Datasets:

semeru

/

text-code-galeras-code-generation-from-docstring-3k-deduped

like 0

def get_train_dataset() -> ray.data.Dataset: data_raw = load_breast_cancer(as_frame=True) df = data_raw["data"] df["target"] = data_raw["target"] return ray.data.from_pandas(df)

python/ray/ml/examples/upload_to_wandb.py

ray

Python

5d9bf4234a038e16eaa73fed7d4ec8cef3f0038f

upload_to_wandb.py

get_train_dataset

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

[air] Example to track runs with Weights & Biases (#24459) This PR - adds an example on how to run Ray Train and log results to weights & biases - adds functionality to the W&B plugin to store checkpoints - fixes a bug introduced in #24017 - Adds a CI utility script to setup credentials - Adds a CI utility script to remove test state from external services cc @simon-mo

def where(self, other, cond, _downcast="infer") -> list[Block]: assert cond.ndim == self.ndim assert not isinstance(other, (ABCIndex, ABCSeries, ABCDataFrame)) transpose = self.ndim == 2 # EABlocks override where values = cast(np.ndarray, self.values) orig_other = other if transpose: values = values.T icond, noop = validate_putmask(values, ~cond) if noop: # GH-39595: Always return a copy; short-circuit up/downcasting return [self.copy()] if other is lib.no_default: other = self.fill_value other = self._standardize_fill_value(other) try: # try/except here is equivalent to a self._can_hold_element check, # but this gets us back 'casted' which we will re-use below; # without using 'casted', expressions.where may do unwanted upcasts. casted = np_can_hold_element(values.dtype, other) except (ValueError, TypeError, LossySetitemError): # we cannot coerce, return a compat dtype if self.ndim == 1 or self.shape[0] == 1: # no need to split columns block = self.coerce_to_target_dtype(other) blocks = block.where(orig_other, cond) return self._maybe_downcast(blocks, downcast=_downcast) else: # since _maybe_downcast would split blocks anyway, we # can avoid some potential upcast/downcast by splitting # on the front end. is_array = isinstance(other, (np.ndarray, ExtensionArray)) res_blocks = [] nbs = self._split() for i, nb in enumerate(nbs): oth = other if is_array: # we have a different value per-column oth = other[:, i : i + 1] submask = cond[:, i : i + 1] rbs = nb.where(oth, submask, _downcast=_downcast) res_blocks.extend(rbs) return res_blocks else: other = casted alt = setitem_datetimelike_compat(values, icond.sum(), other) if alt is not other: if is_list_like(other) and len(other) < len(values): # call np.where with other to get the appropriate ValueError np.where(~icond, values, other) raise NotImplementedError( "This should not be reached; call to np.where above is " "expected to raise ValueError. Please report a bug at " "github.com/pandas-dev/pandas" ) result = values.copy() np.putmask(result, icond, alt) else: # By the time we get here, we should have all Series/Index # args extracted to ndarray if ( is_list_like(other) and not isinstance(other, np.ndarray) and len(other) == self.shape[-1] ): # If we don't do this broadcasting here, then expressions.where # will broadcast a 1D other to be row-like instead of # column-like. other = np.array(other).reshape(values.shape) # If lengths don't match (or len(other)==1), we will raise # inside expressions.where, see test_series_where # Note: expressions.where may upcast. result = expressions.where(~icond, values, other) # The np_can_hold_element check _should_ ensure that we always # have result.dtype == self.dtype here. if transpose: result = result.T return [self.make_block(result)]

pandas/core/internals/blocks.py

pandas

Python

769fc54897953d366d573d4e45cf13177a5f582b

blocks.py

where

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

REF: dispatch Block.fillna to putmask/where (#45911)

def download_ffmpeg() -> Path: os_name = platform.system().lower() os_arch = platform.machine().lower() ffmpeg_url = FFMPEG_URLS.get(os_name, {}).get(os_arch) ffmpeg_path = Path( os.path.join( get_spotdl_path(), "ffmpeg" + (".exe" if os_name == "windows" else "") ) ) if ffmpeg_url is None: raise FFmpegError("FFmpeg binary is not available for your system.") # Download binary and save it to a file in spotdl directory ffmpeg_binary = requests.get(ffmpeg_url, allow_redirects=True, timeout=10).content with open(ffmpeg_path, "wb") as ffmpeg_file: ffmpeg_file.write(ffmpeg_binary) # Set executable permission on linux and mac if os_name in ["linux", "darwin"]: ffmpeg_path.chmod(ffmpeg_path.stat().st_mode | stat.S_IEXEC) return ffmpeg_path

spotdl/utils/ffmpeg.py

spotify-downloader

Python

06a84e0400b7f7f847a7a7d06eedba766cdbced3

ffmpeg.py

download_ffmpeg

https://github.com/spotDL/spotify-downloader.git

added option to preserve original audio

def dis(x=None, *, file=None, depth=None): if x is None: distb(file=file) return # Extract functions from methods. if hasattr(x, '__func__'): x = x.__func__ # Extract compiled code objects from... if hasattr(x, '__code__'): # ...a function, or x = x.__code__ elif hasattr(x, 'gi_code'): #...a generator object, or x = x.gi_code elif hasattr(x, 'ag_code'): #...an asynchronous generator object, or x = x.ag_code elif hasattr(x, 'cr_code'): #...a coroutine. x = x.cr_code # Perform the disassembly. if hasattr(x, '__dict__'): # Class or module items = sorted(x.__dict__.items()) for name, x1 in items: if isinstance(x1, _have_code): print("Disassembly of %s:" % name, file=file) try: dis(x1, file=file, depth=depth) except TypeError as msg: print("Sorry:", msg, file=file) print(file=file) elif hasattr(x, 'co_code'): # Code object _disassemble_recursive(x, file=file, depth=depth) elif isinstance(x, (bytes, bytearray)): # Raw bytecode _disassemble_bytes(x, file=file) elif isinstance(x, str): # Source code _disassemble_str(x, file=file, depth=depth) else: raise TypeError("don't know how to disassemble %s objects" % type(x).__name__)

python3.10.4/Lib/dis.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

dis.py

dis

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

add python 3.10.4 for windows

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

numpy/lib/tests/test_io.py

numpy

Python

66a61b03658f3c9f312505dcf7eab07e4cf91ac6

test_io.py

test_loadtxt_converter_with_unicode_dtype

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

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

def get_batch(self, queue): exhausted = False batch = {} idx = 0 while idx < self.batchsize: item = self._collect_item(queue) if item == "EOF": logger.trace("EOF received") exhausted = True break # Put frames with no faces into the out queue to keep TQDM consistent if not item.detected_faces: self._queues["out"].put(item) continue converted_image = item.get_image_copy(self.color_format) for f_idx, face in enumerate(item.detected_faces): batch.setdefault("image", []).append(converted_image) batch.setdefault("detected_faces", []).append(face) batch.setdefault("filename", []).append(item.filename) idx += 1 if idx == self.batchsize: frame_faces = len(item.detected_faces) if f_idx + 1 != frame_faces: self._rollover = ExtractMedia( item.filename, item.image, detected_faces=item.detected_faces[f_idx + 1:]) logger.trace("Rolled over %s faces of %s to next batch for '%s'", len(self._rollover.detected_faces), frame_faces, item.filename) break if batch: logger.trace("Returning batch: %s", {k: v.shape if isinstance(v, np.ndarray) else v for k, v in batch.items()}) else: logger.trace(item) return exhausted, batch

plugins/extract/align/_base.py

faceswap

Python

5e73437be47f2410439a3c6716de96354e6a0c94

_base.py

get_batch

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

lib.align updates: - alignments.py - Add typed dicts for imported alignments - Explicitly check for presence of thumb value in alignments dict - linting - detected_face.py - Typing - Linting - Legacy support for pre-aligned face - Update dependencies to new property names

def test_sent_event_end_up_in_room_state(self) -> None: event_type = "org.matrix.test_state" # This content will be updated later on, and since we actually use a reference on # the dict it does the right thing. It's a bit hacky but a handy way of making # sure the state actually gets updated. event_content = {"i": -1} api = self.hs.get_module_api() # Define a callback that sends a custom event on power levels update.

tests/rest/client/test_third_party_rules.py

synapse

Python

2ffaf30803f93273a4d8a65c9e6c3110c8433488

test_third_party_rules.py

test_sent_event_end_up_in_room_state

https://github.com/matrix-org/synapse.git

Add type hints to `tests/rest/client` (#12108) * Add type hints to `tests/rest/client` * newsfile * fix imports * add `test_account.py` * Remove one type hint in `test_report_event.py` * change `on_create_room` to `async` * update new functions in `test_third_party_rules.py` * Add `test_filter.py` * add `test_rooms.py` * change to `assertEquals` to `assertEqual` * lint

def image2hmap(self, image_tensor): return self.model.forward(image_tensor, training=False)

easyocr/DBNet/DBNet.py

EasyOCR

Python

803b90729d25fda253011c505d0189e8e63cc039

DBNet.py

image2hmap

https://github.com/JaidedAI/EasyOCR.git

add dbnet

def get_permission_required(self): if self.permission_required is None: raise ImproperlyConfigured( "{0} is missing the permission_required attribute. Define {0}.permission_required, or override " "{0}.get_permission_required().".format(self.__class__.__name__) ) if isinstance(self.permission_required, str): perms = (self.permission_required,) else: perms = self.permission_required return perms

django/contrib/auth/mixins.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

mixins.py

get_permission_required

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

Refs #33476 -- Reformatted code with Black.

def pre_validation(self, config, key_name):

mkdocs/config/base.py

mkdocs

Python

9c0a8e50b11b70f803500cd73e7256b63f64b5e3

base.py

pre_validation

https://github.com/mkdocs/mkdocs.git

Move some documentation into code, add misc API docs page (#2934)

def withEnvironmentVarOverridden(env_var_name, value): if env_var_name in os.environ: old_value = os.environ[env_var_name] else: old_value = None if value is not None: os.environ[env_var_name] = value elif old_value is not None: del os.environ[env_var_name] yield if old_value is None: if value is not None: del os.environ[env_var_name] else: os.environ[env_var_name] = old_value @contextmanager

nuitka/utils/Execution.py

@contextmanager

Nuitka

Python

bf42800f1ddfae3563a20d9e4fbc79265e8d5209

Execution.py

withEnvironmentVarOverridden

https://github.com/Nuitka/Nuitka.git

Minor cleanups * Typo cleanups

def forward(self, data, optimizer=None, return_loss=False, **kwargs): batch_inputs, batch_data_samples = self.preprocss_data(data) if torch.onnx.is_in_onnx_export(): # TODO: Delete assert len(batch_inputs) == 1 return self.onnx_export(batch_inputs, batch_data_samples) if return_loss: losses = self.forward_train(batch_inputs, batch_data_samples, **kwargs) loss, log_vars = self._parse_losses(losses) outputs = dict( loss=loss, log_vars=log_vars, num_samples=len(batch_data_samples)) return outputs else: # TODO: refactor and support aug test later assert isinstance(data[0]['inputs'], torch.Tensor), \ 'Only support simple test currently. Aug-test is ' \ 'not supported yet' return self.forward_simple_test(batch_inputs, batch_data_samples, **kwargs)

mmdet/models/detectors/base.py

mmdetection

Python

924c381a78eb70cede198e042ef34e038e05c15a

base.py

forward

https://github.com/open-mmlab/mmdetection.git

Modify RetinaNet model interface

def matches(self, expr, repl_dict=None, old=False): expr = sympify(expr) if not isinstance(expr, self.__class__): return None if repl_dict is None: repl_dict = {} else: repl_dict = repl_dict.copy() if self == expr: return repl_dict if len(self.args) != len(expr.args): return None d = repl_dict # already a copy for arg, other_arg in zip(self.args, expr.args): if arg == other_arg: continue if arg.is_Relational: try: d = arg.xreplace(d).matches(other_arg, d, old=old) except TypeError: # Should be InvalidComparisonError when introduced d = None else: d = arg.xreplace(d).matches(other_arg, d, old=old) if d is None: return None return d

sympy/core/basic.py

sympy

Python

9d58006fc0a23afcba38f641c9472917c436428a

basic.py

matches

https://github.com/sympy/sympy.git

Code cleanup

def supported_features(self): return self._attr_supported_features

homeassistant/components/xiaomi_miio/vacuum.py

core

Python

424731863423f7b7cd2e29f418ddbce01418828b

vacuum.py

supported_features

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

Use VacuumEntityFeature in xiaomi_miio (#70564)

def overlaying(self): return self["overlaying"]

packages/python/plotly/plotly/graph_objs/layout/_xaxis.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_xaxis.py

overlaying

https://github.com/plotly/plotly.py.git

switch to black .22

def match_hostname(cert, hostname): if not cert: raise ValueError( "empty or no certificate, match_hostname needs a " "SSL socket or SSL context with either " "CERT_OPTIONAL or CERT_REQUIRED" ) try: # Divergence from upstream: ipaddress can't handle byte str host_ip = ipaddress.ip_address(_to_unicode(hostname)) except (UnicodeError, ValueError): # ValueError: Not an IP address (common case) # UnicodeError: Divergence from upstream: Have to deal with ipaddress not taking # byte strings. addresses should be all ascii, so we consider it not # an ipaddress in this case host_ip = None except AttributeError: # Divergence from upstream: Make ipaddress library optional if ipaddress is None: host_ip = None else: # Defensive raise dnsnames = [] san = cert.get("subjectAltName", ()) for key, value in san: if key == "DNS": if host_ip is None and _dnsname_match(value, hostname): return dnsnames.append(value) elif key == "IP Address": if host_ip is not None and _ipaddress_match(value, host_ip): return dnsnames.append(value) if not dnsnames: # The subject is only checked when there is no dNSName entry # in subjectAltName for sub in cert.get("subject", ()): for key, value in sub: # XXX according to RFC 2818, the most specific Common Name # must be used. if key == "commonName": if _dnsname_match(value, hostname): return dnsnames.append(value) if len(dnsnames) > 1: raise CertificateError( "hostname %r " "doesn't match either of %s" % (hostname, ", ".join(map(repr, dnsnames))) ) elif len(dnsnames) == 1: raise CertificateError("hostname %r doesn't match %r" % (hostname, dnsnames[0])) else: raise CertificateError( "no appropriate commonName or subjectAltName fields were found" )

pipenv/patched/notpip/_vendor/urllib3/util/ssl_match_hostname.py

pipenv

Python

c69d55f7c82d5ae2cce542bcfb98d043ca4836a0

ssl_match_hostname.py

match_hostname

https://github.com/pypa/pipenv.git

Vendor in pip 22.1.2

def test_include(self) -> None: # Thread 1 has the user as the root event. thread_1 = self.parent_id self._send_relation( RelationTypes.THREAD, "m.room.test", access_token=self.user2_token ) # Thread 2 has the user replying. res = self.helper.send(self.room, body="Thread Root!", tok=self.user2_token) thread_2 = res["event_id"] self._send_relation(RelationTypes.THREAD, "m.room.test", parent_id=thread_2) # Thread 3 has the user not participating in. res = self.helper.send(self.room, body="Another thread!", tok=self.user2_token) thread_3 = res["event_id"] self._send_relation( RelationTypes.THREAD, "m.room.test", access_token=self.user2_token, parent_id=thread_3, ) # All threads in the room. channel = self.make_request( "GET", f"/_matrix/client/unstable/org.matrix.msc3856/rooms/{self.room}/threads", access_token=self.user_token, ) self.assertEquals(200, channel.code, channel.json_body) thread_roots = [ev["event_id"] for ev in channel.json_body["chunk"]] self.assertEqual( thread_roots, [thread_3, thread_2, thread_1], channel.json_body ) # Only participated threads. channel = self.make_request( "GET", f"/_matrix/client/unstable/org.matrix.msc3856/rooms/{self.room}/threads?include=participated", access_token=self.user_token, ) self.assertEquals(200, channel.code, channel.json_body) thread_roots = [ev["event_id"] for ev in channel.json_body["chunk"]] self.assertEqual(thread_roots, [thread_2, thread_1], channel.json_body)

tests/rest/client/test_relations.py

synapse

Python

3bbe532abb7bfc41467597731ac1a18c0331f539

test_relations.py

test_include

https://github.com/matrix-org/synapse.git

Add an API for listing threads in a room. (#13394) Implement the /threads endpoint from MSC3856. This is currently unstable and behind an experimental configuration flag. It includes a background update to backfill data, results from the /threads endpoint will be partial until that finishes.

def shutdown(self): logger.info("Stopping FreqAI") self._stop_event.set() logger.info("Waiting on Training iteration") for _thread in self._threads: _thread.join()

freqtrade/freqai/freqai_interface.py

freqtrade

Python

dae3b3d86adde0f6c7065ce1d083d9ceac62e5ef

freqai_interface.py

shutdown

https://github.com/freqtrade/freqtrade.git

support shutting down freqai

def render_annotated(self, context): try: return self.render(context) except Exception as e: if context.template.engine.debug: # Store the actual node that caused the exception. if not hasattr(e, "_culprit_node"): e._culprit_node = self if ( not hasattr(e, "template_debug") and context.render_context.template.origin == e._culprit_node.origin ): e.template_debug = ( context.render_context.template.get_exception_info( e, e._culprit_node.token, ) ) raise

django/template/base.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

base.py

render_annotated

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

Refs #33476 -- Reformatted code with Black.

def filter_empty_gradients(grads_and_vars): grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: variable = ([v.name for _, v in grads_and_vars],) raise ValueError( f"No gradients provided for any variable: {variable}. " f"Provided `grads_and_vars` is {grads_and_vars}." ) if vars_with_empty_grads: logging.warning( ( "Gradients do not exist for variables %s when minimizing the loss. " "If you're using `model.compile()`, did you forget to provide a `loss`" "argument?" ), ([v.name for v in vars_with_empty_grads]), ) return filtered

keras/optimizers/optimizer_v2/utils.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

utils.py

filter_empty_gradients

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def expected_parameters(self): raise NotImplementedError( "subclasses of ListFilter must provide an expected_parameters() method" )

django/contrib/admin/filters.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

filters.py

expected_parameters

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

Refs #33476 -- Reformatted code with Black.

def euler_poly(n, x=None, polys=False): r return named_poly(n, dup_euler, QQ, "Euler polynomial", (x,), polys)

sympy/polys/appellseqs.py

sympy

Python

75e3143a934c3427f39b82613d77f6d6f55e00b4

appellseqs.py

euler_poly

https://github.com/sympy/sympy.git

Varying Dirichlet L-series for evalfing the André function

def forward(self, img): i, j, h, w = self.get_params(img, self.scale, self.ratio) return F.resized_crop(img, i, j, h, w, self.size, self.interpolation, antialias=self.antialias)

torchvision/transforms/transforms.py

vision

Python

a5536de95d8e703645e391f4bd885ef489ab35bd

transforms.py

forward

https://github.com/pytorch/vision.git

Added antialias arg to resized crop transform and op (#6193)

def pullsrc(self): return self["pullsrc"]

packages/python/plotly/plotly/graph_objs/_pie.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_pie.py

pullsrc

https://github.com/plotly/plotly.py.git

switch to black .22

def _generate_input_dict(B, T, obs_space, action_space): # generate deterministic inputs # obs obs = np.arange(B * T * obs_space.shape[0], dtype=np.float32).reshape( (B, T, obs_space.shape[0]) ) # actions if isinstance(action_space, gym.spaces.Box): act = np.arange(B * T * action_space.shape[0], dtype=np.float32).reshape( (B, T, action_space.shape[0]) ) else: act = np.mod(np.arange(B * T, dtype=np.int32).reshape((B, T)), action_space.n) # returns to go rtg = np.arange(B * (T + 1), dtype=np.float32).reshape((B, T + 1, 1)) # timesteps timesteps = np.stack([np.arange(T, dtype=np.int32) for _ in range(B)], axis=0) # attention mask mask = np.ones((B, T), dtype=np.float32) input_dict = SampleBatch( { SampleBatch.OBS: obs, SampleBatch.ACTIONS: act, SampleBatch.RETURNS_TO_GO: rtg, SampleBatch.T: timesteps, SampleBatch.ATTENTION_MASKS: mask, } ) input_dict = convert_to_torch_tensor(input_dict) return input_dict

rllib/algorithms/dt/tests/test_dt_model.py

ray

Python

61880591e97c2760d929c16263e039d503b1b9a9

test_dt_model.py

_generate_input_dict

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

[RLlib] Add DTTorchModel (#27872)

async def test_empty_integrations_list_is_only_sent_at_the_end_of_bootstrap(hass): order = []

tests/test_bootstrap.py

core

Python

58d531841bc2197616e7285d0561a9935d9bda73

test_bootstrap.py

test_empty_integrations_list_is_only_sent_at_the_end_of_bootstrap

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

Fix typo SIGNAL_BOOTSTRAP_INTEGRATONS -> SIGNAL_BOOTSTRAP_INTEGRATIONS (#79970)

def test_run_glue_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f.split() if is_cuda_and_apex_available(): testargs.append("--fp16") run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.75) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "glue_no_trainer")))

examples/pytorch/test_accelerate_examples.py

transformers

Python

99eb9b523f9b9ea6096323ce5610ce6633acc88a

test_accelerate_examples.py

test_run_glue_no_trainer

https://github.com/huggingface/transformers.git

Fix `no_trainer` CI (#18242) * Fix all tests

def get_geom_placeholder(self, f, value, compiler): transform_func = self.spatial_function_name("Transform") if hasattr(value, "as_sql"): if value.field.srid == f.srid: placeholder = "%s" else: placeholder = "%s(%%s, %s)" % (transform_func, f.srid) return placeholder # Get the srid for this object if value is None: value_srid = None else: value_srid = value.srid # Adding Transform() to the SQL placeholder if the value srid # is not equal to the field srid. if value_srid is None or value_srid == f.srid: placeholder = "%s" else: placeholder = "%s(%%s, %s)" % (transform_func, f.srid) return placeholder

django/contrib/gis/db/backends/postgis/operations.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

operations.py

get_geom_placeholder

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

Refs #33476 -- Reformatted code with Black.

def truncate(self, size=None): # type: (Optional[int]) -> int return self._file.truncate(size)

.venv/lib/python3.8/site-packages/pip/_internal/network/lazy_wheel.py

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

lazy_wheel.py

truncate

https://github.com/jindongwang/transferlearning.git

upd; format

def test_dqn_compilation(self): num_iterations = 1 config = dqn.dqn.DQNConfig().rollouts(num_rollout_workers=2) for _ in framework_iterator(config, with_eager_tracing=True): # Double-dueling DQN. print("Double-dueling") plain_config = deepcopy(config) trainer = dqn.DQNTrainer(config=plain_config, env="CartPole-v0") for i in range(num_iterations): results = trainer.train() check_train_results(results) print(results) check_compute_single_action(trainer) trainer.stop() # Rainbow. print("Rainbow") rainbow_config = deepcopy(config).training( num_atoms=10, noisy=True, double_q=True, dueling=True, n_step=5 ) trainer = dqn.DQNTrainer(config=rainbow_config, env="CartPole-v0") for i in range(num_iterations): results = trainer.train() check_train_results(results) print(results) check_compute_single_action(trainer) trainer.stop()

rllib/agents/dqn/tests/test_dqn.py

ray

Python

5c96e7223b468fed6b6db763c837728c721f78cd

test_dqn.py

test_dqn_compilation

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

[RLlib] SimpleQ (minor cleanups) and DQN TrainerConfig objects. (#24584)

def on_test_end(self, logs=None): logs = self._process_logs(logs) for callback in self.callbacks: callback.on_test_end(logs)

keras/callbacks.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

callbacks.py

on_test_end

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def _add_contourf_set(self, cset, zdir='z', offset=None): zdir = '-' + zdir midpoints = cset.levels[:-1] + np.diff(cset.levels) / 2 # Linearly interpolate to get levels for any extensions if cset._extend_min: min_level = cset.levels[0] - np.diff(cset.levels[:2]) / 2 midpoints = np.insert(midpoints, 0, min_level) if cset._extend_max: max_level = cset.levels[-1] + np.diff(cset.levels[-2:]) / 2 midpoints = np.append(midpoints, max_level) for z, linec in zip(midpoints, cset.collections): if offset is not None: z = offset art3d.poly_collection_2d_to_3d(linec, z, zdir=zdir) linec.set_sort_zpos(z) return midpoints

lib/mpl_toolkits/mplot3d/axes3d.py

matplotlib

Python

df6f95703b60348e01603f98a439b133da2938a0

axes3d.py

_add_contourf_set

https://github.com/matplotlib/matplotlib.git

Improve mpl_toolkit documentation

def forward(self, input_ids=None, attention_mask=None): if input_ids is None: raise ValueError("Input_ids cannot be None.") inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale inputs_embed_pos = self.encoder_embed_positions(input_ids.shape) hidden_states = inputs_embeds + inputs_embed_pos hidden_states = self.encoder_layernorm_embedding(hidden_states) encoder_input = self.encoder_dropout(hidden_states) if attention_mask is None: attention_mask = paddle.cast( input_ids == self.pad_token_id, dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e4 attention_mask.stop_gradient = True encoder_output = self.encoder(encoder_input, src_mask=attention_mask) return encoder_output

paddlenlp/transformers/blenderbot_small/modeling.py

PaddleNLP

Python

b0c35d5e1ff02a634fa26392b60d3885c2c78677

modeling.py

forward

https://github.com/PaddlePaddle/PaddleNLP.git

Fix the attention mask for fp16 (#1585)

def no_translations(handle_func):

django/core/management/base.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

base.py

no_translations

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

Refs #33476 -- Reformatted code with Black.

def get_return_data_type(func_name): if func_name.startswith("get_"): if func_name.endswith("_list"): return "List" elif func_name.endswith("_count"): return "Integer" return ""

django/contrib/admindocs/views.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

views.py

get_return_data_type

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

Refs #33476 -- Reformatted code with Black.

def print_ir(*prototypes): def lower(f): inputs = tree_util.tree_map(np.array, prototypes) flat_inputs, _ = tree_util.tree_flatten(inputs) shape_strs = " ".join([f"{x.dtype.name}[{','.join(map(str, x.shape))}]" for x in flat_inputs]) name = f.func.__name__ if hasattr(f, "func") else f.__name__ print(f"\nTEST: {name} {shape_strs}") print(jax.jit(f).lower(*inputs).compiler_ir()) return lower

tests/filecheck/jax_filecheck_helpers.py

jax

Python

b8ae8e3fa10f9abe998459fac1513915acee776d

jax_filecheck_helpers.py

print_ir

https://github.com/google/jax.git

(NFC) Prepare for migration from producing MHLO to producing StableHLO This CL renames occurrences of "mhlo" in: 1) names, 2) tests, 3) prose in order to prepare for the upcoming migration. Unchanged occurrences: 1) Public API that contains "mhlo", e.g. XlaLowering.mhlo and the "mhlo" argument value in Lowering.as_text and Lowering.compiler_ir. 2) Documentation (changelog, JEPs, IR examples, etc). 3) One rare situation where prose says "StableHLO" and "MHLO" in one sentence, so both are necessary to disambiguate. PiperOrigin-RevId: 495771153

def test_extract_strings_with_rollout(rollout_option, option_value, expected, set_sentry_option): if rollout_option: set_sentry_option(rollout_option, option_value) outer_message = _construct_outer_message( [ (counter_payload, []), (counter_payload_org_70, []), (distribution_payload, []), (set_payload, []), ] ) batch = IndexerBatch(UseCaseKey.PERFORMANCE, outer_message, rollout_option) assert batch.extract_strings() == expected

tests/sentry/sentry_metrics/test_batch.py

sentry

Python

245b174c30ba3d814043221faa327b1a14d64859

test_batch.py

test_extract_strings_with_rollout

https://github.com/getsentry/sentry.git

feat(indexer): Allow mechanism to not index tag values (#38837) This PR adds mechanism to skip indexing tag values. The code is based on an option introduced in https://github.com/getsentry/sentry/pull/38758/ After the change, when the option is configured, the indexer on performance would send strings for tag values