a frequency dictionary to be used
# by the vectorizers.
from collections import defaultdict
| 6ff214c46bacaf0385125bb47b4d8cb4a305fa3a | 10 | plot_hashing_vs_dict_vectorizer.py | 68 | DOC Rework plot_hashing_vs_dict_vectorizer.py example (#23266)
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Julien Jerphanion <[email protected]> | 76,052 | 0 | 54 | 26 | 47 | 260,068 | 54 | scikit-learn | 9 | examples/text/plot_hashing_vs_dict_vectorizer.py | Python | 2 | {
"docstring": "Extract tokens from doc.\n\n This uses a simple regex that matches word characters to break strings\n into tokens. For a more principled approach, see CountVectorizer or\n TfidfVectorizer.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 27,
"vocab_size": 26
} | https://github.com/scikit-learn/scikit-learn.git |
|
1 | get_first_menu_and_fail | def get_first_menu_and_fail() -> click.testing.Result:
command = ["storage", "create"]
runner = CliRunner()
result = runner.invoke(app, command, input=INVALID_OPTION, catch_exceptions=False)
return result
| 11638691240b7595c0d02542af506a96d344ae8b | 9 | test_storage_cli.py | 72 | Update tests | 11,330 | 0 | 34 | 43 | 16 | 55,479 | 19 | prefect | 13 | tests/cli/test_storage_cli.py | Python | 8 | {
"docstring": "\n Utility function to get output of first step of `prefect storage create` and exit\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 14,
"vocab_size": 13
} | https://github.com/PrefectHQ/prefect.git |
|
5 | get_cat2imgs | def get_cat2imgs(self) -> Dict[int, list]:
classes = self.dataset.metainfo.get('CLASSES', None)
if classes is None:
raise ValueError('dataset metainfo must contain CLASSES')
# sort the label index
cat2imgs = {i: [] for i in range(len(classes))}
for i in range(len(self.dataset)):
cat_ids = set(self.dataset.get_cat_ids(i))
for cat in cat_ids:
cat2imgs[cat].append(i)
return cat2imgs
| 36c1f477b273cb2fb0dea3c921ec267db877c039 | 13 | class_aware_sampler.py | 157 | Refactor OpenImages. | 70,678 | 0 | 143 | 97 | 37 | 245,154 | 46 | mmdetection | 19 | mmdet/datasets/samplers/class_aware_sampler.py | Python | 17 | {
"docstring": "Get a dict with class as key and img_ids as values.\n\n Returns:\n dict[int, list]: A dict of per-label image list,\n the item of the dict indicates a label index,\n corresponds to the image index that contains the label.\n ",
"language": "en",
"n_whitespaces": 85,
"n_words": 38,
"vocab_size": 29
} | https://github.com/open-mmlab/mmdetection.git |
|
2 | print_telemetry_report | def print_telemetry_report():
if is_telemetry_enabled():
user_id = _get_or_create_user_id()
meta_data = _get_or_create_telemetry_meta_data()
print({**{"user_id": user_id}, **meta_data})
else:
print("Telemetry is disabled.")
| ac5617e757e9ace6f30b7291686d9dbbc339f433 | 14 | telemetry.py | 76 | Add basic telemetry features (#2314)
* add basic telemetry features
* change pipeline_config to _component_config
* Update Documentation & Code Style
* add super().__init__() calls to error classes
* make posthog mock work with python 3.7
* Update Documentation & Code Style
* update link to docs web page
* log exceptions, send event for raised HaystackErrors, refactor Path(CONFIG_PATH)
* add comment on send_event in BaseComponent.init() and fix mypy
* mock NonPrivateParameters and fix pylint undefined-variable
* Update Documentation & Code Style
* check model path contains multiple /
* add test for writing to file
* add test for en-/disable telemetry
* Update Documentation & Code Style
* merge file deletion methods and ignore pylint global statement
* Update Documentation & Code Style
* set env variable in demo to activate telemetry
* fix mock of HAYSTACK_TELEMETRY_ENABLED
* fix mypy and linter
* add CI as env variable to execution contexts
* remove threading, add test for custom error event
* Update Documentation & Code Style
* simplify config/log file deletion
* add test for final event being sent
* force writing config file in test
* make test compatible with python 3.7
* switch to posthog production server
* Update Documentation & Code Style
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | 74,974 | 0 | 54 | 40 | 16 | 256,957 | 17 | haystack | 7 | haystack/telemetry.py | Python | 7 | {
"docstring": "\n Prints the user id and the meta data that are sent in events\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 13,
"vocab_size": 12
} | https://github.com/deepset-ai/haystack.git |
|
1 | test_edgeql_for_in_computable_05 | async def test_edgeql_for_in_computable_05(self):
await self.assert_query_result(
r,
[{"select_deck":
tb.bag(["Bog monster", "Dragon", "Giant turtle", "Imp"])}],
)
# This one caused a totally nonsense type error.
await self.assert_query_result(
r,
[{"select_deck":
tb.bag(["Bog monster", "Dragon", "Giant turtle", "Imp"])}],
)
| 26be7d28bdb4eb96c888e373e08f46e6b85711e3 | 15 | test_edgeql_for.py | 117 | Add a `bag` type that tells assert_query_result to ignore order (#3314)
assert_query_result currently supports using sets to ignore order,
but that doesn't work for objects, which can't be hashed or sorted.
There is a system for specifying a sort key for internal data, but it
is way clunkier than just saying we don't care about the order.
I converted some places that were using sort= to use this. | 41,673 | 0 | 138 | 65 | 23 | 176,083 | 34 | edgedb | 5 | tests/test_edgeql_for.py | Python | 29 | {
"docstring": "\n SELECT User {\n select_deck := (\n FOR letter IN {'X'}\n UNION (\n (SELECT .deck.name)\n )\n )\n } FILTER .name = 'Alice';\n \n SELECT User {\n select_deck := (\n FOR letter IN 'X'\n UNION (\n ((SELECT .deck).name)\n )\n )\n } FILTER .name = 'Alice';\n ",
"language": "en",
"n_whitespaces": 394,
"n_words": 42,
"vocab_size": 22
} | https://github.com/edgedb/edgedb.git |
|
3 | stop | async def stop(self, block=False) -> None:
self._stop()
if block:
while self._is_running:
await asyncio.sleep(0.1)
| b7d5fc52e4235370e368e96da218ca49bd32edcc | 12 | loop_service.py | 56 | Change default behavior to block=false | 11,739 | 0 | 60 | 34 | 13 | 58,181 | 13 | prefect | 7 | src/prefect/orion/services/loop_service.py | Python | 15 | {
"docstring": "\n Gracefully stops a running LoopService and optionally blocks until the\n service stops.\n\n Args:\n block (bool): if True, blocks until the service is\n finished running. Otherwise it requests a stop and returns but\n the service may still be running a final loop.\n\n ",
"language": "en",
"n_whitespaces": 111,
"n_words": 41,
"vocab_size": 31
} | https://github.com/PrefectHQ/prefect.git |
|
30 | run | def run(self):
display.debug("in run() - task %s" % self._task._uuid)
try:
try:
items = self._get_loop_items()
except AnsibleUndefinedVariable as e:
# save the error raised here for use later
items = None
self._loop_eval_error = e
if items is not None:
if len(items) > 0:
item_results = self._run_loop(items)
# create the overall result item
res = dict(results=item_results)
# loop through the item results and set the global changed/failed/skipped result flags based on any item.
res['skipped'] = True
for item in item_results:
if 'changed' in item and item['changed'] and not res.get('changed'):
res['changed'] = True
if res['skipped'] and ('skipped' not in item or ('skipped' in item and not item['skipped'])):
res['skipped'] = False
if 'failed' in item and item['failed']:
item_ignore = item.pop('_ansible_ignore_errors')
if not res.get('failed'):
res['failed'] = True
res['msg'] = 'One or more items failed'
self._task.ignore_errors = item_ignore
elif self._task.ignore_errors and not item_ignore:
self._task.ignore_errors = item_ignore
# ensure to accumulate these
for array in ['warnings', 'deprecations']:
if array in item and item[array]:
if array not in res:
res[array] = []
if not isinstance(item[array], list):
item[array] = [item[array]]
res[array] = res[array] + item[array]
del item[array]
if not res.get('failed', False):
res['msg'] = 'All items completed'
if res['skipped']:
res['msg'] = 'All items skipped'
else:
res = dict(changed=False, skipped=True, skipped_reason='No items in the list', results=[])
else:
display.debug("calling self._execute()")
res = self._execute()
display.debug("_execute() done")
# make sure changed is set in the result, if it's not present
if 'changed' not in res:
res['changed'] = False
| a0dede545864b9835f84315b4b3ea17dd1eafe04 | def run(self):
'''
The main executor entrypoint, where we determine if the specified
task requires looping and either runs the task with self._run_loop()
or self._execute(). After that, the returned results are parsed and
returned as a dict.
'''
display.debug("in run() - task %s" % self._task._uuid)
try:
try:
items = self._get_loop_items()
except AnsibleUndefinedVariable as e:
# save the error raised here for use later
items = None
self._loop_eval_error = e
if items is not None:
if len(items) > 0:
item_results = self._run_loop(items)
# create the overall result item
res = dict(results=item_results)
# loop through the item results and set the global changed/failed/skipped result flags based on any item.
res['skipped'] = True
for item in item_results:
if 'changed' in item and item['changed'] and not res.get('changed'):
res['changed'] = True
if res['skipped'] and ('skipped' not in item or ('skipped' in item and not item['skipped'])):
res['skipped'] = False
if 'failed' in item and item['failed']:
item_ignore = item.pop('_ansible_ignore_errors')
if not res.get('failed'):
res['failed'] = True
res['msg'] = 'One or more items failed'
self._task.ignore_errors = item_ignore
elif self._task.ignore_errors and not item_ignore:
self._task.ignore_errors = item_ignore
# ensure to accumulate these
for array in ['warnings', 'deprecations']:
if array in item and item[array]:
if array not in res:
res[array] = []
if not isinstance(item[array], list):
item[array] = [item[array]]
res[array] = res[array] + item[array]
del item[array]
if not res.get('failed', False):
res['msg'] = 'All items completed'
if res['skipped']:
res['msg'] = 'All items skipped'
else:
res = dict(changed=False, skipped=True, skipped_reason='No items in the list', results=[])
else:
display.debug("calling self._execute()")
res = self._execute()
display.debug("_execute() done")
# make sure changed is set in the result, if it's not present
if 'changed' not in res:
res['changed'] = False | 20 | task_executor.py | 625 | ansible-connection verboistery (#77509)
* ansible-connection verboistery
for cli, just use normal parser creation
this also adds --help, but that seems fine
also some error cleanup
Co-authored-by: Nathaniel Case <[email protected]> | 78,689 | 1 | 1,283 | 501 | 121 | 267,014 | 234 | ansible | 29 | lib/ansible/executor/task_executor.py | Python | 63 | {
"docstring": "\n The main executor entrypoint, where we determine if the specified\n task requires looping and either runs the task with self._run_loop()\n or self._execute(). After that, the returned results are parsed and\n returned as a dict.\n ",
"language": "en",
"n_whitespaces": 70,
"n_words": 34,
"vocab_size": 29
} | https://github.com/ansible/ansible.git |
2 | test_orion_full_migration_works_with_data_in_db | async def test_orion_full_migration_works_with_data_in_db(sample_db_data):
try:
await run_sync_in_worker_thread(alembic_downgrade)
finally:
await run_sync_in_worker_thread(alembic_upgrade)
| 36e7e0838aeaffc9492b330297e4905f3ab4b11f | 11 | test_migrations.py | 40 | code review revisions pt3 | 10,770 | 0 | 32 | 20 | 8 | 53,285 | 9 | prefect | 5 | tests/orion/database/test_migrations.py | Python | 5 | {
"docstring": "\n Tests that downgrade migrations work when the database has data in it.\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 12,
"vocab_size": 12
} | https://github.com/PrefectHQ/prefect.git |
|
2 | test_execute_in_batch_with_few_requests | def test_execute_in_batch_with_few_requests(self, api, batch, mock_batch_responses):
mock_batch_responses(
[
{
"json": [{"body": json.dumps({"name": "creative 1"}), "code": 200, "headers": {}}] * 3,
}
]
)
stream = SomeTestStream(api=api)
requests = [FacebookRequest("node", "GET", "endpoint") for _ in range(5)]
result = list(stream.execute_in_batch(requests))
assert batch.add_request.call_count == len(requests)
batch.execute.assert_called_once()
assert len(result) == 3
| 6cd20e6879a42053692c18a16906eb410aad85db | 19 | test_base_streams.py | 190 | 🐛 Source FB Marketing: fix `execute_in_batch` when batch is bigger than 50 (#10588)
* fix execute_in_batch
* add tests
* fix pre-commit config
Co-authored-by: Sherif A. Nada <[email protected]>
Co-authored-by: Eugene Kulak <[email protected]>
Co-authored-by: Sherif A. Nada <[email protected]> | 600 | 0 | 180 | 112 | 42 | 3,985 | 46 | airbyte | 21 | airbyte-integrations/connectors/source-facebook-marketing/unit_tests/test_base_streams.py | Python | 14 | {
"docstring": "Should execute single batch if number of requests less than MAX_BATCH_SIZE.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | https://github.com/airbytehq/airbyte.git |
|
3 | get_multi_io_subclass_model | def get_multi_io_subclass_model(use_bn=False, use_dp=False, num_classes=(2, 3)):
shared_layer = keras.layers.Dense(32, activation='relu')
branch_a = [shared_layer]
if use_dp:
branch_a.append(keras.layers.Dropout(0.5))
branch_a.append(keras.layers.Dense(num_classes[0], activation='softmax'))
branch_b = [shared_layer]
if use_bn:
branch_b.append(keras.layers.BatchNormalization())
branch_b.append(keras.layers.Dense(num_classes[1], activation='softmax'))
model = (
test_utils._MultiIOSubclassModel( # pylint: disable=protected-access
branch_a, branch_b, name='test_model'))
return model
| b96518a22bfd92a29811e507dec0b34248a8a3f5 | 12 | model_subclassing_test_util.py | 214 | - Consolidate disparate test-related files into a single testing_infra folder.
- Cleanup TODO related to removing testing infra as a dependency of the Keras target.
- Standardize import naming: there is now only "test_combinations" for test combinations, and "test_utils" for utilities. The TF utilities module "test_util" is now always imported as "tf_test_utils" to avoid confusion.
PiperOrigin-RevId: 426773173 | 79,770 | 0 | 69 | 135 | 30 | 268,931 | 37 | keras | 18 | keras/tests/model_subclassing_test_util.py | Python | 15 | {
"docstring": "Creates MultiIOModel for the tests of subclass model.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | https://github.com/keras-team/keras.git |
|
2 | inertia | def inertia(frame, ixx, iyy, izz, ixy=0, iyz=0, izx=0):
if not isinstance(frame, ReferenceFrame):
raise TypeError('Need to define the inertia in a frame')
ol = sympify(ixx) * (frame.x | frame.x)
ol += sympify(ixy) * (frame.x | frame.y)
ol += sympify(izx) * (frame.x | frame.z)
ol += sympify(ixy) * (frame.y | frame.x)
ol += sympify(iyy) * (frame.y | frame.y)
ol += sympify(iyz) * (frame.y | frame.z)
ol += sympify(izx) * (frame.z | frame.x)
ol += sympify(iyz) * (frame.z | frame.y)
ol += sympify(izz) * (frame.z | frame.z)
return ol
| 65be461082dda54c8748922f9c29a19af1279fe1 | 10 | functions.py | 286 | Remove abbreviations in documentation | 48,478 | 0 | 129 | 184 | 39 | 197,335 | 86 | sympy | 16 | sympy/physics/mechanics/functions.py | Python | 13 | {
"docstring": "Simple way to create inertia Dyadic object.\n\n Explanation\n ===========\n\n If you do not know what a Dyadic is, just treat this like the inertia\n tensor. Then, do the easy thing and define it in a body-fixed frame.\n\n Parameters\n ==========\n\n frame : ReferenceFrame\n The frame the inertia is defined in\n ixx : Sympifyable\n the xx element in the inertia dyadic\n iyy : Sympifyable\n the yy element in the inertia dyadic\n izz : Sympifyable\n the zz element in the inertia dyadic\n ixy : Sympifyable\n the xy element in the inertia dyadic\n iyz : Sympifyable\n the yz element in the inertia dyadic\n izx : Sympifyable\n the zx element in the inertia dyadic\n\n Examples\n ========\n\n >>> from sympy.physics.mechanics import ReferenceFrame, inertia\n >>> N = ReferenceFrame('N')\n >>> inertia(N, 1, 2, 3)\n (N.x|N.x) + 2*(N.y|N.y) + 3*(N.z|N.z)\n\n ",
"language": "en",
"n_whitespaces": 240,
"n_words": 131,
"vocab_size": 73
} | https://github.com/sympy/sympy.git |
|
1 | hashes_to_frame | def hashes_to_frame(self) -> Dict[str, Dict[str, int]]:
return self._legacy.hashes_to_frame
| e5356a417e7c2124e75c4a2994ed604fc0a3cc74 | 7 | alignments.py | 37 | Alignments update:
- Store face embeddings in PNG header when sorting
- typing + refactor
- Update alignments keys for 'identity' and 'video_meta' + bump to v2.3
- General typing fixes | 21,093 | 0 | 22 | 24 | 7 | 101,689 | 8 | faceswap | 6 | lib/align/alignments.py | Python | 10 | {
"docstring": " dict: The SHA1 hash of the face mapped to the frame(s) and face index within the frame\n that the hash corresponds to.\n\n Notes\n -----\n This method is depractated and exists purely for updating legacy hash based alignments\n to new png header storage in :class:`lib.align.update_legacy_png_header`.\n ",
"language": "en",
"n_whitespaces": 87,
"n_words": 44,
"vocab_size": 36
} | https://github.com/deepfakes/faceswap.git |
|
1 | start | async def start(self):
with ImportExtensions(required=True):
import aiohttp
self.session = aiohttp.ClientSession(**self._session_kwargs)
await self.session.__aenter__()
return self
| 65d6873500f008dafba193340f8731bca39bc7dc | 10 | helper.py | 71 | feat: expose kwargs to Clients (#4947) | 2,350 | 0 | 60 | 39 | 14 | 12,595 | 14 | jina | 9 | jina/clients/base/helper.py | Python | 6 | {
"docstring": "Create ClientSession and enter context\n\n :return: self\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 7,
"vocab_size": 7
} | https://github.com/jina-ai/jina.git |
|
6 | evaluate_accuracy_gpu | def evaluate_accuracy_gpu(net, data_iter, device=None):
if isinstance(net, nn.Module):
net.eval() # Set the model to evaluation mode
if not device:
device = next(iter(net.parameters())).device
# No. of correct predictions, no. of predictions
metric = d2l.Accumulator(2)
with torch.no_grad():
for X, y in data_iter:
if isinstance(X, list):
# Required for BERT Fine-tuning (to be covered later)
X = [x.to(device) for x in X]
else:
X = X.to(device)
y = y.to(device)
metric.add(d2l.accuracy(net(X), y), d2l.size(y))
return metric[0] / metric[1]
| 2b1acfbfe84b6c9c4756a615620f9b376d48085a | 17 | torch.py | 228 | JAX: Fix CI bug; enable build all sections | 74,503 | 0 | 212 | 141 | 58 | 254,229 | 72 | d2l-en | 24 | d2l/torch.py | Python | 15 | {
"docstring": "Compute the accuracy for a model on a dataset using a GPU.\n\n Defined in :numref:`sec_utils`",
"language": "en",
"n_whitespaces": 17,
"n_words": 15,
"vocab_size": 13
} | https://github.com/d2l-ai/d2l-en.git |
|
3 | get_classes_from_csv | def get_classes_from_csv(self, label_file):
index_list = []
classes_names = []
with open(label_file, 'r') as f:
reader = csv.reader(f)
for line in reader:
self.cat2label[line[0]] = line[1]
classes_names.append(line[1])
index_list.append(line[0])
self.index_dict = {index: i for i, index in enumerate(index_list)}
return classes_names
| 1516986a616fee8bb741d0ab2be40683045efccd | 12 | openimages.py | 147 | [Feature] Support OpenImages Dataset (#6331)
* [Feature] support openimage group of eval
* [Feature] support openimage group of eval
* support openimage dataset
* support openimage challenge dataset
* fully support OpenImages-V6 and OpenImages Challenge 2019
* Fix some logic error
* update config file
* fix get data_infos error
* fully support OpenImages evaluation
* update OpenImages config files
* [Feature] support OpenImages datasets
* fix bug
* support load image metas from pipeline
* fix bug
* fix get classes logic error
* update code
* support get image metas
* support openimags
* support collect image metas
* support Open Images
* fix openimages logic
* minor fix
* add a new function to compute openimages tpfp
* minor fix
* fix ci error
* minor fix
* fix indication
* minor fix
* fix returns
* fix returns
* fix returns
* fix returns
* fix returns
* minor fix
* update readme
* support loading image level labels and fix some logic
* minor fix
* minor fix
* add class names
* minor fix
* minor fix
* minor fix
* add openimages test unit
* minor fix
* minor fix
* fix test unit
* minor fix
* fix logic error
* minor fix
* fully support openimages
* minor fix
* fix docstring
* fix docstrings in readthedocs
* update get image metas script
* label_description_file -> label_file
* update openimages readme
* fix test unit
* fix test unit
* minor fix
* update readme file
* Update get_image_metas.py | 70,184 | 0 | 146 | 91 | 29 | 243,994 | 37 | mmdetection | 16 | mmdet/datasets/openimages.py | Python | 11 | {
"docstring": "Get classes name from file.\n\n Args:\n label_file (str): File path of the label description file that\n maps the classes names in MID format to their short\n descriptions.\n\n Returns:\n list[str]: Class name of OpenImages.\n ",
"language": "en",
"n_whitespaces": 106,
"n_words": 33,
"vocab_size": 29
} | https://github.com/open-mmlab/mmdetection.git |
|
1 | _get_dated_items | def _get_dated_items(self, date):
lookup_kwargs = self._make_single_date_lookup(date)
qs = self.get_dated_queryset(**lookup_kwargs)
return (
None,
qs,
{
"day": date,
"previous_day": self.get_previous_day(date),
"next_day": self.get_next_day(date),
"previous_month": self.get_previous_month(date),
"next_month": self.get_next_month(date),
},
)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 10 | dates.py | 123 | Refs #33476 -- Reformatted code with Black. | 51,763 | 0 | 180 | 75 | 25 | 206,862 | 26 | django | 11 | django/views/generic/dates.py | Python | 14 | {
"docstring": "\n Do the actual heavy lifting of getting the dated items; this accepts a\n date object so that TodayArchiveView can be trivial.\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 21,
"vocab_size": 20
} | https://github.com/django/django.git |
|
4 | perform_action | def perform_action(action_id, project, queryset, user, **kwargs):
if action_id not in settings.DATA_MANAGER_ACTIONS:
raise DataManagerException("Can't find '" + action_id + "' in registered actions")
action = settings.DATA_MANAGER_ACTIONS[action_id]
# check user permissions for this action
if not check_permissions(user, action):
raise DRFPermissionDenied(f'Action is not allowed for the current user: {action["id"]}')
try:
result = action['entry_point'](project, queryset, **kwargs)
except Exception as e:
text = 'Error while perform action: ' + action_id + '\n' + tb.format_exc()
logger.error(text, extra={'sentry_skip': True})
raise e
return result
register_actions_from_dir('data_manager.actions', os.path.dirname(__file__))
| 1c4328c5a8b10ee20ac4328ce30612d106350699 | 14 | __init__.py | 212 | feat: DEV-1205: Add task.updated_at column (#1784)
* Update task.updated_at on annotation update (DEV-1205)
* Fix set updated_at on annotation delete (DEV-1205)
* Set update_at for every dm action (DEV-1205)
* Stop changing updated_at on actions (DEV-1205)
* Update experimental.py
Co-authored-by: Max Tkachenko <[email protected]>
Co-authored-by: niklub <[email protected]> | 42,459 | 0 | 143 | 107 | 60 | 177,606 | 78 | label-studio | 26 | label_studio/data_manager/actions/__init__.py | Python | 13 | {
"docstring": " Perform action using entry point from actions\n ",
"language": "en",
"n_whitespaces": 11,
"n_words": 7,
"vocab_size": 7
} | https://github.com/heartexlabs/label-studio.git |
|
4 | fit | def fit(self) -> ResultGrid:
if not self._is_ray_client:
try:
return self._local_tuner.fit()
except Exception as e:
raise TuneError(
f"Tune run failed. "
f'Please use tuner = Tuner.restore("'
f'{self._local_tuner.get_experiment_checkpoint_dir()}") to resume.'
) from e
else:
experiment_checkpoint_dir = ray.get(
self._remote_tuner.get_experiment_checkpoint_dir.remote()
)
try:
return ray.get(self._remote_tuner.fit.remote())
except Exception as e:
raise TuneError(
f"Tune run failed. "
f'Please use tuner = Tuner.restore("'
f'{experiment_checkpoint_dir}") to resume.'
) from e
| 5c500f6308dce526c50a5230029fb4909b492a35 | 18 | tuner.py | 175 | [docs/air] Fix up some minor docstrings (#28361) | 28,451 | 0 | 367 | 93 | 37 | 127,486 | 61 | ray | 14 | python/ray/tune/tuner.py | Python | 41 | {
"docstring": "Executes hyperparameter tuning job as configured and returns result.\n\n Failure handling:\n For the kind of exception that happens during the execution of a trial,\n one may inspect it together with stacktrace through the returned result grid.\n See ``ResultGrid`` for reference. Each trial may fail up to a certain number.\n This is configured by ``RunConfig.FailureConfig.max_failures``.\n\n Exception that happens beyond trials will be thrown by this method as well.\n In such cases, there will be instruction like the following printed out\n at the end of console output to inform users on how to resume.\n\n Please use tuner = Tuner.restore(\"~/ray_results/tuner_resume\")\n to resume.\n\n Raises:\n RayTaskError: If user-provided trainable raises an exception\n TuneError: General Ray Tune error.\n ",
"language": "en",
"n_whitespaces": 218,
"n_words": 112,
"vocab_size": 92
} | https://github.com/ray-project/ray.git |
|
1 | test_task_states_for_dag_run_when_dag_run_not_exists | def test_task_states_for_dag_run_when_dag_run_not_exists(self):
with pytest.raises(DagRunNotFound):
default_date2 = timezone.datetime(2016, 1, 9)
task_command.task_states_for_dag_run(
self.parser.parse_args(
[
'tasks',
'states-for-dag-run',
'not_exists_dag',
default_date2.isoformat(),
'--output',
"json",
]
)
)
| f352ee63a5d09546a7997ba8f2f8702a1ddb4af7 | 14 | test_task_command.py | 97 | Replaced all days_ago functions with datetime functions (#23237)
Co-authored-by: Dev232001 <[email protected]> | 7,671 | 0 | 274 | 56 | 20 | 42,640 | 21 | airflow | 13 | tests/cli/commands/test_task_command.py | Python | 15 | {
"docstring": "\n task_states_for_dag_run should return an AirflowException when invalid dag id is passed\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 11
} | https://github.com/apache/airflow.git |
|
3 | yeardatescalendar | def yeardatescalendar(self, year, width=3):
months = [
self.monthdatescalendar(year, i)
for i in range(January, January+12)
]
return [months[i:i+width] for i in range(0, len(months), width) ]
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 11 | calendar.py | 88 | add python 3.10.4 for windows | 56,289 | 0 | 74 | 60 | 20 | 221,242 | 24 | XX-Net | 10 | python3.10.4/Lib/calendar.py | Python | 6 | {
"docstring": "\n Return the data for the specified year ready for formatting. The return\n value is a list of month rows. Each month row contains up to width months.\n Each month contains between 4 and 6 weeks and each week contains 1-7\n days. Days are datetime.date objects.\n ",
"language": "en",
"n_whitespaces": 81,
"n_words": 45,
"vocab_size": 37
} | https://github.com/XX-net/XX-Net.git |
|
1 | process_downloaded_dataset | def process_downloaded_dataset(self):
df = pd.read_csv(os.path.join(self.raw_dataset_path, self.csv_filename))
df[SPLIT] = np.random.choice(3, len(df), p=(0.7, 0.1, 0.2)).astype(np.int8)
makedirs(self.processed_temp_path, exist_ok=True)
df.to_csv(os.path.join(self.processed_temp_path, self.csv_filename), index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
| c13bc2cf1443ce9339cde0eb5e0d2b568af341f7 | 12 | __init__.py | 159 | Add medical no-show appointments dataset (#2387)
Co-authored-by: Daniel Treiman <[email protected]> | 1,281 | 0 | 62 | 111 | 19 | 7,853 | 20 | ludwig | 25 | ludwig/datasets/noshow_appointments/__init__.py | Python | 6 | {
"docstring": "The final method where we create a concatenated CSV file with both training and test data.",
"language": "en",
"n_whitespaces": 15,
"n_words": 16,
"vocab_size": 16
} | https://github.com/ludwig-ai/ludwig.git |
|
2 | template | def template(self) -> str | None:
if self.value_template is not None:
return self.value_template.template
return None
| dd1463da287f591652e47b00eee0c5b77f5f5b7c | 9 | helpers.py | 43 | Refactor bayesian observations using dataclass (#79590)
* refactor
* remove some changes
* remove typehint
* improve codestyle
* move docstring to comment
* < 88 chars
* avoid short var names
* more readable
* fix rename
* Update homeassistant/components/bayesian/helpers.py
Co-authored-by: epenet <[email protected]>
* Update homeassistant/components/bayesian/binary_sensor.py
Co-authored-by: epenet <[email protected]>
* Update homeassistant/components/bayesian/binary_sensor.py
Co-authored-by: epenet <[email protected]>
* no intermediate
* comment why set before list
Co-authored-by: epenet <[email protected]> | 87,644 | 0 | 47 | 26 | 13 | 288,487 | 15 | core | 4 | homeassistant/components/bayesian/helpers.py | Python | 5 | {
"docstring": "Not all observations have templates and we want to get template strings.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | https://github.com/home-assistant/core.git |
|
2 | get_capacity | def get_capacity(self):
if self.is_container_group:
return self.max_forks
return self.get_instance_capacity()
| e403c603d68def3b4cc6e4b253092343d646da1a | 8 | task_manager_models.py | 37 | use task manager models more consistently in serializer | 17,333 | 0 | 40 | 21 | 7 | 82,225 | 8 | awx | 5 | awx/main/scheduler/task_manager_models.py | Python | 4 | {
"docstring": "This reports any type of capacity, including that of container group jobs.\n\n Container groups don't really have capacity, but if they have max_forks set,\n we can interperet that as how much capacity the user has defined them to have.\n ",
"language": "en",
"n_whitespaces": 60,
"n_words": 39,
"vocab_size": 35
} | https://github.com/ansible/awx.git |
|
8 | create_trial_from_spec | def create_trial_from_spec(spec, output_path, parser, **trial_kwargs):
global _cached_pgf
spec = spec.copy()
resources = spec.pop("resources_per_trial", None)
try:
args, _ = parser.parse_known_args(to_argv(spec))
except SystemExit:
raise TuneError("Error parsing args, see above message", spec)
if resources:
if isinstance(resources, PlacementGroupFactory):
trial_kwargs["placement_group_factory"] = resources
else:
# This will be converted to a placement group factory in the
# Trial object constructor
try:
trial_kwargs["resources"] = json_to_resources(resources)
except (TuneError, ValueError) as exc:
raise TuneError("Error parsing resources_per_trial", resources) from exc
remote_checkpoint_dir = spec.get("remote_checkpoint_dir")
sync_config = spec.get("sync_config", SyncConfig())
if sync_config.syncer is None or isinstance(sync_config.syncer, str):
sync_function_tpl = sync_config.syncer
elif not isinstance(sync_config.syncer, str):
# If a syncer was specified, but not a template, it is a function.
# Functions cannot be used for trial checkpointing on remote nodes,
# so we set the remote checkpoint dir to None to disable this.
sync_function_tpl = None
remote_checkpoint_dir = None
else:
sync_function_tpl = None # Auto-detect
return Trial(
# Submitting trial via server in py2.7 creates Unicode, which does not
# convert to string in a straightforward manner.
trainable_name=spec["run"],
# json.load leads to str -> unicode in py2.7
config=spec.get("config", {}),
local_dir=os.path.join(spec["local_dir"], output_path),
# json.load leads to str -> unicode in py2.7
stopping_criterion=spec.get("stop", {}),
remote_checkpoint_dir=remote_checkpoint_dir,
sync_function_tpl=sync_function_tpl,
checkpoint_freq=args.checkpoint_freq,
checkpoint_at_end=args.checkpoint_at_end,
sync_on_checkpoint=sync_config.sync_on_checkpoint,
keep_checkpoints_num=args.keep_checkpoints_num,
checkpoint_score_attr=args.checkpoint_score_attr,
export_formats=spec.get("export_formats", []),
# str(None) doesn't create None
restore_path=spec.get("restore"),
trial_name_creator=spec.get("trial_name_creator"),
trial_dirname_creator=spec.get("trial_dirname_creator"),
log_to_file=spec.get("log_to_file"),
# str(None) doesn't create None
max_failures=args.max_failures,
**trial_kwargs
)
| 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | 16 | config_parser.py | 511 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 29,682 | 0 | 587 | 313 | 140 | 132,094 | 214 | ray | 46 | python/ray/tune/config_parser.py | Python | 45 | {
"docstring": "Creates a Trial object from parsing the spec.\n\n Args:\n spec (dict): A resolved experiment specification. Arguments should\n The args here should correspond to the command line flags\n in ray.tune.config_parser.\n output_path (str); A specific output path within the local_dir.\n Typically the name of the experiment.\n parser (ArgumentParser): An argument parser object from\n make_parser.\n trial_kwargs: Extra keyword arguments used in instantiating the Trial.\n\n Returns:\n A trial object with corresponding parameters to the specification.\n ",
"language": "en",
"n_whitespaces": 159,
"n_words": 71,
"vocab_size": 55
} | https://github.com/ray-project/ray.git |
|
1 | test_spectralbiclustering_parameter_validation | def test_spectralbiclustering_parameter_validation(params, type_err, err_msg):
data = np.arange(25).reshape((5, 5))
model = SpectralBiclustering(**params)
with pytest.raises(type_err, match=err_msg):
model.fit(data)
@pytest.mark.parametrize("est", (SpectralBiclustering(), SpectralCoclustering())) | 604bc5c605b9587526794de2653624b19c6a8031 | @pytest.mark.parametrize("est", (SpectralBiclustering(), SpectralCoclustering())) | 10 | test_bicluster.py | 117 | MNT use check_scalar in SpectralBiClustering and SpectralCoClustering (#20817)
Co-authored-by: Thomas J. Fan <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]> | 75,345 | 1 | 36 | 52 | 17 | 258,639 | 18 | scikit-learn | 17 | sklearn/cluster/tests/test_bicluster.py | Python | 5 | {
"docstring": "Check parameters validation in `SpectralBiClustering`",
"language": "en",
"n_whitespaces": 4,
"n_words": 5,
"vocab_size": 5
} | https://github.com/scikit-learn/scikit-learn.git |
6 | categorical_mapping | def categorical_mapping(self, data, palette, order):
# -- Identify the order and name of the levels
levels = categorical_order(data, order)
n_colors = len(levels)
# -- Identify the set of colors to use
if isinstance(palette, dict):
missing = set(levels) - set(palette)
if any(missing):
err = "The palette dictionary is missing keys: {}"
raise ValueError(err.format(missing))
lookup_table = palette
else:
if palette is None:
if n_colors <= len(get_color_cycle()):
colors = color_palette(None, n_colors)
else:
colors = color_palette("husl", n_colors)
elif isinstance(palette, list):
colors = self._check_list_length(levels, palette, "palette")
else:
colors = color_palette(palette, n_colors)
lookup_table = dict(zip(levels, colors))
return levels, lookup_table
| 563e96d3be1eaee8db8dfbccf7eed1f1c66dfd31 | 17 | _oldcore.py | 240 | Downgrade exception on mapping list length mismatch to warning (#2856)
* Downgrade exception on mapping list length mismatch to warning
* Lint
* Fix pairplot test
* Set stacklevel to report warning in user code | 7,453 | 0 | 354 | 147 | 57 | 41,880 | 93 | seaborn | 24 | seaborn/_oldcore.py | Python | 21 | {
"docstring": "Determine colors when the hue mapping is categorical.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | https://github.com/mwaskom/seaborn.git |
|
4 | has_all_dags_access | def has_all_dags_access(self, user):
if not user:
user = g.user
return (
self._has_role(['Admin', 'Viewer', 'Op', 'User'], user)
or self.has_access(permissions.ACTION_CAN_READ, permissions.RESOURCE_DAG, user)
or self.has_access(permissions.ACTION_CAN_EDIT, permissions.RESOURCE_DAG, user)
)
| 3e9828022b03b60d9e112f1f64340a528c8407e3 | 12 | security.py | 105 | Simplify fab has access lookup (#19294)
* Use FAB models.
* Remove incorrect conversions to new permission naming scheme.
* Fix missing FAB renames.
* Remove unused FAB compatibility fixes in models.py.
* Set perms directly on user objects.
* Set perms properties on User model.
* Rename missed old naming scheme conversion.
* Remove unused imports.
* Remove unused imports.
* Remeve get_user_roles() method.
* Make permissions eagerload.
* Remove unused imports.
* Clarify query params.
* Modify sort logic so MSSQL passes.
* Add text modifier to order_by values.
* Remove calls to get_*_dags.
* Add back execution_date
* Add back comma to match rest of file.
* Remove unused permission functions.
* Fix failing tests.
* Pass user object to current_app.appbuilder.sm.has_all_dags_access.
* Remove attempts to fix query.
* Update the api_connexion query builders.
* Add typing.
* Apply sorts directly to model objects.
* Apply sorts directly to model objects.
* Standardize custom sort code.
* Code review
* Augment xcom docs (#20755)
* Fix relationship join bug in FAB/SecurityManager with SQLA 1.4 (#21296)
This is fixed in SQLA 1.4.19, but the fix makes the intent clearer here
anyway.
* Docs: Fix task order in overview example (#21282)
* Update stat_name_handler documentation (#21298)
Previously stat_name_handler was under the scheduler section of the
configuration but it was moved to the metrics section since 2.0.0.
* Update recipe for Google Cloud SDK (#21268)
* Use FAB models.
* Remove incorrect conversions to new permission naming scheme.
* Fix missing FAB renames.
* Remove unused FAB compatibility fixes in models.py.
* Set perms directly on user objects.
* Set perms properties on User model.
* Rename missed old naming scheme conversion.
* Remove unused imports.
* Remove unused imports.
* Remeve get_user_roles() method.
* Make permissions eagerload.
* Remove unused imports.
* Clarify query params.
* Modify sort logic so MSSQL passes.
* Add text modifier to order_by values.
* Remove calls to get_*_dags.
* Add back execution_date
* Add back comma to match rest of file.
* Remove unused permission functions.
* Fix failing tests.
* Pass user object to current_app.appbuilder.sm.has_all_dags_access.
* Remove attempts to fix query.
* Update the api_connexion query builders.
* Add typing.
* Apply sorts directly to model objects.
* Apply sorts directly to model objects.
* Standardize custom sort code.
* Make sure joined fields prefetch.
* Dont use cached_property, since its only on > 3.8.
Co-authored-by: Ash Berlin-Taylor <[email protected]>
Co-authored-by: Lewis John McGibbney <[email protected]>
Co-authored-by: Ash Berlin-Taylor <[email protected]>
Co-authored-by: Lucia Kasman <[email protected]>
Co-authored-by: Fran Sánchez <[email protected]>
Co-authored-by: Kamil Breguła <[email protected]> | 8,272 | 0 | 97 | 66 | 21 | 44,454 | 25 | airflow | 10 | airflow/www/security.py | Python | 8 | {
"docstring": "\n Has all the dag access in any of the 3 cases:\n 1. Role needs to be in (Admin, Viewer, User, Op).\n 2. Has can_read action on dags resource.\n 3. Has can_edit action on dags resource.\n ",
"language": "en",
"n_whitespaces": 71,
"n_words": 35,
"vocab_size": 27
} | https://github.com/apache/airflow.git |
|
2 | bootstrap | def bootstrap(pip, options): # type: (str, t.Dict[str, t.Any]) -> None
pip_version = options['pip_version']
packages = options['packages']
url = 'https://ci-files.testing.ansible.com/ansible-test/get-pip-%s.py' % pip_version
cache_path = os.path.expanduser('~/.ansible/test/cache/get_pip_%s.py' % pip_version.replace(".", "_"))
temp_path = cache_path + '.download'
if os.path.exists(cache_path):
log('Using cached pip %s bootstrap script: %s' % (pip_version, cache_path))
else:
log('Downloading pip %s bootstrap script: %s' % (pip_version, url))
make_dirs(os.path.dirname(cache_path))
download_file(url, temp_path)
shutil.move(temp_path, cache_path)
log('Cached pip %s bootstrap script: %s' % (pip_version, cache_path))
env = common_pip_environment()
env.update(GET_PIP=cache_path)
options = common_pip_options()
options.extend(packages)
command = [sys.executable, pip] + options
execute_command(command, env=env)
| 68fb3bf90efa3a722ba5ab7d66b1b22adc73198c | 13 | requirements.py | 272 | ansible-test - Fix consistency of managed venvs. (#77028) | 78,507 | 0 | 169 | 159 | 56 | 266,665 | 84 | ansible | 29 | test/lib/ansible_test/_util/target/setup/requirements.py | Python | 20 | {
"docstring": "Bootstrap pip and related packages in an empty virtual environment.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/ansible/ansible.git |
|
4 | _create_sql_query | def _create_sql_query(self) -> str:
escaper = ParamEscaper()
location = escaper.escape_item(self._file_location)
if self._expression_list is not None:
location = f"(SELECT {self._expression_list} FROM {location})"
files_or_pattern = ""
if self._pattern is not None:
files_or_pattern = f"PATTERN = {escaper.escape_item(self._pattern)}\n"
elif self._files is not None:
files_or_pattern = f"FILES = {escaper.escape_item(self._files)}\n"
format_options = self._generate_options("FORMAT_OPTIONS", escaper, self._format_options)
copy_options = self._generate_options("COPY_OPTIONS", escaper, self._copy_options)
# TODO: think on how to make sure that table_name and expression_list aren't used for SQL injection
sql = f
return sql.strip()
| 27d19e7626ef80687997a6799762fa00162c1328 | 14 | databricks_sql.py | 236 | Databricks SQL operators (#21363) | 8,546 | 0 | 185 | 99 | 55 | 45,314 | 76 | airflow | 21 | airflow/providers/databricks/operators/databricks_sql.py | Python | 18 | {
"docstring": "COPY INTO {self._table_name}\nFROM {location}\nFILEFORMAT = {self._file_format}\n{files_or_pattern}{format_options}{copy_options}\n",
"language": "en",
"n_whitespaces": 5,
"n_words": 9,
"vocab_size": 9
} | https://github.com/apache/airflow.git |
|
9 | map | def map(self, mapper, na_action=None):
from pandas.core.indexes.multi import MultiIndex
new_values = self._map_values(mapper, na_action=na_action)
# we can return a MultiIndex
if new_values.size and isinstance(new_values[0], tuple):
if isinstance(self, MultiIndex):
names = self.names
elif self.name:
names = [self.name] * len(new_values[0])
else:
names = None
return MultiIndex.from_tuples(new_values, names=names)
dtype = None
if not new_values.size:
# empty
dtype = self.dtype
# e.g. if we are floating and new_values is all ints, then we
# don't want to cast back to floating. But if we are UInt64
# and new_values is all ints, we want to try.
same_dtype = lib.infer_dtype(new_values, skipna=False) == self.inferred_type
if same_dtype:
new_values = maybe_cast_pointwise_result(
new_values, self.dtype, same_dtype=same_dtype
)
if self._is_backward_compat_public_numeric_index and is_numeric_dtype(
new_values.dtype
):
return self._constructor(
new_values, dtype=dtype, copy=False, name=self.name
)
return Index._with_infer(new_values, dtype=dtype, copy=False, name=self.name)
# TODO: De-duplicate with map, xref GH#32349 | 521259299f7829da667ba39302ec77acedde9e5e | 15 | base.py | 304 | DOC: Improve doc summaries in series.rst (#45237) | 39,399 | 0 | 437 | 199 | 83 | 163,191 | 130 | pandas | 31 | pandas/core/indexes/base.py | Python | 26 | {
"docstring": "\n Map values using an input mapping or function.\n\n Parameters\n ----------\n mapper : function, dict, or Series\n Mapping correspondence.\n na_action : {None, 'ignore'}\n If 'ignore', propagate NA values, without passing them to the\n mapping correspondence.\n\n Returns\n -------\n applied : Union[Index, MultiIndex], inferred\n The output of the mapping function applied to the index.\n If the function returns a tuple with more than one element\n a MultiIndex will be returned.\n ",
"language": "en",
"n_whitespaces": 197,
"n_words": 67,
"vocab_size": 53
} | https://github.com/pandas-dev/pandas.git |
|
2 | _handle_deprecations | def _handle_deprecations(self) -> None:
if self._args.distributed:
deprecation_warning("`-d`, `--distributed`",
"Please use `-D`, `--distribution-strategy`")
logger.warning("Setting 'distribution-strategy' to 'mirrored'")
setattr(self._args, "distribution_strategy", "mirrored")
del self._args.distributed
| 2ea05623bd684b2d1dd75679ad00441a5c751e7e | 10 | train.py | 79 | Update Distibution Strategies:
- Add Central Storage Stategy
- Deprecate 'distributed' cli argument | 20,513 | 0 | 110 | 43 | 21 | 101,076 | 21 | faceswap | 8 | scripts/train.py | Python | 8 | {
"docstring": " Handle the update of deprecated arguments and output warnings. ",
"language": "en",
"n_whitespaces": 10,
"n_words": 9,
"vocab_size": 9
} | https://github.com/deepfakes/faceswap.git |
|
1 | multi_input_functional | def multi_input_functional():
input_1 = keras.Input(shape=(1,))
input_2 = keras.Input(shape=(1,))
input_3 = keras.Input(shape=(1,))
added = keras.layers.Add()([input_1, input_2, input_3])
output = test_utils.Bias()(added)
return keras.Model([input_1, input_2, input_3], output)
@test_combinations.run_with_all_model_types
@test_combinations.run_all_keras_modes | b96518a22bfd92a29811e507dec0b34248a8a3f5 | @test_combinations.run_with_all_model_types
@test_combinations.run_all_keras_modes | 10 | correctness_test.py | 149 | - Consolidate disparate test-related files into a single testing_infra folder.
- Cleanup TODO related to removing testing infra as a dependency of the Keras target.
- Standardize import naming: there is now only "test_combinations" for test combinations, and "test_utils" for utilities. The TF utilities module "test_util" is now always imported as "tf_test_utils" to avoid confusion.
PiperOrigin-RevId: 426773173 | 79,765 | 1 | 31 | 87 | 19 | 268,905 | 26 | keras | 17 | keras/engine/correctness_test.py | Python | 7 | {
"docstring": "Functional Model that adds its inputs and then adds a bias.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | https://github.com/keras-team/keras.git |
7 | nullify_connected_endpoints | def nullify_connected_endpoints(instance, **kwargs):
logger = logging.getLogger('netbox.dcim.cable')
# Disassociate the Cable from its termination points
if instance.termination_a:
logger.debug(f"Nullifying termination A for cable {instance}")
model = instance.termination_a_type.model_class()
model.objects.filter(pk__in=instance.termination_a_ids).update(_link_peer_type=None, _link_peer_id=None)
if instance.termination_b:
logger.debug(f"Nullifying termination B for cable {instance}")
model = instance.termination_b_type.model_class()
model.objects.filter(pk__in=instance.termination_b_ids).update(_link_peer_type=None, _link_peer_id=None)
# Delete and retrace any dependent cable paths
for cablepath in CablePath.objects.filter(path__contains=instance):
cp = CablePath.from_origin(cablepath.origin)
if cp:
CablePath.objects.filter(pk=cablepath.pk).update(
path=cp.path,
destination_type=ContentType.objects.get_for_model(cp.destination) if cp.destination else None,
destination_id=cp.destination.pk if cp.destination else None,
is_active=cp.is_active,
is_split=cp.is_split
)
else:
cablepath.delete()
| 4bb9b6ee2639db683b70d6ddbee055497e0a3647 | 16 | signals.py | 332 | Extend Cable model to support multiple A/B terminations | 77,796 | 0 | 266 | 205 | 53 | 264,753 | 74 | netbox | 37 | netbox/dcim/signals.py | Python | 22 | {
"docstring": "\n When a Cable is deleted, check for and update its two connected endpoints\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 13,
"vocab_size": 13
} | https://github.com/netbox-community/netbox.git |
|
1 | _async_update | def _async_update(self) -> None:
super()._async_update()
node = self.gateway.sensors[self.node_id]
child = node.children[self.child_id]
position: str = child.values[self.value_type]
latitude, longitude, _ = position.split(",")
self._latitude = float(latitude)
self._longitude = float(longitude)
| df2d0cd3e3ade2339a18415f92c85810308a9926 | 9 | device_tracker.py | 126 | Refactor mysensors device tracker (#84747) | 97,180 | 0 | 82 | 77 | 21 | 298,235 | 26 | core | 21 | homeassistant/components/mysensors/device_tracker.py | Python | 9 | {
"docstring": "Update the controller with the latest value from a device.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | https://github.com/home-assistant/core.git |
|
1 | preprocess_input | def preprocess_input(x, data_format=None): # pylint: disable=unused-argument
return x
@keras_export("keras.applications.convnext.decode_predictions") | 2d1086447a25d281f9428832d046c473d80ad761 | @keras_export("keras.applications.convnext.decode_predictions") | 7 | convnext.py | 33 | Corrected preprocess_input docstring in regnet.py and convnext.py | 80,001 | 1 | 15 | 12 | 9 | 269,284 | 9 | keras | 4 | keras/applications/convnext.py | Python | 2 | {
"docstring": "A placeholder method for backward compatibility.\n\n The preprocessing logic has been included in the convnext model\n implementation. Users are no longer required to call this method to normalize\n the input data. This method does nothing and only kept as a placeholder to\n align the API surface between old and new version of model.\n\n Args:\n x: A floating point `numpy.array` or a `tf.Tensor`.\n data_format: Optional data format of the image tensor/array. Defaults to\n None, in which case the global setting\n `tf.keras.backend.image_data_format()` is used (unless you changed it, it\n defaults to \"channels_last\").{mode}\n\n Returns:\n Unchanged `numpy.array` or `tf.Tensor`.\n ",
"language": "en",
"n_whitespaces": 126,
"n_words": 95,
"vocab_size": 76
} | https://github.com/keras-team/keras.git |
2 | get_delivery_notes_to_be_billed | def get_delivery_notes_to_be_billed(doctype, txt, searchfield, start, page_len, filters, as_dict):
doctype = "Delivery Note"
fields = get_fields(doctype, ["name", "customer", "posting_date"])
return frappe.db.sql(
% {
"fields": ", ".join(["`tabDelivery Note`.{0}".format(f) for f in fields]),
"key": searchfield,
"fcond": get_filters_cond(doctype, filters, []),
"mcond": get_match_cond(doctype),
"start": start,
"page_len": page_len,
"txt": "%(txt)s",
},
{"txt": ("%%%s%%" % txt)},
as_dict=as_dict,
)
@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs | 9baa2229761c5415f29646a1a5bed4a3f4981e05 | @frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs | 16 | queries.py | 210 | fix: specify allowed doctype in queries (#31761) | 14,974 | 1 | 35 | 116 | 47 | 69,105 | 53 | erpnext | 20 | erpnext/controllers/queries.py | Python | 32 | {
"docstring": "\n\t\tselect %(fields)s\n\t\tfrom `tabDelivery Note`\n\t\twhere `tabDelivery Note`.`%(key)s` like %(txt)s and\n\t\t\t`tabDelivery Note`.docstatus = 1\n\t\t\tand status not in ('Stopped', 'Closed') %(fcond)s\n\t\t\tand (\n\t\t\t\t(`tabDelivery Note`.is_return = 0 and `tabDelivery Note`.per_billed < 100)\n\t\t\t\tor (`tabDelivery Note`.grand_total = 0 and `tabDelivery Note`.per_billed < 100)\n\t\t\t\tor (\n\t\t\t\t\t`tabDelivery Note`.is_return = 1\n\t\t\t\t\tand return_against in (select name from `tabDelivery Note` where per_billed < 100)\n\t\t\t\t)\n\t\t\t)\n\t\t\t%(mcond)s order by `tabDelivery Note`.`%(key)s` asc limit %(page_len)s offset %(start)s\n\t",
"language": "en",
"n_whitespaces": 59,
"n_words": 73,
"vocab_size": 41
} | https://github.com/frappe/erpnext.git |
7 | _build_attention_equation | def _build_attention_equation(rank, attn_axes):
target_notation = _CHR_IDX[:rank]
# `batch_dims` includes the head dim.
batch_dims = tuple(np.delete(range(rank), attn_axes + (rank - 1,)))
letter_offset = rank
source_notation = ""
for i in range(rank):
if i in batch_dims or i == rank - 1:
source_notation += target_notation[i]
else:
source_notation += _CHR_IDX[letter_offset]
letter_offset += 1
product_notation = "".join(
[target_notation[i] for i in batch_dims]
+ [target_notation[i] for i in attn_axes]
+ [source_notation[i] for i in attn_axes]
)
dot_product_equation = "%s,%s->%s" % (
source_notation,
target_notation,
product_notation,
)
attn_scores_rank = len(product_notation)
combine_equation = "%s,%s->%s" % (
product_notation,
source_notation,
target_notation,
)
return dot_product_equation, combine_equation, attn_scores_rank
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 13 | multi_head_attention.py | 241 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 81,007 | 0 | 251 | 155 | 56 | 272,394 | 96 | keras | 19 | keras/layers/attention/multi_head_attention.py | Python | 28 | {
"docstring": "Builds einsum equations for the attention computation.\n\n Query, key, value inputs after projection are expected to have the shape as:\n `(bs, <non-attention dims>, <attention dims>, num_heads, channels)`.\n `bs` and `<non-attention dims>` are treated as `<batch dims>`.\n\n The attention operations can be generalized:\n (1) Query-key dot product:\n `(<batch dims>, <query attention dims>, num_heads, channels), (<batch dims>,\n <key attention dims>, num_heads, channels) -> (<batch dims>,\n num_heads, <query attention dims>, <key attention dims>)`\n (2) Combination:\n `(<batch dims>, num_heads, <query attention dims>, <key attention dims>),\n (<batch dims>, <value attention dims>, num_heads, channels) -> (<batch dims>,\n <query attention dims>, num_heads, channels)`\n\n Args:\n rank: Rank of query, key, value tensors.\n attn_axes: List/tuple of axes, `[-1, rank)`,\n that attention will be applied to.\n\n Returns:\n Einsum equations.\n ",
"language": "en",
"n_whitespaces": 186,
"n_words": 119,
"vocab_size": 73
} | https://github.com/keras-team/keras.git |
|
9 | nms | def nms(dets, match_threshold=0.6, match_metric='iou'):
if dets.shape[0] == 0:
return dets[[], :]
scores = dets[:, 0]
x1 = dets[:, 1]
y1 = dets[:, 2]
x2 = dets[:, 3]
y2 = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
ndets = dets.shape[0]
suppressed = np.zeros((ndets), dtype=np.int)
for _i in range(ndets):
i = order[_i]
if suppressed[i] == 1:
continue
ix1 = x1[i]
iy1 = y1[i]
ix2 = x2[i]
iy2 = y2[i]
iarea = areas[i]
for _j in range(_i + 1, ndets):
j = order[_j]
if suppressed[j] == 1:
continue
xx1 = max(ix1, x1[j])
yy1 = max(iy1, y1[j])
xx2 = min(ix2, x2[j])
yy2 = min(iy2, y2[j])
w = max(0.0, xx2 - xx1 + 1)
h = max(0.0, yy2 - yy1 + 1)
inter = w * h
if match_metric == 'iou':
union = iarea + areas[j] - inter
match_value = inter / union
elif match_metric == 'ios':
smaller = min(iarea, areas[j])
match_value = inter / smaller
else:
raise ValueError()
if match_value >= match_threshold:
suppressed[j] = 1
keep = np.where(suppressed == 0)[0]
dets = dets[keep, :]
return dets
| 0f75cb6c640203bc5c2670f6cbd9ac89ad53ecf4 | 16 | utils.py | 553 | [smalldet] add SAHI slice train eval infer deploy (#6465)
* add slice infer for smalldet
* add slice infer for smalldet
* fix slice infer
* fix doc, test=document_fix
* fix eval and configs
* add slice dataset loader
* fix deploy
* fix docs, test=document_fix
* update docs, test=document_fix | 53,054 | 0 | 550 | 365 | 100 | 211,275 | 183 | PaddleDetection | 44 | deploy/python/utils.py | Python | 45 | {
"docstring": " Apply NMS to avoid detecting too many overlapping bounding boxes.\n Args:\n dets: shape [N, 5], [score, x1, y1, x2, y2]\n match_metric: 'iou' or 'ios'\n match_threshold: overlap thresh for match metric.\n ",
"language": "en",
"n_whitespaces": 74,
"n_words": 30,
"vocab_size": 30
} | https://github.com/PaddlePaddle/PaddleDetection.git |
|
1 | test_fetch_period_api_with_minimal_response | async def test_fetch_period_api_with_minimal_response(recorder_mock, hass, hass_client):
now = dt_util.utcnow()
await async_setup_component(hass, "history", {})
hass.states.async_set("sensor.power", 0, {"attr": "any"})
await async_wait_recording_done(hass)
hass.states.async_set("sensor.power", 50, {"attr": "any"})
await async_wait_recording_done(hass)
hass.states.async_set("sensor.power", 23, {"attr": "any"})
last_changed = hass.states.get("sensor.power").last_changed
await async_wait_recording_done(hass)
hass.states.async_set("sensor.power", 23, {"attr": "any"})
await async_wait_recording_done(hass)
client = await hass_client()
response = await client.get(
f"/api/history/period/{now.isoformat()}?filter_entity_id=sensor.power&minimal_response&no_attributes"
)
assert response.status == HTTPStatus.OK
response_json = await response.json()
assert len(response_json[0]) == 3
state_list = response_json[0]
assert state_list[0]["entity_id"] == "sensor.power"
assert state_list[0]["attributes"] == {}
assert state_list[0]["state"] == "0"
assert "attributes" not in state_list[1]
assert "entity_id" not in state_list[1]
assert state_list[1]["state"] == "50"
assert "attributes" not in state_list[2]
assert "entity_id" not in state_list[2]
assert state_list[2]["state"] == "23"
assert state_list[2]["last_changed"] == json.dumps(
process_timestamp(last_changed),
cls=JSONEncoder,
).replace('"', "")
| 31a787558fd312331b55e5c2c4b33341fc3601fc | 13 | test_init.py | 500 | Ensure recorder test fixture is setup before hass fixture (#80528)
* Ensure recorder test fixture is setup before hass fixture
* Adjust more tests | 88,516 | 0 | 223 | 284 | 59 | 289,374 | 112 | core | 28 | tests/components/history/test_init.py | Python | 33 | {
"docstring": "Test the fetch period view for history with minimal_response.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/home-assistant/core.git |
|
11 | describe_operation | def describe_operation(operation, backwards):
prefix = ""
is_error = False
if hasattr(operation, "code"):
code = operation.reverse_code if backwards else operation.code
action = (code.__doc__ or "") if code else None
elif hasattr(operation, "sql"):
action = operation.reverse_sql if backwards else operation.sql
else:
action = ""
if backwards:
prefix = "Undo "
if action is not None:
action = str(action).replace("\n", "")
elif backwards:
action = "IRREVERSIBLE"
is_error = True
if action:
action = " -> " + action
truncated = Truncator(action)
return prefix + operation.describe() + truncated.chars(40), is_error
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 12 | migrate.py | 234 | Refs #33476 -- Reformatted code with Black. | 50,839 | 0 | 275 | 135 | 43 | 204,670 | 84 | django | 18 | django/core/management/commands/migrate.py | Python | 21 | {
"docstring": "Return a string that describes a migration operation for --plan.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | https://github.com/django/django.git |
|
2 | get_binance_available_quotes_for_each_coin | def get_binance_available_quotes_for_each_coin() -> dict:
trading_pairs = _get_trading_pairs()
results = defaultdict(list)
for pair in trading_pairs:
results[pair["baseAsset"]].append(pair["quoteAsset"])
return results
@log_start_end(log=logger) | e1b6022b9cf156ffc0697d0d25a5ed2772ea8d68 | @log_start_end(log=logger) | 12 | binance_model.py | 82 | Global plot styles (#1228)
* Add default stylesheets
* Add terminal style helper class and global style initialization in cfg
* Style comments and docstrings
* Load rich terminal theme from config file
* Add application chart styles to candle charts
* Add todos
* Remove explicit color setting for some ta charts
* Add user styles folder to gitignore
* Update default stylesheets
* Add matplotlib font manager support
* Add matplotlib font manager support
* Update docstrings and default style
* Update stocks candle chart formatting (return fig to style title)
* Style common ta overlap view
* Make up and down market colors a part of the style helper
* Update stylesheets
* Style common ta volume view
* Style common ta momentum view
* Style common ta trend indicators view
* Style common ta volatility view
* Style common ta volume view
* Style common ta custom indicators view
* Fix styling bugs and remove the obvious time x lablel
* Style charts in the covid menu
* Set legend position to upper left in the mpl stylesheet
* Add mpl_rcparams configs for parameters not covered by stylesheets
* Remove font configuration files
* Update style class utility functions
* Implement passing external axes and style utility usage in ema & stoch
* Add theme watermark and output helpers
* Rename style to theme
* Update helper usage in ta/ma and ta/stoch
* Update style to theme in sample menus
* Style forex (#1305)
* Make tight layout optional 'cause mplfinance doesn't support it
* Apply global style to the forex menu
* Update code layout in oanda view and black
* Style common TA (#1315)
* Make tight layout optional 'cause mplfinance doesn't support it
* Apply global style to the forex menu
* Add linewidth to theme for use in mpf's addplots
* Add vwap to the stocks notebook api
* Update common/ta overlap to follow charting style
* Apply style on TerminalStyle init
* Enable infrastructure for excluding non-trading days from plots
* Update notebook api to include there and resolve bandit warning
* Update ta/common/overlap to exclude non-trading days
* Enable external ax, style and non-trading days in common/ta/momentum
* Enable external ax, style and non-trading days in common/ta/trend
* Update vwap to the argument naming convention
* Enable external ax, style and non-trading days in common/ta/volatility
* Enable external ax, style and non-trading days in common/ta/volume
* Enable external ax, style and non-trading days in common/ta/custom
* Fix controller tests
* Forgot to disable rewriting of the cassettes ...
* Fix controller errors that came up because a merge conflict
* Fix price label position on fib
* Fix line having wrong x values in fib
Co-authored-by: Colin Delahunty <[email protected]>
* Style economy (#1308)
* Began converting
* Added alphavan_view
* Added CNN View
* Updated nasdaq view, fixed glitch
* Added fred
* Refactored URL
* Theo's requested changes
* Updated docstrings
* Updated tests
* Fixed pylint
* Fixed tests
* Theo changes
* Econ Fix
* Refactor chart style for Crypto context (#1306)
* Remove mock for gff
* Mock visualize_output helper function
* Refactor
* Fix plot helper
* Update legend loc
* Refactor mplfinance candle plot
* Fix errors in the helper function
* Fix binbook having the wrong call_ function name
* Remove hardcoded style params
* Resolve kwargs future warning from pandas
* Remove warnings import
Co-authored-by: Theodore Aptekarev <[email protected]>
* funds + custom (#1311)
* funds + custom
* cleanup cleanup everybody everywhere
* Fix external axes conditional and a typo
Co-authored-by: Theodore Aptekarev <[email protected]>
* Add external axes mode to covid charts (#1328)
* Add portfolio menu plots (#1318)
* Portfolio view plots (commenting out report stuff)
* PA Menu broken. Commenting out and fix tests
* portfolio optimization
* comment out commented api line
* Add notes on disabling the pa submenu
Co-authored-by: Theodore Aptekarev <[email protected]>
* Plot updates in common BA (#1335)
* Add external axes support to common/ba/finbrain
* Add external axes support to common/ba/twitter
* Add external axes support to common/ba/google
* Add external axes support to common/ba/sentimentinvestor
* Add sentimentinvestor to the notebooks API
* Fix tests
* Etf refactor (#1323)
* Refactored no ETF
* Fixed gtff import
* Fixed tests
* Fix pie chart style
* Refactored etf/candle
* Added pylint fix
* Fixed tests
* Update candle chart layout
* Update etf controller test
* Remove strange binary file
Co-authored-by: Theodore Aptekarev <[email protected]>
* Expose ETF candle function in the notebooks API
* Common BA and Common QA charts update (#1342)
* Add external axes support to common/ba/finbrain
* Add external axes support to common/ba/twitter
* Add external axes support to common/ba/google
* Add external axes support to common/ba/sentimentinvestor
* Add sentimentinvestor to the notebooks API
* Fix tests
* Update stylesheet files
* Refactor charts for common/qa
* Update the forgotten line plot
* Update tests
* Add missing arg to a docstring
* Remove scientific notation
* Black imports
Co-authored-by: Minh Hoang <[email protected]>
* Options refactor (#1324)
* Fixed alphaquery_view
* finished options
* Fixed pylint
* Fixed tests
* Fixed tests
* Fixed tests
* update yfinance
* Tradier + Chartexchange
* change mocks from gtff to theme.visualize output
* tests
Co-authored-by: Theodore Aptekarev <[email protected]>
Co-authored-by: james <[email protected]>
* Refactor Stocks menu (#1325)
* Fix backtesting menu
* Refactor comparison analysis
* Refactor Dark pool shorts
* Refactor rest of menu
* Fix test
* Fix tests failing
* Fix tests fail
* Fix test failing
* Remove record mode=none to record new output
* Rewrite test output
* Rewrite test outputs
* Adding more rewritten test output
* Mock plt.show
* Mock missing plt.show
* Missing @pytest.mark.vcr
* Updating tests : common/behavioural_analysis/finbrain
* Improve notebooks API coverage for CA and DPS
* Silence annoying flake8 warning
Co-authored-by: Chavithra PARANA <[email protected]>
Co-authored-by: Theodore Aptekarev <[email protected]>
* Charts update for common/pred (#1344)
* Add external axes support to common/ba/finbrain
* Add external axes support to common/ba/twitter
* Add external axes support to common/ba/google
* Add external axes support to common/ba/sentimentinvestor
* Add sentimentinvestor to the notebooks API
* Fix tests
* Update stylesheet files
* Refactor charts for common/qa
* Update the forgotten line plot
* Update tests
* Add missing arg to a docstring
* Style pred helper and controllers
* Update ETS plot
* Update plots in KNN and pred helper
* Update plot and pretty table for arima
* Update plot for common/pred/regression
* Refactor mc_view
* Fix linting
* Fix mypy
* Move plot title to the axis level to make more vertical space
Co-authored-by: Minh Hoang <[email protected]>
Co-authored-by: jmaslek <[email protected]>
* linter
* Update common/ba test data
* Change etf candle to match stock candle
* try updating sia test
Co-authored-by: Colin Delahunty <[email protected]>
Co-authored-by: jmaslek <[email protected]>
Co-authored-by: minhhoang1023 <[email protected]>
Co-authored-by: Minh Hoang <[email protected]>
Co-authored-by: Chavithra PARANA <[email protected]> | 84,165 | 1 | 39 | 40 | 16 | 282,485 | 18 | OpenBBTerminal | 12 | gamestonk_terminal/cryptocurrency/due_diligence/binance_model.py | Python | 15 | {
"docstring": "Helper methods that for every coin available on Binance add all quote assets. [Source: Binance]\n\n Returns\n -------\n dict:\n All quote assets for given coin\n {'ETH' : ['BTC', 'USDT' ...], 'UNI' : ['ETH', 'BTC','BUSD', ...]\n\n ",
"language": "en",
"n_whitespaces": 60,
"n_words": 34,
"vocab_size": 30
} | https://github.com/OpenBB-finance/OpenBBTerminal.git |
13 | test_search_tsquery_chars | def test_search_tsquery_chars(self):
# Simple quote should be escaped inside each tsquery term.
results = self.backend.search("L'amour piqué par une abeille", models.Book)
self.assertUnsortedListEqual([r.title for r in results], [])
results = self.backend.search("'starting quote", models.Book)
self.assertUnsortedListEqual([r.title for r in results], [])
results = self.backend.search("ending quote'", models.Book)
self.assertUnsortedListEqual([r.title for r in results], [])
results = self.backend.search("double quo''te", models.Book)
self.assertUnsortedListEqual([r.title for r in results], [])
results = self.backend.search("triple quo | d10f15e55806c6944827d801cd9c2d53f5da4186 | 9 | test_postgres_backend.py | 595 | Reformat with black | 16,435 | 0 | 140 | 378 | 34 | 75,853 | 63 | wagtail | 10 | wagtail/search/tests/test_postgres_backend.py | Python | 25 | {
"docstring": "\n Checks that tsquery characters are correctly escaped\n and do not generate a PostgreSQL syntax error.\n te\", models.Book)\n self.assertUnsortedListEqual([r.title for r in results], [])\n\n # Now suffixes.\n results = self.backend.search(\"Something:B\", models.Book)\n self.assertUnsortedListEqual([r.title for r in results], [])\n results = self.backend.search(\"Something:*\", models.Book)\n self.assertUnsortedListEqual([r.title for r in results], [])\n results = self.backend.search(\"Something:A*BCD\", models.Book)\n self.assertUnsortedListEqual([r.title for r in results], [])\n\n # Now the AND operator.\n results = self.backend.search(\"first & second\", models.Book)\n self.assertUnsortedListEqual([r.title for r in results], [])\n\n # Now the OR operator.\n results = self.backend.search(\"first | second\", models.Book)\n self.assertUnsortedListEqual([r.title for r in results], [])\n\n # Now the NOT operator.\n results = self.backend.search(\"first & !second\", models.Book)\n self.assertUnsortedListEqual([r.title for r in results], [])\n\n # Now the phrase operator.\n results = self.backend.search(\"first <-> second\", models.Book)\n self.assertUnsortedListEqual([r.title for r in results], [])\n",
"language": "en",
"n_whitespaces": 285,
"n_words": 124,
"vocab_size": 43
} | https://github.com/wagtail/wagtail.git |
|
4 | sample_weights_mismatch | def sample_weights_mismatch(self):
# If there is a mismatch between sample weight mode and the placeholders
# created, then recompile the sub-graphs that depend on sample weights.
return (
self.sample_weight_mode is not None and self.sample_weight is None
) or (
self.sample_weight_mode is None and self.sample_weight is not None
)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 10 | training_v1.py | 59 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 80,913 | 0 | 112 | 36 | 31 | 271,957 | 48 | keras | 4 | keras/engine/training_v1.py | Python | 6 | {
"docstring": "Check if the sample weight and the mode match or not.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | https://github.com/keras-team/keras.git |
|
3 | set_undefined_options | def set_undefined_options(self, src_cmd, *option_pairs):
# Option_pairs: list of (src_option, dst_option) tuples
src_cmd_obj = self.distribution.get_command_obj(src_cmd)
src_cmd_obj.ensure_finalized()
for (src_option, dst_option) in option_pairs:
if getattr(self, dst_option) is None:
setattr(self, dst_option, getattr(src_cmd_obj, src_option))
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 13 | cmd.py | 90 | add python 3.10.4 for windows | 56,675 | 0 | 90 | 58 | 26 | 222,614 | 29 | XX-Net | 12 | python3.10.4/Lib/distutils/cmd.py | Python | 6 | {
"docstring": "Set the values of any \"undefined\" options from corresponding\n option values in some other command object. \"Undefined\" here means\n \"is None\", which is the convention used to indicate that an option\n has not been changed between 'initialize_options()' and\n 'finalize_options()'. Usually called from 'finalize_options()' for\n options that depend on some other command rather than another\n option of the same command. 'src_cmd' is the other command from\n which option values will be taken (a command object will be created\n for it if necessary); the remaining arguments are\n '(src_option,dst_option)' tuples which mean \"take the value of\n 'src_option' in the 'src_cmd' command object, and copy it to\n 'dst_option' in the current command object\".\n ",
"language": "en",
"n_whitespaces": 196,
"n_words": 109,
"vocab_size": 71
} | https://github.com/XX-net/XX-Net.git |
|
2 | get_token | def get_token(request):
if "CSRF_COOKIE" in request.META:
csrf_secret = request.META["CSRF_COOKIE"]
# Since the cookie is being used, flag to send the cookie in
# process_response() (even if the client already has it) in order to
# renew the expiry timer.
request.META["CSRF_COOKIE_NEEDS_UPDATE"] = True
else:
csrf_secret = _add_new_csrf_cookie(request)
return _mask_cipher_secret(csrf_secret)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 11 | csrf.py | 79 | Refs #33476 -- Reformatted code with Black. | 51,381 | 0 | 102 | 42 | 35 | 206,140 | 48 | django | 6 | django/middleware/csrf.py | Python | 7 | {
"docstring": "\n Return the CSRF token required for a POST form. The token is an\n alphanumeric value. A new token is created if one is not already set.\n\n A side effect of calling this function is to make the csrf_protect\n decorator and the CsrfViewMiddleware add a CSRF cookie and a 'Vary: Cookie'\n header to the outgoing response. For this reason, you may need to use this\n function lazily, as is done by the csrf context processor.\n ",
"language": "en",
"n_whitespaces": 97,
"n_words": 74,
"vocab_size": 54
} | https://github.com/django/django.git |
|
13 | as_sql | def as_sql(self, compiler, connection):
result = []
result_params = []
if self.connector == AND:
full_needed, empty_needed = len(self.children), 1
else:
full_needed, empty_needed = 1, len(self.children)
for child in self.children:
try:
sql, params = compiler.compile(child)
except EmptyResultSet:
empty_needed -= 1
else:
if sql:
result.append(sql)
result_params.extend(params)
else:
full_needed -= 1
# Check if this node matches nothing or everything.
# First check the amount of full nodes and empty nodes
# to make this node empty/full.
# Now, check if this node is full/empty using the
# counts.
if empty_needed == 0:
if self.negated:
return "", []
else:
raise EmptyResultSet
if full_needed == 0:
if self.negated:
raise EmptyResultSet
else:
return "", []
conn = " %s " % self.connector
sql_string = conn.join(result)
if sql_string:
if self.negated:
# Some backends (Oracle at least) need parentheses
# around the inner SQL in the negated case, even if the
# inner SQL contains just a single expression.
sql_string = "NOT (%s)" % sql_string
elif len(result) > 1 or self.resolved:
sql_string = "(%s)" % sql_string
return sql_string, result_params
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 14 | where.py | 320 | Refs #33476 -- Reformatted code with Black. | 51,276 | 0 | 724 | 185 | 98 | 205,905 | 172 | django | 24 | django/db/models/sql/where.py | Python | 36 | {
"docstring": "\n Return the SQL version of the where clause and the value to be\n substituted in. Return '', [] if this node matches everything,\n None, [] if this node is empty, and raise EmptyResultSet if this\n node can't match anything.\n ",
"language": "en",
"n_whitespaces": 75,
"n_words": 39,
"vocab_size": 28
} | https://github.com/django/django.git |
|
1 | test_pageurl_with_unknown_site | def test_pageurl_with_unknown_site(self):
page = Page.objects.get(url_path="/home/events/")
tpl = template.Template(
)
# 'request' object in context, but site is None
request = HttpRequest()
request.META["HTTP_HOST"] = "unknown.example.com"
request.META["SERVER_PORT"] = 80
result = tpl.render(template.Context({"page": page, "request": request}))
self.assertIn('<a href="/events/">Events</a>', result)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 13 | tests.py | 135 | Reformat with black | 16,286 | 0 | 110 | 75 | 31 | 74,670 | 36 | wagtail | 17 | wagtail/core/tests/tests.py | Python | 10 | {
"docstring": "{% load wagtailcore_tags %}<a href=\"{% pageurl page %}\">{{ page.title }}</a>",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/wagtail/wagtail.git |
|
1 | get_labels | def get_labels(self):
return ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"], \
self.BUILDER_CONFIGS[self.name]['pos_tags']
| 7b455cce47204d4d664deea9661670a838ec8d35 | 9 | conll2002.py | 72 | feat: add conll2002 dataset (#1561)
Co-authored-by: Zeyu Chen <[email protected]> | 118,099 | 0 | 42 | 39 | 14 | 322,232 | 14 | PaddleNLP | 4 | paddlenlp/datasets/conll2002.py | Python | 3 | {
"docstring": "\n Returns labels of ner tags and pos tags.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | https://github.com/PaddlePaddle/PaddleNLP.git |
|
4 | format_value | def format_value(self, value):
if value == "" or value is None:
return None
if self.is_localized:
return formats.localize_input(value)
return str(value)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 9 | widgets.py | 61 | Refs #33476 -- Reformatted code with Black. | 51,328 | 0 | 69 | 36 | 15 | 206,022 | 19 | django | 7 | django/forms/widgets.py | Python | 6 | {
"docstring": "\n Return a value as it should appear when rendered in a template.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 12,
"vocab_size": 11
} | https://github.com/django/django.git |
|
2 | __iter__ | def __iter__(self) -> Iterator[tuple[Widget, Region, Region, Size, Size]]:
layers = sorted(self.map.items(), key=lambda item: item[1].order, reverse=True)
intersection = Region.intersection
for widget, (region, _order, clip, virtual_size, container_size) in layers:
yield (
widget,
intersection(region, clip),
region,
virtual_size,
container_size,
)
| 1a20b9de7d4cef7f93e4500757d3fb42e680f40c | 12 | _compositor.py | 126 | docstring | 43,896 | 0 | 161 | 90 | 32 | 182,658 | 36 | textual | 22 | src/textual/_compositor.py | Python | 17 | {
"docstring": "Iterate map with information regarding each widget and is position\n\n Yields:\n Iterator[tuple[Widget, Region, Region, Size, Size]]: Iterates a tuple of\n Widget, clip region, region, virtual size, and container size.\n ",
"language": "en",
"n_whitespaces": 69,
"n_words": 29,
"vocab_size": 26
} | https://github.com/Textualize/textual.git |
|
3 | _express_axis | def _express_axis(self, axis, frame):
try:
ax_mat = axis.to_matrix(self.parent_interframe)
except ValueError:
ax_mat = axis.to_matrix(self.child_interframe)
try:
self.parent_interframe.dcm(frame) # Check if connected
int_frame = self.parent_interframe
except ValueError:
int_frame = self.child_interframe
return self._to_vector(ax_mat, int_frame).express(frame)
| 7c199e306648513c13c9b5c5b5fad06e5f1c3020 | 12 | joint.py | 118 | Fix failing test and simplify joint orient methods | 49,253 | 0 | 128 | 72 | 22 | 199,355 | 30 | sympy | 13 | sympy/physics/mechanics/joint.py | Python | 11 | {
"docstring": "Helper function to get an axis expressed in a specified frame.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | https://github.com/sympy/sympy.git |
|
1 | run_api_experiment | def run_api_experiment(input_features, output_features):
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "output_size": 14},
"training": {"epochs": 2},
}
model = LudwigModel(config)
return model
@pytest.fixture(scope="module") | 69604268c2ddc06a4ee0b3dce0e05a8fb73b5d16 | @pytest.fixture(scope="module") | 11 | test_visualization_api.py | 105 | Rename fc_size to output_size (#1641)
* Rename fc_size to output_size
* Responding to comments | 894 | 1 | 67 | 48 | 23 | 5,940 | 25 | ludwig | 9 | tests/integration_tests/test_visualization_api.py | Python | 9 | {
"docstring": "Helper method to avoid code repetition in running an experiment.\n\n :param input_features: input schema\n :param output_features: output schema\n :return: None\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 20,
"vocab_size": 18
} | https://github.com/ludwig-ai/ludwig.git |
1 | label_axes | def label_axes(ax, text):
ax.text(.5, .5, text, transform=ax.transAxes,
horizontalalignment="center", verticalalignment="center")
ax.tick_params(bottom=False, labelbottom=False,
left=False, labelleft=False)
##############################################################################
# Fixed axes sizes; fixed paddings.
fig = plt.figure(figsize=(6, 6))
fig.suptitle("Fixed axes sizes, fixed paddings")
# Sizes are in inches.
horiz = [Size.Fixed(1.), Size.Fixed(.5), Size.Fixed(1.5), Size.Fixed(.5)]
vert = [Size.Fixed(1.5), Size.Fixed(.5), Size.Fixed(1.)]
rect = (0.1, 0.1, 0.8, 0.8)
# Divide the axes rectangle into a grid with sizes specified by horiz * vert.
div = Divider(fig, rect, horiz, vert, aspect=False)
# The rect parameter will actually be ignored and overridden by axes_locator.
ax1 = fig.add_axes(rect, axes_locator=div.new_locator(nx=0, ny=0))
label_axes(ax1, "nx=0, ny=0")
ax2 = fig.add_axes(rect, axes_locator=div.new_locator(nx=0, ny=2))
label_axes(ax2, "nx=0, ny=2")
ax3 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, ny=2))
label_axes(ax3, "nx=2, ny=2")
ax4 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, nx1=4, ny=0))
label_axes(ax4, "nx=2, nx1=4, ny=0")
##############################################################################
# Axes sizes that scale with the figure size; fixed paddings.
fig = plt.figure(figsize=(6, 6))
fig.suptitle("Scalable axes sizes, fixed paddings")
horiz = [Size.Scaled(1.5), Size.Fixed(.5), Size.Scaled(1.), Size.Scaled(.5)]
vert = [Size.Scaled(1.), Size.Fixed(.5), Size.Scaled(1.5)]
rect = (0.1, 0.1, 0.8, 0.8)
# Divide the axes rectangle into a grid with sizes specified by horiz * vert.
div = Divider(fig, rect, horiz, vert, aspect=False)
# The rect parameter will actually be ignored and overridden by axes_locator.
ax1 = fig.add_axes(rect, axes_locator=div.new_locator(nx=0, ny=0))
label_axes(ax1, "nx=0, ny=0")
ax2 = fig.add_axes(rect, axes_locator=div.new_locator(nx=0, ny=2))
label_axes(ax2, "nx=0, ny=2")
ax3 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, ny=2))
label_axes(ax3, "nx=2, ny=2")
ax4 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, nx1=4, ny=0))
label_axes(ax4, "nx=2, nx1=4, ny=0")
plt.show()
| 162bd59f50a9c59a51574aee5dc9e932133bb971 | 10 | simple_axes_divider1.py | 767 | DOC: More capitalization of Axes
In line with #18726.
Triggered by #22242. | 22,676 | 0 | 226 | 54 | 101 | 107,291 | 226 | matplotlib | 37 | examples/axes_grid1/simple_axes_divider1.py | Python | 5 | {
"docstring": "Place a label at the center of an Axes, and remove the axis ticks.",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 13
} | https://github.com/matplotlib/matplotlib.git |
|
2 | max_mireds | def max_mireds(self) -> int:
max_mireds = self.cluster.get("color_temp_physical_max", self.MAX_MIREDS)
if max_mireds == 0:
self.warning(
"[Max mireds is 0, setting to %s] Please open an issue on the quirks repo to have this device corrected",
self.MAX_MIREDS,
)
max_mireds = self.MAX_MIREDS
return max_mireds
| 83c6a7e18b1b0e4d5a302e304f117dee11d3aa51 | 10 | lighting.py | 76 | Fix invalid min and max color temp in bad ZHA light devices (#81604)
* Fix ZHA default color temps
* update test | 89,352 | 0 | 131 | 45 | 35 | 290,234 | 40 | core | 7 | homeassistant/components/zha/core/channels/lighting.py | Python | 10 | {
"docstring": "Return the warmest color_temp that this channel supports.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | https://github.com/home-assistant/core.git |
|
3 | get_names | def get_names(adtype):
listnames = []
names = adtype.names
for name in names:
current = adtype[name]
if current.names is not None:
listnames.append((name, tuple(get_names(current))))
else:
listnames.append(name)
return tuple(listnames)
| 569fc6a40ea53054409e00c7d1c0e7f5f53cb0ce | 16 | recfunctions.py | 103 | Fix docstring and examples for rfn.get_names* | 38,607 | 0 | 84 | 63 | 24 | 160,357 | 26 | numpy | 8 | numpy/lib/recfunctions.py | Python | 10 | {
"docstring": "\n Returns the field names of the input datatype as a tuple. Input datatype\n has to have fields otherwise error is raised.\n\n Parameters\n ----------\n adtype : dtype\n Input datatype\n\n Examples\n --------\n >>> from numpy.lib import recfunctions as rfn\n >>> rfn.get_names(np.empty((1,), dtype=[('A', int)]).dtype)\n ('A',)\n >>> rfn.get_names(np.empty((1,), dtype=[('A',int), ('B', float)]).dtype)\n ('A', 'B')\n >>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])\n >>> rfn.get_names(adtype)\n ('a', ('b', ('ba', 'bb')))\n ",
"language": "en",
"n_whitespaces": 121,
"n_words": 65,
"vocab_size": 52
} | https://github.com/numpy/numpy.git |
|
7 | pipe | def pipe(*, duplex=False, overlapped=(True, True), bufsize=BUFSIZE):
address = tempfile.mktemp(
prefix=r'\\.\pipe\python-pipe-{:d}-{:d}-'.format(
os.getpid(), next(_mmap_counter)))
if duplex:
openmode = _winapi.PIPE_ACCESS_DUPLEX
access = _winapi.GENERIC_READ | _winapi.GENERIC_WRITE
obsize, ibsize = bufsize, bufsize
else:
openmode = _winapi.PIPE_ACCESS_INBOUND
access = _winapi.GENERIC_WRITE
obsize, ibsize = 0, bufsize
openmode |= _winapi.FILE_FLAG_FIRST_PIPE_INSTANCE
if overlapped[0]:
openmode |= _winapi.FILE_FLAG_OVERLAPPED
if overlapped[1]:
flags_and_attribs = _winapi.FILE_FLAG_OVERLAPPED
else:
flags_and_attribs = 0
h1 = h2 = None
try:
h1 = _winapi.CreateNamedPipe(
address, openmode, _winapi.PIPE_WAIT,
1, obsize, ibsize, _winapi.NMPWAIT_WAIT_FOREVER, _winapi.NULL)
h2 = _winapi.CreateFile(
address, access, 0, _winapi.NULL, _winapi.OPEN_EXISTING,
flags_and_attribs, _winapi.NULL)
ov = _winapi.ConnectNamedPipe(h1, overlapped=True)
ov.GetOverlappedResult(True)
return h1, h2
except:
if h1 is not None:
_winapi.CloseHandle(h1)
if h2 is not None:
_winapi.CloseHandle(h2)
raise
# Wrapper for a pipe handle
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 13 | windows_utils.py | 355 | add python 3.10.4 for windows | 56,180 | 0 | 346 | 232 | 70 | 221,015 | 111 | XX-Net | 38 | python3.10.4/Lib/asyncio/windows_utils.py | Python | 37 | {
"docstring": "Like os.pipe() but with overlapped support and using handles not fds.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | https://github.com/XX-net/XX-Net.git |
|
1 | test_unload_config_entry | async def test_unload_config_entry(hass, entry, lcn_connection):
await hass.config_entries.async_unload(entry.entry_id)
assert hass.states.get("light.light_output1").state == STATE_UNAVAILABLE
| d5ec2fe842411aea086333889c4357d8cd6cc394 | 10 | test_light.py | 58 | Add tests for LCN light platform (#64741) | 109,403 | 0 | 20 | 34 | 11 | 310,731 | 11 | core | 11 | tests/components/lcn/test_light.py | Python | 3 | {
"docstring": "Test the light is removed when the config entry is unloaded.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 9
} | https://github.com/home-assistant/core.git |
|
6 | real_get_relative_path | def real_get_relative_path(config, path):
requests_pathname = config.requests_pathname_prefix
if requests_pathname == "/" and path == "":
return "/"
if requests_pathname != "/" and path == "":
return requests_pathname
if not path.startswith("/"):
raise exceptions.UnsupportedRelativePath(
.format(
path
)
)
return "/".join([requests_pathname.rstrip("/"), path.lstrip("/")])
| cd13276a2df1c33b5daabdd3a07f57e3128e471c | 12 | _get_paths.py | 139 | Added dash.get_relative_path, dash.strip_relative_path, dash.get_asset_url | 7,266 | 0 | 117 | 76 | 22 | 39,818 | 38 | dash | 12 | dash/_get_paths.py | Python | 16 | {
"docstring": "\n Paths that aren't prefixed with a leading / are not supported.\n You supplied: {}\n ",
"language": "en",
"n_whitespaces": 48,
"n_words": 14,
"vocab_size": 14
} | https://github.com/plotly/dash.git |
|
4 | load_model | def load_model(filepath, custom_objects=None, compile=True, **kwargs):
if str(filepath).endswith(".keras") and zipfile.is_zipfile(filepath):
if kwargs:
raise ValueError(
"The following argument(s) are not supported "
f"with the native Keras format: {list(kwargs.keys())}"
)
return saving_lib.load_model(
filepath, custom_objects=custom_objects, compile=compile
)
# Legacy case.
return legacy_sm_saving_lib.load_model(
filepath, custom_objects=custom_objects, compile=compile, **kwargs
)
| c9068087d9142bab573e0c300bf9874a957accff | 18 | saving_api.py | 140 | Prepare public API surface for v3 saving.
PiperOrigin-RevId: 484397600 | 83,345 | 0 | 149 | 78 | 37 | 280,315 | 43 | keras | 14 | keras/saving/saving_api.py | Python | 13 | {
"docstring": "Loads a model saved via `model.save()`.\n\n Args:\n filepath: `str` or `pathlib.Path` object, path to the saved model file.\n custom_objects: Optional dictionary mapping names\n (strings) to custom classes or functions to be\n considered during deserialization.\n compile: Boolean, whether to compile the model after loading.\n\n SavedModel format arguments:\n options: Only applies to SavedModel format.\n Optional `tf.saved_model.LoadOptions` object that specifies\n SavedModel loading options.\n\n Returns:\n A Keras model instance. If the original model was compiled,\n and the argument `compile=True` is set, then the returned model\n will be compiled. Otherwise, the model will be left uncompiled.\n\n Example:\n\n ```python\n model = tf.keras.Sequential([\n tf.keras.layers.Dense(5, input_shape=(3,)),\n tf.keras.layers.Softmax()])\n model.save(\"model.keras\")\n loaded_model = tf.keras.models.load_model(\"model.keras\")\n x = tf.random.uniform((10, 3))\n assert np.allclose(model.predict(x), loaded_model.predict(x))\n ```\n\n Note that the model variables may have different name values\n (`var.name` property, e.g. `\"dense_1/kernel:0\"`) after being reloaded.\n It is recommended that you use layer attributes to\n access specific variables, e.g. `model.get_layer(\"dense_1\").kernel`.\n ",
"language": "en",
"n_whitespaces": 297,
"n_words": 142,
"vocab_size": 108
} | https://github.com/keras-team/keras.git |
|
1 | test_cache_invalidation | def test_cache_invalidation() -> None:
ledger_store = DictLedgerStore()
user_key = b"1483"
ledger = DataSubjectLedger.get_or_create(store=ledger_store, user_key=user_key)
assert ledger.delta == 1e-6, "The cache has been changed or is invalid."
assert (
ledger._cache_constant2epsilon.all()
), "There is a zero epsilon value in the cache- major security flaw."
assert (
ledger._cache_constant2epsilon > 0
).all(), "Negative epsilon value in the cache- major security flaw."
| 61f4138eeb028287425f6007d692bf7faa808e75 | 10 | data_subject_ledger_test.py | 108 | Add tests for ledger and cache | 411 | 0 | 98 | 66 | 43 | 3,243 | 57 | PySyft | 11 | packages/syft/tests/syft/core/adp/data_subject_ledger_test.py | Python | 12 | {
"docstring": "The cache was built assuming a particular value of delta (1e-6), and shouldn't contain any zero values.",
"language": "en",
"n_whitespaces": 16,
"n_words": 17,
"vocab_size": 17
} | https://github.com/OpenMined/PySyft.git |
|
1 | test_cancellation | def test_cancellation(self):
deferred: "Deferred[str]" = Deferred()
wrapper_deferred = stop_cancellation(deferred)
# Cancel the new `Deferred`.
wrapper_deferred.cancel()
self.assertTrue(wrapper_deferred.called)
self.failureResultOf(wrapper_deferred, CancelledError)
self.assertFalse(
deferred.called, "Original `Deferred` was unexpectedly cancelled."
)
# Now make the inner `Deferred` fail.
# The `Failure` must be consumed, otherwise unwanted tracebacks will be printed
# in logs.
deferred.errback(ValueError("abc"))
self.assertIsNone(deferred.result, "`Failure` was not consumed")
| 91bc15c772d22fbe814170ab2e0fdbfa50f9c372 | 10 | test_async_helpers.py | 126 | Add `stop_cancellation` utility function (#12106) | 71,509 | 0 | 163 | 69 | 46 | 247,147 | 54 | synapse | 16 | tests/util/test_async_helpers.py | Python | 11 | {
"docstring": "Test that cancellation of the new `Deferred` leaves the original running.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | https://github.com/matrix-org/synapse.git |
|
1 | test_from | def test_from(self) -> None:
self._create_users_with_media(20, 2)
channel = self.make_request(
"GET",
self.url + "?from=5",
access_token=self.admin_user_tok,
)
self.assertEqual(200, channel.code, msg=channel.json_body)
self.assertEqual(channel.json_body["total"], 20)
self.assertEqual(len(channel.json_body["users"]), 15)
self.assertNotIn("next_token", channel.json_body)
self._check_fields(channel.json_body["users"])
| c97042f7eef3748e17c90e48a4122389a89c4735 | 11 | test_statistics.py | 168 | Use literals in place of `HTTPStatus` constants in tests (#13469) | 72,674 | 0 | 121 | 103 | 25 | 249,167 | 25 | synapse | 15 | tests/rest/admin/test_statistics.py | Python | 15 | {
"docstring": "\n Testing list of media with a defined starting point (from)\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 10,
"vocab_size": 10
} | https://github.com/matrix-org/synapse.git |
|
1 | get_internal_block_refs | def get_internal_block_refs(self) -> List[ObjectRef[Block]]:
return self._plan.execute().get_blocks()
| 35a157948efa7ba1adf1d1507c2af1d6d84a7db7 | 10 | dataset.py | 44 | Lay the groundwork for lazy dataset optimization (no behavior changes) (#22233)
This PR refactors Dataset execution to enable lazy mode in the future, which can reduce memory usage in large-scale ingest pipelines. There should be no behavior changes in this PR. Many of the optimizations are also punted for future work. | 33,326 | 0 | 20 | 26 | 6 | 144,867 | 6 | ray | 8 | python/ray/data/dataset.py | Python | 12 | {
"docstring": "Get a list of references to the underlying blocks of this dataset.\n\n This function can be used for zero-copy access to the data. It blocks\n until the underlying blocks are computed.\n\n Time complexity: O(1)\n\n Returns:\n A list of references to this dataset's blocks.\n ",
"language": "en",
"n_whitespaces": 89,
"n_words": 43,
"vocab_size": 31
} | https://github.com/ray-project/ray.git |
|
1 | get_group_tag_value_count | def get_group_tag_value_count(self, group, environment_id, key):
raise NotImplementedError
| 72e351082168f68cbaa5700a51e8ed577222e887 | 6 | base.py | 22 | feat(perf_issues): Fix `GroupTagKeyDetailsEndpoint` to work for performance issues (#38860)
This allows this endpoint to return results for performance issues. | 18,042 | 0 | 21 | 14 | 7 | 85,773 | 7 | sentry | 6 | src/sentry/tagstore/base.py | Python | 2 | {
"docstring": "\n >>> get_group_tag_value_count(group, 3, 'key1')\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 4,
"vocab_size": 4
} | https://github.com/getsentry/sentry.git |
|
2 | _create_local_rank_map | def _create_local_rank_map(self) -> Dict:
rank_mapping = {}
ip_dict = defaultdict(int)
for world_rank in range(len(self.worker_group)):
worker = self.worker_group.workers[world_rank]
node_ip = worker.metadata.node_ip
rank_mapping[world_rank] = ip_dict[node_ip]
ip_dict[node_ip] += 1
return rank_mapping
| 80ae651f259e1ea13c21b285d6bfcc7fd834ef9c | 11 | backend_executor.py | 104 | [Train] Clean up `ray.train` package (#25566) | 32,355 | 0 | 107 | 65 | 22 | 141,415 | 28 | ray | 15 | python/ray/train/_internal/backend_executor.py | Python | 30 | {
"docstring": "Create mapping from worker world_rank to local_rank.\n\n Example:\n Worker 0: 0.0.0.0\n Worker 1: 0.0.0.0\n Worker 2: 0.0.0.1\n Worker 3: 0.0.0.0\n Worker 4: 0.0.0.1\n\n Workers 0, 1, 3 are on 0.0.0.0.\n Workers 2, 4 are on 0.0.0.1.\n\n Expected Output:\n {\n 0 -> 0,\n 1 -> 1,\n 2 -> 0,\n 3 -> 2,\n 4 -> 1\n }\n ",
"language": "en",
"n_whitespaces": 254,
"n_words": 55,
"vocab_size": 34
} | https://github.com/ray-project/ray.git |
|
1 | iloc | def iloc(self):
from dask.dataframe.indexing import _iLocIndexer
# For dataframes with unique column names, this will be transformed into a __getitem__ call
return _iLocIndexer(self)
| cccb9d8d8e33a891396b1275c2448c352ef40c27 | 7 | core.py | 33 | absolufy-imports - No relative - PEP8 (#8796)
Conversation in https://github.com/dask/distributed/issues/5889 | 36,552 | 0 | 51 | 19 | 23 | 156,093 | 23 | dask | 6 | dask/dataframe/core.py | Python | 3 | {
"docstring": "Purely integer-location based indexing for selection by position.\n\n Only indexing the column positions is supported. Trying to select\n row positions will raise a ValueError.\n\n See :ref:`dataframe.indexing` for more.\n\n Examples\n --------\n >>> df.iloc[:, [2, 0, 1]] # doctest: +SKIP\n ",
"language": "en",
"n_whitespaces": 88,
"n_words": 38,
"vocab_size": 35
} | https://github.com/dask/dask.git |
|
3 | check_for_non_existent_openapi_endpoints | def check_for_non_existent_openapi_endpoints(self) -> None:
openapi_paths = set(get_openapi_paths())
undocumented_paths = openapi_paths - self.checked_endpoints
undocumented_paths -= self.buggy_documentation_endpoints
undocumented_paths -= self.pending_endpoints
try:
self.assert_length(undocumented_paths, 0)
except AssertionError: # nocoverage
msg = "The following endpoints have been documented but can't be found in urls.py:"
for undocumented_path in undocumented_paths:
msg += f"\n + {undocumented_path}"
raise AssertionError(msg)
| b0ce4f1bce8031881addecb1e86073483517f392 | 13 | test_openapi.py | 113 | docs: Fix many spelling mistakes.
Signed-off-by: Anders Kaseorg <[email protected]> | 17,642 | 0 | 159 | 63 | 42 | 83,261 | 50 | zulip | 13 | zerver/tests/test_openapi.py | Python | 18 | {
"docstring": "Here, we check to see if every endpoint documented in the OpenAPI\n documentation actually exists in urls.py and thus in actual code.\n Note: We define this as a helper called at the end of\n test_openapi_arguments instead of as a separate test to ensure that\n this test is only executed after test_openapi_arguments so that it's\n results can be used here in the set operations.",
"language": "en",
"n_whitespaces": 97,
"n_words": 63,
"vocab_size": 50
} | https://github.com/zulip/zulip.git |
|
3 | _send_connect | def _send_connect(self, identifier):
# type: (int) -> List[XCPScannerResult]
all_slaves = []
body = Connect(connection_mode=0x00)
xcp_req_and_res_list = self._scan(identifier, body, 0xFF,
ConnectPositiveResponse)
for req_and_res in xcp_req_and_res_list:
result = XCPScannerResult(response_id=req_and_res[1].identifier,
request_id=identifier)
all_slaves.append(result)
log_automotive.info(
"Detected XCP slave for broadcast identifier: " + str(
identifier) + "\nResponse: " + str(result))
if len(all_slaves) == 0:
log_automotive.info(
"No XCP slave detected for identifier: " + str(identifier))
return all_slaves
| 495b21f2867e48286767085c8cf2918e4092e9dc | 14 | scanner.py | 167 | Add Automotive Logger for all debug outputs of the automotive layer | 52,743 | 0 | 288 | 101 | 46 | 209,600 | 61 | scapy | 20 | scapy/contrib/automotive/xcp/scanner.py | Python | 16 | {
"docstring": "\n Sends CONNECT Message on the Control Area Network\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | https://github.com/secdev/scapy.git |
|
2 | _calc_mode | def _calc_mode(path):
try:
mode = _path_stat(path).st_mode
except OSError:
mode = 0o666
# We always ensure write access so we can update cached files
# later even when the source files are read-only on Windows (#6074)
mode |= 0o200
return mode
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 11 | _bootstrap_external.py | 50 | add python 3.10.4 for windows | 55,174 | 0 | 75 | 27 | 34 | 218,170 | 40 | XX-Net | 6 | python3.10.4/Lib/importlib/_bootstrap_external.py | Python | 7 | {
"docstring": "Calculate the mode permissions for a bytecode file.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | https://github.com/XX-net/XX-Net.git |
|
3 | download | def download(self, url_or_urls):
download_config = self.download_config.copy()
download_config.extract_compressed_file = False
# Default to using 16 parallel thread for downloading
# Note that if we have less than or equal to 16 files, multi-processing is not activated
if download_config.num_proc is None:
download_config.num_proc = 16
if download_config.download_desc is None:
download_config.download_desc = "Downloading data"
download_func = partial(self._download, download_config=download_config)
start_time = datetime.now()
downloaded_path_or_paths = map_nested(
download_func,
url_or_urls,
map_tuple=True,
num_proc=download_config.num_proc,
parallel_min_length=16,
disable_tqdm=not is_progress_bar_enabled(),
desc="Downloading data files",
)
duration = datetime.now() - start_time
logger.info(f"Downloading took {duration.total_seconds() // 60} min")
url_or_urls = NestedDataStructure(url_or_urls)
downloaded_path_or_paths = NestedDataStructure(downloaded_path_or_paths)
self.downloaded_paths.update(dict(zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten())))
start_time = datetime.now()
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
duration = datetime.now() - start_time
logger.info(f"Checksum Computation took {duration.total_seconds() // 60} min")
return downloaded_path_or_paths.data
| a5e05ccffa49051cbad84c007f31725ad4b2e7f7 | 13 | download_manager.py | 330 | Fix multiprocessing in map_nested (#4740)
* Do multiprocessing in map_nested when num_proc >= len(iterable)
* Add missing type hint
* Update docstring
* Fix tests
* Document download default and fix docstring
* Fix docstring syntax
* Add tests
* Fix test for Python<3.8
* Download with multiprocessing only if at least 16 files
* Revert test fix
* Rename multiprocessing_min_length to parallel_min_length | 22,123 | 0 | 355 | 186 | 73 | 105,462 | 109 | datasets | 33 | src/datasets/download/download_manager.py | Python | 28 | {
"docstring": "Download given URL(s).\n\n By default, if there is more than one URL to download, multiprocessing is used with maximum `num_proc = 16`.\n Pass customized `download_config.num_proc` to change this behavior.\n\n Args:\n url_or_urls (`str` or `list` or `dict`): URL or list/dict of URLs to download. Each URL is a `str`.\n\n Returns:\n `str` or `list` or `dict`: The downloaded paths matching the given input `url_or_urls`.\n\n Example:\n\n ```py\n >>> downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')\n ```\n ",
"language": "en",
"n_whitespaces": 154,
"n_words": 69,
"vocab_size": 56
} | https://github.com/huggingface/datasets.git |
|
2 | execute_test_case | def execute_test_case(self, test_case):
# type: (AutomotiveTestCaseABC) -> None
test_case.pre_execute(
self.socket, self.target_state, self.configuration)
try:
test_case_kwargs = self.configuration[test_case.__class__.__name__]
except KeyError:
test_case_kwargs = dict()
log_interactive.debug("[i] Execute test_case %s with args %s",
test_case.__class__.__name__, test_case_kwargs)
test_case.execute(self.socket, self.target_state, **test_case_kwargs)
test_case.post_execute(
self.socket, self.target_state, self.configuration)
self.check_new_states(test_case)
self.check_new_testcases(test_case)
| f549b66f3f523c1c1a4a21aadc40c6153c5ad77e | 11 | executor.py | 162 | Stabilize UDS_Scanner unit tests for appveyor (#3615)
* Different approach
* add event
* fix test
* cleanup
* cleanup
* lower timeout of GMLAN Scanner
* apply feedback | 52,642 | 0 | 182 | 103 | 33 | 209,211 | 39 | scapy | 18 | scapy/contrib/automotive/scanner/executor.py | Python | 14 | {
"docstring": "\n This function ensures the correct execution of a testcase, including\n the pre_execute, execute and post_execute.\n Finally the testcase is asked if a new edge or a new testcase was\n generated.\n :param test_case: A test case to be executed\n :return: None\n ",
"language": "en",
"n_whitespaces": 90,
"n_words": 40,
"vocab_size": 34
} | https://github.com/secdev/scapy.git |
|
1 | backfill | def backfill(self, limit=None):
warnings.warn(
"backfill is deprecated and will be removed in a future version. "
"Use bfill instead.",
FutureWarning,
stacklevel=find_stack_level(inspect.currentframe()),
)
return self.bfill(limit=limit)
| 709f274245dc2f3fe2d91ad53a4cfa57f9d41d59 | 12 | groupby.py | 68 | DOC: Add deprecation infos to deprecated functions (#48599)
* DOC: Add deprecation infos to deprecated functions
* Add sections
* Fix | 40,410 | 0 | 96 | 40 | 24 | 169,282 | 24 | pandas | 11 | pandas/core/groupby/groupby.py | Python | 8 | {
"docstring": "\n Backward fill the values.\n\n .. deprecated:: 1.4\n Use bfill instead.\n\n Parameters\n ----------\n limit : int, optional\n Limit of how many values to fill.\n\n Returns\n -------\n Series or DataFrame\n Object with missing values filled.\n ",
"language": "en",
"n_whitespaces": 130,
"n_words": 33,
"vocab_size": 32
} | https://github.com/pandas-dev/pandas.git |
|
1 | disable_tf_random_generator | def disable_tf_random_generator():
global _USE_GENERATOR_FOR_RNG
_USE_GENERATOR_FOR_RNG = False
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 6 | backend.py | 20 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 80,141 | 0 | 16 | 10 | 6 | 269,510 | 7 | keras | 2 | keras/backend.py | Python | 3 | {
"docstring": "Disable the `tf.random.Generator` as the RNG for Keras.\n\n See `tf.keras.backend.experimental.is_tf_random_generator_enabled` for more\n details.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 13,
"vocab_size": 11
} | https://github.com/keras-team/keras.git |
|
1 | test_exception_handling | def test_exception_handling():
# Raising an error in a subprocess should propagate to a runtime error in the master process.
with pytest.raises(RuntimeError) as ex_info:
isolated.call(fail)
# The exception should include the offending function's name and the child's traceback which itself includes the
# breaking line of code and the exception type and message (in that order). Unfortunately, there seems to be a bug
# in pytest's exception rewriting which makes the exception type and message unverifiable when ran under pytest.
assert ex_info.match(r)
# It should have removed the following line.
assert "Traceback (most recent call last)" not in str(ex_info.value)
| f4a8368fb0d1f4e0a902cccfabccde3c8c0d93d1 | 10 | test_isolation.py | 76 | Allow sources to be formatted using yapf 0.32.0
Allow sources to be formatted using yapf 0.32.0 in addition to
currently pinned 0.31.0.
Version 0.32.0 enables `blank_line_before_nested_class_or_def` by
default. As this was disabled by default in earlier versions, we
now need to explicitly turn it off in configuration.
The two versions also seem to disagree about alignment of split
string fragments (same line in 0.31, vertically aligned in 0.32).
In our code, some cases look better vertically aligned (disable
yapf on those via comment to prevent changes on older version),
while the remaining cases should have been merged into a single
string in the first place; so do that now. | 77,458 | 0 | 132 | 39 | 69 | 263,836 | 98 | pyinstaller | 11 | tests/unit/test_isolation.py | Python | 5 | {
"docstring": "\n Test the behaviour which an error is raised in the child process.\n (?s) call to fail\\(\\) failed .* assert 0, \"It's broken!\".*",
"language": "en",
"n_whitespaces": 28,
"n_words": 22,
"vocab_size": 21
} | https://github.com/pyinstaller/pyinstaller.git |
|
2 | _set_gradient_checkpointing | def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (Wav2Vec2ConformerEncoder, Wav2Vec2ConformerFeatureEncoder)):
module.gradient_checkpointing = value
WAV2VEC2_CONFORMER_START_DOCSTRING = r
WAV2VEC2_CONFORMER_INPUTS_DOCSTRING = r
@add_start_docstrings(
"The bare Wav2Vec2Conformer Model transformer outputting raw hidden-states without any specific head on top.",
WAV2VEC2_CONFORMER_START_DOCSTRING,
) | 5a9957358cebd616e58b2d1ab3b887c2f2793b45 | @add_start_docstrings(
"The bare Wav2Vec2Conformer Model transformer outputting raw hidden-states without any specific head on top.",
WAV2VEC2_CONFORMER_START_DOCSTRING,
) | 9 | modeling_wav2vec2_conformer.py | 69 | Add Wav2Vec2Conformer (#16812)
* save intermediate
* add wav2vec2 conformer
* add more code
* more
* first test passes
* make all checkpoints work
* update
* up
* more clean ups
* save clean-up
* save clean-up
* save more
* remove bogus
* finalize design conformer
* remove vision
* finish all tests
* more changes
* finish code
* add doc tests
* add slow tests
* fix autoconfig test
* up
* correct docstring
* up
* update
* fix
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
Co-authored-by: Anton Lozhkov <[email protected]>
* Update docs/source/en/model_doc/wav2vec2-conformer.mdx
* upload
* save copied from
* correct configs
* fix model outputs
* add to docs
* fix imports
* finish
* finish code
* correct copied from
* correct again
* correct make fix
* improve make fix copies
* save
* correct fix copy from
* correct init structure
* correct
* fix import
* apply suggestions
Co-authored-by: Sylvain Gugger <[email protected]>
Co-authored-by: Anton Lozhkov <[email protected]> | 6,985 | 1 | 53 | 28 | 31 | 38,500 | 34 | transformers | 11 | src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | Python | 3 | {
"docstring": "\n Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech\n Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael\n Auli.\n\n This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n library implements for all its model (such as downloading or saving etc.).\n\n This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#nn.Module) sub-class. Use it as a\n regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.\n\n Parameters:\n config ([`Wav2Vec2ConformerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n\n Args:\n input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):\n Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file\n into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install\n soundfile*). To prepare the array into *input_values*, the [`Wav2Vec2Processor`] should be used for padding\n and conversion into a tensor of type *torch.FloatTensor*. See [`Wav2Vec2Processor.__call__`] for details.\n attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,\n 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n\n <Tip warning={true}>\n\n `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==\n True`. For all models whose processor has `config.return_attention_mask == False`, such as\n [wav2vec2_conformer-base](https://huggingface.co/facebook/wav2vec2-conformer-large-rel-pos),\n `attention_mask` should **not** be passed to avoid degraded performance when doing batched inference. For\n such models `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware\n that these models also yield slightly different results depending on whether `input_values` is padded or\n not.\n\n </Tip>\n\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n",
"language": "en",
"n_whitespaces": 706,
"n_words": 362,
"vocab_size": 217
} | https://github.com/huggingface/transformers.git |
2 | __call__ | def __call__(self, results):
img = results['img']
if self.to_float32:
img = img.astype(np.float32)
results['img_path'] = None
results['img'] = img
height, width = img.shape[:2]
results['height'] = height
results['width'] = width
results['ori_height'] = height
results['ori_width'] = width
return results
@TRANSFORMS.register_module() | c71a160c5193b92f6a4f56c113e96b63decf8354 | @TRANSFORMS.register_module() | 11 | loading.py | 147 | Refacter Visualization | 70,426 | 1 | 123 | 78 | 22 | 244,549 | 36 | mmdetection | 13 | mmdet/datasets/pipelines/loading.py | Python | 12 | {
"docstring": "Call functions to add image meta information.\n\n Args:\n results (dict): Result dict with Webcam read image in\n ``results['img']``.\n\n Returns:\n dict: The dict contains loaded image and meta information.\n ",
"language": "en",
"n_whitespaces": 86,
"n_words": 28,
"vocab_size": 23
} | https://github.com/open-mmlab/mmdetection.git |
4 | set_linestyle | def set_linestyle(self, ls):
try:
dashes = [mlines._get_dash_pattern(ls)]
except ValueError:
try:
dashes = [mlines._get_dash_pattern(x) for x in ls]
except ValueError as err:
raise ValueError('Do not know how to convert {!r} to '
'dashes'.format(ls)) from err
# get the list of raw 'unscaled' dash patterns
self._us_linestyles = dashes
# broadcast and scale the lw and dash patterns
self._linewidths, self._linestyles = self._bcast_lwls(
self._us_lw, self._us_linestyles)
| 511232b14b6fca586ac92bc5f8e0b0924f23172f | 17 | collections.py | 138 | remove redundant string handling | 24,344 | 0 | 216 | 82 | 48 | 110,880 | 61 | matplotlib | 15 | lib/matplotlib/collections.py | Python | 12 | {
"docstring": "\n Set the linestyle(s) for the collection.\n\n =========================== =================\n linestyle description\n =========================== =================\n ``'-'`` or ``'solid'`` solid line\n ``'--'`` or ``'dashed'`` dashed line\n ``'-.'`` or ``'dashdot'`` dash-dotted line\n ``':'`` or ``'dotted'`` dotted line\n =========================== =================\n\n Alternatively a dash tuple of the following form can be provided::\n\n (offset, onoffseq),\n\n where ``onoffseq`` is an even length tuple of on and off ink in points.\n\n Parameters\n ----------\n ls : str or tuple or list thereof\n Valid values for individual linestyles include {'-', '--', '-.',\n ':', '', (offset, on-off-seq)}. See `.Line2D.set_linestyle` for a\n complete description.\n ",
"language": "en",
"n_whitespaces": 288,
"n_words": 90,
"vocab_size": 69
} | https://github.com/matplotlib/matplotlib.git |
|
3 | test_valid_incremental_read_with_slices | def test_valid_incremental_read_with_slices(mocker):
slices = [{"1": "1"}, {"2": "2"}]
stream_output = [{"k1": "v1"}, {"k2": "v2"}, {"k3": "v3"}]
s1 = MockStream(
[({"sync_mode": SyncMode.incremental, "stream_slice": s, "stream_state": mocker.ANY}, stream_output) for s in slices], name="s1"
)
s2 = MockStream(
[({"sync_mode": SyncMode.incremental, "stream_slice": s, "stream_state": mocker.ANY}, stream_output) for s in slices], name="s2"
)
state = {"cursor": "value"}
mocker.patch.object(MockStream, "get_updated_state", return_value=state)
mocker.patch.object(MockStream, "supports_incremental", return_value=True)
mocker.patch.object(MockStream, "get_json_schema", return_value={})
mocker.patch.object(MockStream, "stream_slices", return_value=slices)
src = MockSource(streams=[s1, s2])
catalog = ConfiguredAirbyteCatalog(streams=[_configured_stream(s1, SyncMode.incremental), _configured_stream(s2, SyncMode.incremental)])
expected = [
# stream 1 slice 1
*_as_records("s1", stream_output),
_state({"s1": state}),
# stream 1 slice 2
*_as_records("s1", stream_output),
_state({"s1": state}),
# stream 2 slice 1
*_as_records("s2", stream_output),
_state({"s1": state, "s2": state}),
# stream 2 slice 2
*_as_records("s2", stream_output),
_state({"s1": state, "s2": state}),
]
messages = _fix_emitted_at(list(src.read(logger, {}, catalog, state=defaultdict(dict))))
assert expected == messages
| f83eca58eaf2129d21b5796a301732ab22675130 | 15 | test_abstract_source.py | 544 | CDK: Fix typing errors (#9037)
* fix typing, drop AirbyteLogger
* format
* bump the version
* use logger instead of fixture logger
Co-authored-by: Eugene Kulak <[email protected]>
Co-authored-by: auganbay <[email protected]> | 455 | 0 | 280 | 326 | 74 | 3,353 | 128 | airbyte | 32 | airbyte-cdk/python/unit_tests/sources/test_abstract_source.py | Python | 28 | {
"docstring": "Tests that an incremental read which uses slices outputs each record in the slice followed by a STATE message, for each slice",
"language": "en",
"n_whitespaces": 21,
"n_words": 22,
"vocab_size": 20
} | https://github.com/airbytehq/airbyte.git |
|
7 | setup_server | async def setup_server(self):
from jina.helper import extend_rest_interface
self.app = extend_rest_interface(
get_fastapi_app(
streamer=self.streamer,
title=self.title,
description=self.description,
no_debug_endpoints=self.no_debug_endpoints,
no_crud_endpoints=self.no_crud_endpoints,
expose_endpoints=self.expose_endpoints,
expose_graphql_endpoint=self.expose_graphql_endpoint,
cors=self.cors,
logger=self.logger,
)
)
with ImportExtensions(required=True):
from uvicorn import Config, Server
| 243639dd2b953fab8654747cbfdf6a0953b95578 | 12 | gateway.py | 137 | refactor: extract gateway app logic into custom gateway class (#5153) | 2,529 | 0 | 232 | 239 | 26 | 13,058 | 29 | jina | 21 | jina/serve/runtimes/gateway/http/gateway.py | Python | 40 | {
"docstring": "\n Initialize and return GRPC server\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 5,
"vocab_size": 5
} | https://github.com/jina-ai/jina.git |
|
2 | get_sortable_by | def get_sortable_by(self, request):
return (
self.sortable_by
if self.sortable_by is not None
else self.get_list_display(request)
)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 9 | options.py | 44 | Refs #33476 -- Reformatted code with Black. | 50,365 | 0 | 68 | 28 | 13 | 203,420 | 14 | django | 5 | django/contrib/admin/options.py | Python | 6 | {
"docstring": "Hook for specifying which fields can be sorted in the changelist.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | https://github.com/django/django.git |
|
1 | test_class_weight_does_not_contains_more_classses | def test_class_weight_does_not_contains_more_classses():
tree = DecisionTreeClassifier(class_weight={0: 1, 1: 10, 2: 20})
# Does not raise
tree.fit([[0, 0, 1], [1, 0, 1], [1, 2, 0]], [0, 0, 1])
| 3605c140af992b6ac52f04f1689c58509cc0b5b2 | 11 | test_class_weight.py | 89 | FIX Support extra class_weights in compute_class_weight (#22595) | 75,568 | 0 | 38 | 63 | 22 | 259,103 | 26 | scikit-learn | 5 | sklearn/utils/tests/test_class_weight.py | Python | 3 | {
"docstring": "Check that class_weight can contain more labels than in y.\n\n Non-regression test for #22413\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 14,
"vocab_size": 14
} | https://github.com/scikit-learn/scikit-learn.git |
|
4 | _make_replica_execution_function | def _make_replica_execution_function(model, mode):
if mode == ModeKeys.TRAIN:
func = model.train_on_batch
elif mode == ModeKeys.TEST:
func = model.test_on_batch
else:
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 10 | distributed_training_utils_v1.py | 57 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 80,457 | 0 | 44 | 60 | 14 | 270,367 | 18 | keras | 9 | keras/distribute/distributed_training_utils_v1.py | Python | 11 | {
"docstring": "A single step of the distributed execution on a replica.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/keras-team/keras.git |
|
1 | perform_long_operation | def perform_long_operation(self, func, end_key):
thread = threading.Thread(target=_long_func_thread, args=(self, end_key, func), daemon=True)
thread.start()
return thread
| 992c5db3bcde5d735b0d00af66d9accfa25a1da7 | 10 | PySimpleGUI.py | 62 | Updated Window.perform_long_operation docstring to warn that Threads are used | 53,360 | 0 | 42 | 41 | 13 | 212,715 | 14 | PySimpleGUI | 12 | PySimpleGUI.py | Python | 4 | {
"docstring": "\n Call your function that will take a long time to execute. When it's complete, send an event\n specified by the end_key.\n This is a way for you to \"ease into\" threading without learning the details of threading.\n Your function will run, and when it returns 2 things will happen:\n 1. The value you provide for end_key will be returned to you when you call window.read()\n 2. If your function returns a value, then the value returned will also be included in your windows.read call in the values dictionary\n\n IMPORTANT - This method uses THREADS... this means you CANNOT make any PySimpleGUI calls from\n the function you provide with the exception of one function, Window.write_event_value.\n\n\n :param func: A lambda or a function name with no parms\n :type func: Any\n :param end_key: The key that will be generated when the function returns\n :type end_key: (Any)\n :return: The id of the thread\n :rtype: threading.Thread\n ",
"language": "en",
"n_whitespaces": 281,
"n_words": 151,
"vocab_size": 99
} | https://github.com/PySimpleGUI/PySimpleGUI.git |
|
2 | mixin_base_pod_parser | def mixin_base_pod_parser(parser):
gp = add_arg_group(parser, title='Pod')
gp.add_argument(
'--uses-before',
type=str,
help='The executor attached after the Peas described by --uses, typically before sending to all '
'shards, accepted type follows `--uses`',
)
gp.add_argument(
'--uses-after',
type=str,
help='The executor attached after the Peas described by --uses, typically used for receiving from '
'all shards, accepted type follows `--uses`',
)
gp.add_argument(
'--external',
action='store_true',
default=False,
help='The Pod will be considered an external Pod that has been started independently from the Flow.'
'This Pod will not be context managed by the Flow.',
)
# hidden CLI used for internal only
gp.add_argument(
'--pod-role',
type=PodRoleType.from_string,
choices=list(PodRoleType),
help='The role of this pod in the flow'
if _SHOW_ALL_ARGS
else argparse.SUPPRESS,
)
| 6f22e6824cbe9e95528e53e0613d5b1babd4af8e | 10 | pod.py | 171 | refactor: take out peas hosts from cli (#4171) | 1,871 | 0 | 276 | 117 | 75 | 10,586 | 110 | jina | 18 | jina/parsers/peapods/pod.py | Python | 37 | {
"docstring": "Add mixin arguments required by :class:`BasePod` into the given parser.\n\n :param parser: the parser instance to which we add arguments\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 20,
"vocab_size": 18
} | https://github.com/jina-ai/jina.git |
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