Datasets:

semeru

/

code-code-galeras-code-completion-from-docstring-3k-deduped

like 0

lib/matplotlib/axes/_base.py

def set_aspect(self, aspect, adjustable=None, anchor=None, share=False): if cbook._str_equal(aspect, 'equal'): aspect = 1 if not cbook._str_equal(aspect, 'auto'): aspect = float(aspect) # raise ValueError if

Update _base.py

set_aspect

9d616615417eac104e12f2915f3fe875177bb2e4

matplotlib

_base.py

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

Python

def set_aspect(self, aspect, adjustable=None, anchor=None, share=False): if cbook._str_equal(aspect, 'equal'): aspect = 1 if not cbook._str_equal(aspect, 'auto'): aspect = float(aspect) # raise ValueError if necessary if aspect<0: raise ValueError("aspect must be positive") if share: axes = {sibling for name in self._axis_names for sibling in self._shared_axes[name].get_siblings(self)} else: axes = [self] for ax in axes: ax._aspect = aspect if adjustable is None: adjustable = self._adjustable self.set_adjustable(adjustable, share=share) # Handle sharing. if anchor is not None: self.set_anchor(anchor, share=share) self.stale = True

tests/custom_pk/tests.py

def test_pk_attributes(self): # pk can be used as a substitute for the primary key. # The primary key can be accessed via the pk property on the model. e = Employee.objects.get(pk=123) self.ass

Refs #33476 -- Reformatted code with Black.

test_pk_attributes

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_pk_attributes(self): # pk can be used as a substitute for the primary key. # The primary key can be accessed via the pk property on the model. e = Employee.objects.get(pk=123) self.assertEqual(e.pk, 123) # Or we can use the real attribute name for the primary key: self.assertEqual(e.employee_code, 123) with self.assertRaisesMessage( AttributeError, "'Employee' object has no attribute 'id'" ): e.id

src/prefect/client.py

async def __aenter__(self): if self._closed: # httpx.AsyncClient does not allow reuse so we will not either. raise RuntimeError( "The client cannot be started again after closing. "

Disable lifespan management during logging

__aenter__

05b92d7c7f6cf21c5d6033df7242c331fc66b92e

prefect

client.py

https://github.com/PrefectHQ/prefect.git

Python

async def __aenter__(self): if self._closed: # httpx.AsyncClient does not allow reuse so we will not either. raise RuntimeError( "The client cannot be started again after closing. " "Retrieve a new client with `get_client()` instead." ) if self._started: # httpx.AsyncClient does not allow reentrancy so we will not either. raise RuntimeError("The client cannot be started more than once.") await self._exit_stack.__aenter__() # Enter a lifespan context if using an ephemeral application. # See https://github.com/encode/httpx/issues/350 if self._ephemeral_app and self.manage_lifespan: self._ephemeral_lifespan = await self._exit_stack.enter_async_context( app_lifespan_context(self._ephemeral_app) ) # Enter the httpx client's context await self._exit_stack.enter_async_context(self._client) self._started = True return self

rllib/agents/qmix/qmix.py

def training_iteration(self) -> ResultDict: # Sample n batches from n workers. new_sample_batches = synchronous_parallel_sample( worker_set=self.workers, concat=False ) for batch in new_sample_batches: # Update counters. self._counters[NUM_ENV_STEPS_SAMPLED] += batch.env_steps() self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps() # Store new samples in the replay buffer. self.local_replay_buffer.add(batch) # Sample n batches from replay buffer until the total number of timesteps # reaches `train_batch_size`. train_batch = sample_min_n_steps_from_buffer( replay_buffer=self.local_replay_buffer, min_steps=self.config["train_batch_size"], count_by_agent_steps=self._by_agent_steps, ) if train_batch is None: return {} # Learn on the training batch. # Use simple optimizer (only for multi-agent or tf-eager; all other # cases should use the multi-GPU optimizer, even if only using 1 GPU) if self.config.get("simple_optimizer") is True: train_results = train_one_step(self, train_batch) else: train_results = multi_gpu_train_one_step(self, train_batch) # TODO: Move training steps counter update outside of `train_one_step()` method. # # Update train step counters. # self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps() # self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps() # Update target network every `target_network_update_freq` steps.

[RLlib] QMIX training iteration function and new replay buffer API. (#24164)

training_iteration

627b9f2e888b05434bb67f547b390409f26538e7

ray

qmix.py

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

Python

def training_iteration(self) -> ResultDict: # Sample n batches from n workers. new_sample_batches = synchronous_parallel_sample( worker_set=self.workers, concat=False ) for batch in new_sample_batches: # Update counters. self._counters[NUM_ENV_STEPS_SAMPLED] += batch.env_steps() self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps() # Store new samples in the replay buffer. self.local_replay_buffer.add(batch) # Sample n batches from replay buffer until the total number of timesteps # reaches `train_batch_size`. train_batch = sample_min_n_steps_from_buffer( replay_buffer=self.local_replay_buffer, min_steps=self.config["train_batch_size"], count_by_agent_steps=self._by_agent_steps, ) if train_batch is None: return {} # Learn on the training batch. # Use simple optimizer (only for multi-agent or tf-eager; all other # cases should use the multi-GPU optimizer, even if only using 1 GPU) if self.config.get("simple_optimizer") is True: train_results = train_one_step(self, train_batch) else: train_results = multi_gpu_train_one_step(self, train_batch) # TODO: Move training steps counter update outside of `train_one_step()` method. # # Update train step counters. # self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps() # self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps() # Update target network every `target_network_update_freq` steps. cur_ts = self._counters[NUM_ENV_STEPS_SAMPLED] last_update = self._counters[LAST_TARGET_UPDATE_TS] if cur_ts - last_update >= self.config["target_network_update_freq"]: to_update = self.workers.local_worker().get_policies_to_train() self.workers.local_worker().foreach_policy_to_train( lambda p, pid: pid in to_update and p.update_target() ) self._counters[NUM_TARGET_UPDATES] += 1 self._counters[LAST_TARGET_UPDATE_TS] = cur_ts # Update weights and global_vars - after learning on the local worker - on all # remote workers. global_vars = { "timestep": self._counters[NUM_ENV_STEPS_SAMPLED], } # Update remote workers' weights and global vars after learning on local worker. with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]: self.workers.sync_weights(global_vars=global_vars) # Return all collected metrics for the iteration. return train_results

@cli.command() @click.option("--gh-token", envvar=["GH_TOKEN", "GITHUB_TOKEN"], required=True)

scripts-dev/release.py

def _announce() -> None: current_version = get_package_version() tag_name = f"v{current_version}" click.echo( f

Extend the release script to wait for GitHub Actions to finish and to be usable as a guide for the whole process. (#13483)

_announce

c7b18d9d44c90acfd4ceaec1fa2f8275e03f14af

synapse

release.py

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

Python

def _announce() -> None: current_version = get_package_version() tag_name = f"v{current_version}" click.echo( f ) if "rc" in tag_name: click.echo( ) else: click.echo( ) @cli.command() @click.option("--gh-token", envvar=["GH_TOKEN", "GITHUB_TOKEN"], required=True)

PySimpleGUI.py

def update(self, value=None, visible=None):

Completed switching all elements over to the new way of handling visiblity

update

ed2bc288ff17344f6406c49623036620f18e65bb

PySimpleGUI

PySimpleGUI.py

https://github.com/PySimpleGUI/PySimpleGUI.git

Python

def update(self, value=None, visible=None): if not self._widget_was_created(): # if widget hasn't been created yet, then don't allow return if value is not None: self._TKOut.output.delete('1.0', tk.END) self._TKOut.output.insert(tk.END, value) if visible is False: self._pack_forget_save_settings(self._TKOut.frame) elif visible is True: self._pack_restore_settings(self._TKOut.frame) if visible is not None: self._visible = visible

freqtrade/freqai/prediction_models/RLPredictionModel.py

def example(self): result = getattr(self, "_example", None) if result is None: # No example batch was found, so get one from the `.train` dataset result = next(iter(self.train)) # And cache it for next time self._example = result return result

callback function and TDQN model added

example

01232e9a1f8e28e3611e38af3816edb026600767

freqtrade

RLPredictionModel.py

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

Python

def example(self): result = getattr(self, "_example", None) if result is None: # No example batch was found, so get one from the `.train` dataset result = next(iter(self.train)) # And cache it for next time self._example = result return result

django/contrib/gis/measure.py

def unit_attname(cls, unit_str): lower = unit_str.lower() if unit_str in cls.UNITS: return unit_str elif lower in cls.UNITS: return lower elif lower in cls.LALIAS: return cls.LALIAS[lower] else: raise Exception( 'Could not find a unit keyword associated

Refs #33476 -- Reformatted code with Black.

unit_attname

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

measure.py

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

Python

def unit_attname(cls, unit_str): lower = unit_str.lower() if unit_str in cls.UNITS: return unit_str elif lower in cls.UNITS: return lower elif lower in cls.LALIAS: return cls.LALIAS[lower] else: raise Exception( 'Could not find a unit keyword associated with "%s"' % unit_str )

rllib/tests/test_nn_framework_import_errors.py

def test_dont_import_tf_error(): # Do n

[RLlib] AlgorithmConfigs: Make None a valid value for methods to set properties; Use new `NotProvided` singleton, instead, to indicate no changes wanted on that property. (#30020)

test_dont_import_tf_error

087548031bcf22dd73364b58acb70e61a49f2427

ray

test_nn_framework_import_errors.py

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

Python

def test_dont_import_tf_error(): # Do not import tf for testing purposes. os.environ["RLLIB_TEST_NO_TF_IMPORT"] = "1" config = ppo.PPOConfig().environment("CartPole-v1") for _ in framework_iterator(config, frameworks=("tf", "tf2")): with pytest.raises(ImportError, match="However, no installation was found"): config.build()

python3.10.4/Lib/hmac.py

def _current(self): if self._hmac: return self._hmac else: h = self._outer.copy()

add python 3.10.4 for windows

_current

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

hmac.py

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

Python

def _current(self): if self._hmac: return self._hmac else: h = self._outer.copy() h.update(self._inner.digest()) return h

erpnext/patches/v13_0/delete_old_sales_reports.py

def delete_links_from_desktop_icons(report): desktop_icons = frappe.db.get_values("Desktop Icon", {"_report": report}, ["name"]) for desktop_icon in desktop_icons: frappe.delete_doc("Desktop Icon", desktop_icon[0])

fix: broken patches (backport #29067) (#29406) * chore: patch fixes (cherry picked from commit 8b5b146f6d2720587a16f78a8d47840be8dca2b7) # Conflicts: # erpnext/patches/v13_0/make_homepage_products_website_items.py * fix: remove desktop icons while deleting sales reports (cherry picked from commit 5f72026cb932d01fc827c382747e996a94b441fd) * refactor: dont ignore dangerous exceptions in patches (cherry picked from commit 0aa1ea8aeb7757592616bd491de98c69fef08854) * fix: make patch kinda idempotent with previous query rerunning would've caused all values to become 0. * chore: conflicts * fix: check type before patching Co-authored-by: Saurabh <saurabh6790@gmail.com> Co-authored-by: Ankush Menat <ankush@frappe.io>

delete_links_from_desktop_icons

f469ec87d94d4639ff4eb99a45496721c4779bf3

erpnext

delete_old_sales_reports.py

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

Python

def delete_links_from_desktop_icons(report): desktop_icons = frappe.db.get_values("Desktop Icon", {"_report": report}, ["name"]) for desktop_icon in desktop_icons: frappe.delete_doc("Desktop Icon", desktop_icon[0])

ppstructure/table/table_master_match.py

def get_bboxes_list(end2end_result, structure_master_result): # end2end e

add SLANet

get_bboxes_list

ddaa2c2552e19635cd6cdf38619f1f176c358f89

PaddleOCR

table_master_match.py

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

Python

def get_bboxes_list(end2end_result, structure_master_result): # end2end end2end_xyxy_list = [] end2end_xywh_list = [] for end2end_item in end2end_result: src_bbox = end2end_item['bbox'] end2end_xyxy_list.append(src_bbox) xywh_bbox = xyxy2xywh(src_bbox) end2end_xywh_list.append(xywh_bbox) end2end_xyxy_bboxes = np.array(end2end_xyxy_list) end2end_xywh_bboxes = np.array(end2end_xywh_list) # structure master src_bboxes = structure_master_result['bbox'] src_bboxes = remove_empty_bboxes(src_bboxes) # structure_master_xywh_bboxes = src_bboxes # xyxy_bboxes = xywh2xyxy(src_bboxes) # structure_master_xyxy_bboxes = xyxy_bboxes structure_master_xyxy_bboxes = src_bboxes xywh_bbox = xyxy2xywh(src_bboxes) structure_master_xywh_bboxes = xywh_bbox return end2end_xyxy_bboxes, end2end_xywh_bboxes, structure_master_xywh_bboxes, structure_master_xyxy_bboxes

pipenv/patched/notpip/_vendor/pyparsing/core.py

def autoname_elements() -> None: for name, var in sys._getframe().f_back.f_locals.items(): if isinstance(var, ParserElement) and not var.customName: var.set_name(name) dbl_quoted_string = Combine( Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '"' ).set_name("string enclosed in double quotes") sgl_quoted_string = Combine( Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*") + "'" ).set_name("string enclosed in single quotes") quoted_string = Combine( Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '"' | Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*") + "'" ).set_name("quotedString using single or double quotes") unicode_string = Combine("u" + quoted_string.copy()).set_name("unicode string literal") alphas8bit = srange(r"[\0xc0-\0xd6\0xd8-\0xf6\0xf8-\0xff]") punc8bit = srange(r"[\0xa1-\0xbf\0xd7\0xf7]") # build list of built-in expressions, for future reference if a global default value # gets updated _builtin_expr

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

autoname_elements

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

core.py

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

Python

def autoname_elements() -> None: for name, var in sys._getframe().f_back.f_locals.items(): if isinstance(var, ParserElement) and not var.customName: var.set_name(name) dbl_quoted_string = Combine( Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '"' ).set_name("string enclosed in double quotes") sgl_quoted_string = Combine( Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*") + "'" ).set_name("string enclosed in single quotes") quoted_string = Combine( Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '"' | Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*") + "'" ).set_name("quotedString using single or double quotes") unicode_string = Combine("u" + quoted_string.copy()).set_name("unicode string literal") alphas8bit = srange(r"[\0xc0-\0xd6\0xd8-\0xf6\0xf8-\0xff]") punc8bit = srange(r"[\0xa1-\0xbf\0xd7\0xf7]") # build list of built-in expressions, for future reference if a global default value # gets updated _builtin_exprs = [v for v in vars().values() if isinstance(v, ParserElement)] # backward compatibility names tokenMap = token_map conditionAsParseAction = condition_as_parse_action nullDebugAction = null_debug_action sglQuotedString = sgl_quoted_string dblQuotedString = dbl_quoted_string quotedString = quoted_string unicodeString = unicode_string lineStart = line_start lineEnd = line_end stringStart = string_start stringEnd = string_end traceParseAction = trace_parse_action

tools/test/test_gen_backend_stubs.py

def test_valid_zero_ops_doesnt_require_backend_dispatch_key(self) -> None: yaml_str = # External codegen on a yaml file with no operators is effectively a no-op,

Revert "Revert D32498569: allow external backend codegen to toggle whether to generate out= and inplace kernels" (#69950) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69950 This reverts commit f6cad53443704dfe5a20cc62bee14d91e3bffcaa. Test Plan: Imported from OSS Reviewed By: albanD Differential Revision: D33113545 Pulled By: bdhirsh fbshipit-source-id: d6590294662588d36c09662dea65919ad4e1e288

test_valid_zero_ops_doesnt_require_backend_dispatch_key

bb5b4cceb6f737448eaaa6817cd773b6f4b0e77d

pytorch

test_gen_backend_stubs.py

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

Python

def test_valid_zero_ops_doesnt_require_backend_dispatch_key(self) -> None: yaml_str = # External codegen on a yaml file with no operators is effectively a no-op, # so there's no reason to parse the backend self.assert_success_from_gen_backend_stubs(yaml_str)

tests/distributed/test_remote_peas/test_remote_peas.py

async def test_pseudo_remote_peas_topologies(gateway, head, worker): worker_port = random_port() head_port = random_port() port_expose = random_port() graph_description = '{"start-gateway": ["pod0"], "pod0": ["end-gateway"]}' if head == 'remote': pods_addresses = f'{{"pod0": ["{HOST}:{head_port}"]}}' else: pods_addresses = f'{{"pod0": ["0.0.0.0:{head_port}"]}}' # create a single head pea head_pea = _create_head_pea(head, head_port) # create a single worker pea worker_pea = _create_worker_pea(worker, worker_port) # create a single gateway pea gateway_pea = _create_gateway_pea( gateway, graph_description, pods_addresses, port_expose ) with gateway_pea, worker_pea, head_pea: await asyncio.sleep(1.0) # this would be done by the Pod, its adding the worker to the head activate_msg = ControlRequest(command='ACTIVATE') worker_host, worker_port = worker_pea.runtime_ctrl_address.split(':') if head == 'remote': worker_host = __docker_host__ activate_msg.add_related_entity('worker', worker_host, int(worker_port)) assert GrpcConnectionPool.send_request_sync( activate_msg, head_pea.runtime_ctrl_address ) # send requests to the gateway c = Client(host='127.0.0.1', port=port_expose, asyncio=True) responses = c.post(

feat: star routing (#3900) * feat(proto): adjust proto for star routing (#3844) * feat(proto): adjust proto for star routing * feat(proto): generate proto files * feat(grpc): refactor grpclet interface (#3846) * feat: refactor connection pool for star routing (#3872) * feat(k8s): add more labels to k8s deployments * feat(network): refactor connection pool * feat(network): refactor k8s pool * feat: star routing graph gateway (#3877) * feat: star routing - refactor grpc data runtime (#3887) * feat(runtimes): refactor grpc dataruntime * fix(tests): adapt worker runtime tests * fix(import): fix import * feat(proto): enable sending multiple lists (#3891) * feat: star routing gateway (#3893) * feat: star routing gateway all protocols (#3897) * test: add streaming and prefetch tests (#3901) * feat(head): new head runtime for star routing (#3899) * feat(head): new head runtime * feat(head): new head runtime * style: fix overload and cli autocomplete * feat(network): improve proto comments Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * feat(worker): merge docs in worker runtime (#3905) * feat(worker): merge docs in worker runtime * feat(tests): assert after clean up * feat(tests): star routing runtime integration tests (#3908) * fix(tests): fix integration tests * test: test runtimes fast slow request (#3910) * feat(zmq): purge zmq, zed, routing_table (#3915) * feat(zmq): purge zmq, zed, routing_table * style: fix overload and cli autocomplete * feat(zmq): adapt comment in dependency list * style: fix overload and cli autocomplete * fix(tests): fix type tests Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * test: add test gateway to worker connection (#3921) * feat(pea): adapt peas for star routing (#3918) * feat(pea): adapt peas for star routing * style: fix overload and cli autocomplete * feat(pea): add tests * feat(tests): add failing head pea test Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * feat(tests): integration tests for peas (#3923) * feat(tests): integration tests for peas * feat(pea): remove _inner_pea function * feat: star routing container pea (#3922) * test: rescue tests (#3942) * fix: fix streaming tests (#3945) * refactor: move docker run to run (#3948) * feat: star routing pods (#3940) * feat(pod): adapt pods for star routing * feat(pods): adapt basepod to star routing * feat(pod): merge pod and compound pod * feat(tests): fix tests * style: fix overload and cli autocomplete * feat(test): add container pea int test * feat(ci): remove more unnecessary tests * fix(tests): remove jinad runtime * feat(ci): remove latency tracking * fix(ci): fix ci def * fix(runtime): enable runtime to be exited * fix(tests): wrap runtime test in process * fix(runtimes): remove unused runtimes * feat(runtimes): improve cancel wait * fix(ci): build test pip again in ci * fix(tests): fix a test * fix(test): run async in its own process * feat(pod): include shard in activate msg * fix(pea): dont join * feat(pod): more debug out * feat(grpc): manage channels properly * feat(pods): remove exitfifo * feat(network): add simple send retry mechanism * fix(network): await pool close * fix(test): always close grpc server in worker * fix(tests): remove container pea from tests * fix(tests): reorder tests * fix(ci): split tests * fix(ci): allow alias setting * fix(test): skip a test * feat(pods): address comments Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * test: unblock skipped test (#3957) * feat: jinad pea (#3949) * feat: jinad pea * feat: jinad pea * test: remote peas * test: toplogy tests with jinad * ci: parallel jobs * feat(tests): add pod integration tests (#3958) * feat(tests): add pod integration tests * fix(tests): make tests less flaky * fix(test): fix test * test(pea): remote pea topologies (#3961) * test(pea): remote pea simple topology * test: remote pea topologies * refactor: refactor streamer result handling (#3960) * feat(k8s): adapt K8s Pod for StarRouting (#3964) * test: optimize k8s test * test: increase timeout and use different namespace * test: optimize k8s test * test: build and load image when needed * test: refactor k8s test * test: fix image name error * test: fix k8s image load * test: fix typoe port expose * test: update tests in connection pool and handling * test: remove unused fixture * test: parameterize docker images * test: parameterize docker images * test: parameterize docker images * feat(k8s): adapt k8s pod for star routing * fix(k8s): dont overwrite add/remove function in pool * fix(k8s): some fixes * fix(k8s): some more fixes * fix(k8s): linting * fix(tests): fix tests * fix(tests): fix k8s unit tests * feat(k8s): complete k8s integration test * feat(k8s): finish k8s tests * feat(k8s): fix test * fix(tests): fix test with no name * feat(k8s): unify create/replace interface * feat(k8s): extract k8s port constants * fix(tests): fix tests * fix(tests): wait for runtime being ready in tests * feat(k8s): address comments Co-authored-by: bwanglzu <bo.wang@jina.ai> * feat(flow): adapt Flow for StarRouting (#3986) * feat(flow): add routes * feat(flow): adapt flow to star routing * style: fix overload and cli autocomplete * feat(flow): handle empty topologies * feat(k8s): allow k8s pool disabling * style: fix overload and cli autocomplete * fix(test): fix test with mock * fix(tests): fix more tests * feat(flow): clean up tests * style: fix overload and cli autocomplete * fix(tests): fix more tests * feat: add plot function (#3994) * fix(tests): avoid hanging tests * feat(flow): add type hinting * fix(test): fix duplicate exec name in test * fix(tests): fix more tests * fix(tests): enable jinad test again * fix(tests): random port fixture * fix(style): replace quotes Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * feat(ci): bring back ci (#3997) * feat(ci): enable ci again * style: fix overload and cli autocomplete * feat(ci): add latency tracking * feat(ci): bring back some tests * fix(tests): remove invalid port test * feat(ci): disable daemon and distributed tests * fix(tests): fix entrypoint in hub test * fix(tests): wait for gateway to be ready * fix(test): fix more tests * feat(flow): do rolling update and scale sequentially * fix(tests): fix more tests * style: fix overload and cli autocomplete * feat: star routing hanging pods (#4011) * fix: try to handle hanging pods better * test: hanging pods test work * fix: fix topology graph problem * test: add unit test to graph * fix(tests): fix k8s tests * fix(test): fix k8s test * fix(test): fix k8s pool test * fix(test): fix k8s test * fix(test): fix k8s connection pool setting * fix(tests): make runtime test more reliable * fix(test): fix routes test * fix(tests): make rolling update test less flaky * feat(network): gurantee unique ports * feat(network): do round robin for shards * fix(ci): increase pytest timeout to 10 min Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * fix(ci): fix ci file * feat(daemon): jinad pod for star routing * Revert "feat(daemon): jinad pod for star routing" This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92. * feat(daemon): remote jinad pod support (#4042) * feat(daemon): add pod tests for star routing * feat(daemon): add remote pod test * test(daemon): add remote pod arguments test * test(daemon): add async scale test * test(daemon): add rolling update test * test(daemon): fix host * feat(proto): remove message proto (#4051) * feat(proto): remove message proto * fix(tests): fix tests * fix(tests): fix some more tests * fix(tests): fix more tests * fix(tests): fix more tests * fix(tests): fix more tests * fix(tests): fix more tests * feat(proto): put docs back in data * fix(proto): clean up * feat(proto): clean up * fix(tests): skip latency tracking * fix(test): fix hub test * fix(tests): fix k8s test * fix(test): some test clean up * fix(style): clean up style issues * feat(proto): adjust for rebase * fix(tests): bring back latency tracking * fix(tests): fix merge accident * feat(proto): skip request serialization (#4074) * feat: add reduce to star routing (#4070) * feat: add reduce on shards to head runtime * test: add reduce integration tests with fixed order * feat: add reduce on needs * chore: get_docs_matrix_from_request becomes public * style: fix overload and cli autocomplete * docs: remove undeterministic results warning * fix: fix uses_after * test: assert correct num docs after reducing in test_external_pod * test: correct asserts after reduce in test_rolling_update * fix: no reduce if uses_after_address is set * fix: get_docs_from_request only if needed * fix: fix tests after merge * refactor: move reduce from data_request_handler to head * style: fix overload and cli autocomplete * chore: apply suggestions * fix: fix asserts * chore: minor test fix * chore: apply suggestions * test: remove flow tests with external executor (pea) * fix: fix test_expected_messages_routing * fix: fix test_func_joiner * test: adapt k8s test Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * fix(k8s): fix static pool config * fix: use custom protoc doc generator image (#4088) * fix: use custom protoc doc generator image * fix(docs): minor doc improvement * fix(docs): use custom image * fix(docs): copy docarray * fix: doc building local only * fix: timeout doc building * fix: use updated args when building ContainerPea * test: add container PeaFactory test * fix: force pea close on windows (#4098) * fix: dont reduce if uses exist (#4099) * fix: dont use reduce if uses exist * fix: adjust reduce tests * fix: adjust more reduce tests * fix: fix more tests * fix: adjust more tests * fix: ignore non jina resources (#4101) * feat(executor): enable async executors (#4102) * feat(daemon): daemon flow on star routing (#4096) * test(daemon): add remote flow test * feat(daemon): call scale in daemon * feat(daemon): remove tail args and identity * test(daemon): rename scalable executor * test(daemon): add a small delay in async test * feat(daemon): scale partial flow only * feat(daemon): call scale directly in partial flow store * test(daemon): use asyncio sleep * feat(daemon): enable flow level distributed tests * test(daemon): fix jinad env workspace config * test(daemon): fix pod test use new port rolling update * feat(daemon): enable distribuetd tests * test(daemon): remove duplicate tests and zed runtime test * test(daemon): fix stores unit test * feat(daemon): enable part of distributed tests * feat(daemon): enable part of distributed tests * test: correct test paths * test(daemon): add client test for remote flows * test(daemon): send a request with jina client * test(daemon): assert async generator * test(daemon): small interval between tests * test(daemon): add flow test for container runtime * test(daemon): add flow test for container runtime * test(daemon): fix executor name * test(daemon): fix executor name * test(daemon): use async client fetch result * test(daemon): finish container flow test * test(daemon): enable distributed in ci * test(daemon): enable distributed in ci * test(daemon): decare flows and pods * test(daemon): debug ci if else * test(daemon): debug ci if else * test(daemon): decare flows and pods * test(daemon): correct test paths * test(daemon): add small delay for async tests * fix: star routing fixes (#4100) * docs: update docs * fix: fix Request.__repr__ * docs: update flow remarks * docs: fix typo * test: add non_empty_fields test * chore: remove non_empty_fields test * feat: polling per endpoint (#4111) * feat(polling): polling per endpoint configurable * fix: adjust tests * feat(polling): extend documentation * style: fix overload and cli autocomplete * fix: clean up * fix: adjust more tests * fix: remove repeat from flaky test * fix: k8s test * feat(polling): address pr feedback * feat: improve docs Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * feat(grpc): support connect grpc server via ssl tunnel (#4092) * feat(grpc): support ssl grpc connect if port is 443 * fix(grpc): use https option instead of detect port automatically * chore: fix typo * fix: update jina/peapods/networking.py Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * fix: update jina/peapods/networking.py Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * fix: update jina/peapods/networking.py Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * test(networking): add test for peapods networking * fix: address comments Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * feat(polling): unify polling args (#4113) * fix: several issues for jinad pods (#4119) * fix: activate for jinad pods * fix: dont expose worker pod in partial daemon * fix: workspace setting * fix: containerized flows * fix: hub test * feat(daemon): remote peas on star routing (#4112) * test(daemon): fix request in peas * test(daemon): fix request in peas * test(daemon): fix sync async client test * test(daemon): enable remote peas test * test(daemon): replace send message to send request * test(daemon): declare pea tests in ci * test(daemon): use pea args fixture * test(daemon): head pea use default host * test(daemon): fix peas topologies * test(daemon): fix pseudo naming * test(daemon): use default host as host * test(daemon): fix executor path * test(daemon): add remote worker back * test(daemon): skip local remote remote topology * fix: jinad pea test setup * fix: jinad pea tests * fix: remove invalid assertion Co-authored-by: jacobowitz <tobias.jacobowitz@posteo.de> * feat: enable daemon tests again (#4132) * feat: enable daemon tests again * fix: remove bogy empty script file * fix: more jinad test fixes * style: fix overload and cli autocomplete * fix: scale and ru in jinad * fix: fix more jinad tests Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * fix: fix flow test * fix: improve pea tests reliability (#4136) Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> Co-authored-by: Deepankar Mahapatro <deepankar.mahapatro@jina.ai> Co-authored-by: bwanglzu <bo.wang@jina.ai> Co-authored-by: AlaeddineAbdessalem <alaeddine-13@live.fr> Co-authored-by: Zhaofeng Miao <522856232@qq.com>

test_pseudo_remote_peas_topologies

933415bfa1f9eb89f935037014dfed816eb9815d

jina

test_remote_peas.py

https://github.com/jina-ai/jina.git

Python

async def test_pseudo_remote_peas_topologies(gateway, head, worker): worker_port = random_port() head_port = random_port() port_expose = random_port() graph_description = '{"start-gateway": ["pod0"], "pod0": ["end-gateway"]}' if head == 'remote': pods_addresses = f'{{"pod0": ["{HOST}:{head_port}"]}}' else: pods_addresses = f'{{"pod0": ["0.0.0.0:{head_port}"]}}' # create a single head pea head_pea = _create_head_pea(head, head_port) # create a single worker pea worker_pea = _create_worker_pea(worker, worker_port) # create a single gateway pea gateway_pea = _create_gateway_pea( gateway, graph_description, pods_addresses, port_expose ) with gateway_pea, worker_pea, head_pea: await asyncio.sleep(1.0) # this would be done by the Pod, its adding the worker to the head activate_msg = ControlRequest(command='ACTIVATE') worker_host, worker_port = worker_pea.runtime_ctrl_address.split(':') if head == 'remote': worker_host = __docker_host__ activate_msg.add_related_entity('worker', worker_host, int(worker_port)) assert GrpcConnectionPool.send_request_sync( activate_msg, head_pea.runtime_ctrl_address ) # send requests to the gateway c = Client(host='127.0.0.1', port=port_expose, asyncio=True) responses = c.post( '/', inputs=async_inputs, request_size=1, return_results=True ) response_list = []

jina/peapods/peas/__init__.py

async def async_wait_start_success(self): import asyncio _timeout = self.args.timeout_ready if _timeout <= 0: _timeout = None else: _timeout /= 1e3 timeout_ns = 1e9 * _timeout if _timeout else None now = time.time_ns() while timeout_ns is None or time.ti

feat: star routing (#3900) * feat(proto): adjust proto for star routing (#3844) * feat(proto): adjust proto for star routing * feat(proto): generate proto files * feat(grpc): refactor grpclet interface (#3846) * feat: refactor connection pool for star routing (#3872) * feat(k8s): add more labels to k8s deployments * feat(network): refactor connection pool * feat(network): refactor k8s pool * feat: star routing graph gateway (#3877) * feat: star routing - refactor grpc data runtime (#3887) * feat(runtimes): refactor grpc dataruntime * fix(tests): adapt worker runtime tests * fix(import): fix import * feat(proto): enable sending multiple lists (#3891) * feat: star routing gateway (#3893) * feat: star routing gateway all protocols (#3897) * test: add streaming and prefetch tests (#3901) * feat(head): new head runtime for star routing (#3899) * feat(head): new head runtime * feat(head): new head runtime * style: fix overload and cli autocomplete * feat(network): improve proto comments Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * feat(worker): merge docs in worker runtime (#3905) * feat(worker): merge docs in worker runtime * feat(tests): assert after clean up * feat(tests): star routing runtime integration tests (#3908) * fix(tests): fix integration tests * test: test runtimes fast slow request (#3910) * feat(zmq): purge zmq, zed, routing_table (#3915) * feat(zmq): purge zmq, zed, routing_table * style: fix overload and cli autocomplete * feat(zmq): adapt comment in dependency list * style: fix overload and cli autocomplete * fix(tests): fix type tests Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * test: add test gateway to worker connection (#3921) * feat(pea): adapt peas for star routing (#3918) * feat(pea): adapt peas for star routing * style: fix overload and cli autocomplete * feat(pea): add tests * feat(tests): add failing head pea test Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * feat(tests): integration tests for peas (#3923) * feat(tests): integration tests for peas * feat(pea): remove _inner_pea function * feat: star routing container pea (#3922) * test: rescue tests (#3942) * fix: fix streaming tests (#3945) * refactor: move docker run to run (#3948) * feat: star routing pods (#3940) * feat(pod): adapt pods for star routing * feat(pods): adapt basepod to star routing * feat(pod): merge pod and compound pod * feat(tests): fix tests * style: fix overload and cli autocomplete * feat(test): add container pea int test * feat(ci): remove more unnecessary tests * fix(tests): remove jinad runtime * feat(ci): remove latency tracking * fix(ci): fix ci def * fix(runtime): enable runtime to be exited * fix(tests): wrap runtime test in process * fix(runtimes): remove unused runtimes * feat(runtimes): improve cancel wait * fix(ci): build test pip again in ci * fix(tests): fix a test * fix(test): run async in its own process * feat(pod): include shard in activate msg * fix(pea): dont join * feat(pod): more debug out * feat(grpc): manage channels properly * feat(pods): remove exitfifo * feat(network): add simple send retry mechanism * fix(network): await pool close * fix(test): always close grpc server in worker * fix(tests): remove container pea from tests * fix(tests): reorder tests * fix(ci): split tests * fix(ci): allow alias setting * fix(test): skip a test * feat(pods): address comments Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * test: unblock skipped test (#3957) * feat: jinad pea (#3949) * feat: jinad pea * feat: jinad pea * test: remote peas * test: toplogy tests with jinad * ci: parallel jobs * feat(tests): add pod integration tests (#3958) * feat(tests): add pod integration tests * fix(tests): make tests less flaky * fix(test): fix test * test(pea): remote pea topologies (#3961) * test(pea): remote pea simple topology * test: remote pea topologies * refactor: refactor streamer result handling (#3960) * feat(k8s): adapt K8s Pod for StarRouting (#3964) * test: optimize k8s test * test: increase timeout and use different namespace * test: optimize k8s test * test: build and load image when needed * test: refactor k8s test * test: fix image name error * test: fix k8s image load * test: fix typoe port expose * test: update tests in connection pool and handling * test: remove unused fixture * test: parameterize docker images * test: parameterize docker images * test: parameterize docker images * feat(k8s): adapt k8s pod for star routing * fix(k8s): dont overwrite add/remove function in pool * fix(k8s): some fixes * fix(k8s): some more fixes * fix(k8s): linting * fix(tests): fix tests * fix(tests): fix k8s unit tests * feat(k8s): complete k8s integration test * feat(k8s): finish k8s tests * feat(k8s): fix test * fix(tests): fix test with no name * feat(k8s): unify create/replace interface * feat(k8s): extract k8s port constants * fix(tests): fix tests * fix(tests): wait for runtime being ready in tests * feat(k8s): address comments Co-authored-by: bwanglzu <bo.wang@jina.ai> * feat(flow): adapt Flow for StarRouting (#3986) * feat(flow): add routes * feat(flow): adapt flow to star routing * style: fix overload and cli autocomplete * feat(flow): handle empty topologies * feat(k8s): allow k8s pool disabling * style: fix overload and cli autocomplete * fix(test): fix test with mock * fix(tests): fix more tests * feat(flow): clean up tests * style: fix overload and cli autocomplete * fix(tests): fix more tests * feat: add plot function (#3994) * fix(tests): avoid hanging tests * feat(flow): add type hinting * fix(test): fix duplicate exec name in test * fix(tests): fix more tests * fix(tests): enable jinad test again * fix(tests): random port fixture * fix(style): replace quotes Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * feat(ci): bring back ci (#3997) * feat(ci): enable ci again * style: fix overload and cli autocomplete * feat(ci): add latency tracking * feat(ci): bring back some tests * fix(tests): remove invalid port test * feat(ci): disable daemon and distributed tests * fix(tests): fix entrypoint in hub test * fix(tests): wait for gateway to be ready * fix(test): fix more tests * feat(flow): do rolling update and scale sequentially * fix(tests): fix more tests * style: fix overload and cli autocomplete * feat: star routing hanging pods (#4011) * fix: try to handle hanging pods better * test: hanging pods test work * fix: fix topology graph problem * test: add unit test to graph * fix(tests): fix k8s tests * fix(test): fix k8s test * fix(test): fix k8s pool test * fix(test): fix k8s test * fix(test): fix k8s connection pool setting * fix(tests): make runtime test more reliable * fix(test): fix routes test * fix(tests): make rolling update test less flaky * feat(network): gurantee unique ports * feat(network): do round robin for shards * fix(ci): increase pytest timeout to 10 min Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * fix(ci): fix ci file * feat(daemon): jinad pod for star routing * Revert "feat(daemon): jinad pod for star routing" This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92. * feat(daemon): remote jinad pod support (#4042) * feat(daemon): add pod tests for star routing * feat(daemon): add remote pod test * test(daemon): add remote pod arguments test * test(daemon): add async scale test * test(daemon): add rolling update test * test(daemon): fix host * feat(proto): remove message proto (#4051) * feat(proto): remove message proto * fix(tests): fix tests * fix(tests): fix some more tests * fix(tests): fix more tests * fix(tests): fix more tests * fix(tests): fix more tests * fix(tests): fix more tests * feat(proto): put docs back in data * fix(proto): clean up * feat(proto): clean up * fix(tests): skip latency tracking * fix(test): fix hub test * fix(tests): fix k8s test * fix(test): some test clean up * fix(style): clean up style issues * feat(proto): adjust for rebase * fix(tests): bring back latency tracking * fix(tests): fix merge accident * feat(proto): skip request serialization (#4074) * feat: add reduce to star routing (#4070) * feat: add reduce on shards to head runtime * test: add reduce integration tests with fixed order * feat: add reduce on needs * chore: get_docs_matrix_from_request becomes public * style: fix overload and cli autocomplete * docs: remove undeterministic results warning * fix: fix uses_after * test: assert correct num docs after reducing in test_external_pod * test: correct asserts after reduce in test_rolling_update * fix: no reduce if uses_after_address is set * fix: get_docs_from_request only if needed * fix: fix tests after merge * refactor: move reduce from data_request_handler to head * style: fix overload and cli autocomplete * chore: apply suggestions * fix: fix asserts * chore: minor test fix * chore: apply suggestions * test: remove flow tests with external executor (pea) * fix: fix test_expected_messages_routing * fix: fix test_func_joiner * test: adapt k8s test Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * fix(k8s): fix static pool config * fix: use custom protoc doc generator image (#4088) * fix: use custom protoc doc generator image * fix(docs): minor doc improvement * fix(docs): use custom image * fix(docs): copy docarray * fix: doc building local only * fix: timeout doc building * fix: use updated args when building ContainerPea * test: add container PeaFactory test * fix: force pea close on windows (#4098) * fix: dont reduce if uses exist (#4099) * fix: dont use reduce if uses exist * fix: adjust reduce tests * fix: adjust more reduce tests * fix: fix more tests * fix: adjust more tests * fix: ignore non jina resources (#4101) * feat(executor): enable async executors (#4102) * feat(daemon): daemon flow on star routing (#4096) * test(daemon): add remote flow test * feat(daemon): call scale in daemon * feat(daemon): remove tail args and identity * test(daemon): rename scalable executor * test(daemon): add a small delay in async test * feat(daemon): scale partial flow only * feat(daemon): call scale directly in partial flow store * test(daemon): use asyncio sleep * feat(daemon): enable flow level distributed tests * test(daemon): fix jinad env workspace config * test(daemon): fix pod test use new port rolling update * feat(daemon): enable distribuetd tests * test(daemon): remove duplicate tests and zed runtime test * test(daemon): fix stores unit test * feat(daemon): enable part of distributed tests * feat(daemon): enable part of distributed tests * test: correct test paths * test(daemon): add client test for remote flows * test(daemon): send a request with jina client * test(daemon): assert async generator * test(daemon): small interval between tests * test(daemon): add flow test for container runtime * test(daemon): add flow test for container runtime * test(daemon): fix executor name * test(daemon): fix executor name * test(daemon): use async client fetch result * test(daemon): finish container flow test * test(daemon): enable distributed in ci * test(daemon): enable distributed in ci * test(daemon): decare flows and pods * test(daemon): debug ci if else * test(daemon): debug ci if else * test(daemon): decare flows and pods * test(daemon): correct test paths * test(daemon): add small delay for async tests * fix: star routing fixes (#4100) * docs: update docs * fix: fix Request.__repr__ * docs: update flow remarks * docs: fix typo * test: add non_empty_fields test * chore: remove non_empty_fields test * feat: polling per endpoint (#4111) * feat(polling): polling per endpoint configurable * fix: adjust tests * feat(polling): extend documentation * style: fix overload and cli autocomplete * fix: clean up * fix: adjust more tests * fix: remove repeat from flaky test * fix: k8s test * feat(polling): address pr feedback * feat: improve docs Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * feat(grpc): support connect grpc server via ssl tunnel (#4092) * feat(grpc): support ssl grpc connect if port is 443 * fix(grpc): use https option instead of detect port automatically * chore: fix typo * fix: update jina/peapods/networking.py Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * fix: update jina/peapods/networking.py Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * fix: update jina/peapods/networking.py Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * test(networking): add test for peapods networking * fix: address comments Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> * feat(polling): unify polling args (#4113) * fix: several issues for jinad pods (#4119) * fix: activate for jinad pods * fix: dont expose worker pod in partial daemon * fix: workspace setting * fix: containerized flows * fix: hub test * feat(daemon): remote peas on star routing (#4112) * test(daemon): fix request in peas * test(daemon): fix request in peas * test(daemon): fix sync async client test * test(daemon): enable remote peas test * test(daemon): replace send message to send request * test(daemon): declare pea tests in ci * test(daemon): use pea args fixture * test(daemon): head pea use default host * test(daemon): fix peas topologies * test(daemon): fix pseudo naming * test(daemon): use default host as host * test(daemon): fix executor path * test(daemon): add remote worker back * test(daemon): skip local remote remote topology * fix: jinad pea test setup * fix: jinad pea tests * fix: remove invalid assertion Co-authored-by: jacobowitz <tobias.jacobowitz@posteo.de> * feat: enable daemon tests again (#4132) * feat: enable daemon tests again * fix: remove bogy empty script file * fix: more jinad test fixes * style: fix overload and cli autocomplete * fix: scale and ru in jinad * fix: fix more jinad tests Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> * fix: fix flow test * fix: improve pea tests reliability (#4136) Co-authored-by: Joan Fontanals <joan.martinez@jina.ai> Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> Co-authored-by: Deepankar Mahapatro <deepankar.mahapatro@jina.ai> Co-authored-by: bwanglzu <bo.wang@jina.ai> Co-authored-by: AlaeddineAbdessalem <alaeddine-13@live.fr> Co-authored-by: Zhaofeng Miao <522856232@qq.com>

async_wait_start_success

933415bfa1f9eb89f935037014dfed816eb9815d

jina

__init__.py

https://github.com/jina-ai/jina.git

Python

async def async_wait_start_success(self): import asyncio _timeout = self.args.timeout_ready if _timeout <= 0: _timeout = None else: _timeout /= 1e3 timeout_ns = 1e9 * _timeout if _timeout else None now = time.time_ns() while timeout_ns is None or time.time_ns() - now < timeout_ns: if self.ready_or_shutdown.event.is_set(): self._check_failed_to_start() self.logger.debug(__ready_msg__) return else: await asyncio.sleep(0.1) self._fail_start_timeout(_timeout)

lib/matplotlib/offsetbox.py

def set_fontsize(self, s=None): if s is None: s = mpl.rcParams["legend.fontsize"] self.prop = FontProperties(size=s) self.stale = True

Get rcParams from mpl

set_fontsize

438d30b227b1fef7e8733578f851e76a8e360f24

matplotlib

offsetbox.py

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

Python

def set_fontsize(self, s=None): if s is None: s = mpl.rcParams["legend.fontsize"] self.prop = FontProperties(size=s) self.stale = True

python/ray/util/actor_pool.py

def get_next(self, timeout=None): if not s

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

get_next

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

actor_pool.py

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

Python

def get_next(self, timeout=None): if not self.has_next(): raise StopIteration("No more results to get") if self._next_return_index >= self._next_task_index: raise ValueError( "It is not allowed to call get_next() after " "get_next_unordered()." ) future = self._index_to_future[self._next_return_index] if timeout is not None: res, _ = ray.wait([future], timeout=timeout) if not res: raise TimeoutError("Timed out waiting for result") del self._index_to_future[self._next_return_index] self._next_return_index += 1 future_key = tuple(future) if isinstance(future, list) else future i, a = self._future_to_actor.pop(future_key) self._return_actor(a) return ray.get(future)

pipenv/environment.py

def expand_egg_links(self) -> None: prefixes = [ Path(prefix) for prefix in self.base_paths["libdirs"].split(os.pathsep) if vistir.path.is_in_path(prefix, self.prefix.as_posix()) ] for loc in prefixes: if not loc.exists(): continue for pth in loc.iterdir(): if not pth.suffix == ".egg-link": continue contents = [ vistir.path.normalize_path(line.strip()) for line in pth

Convert type comments to type annotations

expand_egg_links

4b996c0fa85824b323ad9eff3364dbe2213ebb4c

pipenv

environment.py

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

Python

def expand_egg_links(self) -> None: prefixes = [ Path(prefix) for prefix in self.base_paths["libdirs"].split(os.pathsep) if vistir.path.is_in_path(prefix, self.prefix.as_posix()) ] for loc in prefixes: if not loc.exists(): continue for pth in loc.iterdir(): if not pth.suffix == ".egg-link": continue contents = [ vistir.path.normalize_path(line.strip()) for line in pth.read_text().splitlines() ] pth.write_text("\n".join(contents))

zerver/tests/test_link_embed.py

def test_page_with_og(self) -> None:

preview: Use a dataclass for the embed data. This is significantly cleaner than passing around `Dict[str, Any]` all of the time.

test_page_with_og

327ff9ea0f5e4712a34d767fee55a549cc1d3f39

zulip

test_link_embed.py

https://github.com/zulip/zulip.git

Python

def test_page_with_og(self) -> None: html = b parser = OpenGraphParser(html, "text/html; charset=UTF-8") result = parser.extract_data() self.assertEqual(result.title, "The Rock") self.assertEqual(result.description, "The Rock film")

sympy/core/expr.py

def is_rational_function(self, *syms): if self in _illegal: return False if syms: syms = set(map(sympify, syms)) else: syms = self.free_symbols if not syms: return True return self._eval_is_rational_function(syms)

Moved definition of illegal

is_rational_function

117f9554466e08aa4178137ad65fae1f2d49b340

sympy

expr.py

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

Python

def is_rational_function(self, *syms): if self in _illegal: return False if syms: syms = set(map(sympify, syms)) else: syms = self.free_symbols if not syms: return True return self._eval_is_rational_function(syms)

sympy/polys/numberfields/primes.py

def reduce_alg_num(self, a): elt = self.ZK.parent.element_from_alg_num(a) red = self.reduce_element(elt) return a.field_element(list(reversed(red.QQ_col.f

Improve `PrimeIdeal` reduction methods.

reduce_alg_num

af44b30d68265acb25340374b648e198fb5570e7

sympy

primes.py

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

Python

def reduce_alg_num(self, a): elt = self.ZK.parent.element_from_alg_num(a) red = self.reduce_element(elt) return a.field_element(list(reversed(red.QQ_col.flat())))

paddlenlp/taskflow/task.py

def _auto_joiner(self, short_results, input_mapping, is_dict=False): concat_results = [] elem_type = {} if is_dict else [] for k, vs in input_mapping.items(): single_results = elem_type for v in vs: if len(single_results) == 0: single_results = short_results[v] elif isinstance(elem_type, list): single_results.extend(short_results[v]) elif isinstance(elem_type, dict): for sk in single_results.keys(): if isinstance(single_results[sk], str): single_results[sk] += short_results[v][sk] else: single_results[sk].extend(short_results[v][sk]) else: raise ValueError( "Invalid element type, the type of results " "for each element should be list of dict, " "but {} received.".format(t

Update Taskflow word_segmentation and ner tasks (#1666) * Add AutoSplitter & AutoJoiner * codestyle fix * unify auto joiner * add comments * add sentence split mode * update params * add paddle version check * add wordtag for word_segmentation * add wordtag for word_segmentation * add ner-lac and word_segmentation-jieba * add return entities only for ner * fix ci * fix ci * fix ci * fix ci * fix ci * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * fix bugs of dataloader * remove guard * use fast mode for rnn example * Update README.md * Update README.md

_auto_joiner

1e2ee01dade0d4076ba98aa613c3eb150c615abb

PaddleNLP

task.py

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

Python

def _auto_joiner(self, short_results, input_mapping, is_dict=False): concat_results = [] elem_type = {} if is_dict else [] for k, vs in input_mapping.items(): single_results = elem_type for v in vs: if len(single_results) == 0: single_results = short_results[v] elif isinstance(elem_type, list): single_results.extend(short_results[v]) elif isinstance(elem_type, dict): for sk in single_results.keys(): if isinstance(single_results[sk], str): single_results[sk] += short_results[v][sk] else: single_results[sk].extend(short_results[v][sk]) else: raise ValueError( "Invalid element type, the type of results " "for each element should be list of dict, " "but {} received.".format(type(single_results))) concat_results.append(single_results) return concat_results

keras/applications/efficientnet_weight_update_util.py

def get_variable_names_from_ckpt(path_ckpt, use_ema=True): v_all = tf.train.list_variables(path_ckpt) # keep name only v_name_all = [x[0] for x in v_all] if use_ema: v_name_all = [x for x in v_name_all if "ExponentialMovingAverage" in x] else: v_name_all = [ x for x in v_name_all if "ExponentialMovingAverage" not in x ] # remove util variables used for RMSprop v_

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

get_variable_names_from_ckpt

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

efficientnet_weight_update_util.py

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

Python

def get_variable_names_from_ckpt(path_ckpt, use_ema=True): v_all = tf.train.list_variables(path_ckpt) # keep name only v_name_all = [x[0] for x in v_all] if use_ema: v_name_all = [x for x in v_name_all if "ExponentialMovingAverage" in x] else: v_name_all = [ x for x in v_name_all if "ExponentialMovingAverage" not in x ] # remove util variables used for RMSprop v_name_all = [x for x in v_name_all if "RMS" not in x] return v_name_all

sympy/solvers/deutils.py

def ode_order(expr, func):

Improve loop performance in solvers

ode_order

bd9f607176c58dfba01e27c05c2b7d49ff97c901

sympy

deutils.py

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

Python

def ode_order(expr, func): a = Wild('a', exclude=[func]) if expr.match(a): return 0 if isinstance(expr, Derivative): if expr.args[0] == func: return len(expr.variables) else: return max(ode_order(arg, func) for arg in expr.args[0].args) + len(expr.variables) else: return max(ode_order(arg, func) for arg in expr.args)

homeassistant/components/discord/notify.py

async def async_send_message(self, message, **kwargs): nextcord.VoiceClient.warn_nacl = False discord_bot = nextcord.Client() images = None embedding = None if ATTR_TARGET not in kwargs: _LOGGER.error("No target specified") return None data = kwargs.get(ATTR_DATA) or {} embeds: list[nextcord.Embed] = [] if ATTR_EMBED in data: embedding = data[ATTR_EMBED] fields = embedding.get(ATTR_EMBED_FIELDS) or [] if embedding: embed = nextcord.Embed(**embedding) for field in fields: embed.add_field(**field) if ATTR_EMBED_FOOTER in embedding: embed.set_footer(**embedding[ATTR_EMBED_FOOTER]) if ATTR_EMBED_AUTHOR in embedding: embed.set_author(**embedding[ATTR_EMBED_AUTHOR]) if ATTR_EMBED_THUMBNAIL in embedding: embed.set_thumbnail(**embedding[ATTR_EMBED_THUMBNAIL]) embeds.append(embed) if ATTR_IMAGES in data: images = [] for image in data.get(ATTR_IMAGES, []): image_exists = await self.hass.async_add_executor_job( self.file_exists, image ) if image_exists: images.

Replace discord.py with nextcord (#66540) * Replace discord.py with nextcord * Typing tweak * Another pip check decrease :)

async_send_message

cb03db8df4bf8b50945b36a4b0debcaaed1190a8

core

notify.py

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

Python

async def async_send_message(self, message, **kwargs): nextcord.VoiceClient.warn_nacl = False discord_bot = nextcord.Client() images = None embedding = None if ATTR_TARGET not in kwargs: _LOGGER.error("No target specified") return None data = kwargs.get(ATTR_DATA) or {} embeds: list[nextcord.Embed] = [] if ATTR_EMBED in data: embedding = data[ATTR_EMBED] fields = embedding.get(ATTR_EMBED_FIELDS) or [] if embedding: embed = nextcord.Embed(**embedding) for field in fields: embed.add_field(**field) if ATTR_EMBED_FOOTER in embedding: embed.set_footer(**embedding[ATTR_EMBED_FOOTER]) if ATTR_EMBED_AUTHOR in embedding: embed.set_author(**embedding[ATTR_EMBED_AUTHOR]) if ATTR_EMBED_THUMBNAIL in embedding: embed.set_thumbnail(**embedding[ATTR_EMBED_THUMBNAIL]) embeds.append(embed) if ATTR_IMAGES in data: images = [] for image in data.get(ATTR_IMAGES, []): image_exists = await self.hass.async_add_executor_job( self.file_exists, image ) if image_exists: images.append(image) else: _LOGGER.warning("Image not found: %s", image) await discord_bot.login(self.token) try: for channelid in kwargs[ATTR_TARGET]: channelid = int(channelid) try: channel = await discord_bot.fetch_channel(channelid) except nextcord.NotFound: try: channel = await discord_bot.fetch_user(channelid) except nextcord.NotFound: _LOGGER.warning("Channel not found for ID: %s", channelid) continue # Must create new instances of File for each channel. files = [nextcord.File(image) for image in images] if images else [] await channel.send(message, files=files, embeds=embeds) except (nextcord.HTTPException, nextcord.NotFound) as error: _LOGGER.warning("Communication error: %s", error) await discord_bot.close()

@pytest.fixture( params=[ ( Interval(left=0, right=5, inclusive="right"), IntervalDtype("int64", inclusive="right"), ), ( Interval(left=0.1, right=0.5, inclusive="right"), IntervalDtype("float64", inclusive="right"), ), (Period("2012-01", freq="M"), "period[M]"), (Period("2012-02-01", freq="D"), "period[D]"), ( Timestamp("2011-01-01", tz="US/Eastern"), DatetimeTZDtype(tz="US/Eastern"), ), (Timedelta(seconds=500), "timedelta64[ns]"), ] )

pandas/conftest.py

def rand_series_with_duplicate_datetimeindex() -> Series: dates = [ datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 3), datetime(2000, 1, 3), datetime(2000, 1, 3), datetime(2000, 1, 4), datetime(2000, 1, 4), datetime(2000, 1, 4), datetime(2000, 1, 5), ] return Series(np.random.randn(len(dates)), index=dates) # ---------------------------------------------------------------- # Scalars # ---------------------------------------------------------------- @pytest.fixture( params=[ ( Interval(left=0, right=5, inclusive="right"), IntervalDtype("int64", inclusive="right"), ), ( Interval(lef

TYP: misc return type annotations (#47558)

rand_series_with_duplicate_datetimeindex

f538568afc2c76c2d738d32e3544cf9fe6742960

pandas

conftest.py

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

Python

def rand_series_with_duplicate_datetimeindex() -> Series: dates = [ datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 3), datetime(2000, 1, 3), datetime(2000, 1, 3), datetime(2000, 1, 4), datetime(2000, 1, 4), datetime(2000, 1, 4), datetime(2000, 1, 5), ] return Series(np.random.randn(len(dates)), index=dates) # ---------------------------------------------------------------- # Scalars # ---------------------------------------------------------------- @pytest.fixture( params=[ ( Interval(left=0, right=5, inclusive="right"), IntervalDtype("int64", inclusive="right"), ), ( Interval(left=0.1, right=0.5, inclusive="right"), IntervalDtype("float64", inclusive="right"), ), (Period("2012-01", freq="M"), "period[M]"), (Period("2012-02-01", freq="D"), "period[D]"), ( Timestamp("2011-01-01", tz="US/Eastern"), DatetimeTZDtype(tz="US/Eastern"), ), (Timedelta(seconds=500), "timedelta64[ns]"), ] )

.venv/lib/python3.8/site-packages/pip/_internal/locations/base.py

def get_major_minor_version(): # typ

upd; format

get_major_minor_version

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

base.py

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

Python

def get_major_minor_version(): # type: () -> str return "{}.{}".format(*sys.version_info)

tests/test_models/test_dense_heads/test_ssd_head.py

def test_ssd_head_loss(self): s = 300 img_metas = [{ 'img_shape': (s, s, 3), 'scale_factor': 1, }] cfg = Config( dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0., ignore_iof_thr=-1, gt_max_assign_all=False), sampler=dict(type='PseudoSampler'), smoothl1_beta=1., allowed_border=-1, pos_weight=-1, neg_pos_ratio=3, debug=False)) ssd_head = SSDHe

Update SSD and PISA-SSD model config

test_ssd_head_loss

9d7511d8c35df1f9c13b17eb770136859bf370be

mmdetection

test_ssd_head.py

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

Python

def test_ssd_head_loss(self): s = 300 img_metas = [{ 'img_shape': (s, s, 3), 'scale_factor': 1, }] cfg = Config( dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0., ignore_iof_thr=-1, gt_max_assign_all=False), sampler=dict(type='PseudoSampler'), smoothl1_beta=1., allowed_border=-1, pos_weight=-1, neg_pos_ratio=3, debug=False)) ssd_head = SSDHead( num_classes=4, in_channels=(1, 1, 1, 1, 1, 1), stacked_convs=1, feat_channels=1, use_depthwise=True, anchor_generator=dict( type='SSDAnchorGenerator', scale_major=False, input_size=s, basesize_ratio_range=(0.15, 0.9), strides=[8, 16, 32, 64, 100, 300], ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), train_cfg=cfg) # SSD head expects a multiple levels of features per image feats = ( torch.rand(1, 1, ceil(s / stride[0]), ceil(s / stride[0])) for stride in ssd_head.prior_generator.strides) cls_scores, bbox_preds = ssd_head.forward(feats) # Test that empty ground truth encourages the network to # predict background gt_instances = InstanceData() gt_instances.bboxes = torch.empty((0, 4)) gt_instances.labels = torch.LongTensor([]) empty_gt_losses = ssd_head.loss(cls_scores, bbox_preds, [gt_instances], img_metas) # When there is no truth, cls_loss and box_loss should all be zero. empty_cls_loss = sum(empty_gt_losses['loss_cls']) empty_box_loss = sum(empty_gt_losses['loss_bbox']) self.assertEqual( empty_cls_loss.item(), 0, 'there should be no cls loss when there are no true boxes') self.assertEqual( empty_box_loss.item(), 0, 'there should be no box loss when there are no true boxes') # When truth is non-empty then both cls and box loss # should be nonzero for random inputs gt_instances = InstanceData() gt_instances.bboxes = torch.Tensor( [[23.6667, 23.8757, 238.6326, 151.8874]]) gt_instances.labels = torch.LongTensor([2]) one_gt_losses = ssd_head.loss(cls_scores, bbox_preds, [gt_instances], img_metas) onegt_cls_loss = sum(one_gt_losses['loss_cls']) onegt_box_loss = sum(one_gt_losses['loss_bbox']) self.assertGreater(onegt_cls_loss.item(), 0, 'cls loss should be non-zero') self.assertGreater(onegt_box_loss.item(), 0, 'box loss should be non-zero')

tools/alignments/jobs.py

def _legacy_check(self) -> None: if self._min_size > 0 or self._arguments.extract_every_n != 1: logger.warning("This alignments file was generated with the legacy extraction method.") logger.warning("You should run this extraction job, but with 'min_size' set to 0 and " "'extract-every-n' set to 1 to update the alignments file.") logger.warning("You can then re-run this extraction job with your chosen options.") sys.exit(0) maskers = ["components", "extended"] nn_masks = [mask for mask in list(self._alignments.mask_summary) if mask not in maskers] logtype = logger.warning if nn_masks else logger.info logtype("This alignments file was created with the legacy extraction method and will be " "updated.") logtype("Faces will be extracted using the new method and landmarks based masks will be " "regenerated.") if nn_masks: logtype("However, the NN based masks '%s' will be cropped to the legacy extraction " "method, so you may want to run the mask tool to regenerate these " "masks.", "', '".join(nn_masks)) self._mask_pipeline = Extractor(None, None, maskers, multiproce

Alignments tool - Replace 'extract-large' with 'min-size'

_legacy_check

a9908b46f77dc66ac7efe7100ea0eed4b1f2b460

faceswap

jobs.py

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

Python

def _legacy_check(self) -> None: if self._min_size > 0 or self._arguments.extract_every_n != 1: logger.warning("This alignments file was generated with the legacy extraction method.") logger.warning("You should run this extraction job, but with 'min_size' set to 0 and " "'extract-every-n' set to 1 to update the alignments file.") logger.warning("You can then re-run this extraction job with your chosen options.") sys.exit(0) maskers = ["components", "extended"] nn_masks = [mask for mask in list(self._alignments.mask_summary) if mask not in maskers] logtype = logger.warning if nn_masks else logger.info logtype("This alignments file was created with the legacy extraction method and will be " "updated.") logtype("Faces will be extracted using the new method and landmarks based masks will be " "regenerated.") if nn_masks: logtype("However, the NN based masks '%s' will be cropped to the legacy extraction " "method, so you may want to run the mask tool to regenerate these " "masks.", "', '".join(nn_masks)) self._mask_pipeline = Extractor(None, None, maskers, multiprocess=True) self._mask_pipeline.launch() # Update alignments versioning self._alignments._version = _VERSION # pylint:disable=protected-access

sympy/stats/crv_types.py

def FisherZ(name, d1, d2): r return rv(name, FisherZDistribution, (d1, d2)) #------------------------------------------------------------------------------- # Frechet distribution -----

Documentation cleanup 5

FisherZ

9ad8ab9fe58051cf11626ba6654852fcfec60147

sympy

crv_types.py

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

Python

def FisherZ(name, d1, d2): r return rv(name, FisherZDistribution, (d1, d2)) #------------------------------------------------------------------------------- # Frechet distribution ---------------------------------------------------------

src/transformers/trainer_pt_utils.py

def find_batch_size(tensors): if isinstance(tensors, (list, tuple)): for t in tensors: result = find_batch_size(t) if result is not None: return result elif isinstance(tensors, Mapping): for key, value in tensors.items(): result = find_batch_size(value) if result is not None: return result elif isinstance(tensors, torch.Tensor): return tensors.shape[0] if len(tensor

Replace dict/BatchEncoding instance checks by Mapping (#17014) * Replace dict/BatchEncoding instance checks by Mapping * Typo

find_batch_size

18df440709f1b19d1c5617c0d987c5ff8fd0915d

transformers

trainer_pt_utils.py

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

Python

def find_batch_size(tensors): if isinstance(tensors, (list, tuple)): for t in tensors: result = find_batch_size(t) if result is not None: return result elif isinstance(tensors, Mapping): for key, value in tensors.items(): result = find_batch_size(value) if result is not None: return result elif isinstance(tensors, torch.Tensor): return tensors.shape[0] if len(tensors.shape) >= 1 else None elif isinstance(tensors, np.ndarray): return tensors.shape[0] if len(tensors.shape) >= 1 else None

src/transformers/models/yolos/feature_extraction_yolos.py

def post_process_segmentation(self, outputs, target_sizes, threshold=0.9, mask_threshold=0.5): out_logits, raw_masks = outputs.logits, outputs.pred_masks preds = []

Add YOLOS (#16848) * First draft * Add YolosForObjectDetection * Make forward pass work * Add mid position embeddings * Add interpolation of position encodings * Add expected values * Add YOLOS to tests * Add integration test * Support tiny model as well * Support all models in conversion script * Remove mid_pe_size attribute * Make more tests pass * Add model to README and fix config * Add copied from statements * Rename base_model_prefix to vit * Add missing YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP * Apply suggestions from code review * Apply more suggestions from code review * Convert remaining checkpoints * Improve docstrings * Add YolosFeatureExtractor * Add feature extractor to docs * Add corresponding tests * Fix style * Fix docs * Apply suggestion from code review * Fix bad rebase * Fix some more bad rebase * Fix missing character * Improve docs and variable names Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

post_process_segmentation

1ac698744c4dbdf1495d303246d08ffacdf4f5b8

transformers

feature_extraction_yolos.py

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

Python

def post_process_segmentation(self, outputs, target_sizes, threshold=0.9, mask_threshold=0.5): out_logits, raw_masks = outputs.logits, outputs.pred_masks preds = []

sympy/combinatorics/subsets.py

def iterate_graycode(self, k): unranked_code = GrayCode.unrank(self.superset_size,

Updated import locations

iterate_graycode

498015021131af4dbb07eb110e5badaba8250c7b

sympy

subsets.py

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

Python

def iterate_graycode(self, k): unranked_code = GrayCode.unrank(self.superset_size, (self.rank_gray + k) % self.cardinality) return Subset.subset_from_bitlist(self.superset, unranked_code)

keras/layers/rnn/gru_lstm_utils.py

def is_sequence_right_padded(mask): max_seq_length = tf.shape(mask)[1] count_of_true = tf.reduce_sum(tf.cast(mask, tf.int32), axis=1) right_padded_mask = tf.sequence_mask(count_of_true, maxlen=max_seq_length)

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

is_sequence_right_padded

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

gru_lstm_utils.py

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

Python

def is_sequence_right_padded(mask): max_seq_length = tf.shape(mask)[1] count_of_true = tf.reduce_sum(tf.cast(mask, tf.int32), axis=1) right_padded_mask = tf.sequence_mask(count_of_true, maxlen=max_seq_length) return tf.reduce_all(tf.equal(mask, right_padded_mask))

tests/rest/client/test_third_party_rules.py

def _send_event_over_federation(self) -> None: body = { "pdus": [ { "sender": self.user_id, "type": EventTypes.Message, "state_key": "", "content": {"body": "hello world", "msgtype": "m.text"}, "room_id": self.room_id, "depth": 0, "origin_server_ts": self.clock.time_msec(),

Tests: replace mocked Authenticator with the real thing (#11913) If we prepopulate the test homeserver with a key for a remote homeserver, we can make federation requests to it without having to stub out the authenticator. This has two advantages: * means that what we are testing is closer to reality (ie, we now have complete tests for the incoming-request-authorisation flow) * some tests require that other objects be signed by the remote server (eg, the event in `/send_join`), and doing that would require a whole separate set of mocking out. It's much simpler just to use real keys.

_send_event_over_federation

c3db7a0b59d48b8872bc24096f9a2467ef35f703

synapse

test_third_party_rules.py

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

Python

def _send_event_over_federation(self) -> None: body = { "pdus": [ { "sender": self.user_id, "type": EventTypes.Message, "state_key": "", "content": {"body": "hello world", "msgtype": "m.text"}, "room_id": self.room_id, "depth": 0, "origin_server_ts": self.clock.time_msec(), "prev_events": [], "auth_events": [], "signatures": {}, "unsigned": {}, } ], } channel = self.make_signed_federation_request( method="PUT", path="/_matrix/federation/v1/send/1", content=body, ) self.assertEqual(channel.code, 200, channel.result)

plugins/train/trainer/_base.py

def _print_loss(self, loss): output = ", ".join([f"Loss {side}: {side_loss:.5f}" for side, side_loss in zip(("A", "B"), loss)]) timestamp = time.strftime("%H:%M:%S") output = f"[{timestamp}] [#{self._model.iterations:05d}] {output}"

Update code to support Tensorflow versions up to 2.8 (#1213) * Update maximum tf version in setup + requirements * - bump max version of tf version in launcher - standardise tf version check * update keras get_custom_objects for tf>2.6 * bugfix: force black text in GUI file dialogs (linux) * dssim loss - Move to stock tf.ssim function * Update optimizer imports for compatibility * fix logging for tf2.8 * Fix GUI graphing for TF2.8 * update tests * bump requirements.txt versions * Remove limit on nvidia-ml-py * Graphing bugfixes - Prevent live graph from displaying if data not yet available * bugfix: Live graph. Collect loss labels correctly * fix: live graph - swallow inconsistent loss errors * Bugfix: Prevent live graph from clearing during training * Fix graphing for AMD

_print_loss

c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf

faceswap

_base.py

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

Python

def _print_loss(self, loss): output = ", ".join([f"Loss {side}: {side_loss:.5f}" for side, side_loss in zip(("A", "B"), loss)]) timestamp = time.strftime("%H:%M:%S") output = f"[{timestamp}] [#{self._model.iterations:05d}] {output}" print(f"\r{output}", end="")

python3.10.4/Lib/calendar.py

def itermonthdays2(self, year, month): for i, d in enumerate(self.itermonthdays(year, mont

add python 3.10.4 for windows

itermonthdays2

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

calendar.py

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

Python

def itermonthdays2(self, year, month): for i, d in enumerate(self.itermonthdays(year, month), self.firstweekday): yield d, i % 7

tests/helpers/test_template.py

def test_distance_function_return_none_if_invalid_state(hass): hass.states.async_set("test.object_2", "happy", {"latitude": 10}) tpl = template.Template("{{ distance(states.test.object_2) | round }}", hass) with pytes

Fail template functions when no default specified (#71687)

test_distance_function_return_none_if_invalid_state

4885331509eeffe50f42d76b234996467b06170f

core

test_template.py

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

Python

def test_distance_function_return_none_if_invalid_state(hass): hass.states.async_set("test.object_2", "happy", {"latitude": 10}) tpl = template.Template("{{ distance(states.test.object_2) | round }}", hass) with pytest.raises(TemplateError): tpl.async_render()

elif sys.version_info[:2] >= (3, 7):sys

pipenv/patched/notpip/_vendor/typing_extensions.py

def Concatenate(self, parameters): return _con

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

Concatenate

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

typing_extensions.py

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

Python

def Concatenate(self, parameters): return _concatenate_getitem(self, parameters) # 3.7-8 elif sys.version_info[:2] >= (3, 7):

tests/ann_test.py

def compute_recall(result_neighbors, ground_truth_neighbors) -> float:

[JAX] Move ann.ann_recall back to tests. The function is simple enough for users to implement their own on the host. PiperOrigin-RevId: 430696789

compute_recall

8372b98c4856b6b2363b7bb28abdb4579440a656

jax

ann_test.py

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

Python

def compute_recall(result_neighbors, ground_truth_neighbors) -> float: assert len( result_neighbors.shape) == 2, "shape = [num_queries, neighbors_per_query]" assert len(ground_truth_neighbors.shape ) == 2, "shape = [num_queries, ground_truth_neighbors_per_query]" assert result_neighbors.shape[0] == ground_truth_neighbors.shape[0] gt_sets = [set(np.asarray(x)) for x in ground_truth_neighbors] hits = sum( len(list(x for x in nn_per_q if x.item() in gt_sets[q])) for q, nn_per_q in enumerate(result_neighbors)) return hits / ground_truth_neighbors.size

pandas/core/arrays/datetimes.py

def isocalendar(self) -> DataFrame: from pandas import DataFrame values = self._local_timestamps() sarray = fields.build_isocalendar_sarray(values) iso_calendar_df = DataFrame( sarray, columns=["year", "week", "day"], dtype="UInt32" ) if self._hasna: iso_calendar_df.iloc[self._isnan] = None

EA interface: rename ExtensionArray._hasnans to ._hasna (#45519)

isocalendar

a0b40c0f2ad73420a54e48ec4f564b9667e3f452

pandas

datetimes.py

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

Python

def isocalendar(self) -> DataFrame: from pandas import DataFrame values = self._local_timestamps() sarray = fields.build_isocalendar_sarray(values) iso_calendar_df = DataFrame( sarray, columns=["year", "week", "day"], dtype="UInt32" ) if self._hasna: iso_calendar_df.iloc[self._isnan] = None return iso_calendar_df

src/PIL/Image.py

def putpixel(self, xy, value): if self.readonly: self._copy() self.load() if self.pyaccess: return self.pyaccess.putpixel(xy, value) if ( self.mode in ("P", "PA") and isinstance(value, (list, tuple)) and len(value) in [3, 4] ): # RGB or RGBA value for a P or PA image if self.mode == "PA": alpha = value[3] if len(value) == 4 else 255 valu

Allow RGB and RGBA values for PA image putpixel

putpixel

a37593f004247ebf69d5582524da6dc5143cb023

Pillow

Image.py

https://github.com/python-pillow/Pillow.git

Python

def putpixel(self, xy, value): if self.readonly: self._copy() self.load() if self.pyaccess: return self.pyaccess.putpixel(xy, value) if ( self.mode in ("P", "PA") and isinstance(value, (list, tuple)) and len(value) in [3, 4] ): # RGB or RGBA value for a P or PA image if self.mode == "PA": alpha = value[3] if len(value) == 4 else 255 value = value[:3] value = self.palette.getcolor(value, self) if self.mode == "PA": value = (value, alpha) return self.im.putpixel(xy, value)

tests/components/template/test_switch.py

async def test_available_template_with_entities(hass): await setup.async_setup_component( hass, "switch", { "switch": { "platform": "template", "switches": { "test_template_switch": { **OPTIMISTIC_SWITCH_CONFIG,

Tweak template switch tests (#71738)

test_available_template_with_entities

11cc1feb853bcfd9633ebfc44eae142c10a7f983

core

test_switch.py

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

Python

async def test_available_template_with_entities(hass): await setup.async_setup_component( hass, "switch", { "switch": { "platform": "template", "switches": { "test_template_switch": { **OPTIMISTIC_SWITCH_CONFIG, "value_template": "{{ 1 == 1 }}", "availability_template": "{{ is_state('availability_state.state', 'on') }}", } }, } }, ) await hass.async_block_till_done() await hass.async_start() await hass.async_block_till_done() hass.states.async_set("availability_state.state", STATE_ON) await hass.async_block_till_done() assert hass.states.get("switch.test_template_switch").state != STATE_UNAVAILABLE hass.states.async_set("availability_state.state", STATE_OFF) await hass.async_block_till_done() assert hass.states.get("switch.test_template_switch").state == STATE_UNAVAILABLE

bots/etf/tops.py

def etfs_disc_command(sort=""): # Debug i

Discord bot massive improvement (#1481) * allow logs feature flag * Adding log collection md * upload last log at startup * additions/refractor * refactor * lint/black ++ * disc * TimeRotating Logger and upload to s3 * corrected regex error * makeup for config * logging/disc/sia/etf/++ * append .log before uploading * process to upload logs to s3 * candle ta/etfmcds * fix * ta candles * implement presignedURL * fixed regex * ma's in 1 cmd, delete older files * refactor ta candle * updates * black * moon? * Logger uploader * rotate every hour * only archive if successful * chavis suggestions * windows * ta * commands_dict update * discord tacmds * log_collection error fix * fix * fix * pylint * bb fix * only log filesize * fixes * discord logs * Delete log_collection.md * fixes for other bots on images * bots image upload fix * updated helpers/load candle * more ta cc/housekeeping/refactors/slashcmds * update bots cmds_dict * adjustments to font size/fixes * test fixs/disc earnings * missed a spot * fixes had > revesred * reversed the >< again oops * remove logger branch code blocking tests * black fix * fix missing sources in docstr/daily candle dt tz * load_candle refactor with docstring * moved insiders to disc * Lucas logging changes * Fixing log_collection.md * testing scenario * more ta converted * more ta * Update config_terminal : remove print of verbosity * table cfg/fix matplt/ screener + * fix * what's sleep? 1 more line.. or 2. scr df2img * juan more. fix news 1m chart issue * ticker.upper() fixes * Update log collection * Updating log collection - change tmp folder Co-authored-by: LBolte29 <lbolte@gmx.net> Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: LBolte29 <97528701+LBolte29@users.noreply.github.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt>

etfs_disc_command

50cafd500ece43df98e3cf076d81084b2806ea03

OpenBBTerminal

tops.py

https://github.com/OpenBB-finance/OpenBBTerminal.git

Python

def etfs_disc_command(sort=""): # Debug if cfg.DEBUG: logger.debug("etfs") df_etfs = wsj_model.etf_movers(sort, export=True) if df_etfs.empty: raise Exception("No available data found") df_etfs.set_index(" ", inplace=True) prfx = "Top" if sort == "active": prfx = "Most" title = f"ETF Movers ({prfx} {sort.capitalize()})" dindex = len(df_etfs.index) if dindex > 15: embeds: list = [] # Output i, i2, end = 0, 0, 15 df_pg, embeds_img, images_list = [], [], [] while i < dindex: df_pg = df_etfs.iloc[i:end] df_pg.append(df_pg) fig = df2img.plot_dataframe( df_pg, fig_size=(1200, (40 + (40 * dindex))), col_width=[1, 9, 1.5, 1.5, 1.5, 1.5], tbl_header=cfg.PLT_TBL_HEADER, tbl_cells=cfg.PLT_TBL_CELLS, font=cfg.PLT_TBL_FONT, row_fill_color=cfg.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["left"]))) imagefile = "disc-etfs.png" imagefile = helpers.save_image(imagefile, fig) if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": image_link = cfg.IMAGES_URL + imagefile images_list.append(imagefile) else: imagefile_save = cfg.IMG_DIR / imagefile uploaded_image = gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link os.remove(imagefile_save) embeds_img.append( f"{image_link}", ) embeds.append( disnake.Embed( title=title, colour=cfg.COLOR, ), ) i2 += 1 i += 15 end += 15 # Author/Footer for i in range(0, i2): embeds[i].set_author( name=cfg.AUTHOR_NAME, url=cfg.AUTHOR_URL, icon_url=cfg.AUTHOR_ICON_URL, ) embeds[i].set_footer( text=cfg.AUTHOR_NAME, icon_url=cfg.AUTHOR_ICON_URL, ) i = 0 for i in range(0, i2): embeds[i].set_image(url=embeds_img[i]) i += 1 embeds[0].set_footer(text=f"Page 1 of {len(embeds)}") choices = [ disnake.SelectOption(label="Home", value="0", emoji="🟢"), ] output = { "view": Menu, "title": title, "embed": embeds, "choices": choices, "embeds_img": embeds_img, "images_list": images_list, } else: fig = df2img.plot_dataframe( df_etfs, fig_size=(1200, (40 + (40 * dindex))), col_width=[1, 9, 1.5, 1.5, 1.5, 1.5], tbl_header=cfg.PLT_TBL_HEADER, tbl_cells=cfg.PLT_TBL_CELLS, font=cfg.PLT_TBL_FONT, row_fill_color=cfg.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["left"]))) imagefile = helpers.save_image("disc-etfs.png", fig) output = { "title": title, "imagefile": imagefile, } return output

modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py

def get(self):

FEAT-#4371: Add logging to Modin (#4372) Co-authored-by: Devin Petersohn <devin.petersohn@gmail.com> Co-authored-by: Mahesh Vashishtha <mvashishtha@users.noreply.github.com> Co-authored-by: Anatoly Myachev <anatoliimyachev@mail.com> Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru> Signed-off-by: Naren Krishna <naren@ponder.io>

get

49fc2cf3733f20ac6cf8a7c61e42ef7aa5cf4b03

modin

partition.py

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

Python

def get(self): logger = get_logger() logger.debug(f"ENTER::Partition.get::{self._identity}") if len(self.call_queue): self.drain_call_queue() result = ray.get(self.oid) logger.debug(f"EXIT::Partition.get::{self._identity}") return result

@pytest.fixture

tests/sensors/test_external_task_sensor.py

def dag_bag_ext(): clear_db_runs() dag_bag = DagBag(dag_folder=DEV_NULL, include_examples=False) dag_0 = DAG("dag_0", start_date=DEFAULT_DATE, schedule_interval=None) task_a_0 = EmptyOperator(task_id="task_a_0", dag=dag_0) task_b_0 = ExternalTaskMarker( task_id="task_b_0", external_dag_id="dag_1", external_task_id="task_a_1", recursion_depth=3, dag=dag_0 ) task_a_0 >> task_b_0 dag_1 = DAG("dag_1", start_date=DEFAULT_DATE, schedule_interval=None) task_a_1 = ExternalTaskSensor( task_id="task_a_1", external_dag_id=dag_0.dag_id, external_task_id=task_b_0.task_id, dag=dag_1 ) task_b_1 = ExternalTaskMarker( task_id="task_b_1", external_dag_id="dag_2", external_task_id="task_a_2", recursion_depth=2, dag=dag_1 ) task_a_1 >> task_b_1 dag_2 = DAG("dag_2", start_date=DEFAULT_DATE, schedule_interval=None) task_a_2 = ExternalTaskSensor( task_id="task_a_2", external_dag_id=dag_1.dag_id, external_task_id=task_b_1.task_id, dag=dag_2 ) task_b_2 = ExternalTaskMarker( task_id="task_b_2", external_dag_id="dag_3", external_task_id="task_a_3", recursion_depth=1, dag=dag_2 ) task_a_2 >> task_b_2 dag_3 = DAG("dag_3", start_date=DEFAULT_DATE, schedule_interval=None) task_a_3 = ExternalTaskSensor( task_id="task_a_3", external_dag_id=dag_2.

Replace usage of `DummyOperator` with `EmptyOperator` (#22974) * Replace usage of `DummyOperator` with `EmptyOperator`

dag_bag_ext

49e336ae0302b386a2f47269a6d13988382d975f

airflow

test_external_task_sensor.py

https://github.com/apache/airflow.git

Python

def dag_bag_ext(): clear_db_runs() dag_bag = DagBag(dag_folder=DEV_NULL, include_examples=False) dag_0 = DAG("dag_0", start_date=DEFAULT_DATE, schedule_interval=None) task_a_0 = EmptyOperator(task_id="task_a_0", dag=dag_0) task_b_0 = ExternalTaskMarker( task_id="task_b_0", external_dag_id="dag_1", external_task_id="task_a_1", recursion_depth=3, dag=dag_0 ) task_a_0 >> task_b_0 dag_1 = DAG("dag_1", start_date=DEFAULT_DATE, schedule_interval=None) task_a_1 = ExternalTaskSensor( task_id="task_a_1", external_dag_id=dag_0.dag_id, external_task_id=task_b_0.task_id, dag=dag_1 ) task_b_1 = ExternalTaskMarker( task_id="task_b_1", external_dag_id="dag_2", external_task_id="task_a_2", recursion_depth=2, dag=dag_1 ) task_a_1 >> task_b_1 dag_2 = DAG("dag_2", start_date=DEFAULT_DATE, schedule_interval=None) task_a_2 = ExternalTaskSensor( task_id="task_a_2", external_dag_id=dag_1.dag_id, external_task_id=task_b_1.task_id, dag=dag_2 ) task_b_2 = ExternalTaskMarker( task_id="task_b_2", external_dag_id="dag_3", external_task_id="task_a_3", recursion_depth=1, dag=dag_2 ) task_a_2 >> task_b_2 dag_3 = DAG("dag_3", start_date=DEFAULT_DATE, schedule_interval=None) task_a_3 = ExternalTaskSensor( task_id="task_a_3", external_dag_id=dag_2.dag_id, external_task_id=task_b_2.task_id, dag=dag_3 ) task_b_3 = EmptyOperator(task_id="task_b_3", dag=dag_3) task_a_3 >> task_b_3 for dag in [dag_0, dag_1, dag_2, dag_3]: dag_bag.bag_dag(dag=dag, root_dag=dag) yield dag_bag clear_db_runs() @pytest.fixture

@image_comparison(['constrained_layout4.png'])

lib/matplotlib/tests/test_constrainedlayout.py

def test_constrained_layout3(): fig, axs = plt.subplots(2, 2, layout="constrained") for nn, ax in enumerate(axs.flat): pcm = example_pcolor(ax, fontsize=24) if nn == 3: pad = 0.08 else: pad = 0.02 # default fig.colorbar(pcm, ax=ax, pad=pad) @image_comparison(['constraine

ENH: implement and use base layout_engine for more flexible layout.

test_constrained_layout3

ec4dfbc3c83866f487ff0bc9c87b0d43a1c02b22

matplotlib

test_constrainedlayout.py

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

Python

def test_constrained_layout3(): fig, axs = plt.subplots(2, 2, layout="constrained") for nn, ax in enumerate(axs.flat): pcm = example_pcolor(ax, fontsize=24) if nn == 3: pad = 0.08 else: pad = 0.02 # default fig.colorbar(pcm, ax=ax, pad=pad) @image_comparison(['constrained_layout4.png'])

pandas/core/groupby/groupby.py

def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]: return self.grouper.indi

TYP: more return annotations in core/ (#47618) * TYP: more return annotations in core/ * from __future__ import annotations * more __future__

indices

f65417656ba8c59438d832b6e2a431f78d40c21c

pandas

groupby.py

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

Python

def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]: return self.grouper.indices

.venv/lib/python3.8/site-packages/pip/_vendor/distlib/database.py

def get_hash(self, data, hasher=None): if

upd; format

get_hash

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

database.py

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

Python

def get_hash(self, data, hasher=None): if hasher is None: hasher = self.hasher if hasher is None: hasher = hashlib.md5 prefix = '' else: hasher = getattr(hashlib, hasher) prefix = '%s=' % self.hasher digest = hasher(data).digest() digest = base64.urlsafe_b64encode(digest).rstrip(b'=').decode('ascii') return '%s%s' % (prefix, digest)

rllib/utils/tests/test_actor_manager.py

def test_async_call_same_actor_multiple_times(self): actors = [Actor.remote(i, maybe_crash=False) for i in range(4)] manager = FaultTolerantActorManager(actors=actors) # 2 asynchronous call to actor 0. num_of_calls = manager.foreach_actor_async( lambda w: w.call(), h

[RLlib] Introduce FaultTolerantActorManager (#29703) Signed-off-by: Jun Gong <jungong@anyscale.com>

test_async_call_same_actor_multiple_times

d329147ae28c57b290f6b932f9f3044523f67c4e

ray

test_actor_manager.py

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

Python

def test_async_call_same_actor_multiple_times(self): actors = [Actor.remote(i, maybe_crash=False) for i in range(4)] manager = FaultTolerantActorManager(actors=actors) # 2 asynchronous call to actor 0. num_of_calls = manager.foreach_actor_async( lambda w: w.call(), healthy_only=False, remote_actor_indices=[0, 0], ) self.assertEqual(num_of_calls, 2) # Now, let's actually fetch the results. results = manager.fetch_ready_async_reqs(timeout_seconds=None) # Returns 1 and 2, representing the first and second calls to actor 0. self.assertEqual([r.get() for r in results.ignore_errors()], [1, 2])

scripts/fsmedia.py

def _load(self): data = {} if not self._is_extract: if not self.have_alignments_file: return data data = super()._load() return data skip_existing = hasattr(self._args, 'skip_existing') and self._args.skip_existing skip_faces = hasattr(self._args, 'skip_faces') and self._args.skip_faces if not skip_existing and not skip_faces: logge

bugfix: debug landmarks

_load

9e503bdaa2bfe2baaea50ad2e4bf742f309d9d10

faceswap

fsmedia.py

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

Python

def _load(self): data = {} if not self._is_extract: if not self.have_alignments_file: return data data = super()._load() return data skip_existing = hasattr(self._args, 'skip_existing') and self._args.skip_existing skip_faces = hasattr(self._args, 'skip_faces') and self._args.skip_faces if not skip_existing and not skip_faces: logger.debug("No skipping selected. Returning empty dictionary") return data if not self.have_alignments_file and (skip_existing or skip_faces): logger.warning("Skip Existing/Skip Faces selected, but no alignments file found!") return data data = super()._load() if skip_faces: # Remove items from alignments that have no faces so they will # be re-detected del_keys = [key for key, val in data.items() if not val["faces"]] logger.debug("Frames with no faces selected for redetection: %s", len(del_keys)) for key in del_keys: if key in data: logger.trace("Selected for redetection: '%s'", key) del data[key] return data

@log_start_end(log=logger)

openbb_terminal/stocks/discovery/yahoofinance_model.py

def get_gtech() -> pd.DataFrame: return get_df( "https://finance.y

Fixed bad yfinance urls (#2282)

get_gtech

bd12c203a0585dab6ca3ff81c3b4500e088b41d6

OpenBBTerminal

yahoofinance_model.py

https://github.com/OpenBB-finance/OpenBBTerminal.git

Python

def get_gtech() -> pd.DataFrame: return get_df( "https://finance.yahoo.com/screener/predefined/growth_technology_stocks" ) @log_start_end(log=logger)

@require_torch

tests/encoder_decoder/test_modeling_encoder_decoder.py

def test_bert2gpt2_summarization(self): model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2gpt2-cnn_dailymail-fp16") model.to(torch_device) tokenizer_in = AutoTokenizer.from_pretrained("bert-base-case

[Test refactor 1/5] Per-folder tests reorganization (#15725) * Per-folder tests reorganization Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Stas Bekman <stas@stason.org>

test_bert2gpt2_summarization

29c10a41d04f855c433a6cde7797b325651417d2

transformers

test_modeling_encoder_decoder.py

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

Python

def test_bert2gpt2_summarization(self): model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2gpt2-cnn_dailymail-fp16") model.to(torch_device) tokenizer_in = AutoTokenizer.from_pretrained("bert-base-cased") tokenizer_out = AutoTokenizer.from_pretrained("../gpt2") ARTICLE_STUDENTS = EXPECTED_SUMMARY_STUDENTS = input_dict = tokenizer_in(ARTICLE_STUDENTS, return_tensors="pt") output_ids = model.generate(input_dict["input_ids"].to(torch_device)) summary = tokenizer_out.batch_decode(output_ids, skip_special_tokens=True) self.assertEqual(summary, [EXPECTED_SUMMARY_STUDENTS]) @require_torch

saleor/permission/management.py

def _get_builtin_permissions(opts): # noqa: D205, D212 perms = [] for action in opts.default_permissions: perms.append( ( get_permission_codename(action, opts), "Can %s %s" % (action, opts.verbose_name_raw), ) )

Move create_permission post migrate signal

_get_builtin_permissions

3981ae09888569eafe9cbb3a0c659dd337028fa4

saleor

management.py

https://github.com/saleor/saleor.git

Python

def _get_builtin_permissions(opts): # noqa: D205, D212 perms = [] for action in opts.default_permissions: perms.append( ( get_permission_codename(action, opts), "Can %s %s" % (action, opts.verbose_name_raw), ) ) return perms

src/accelerate/accelerator.py

def accumulate(self, model): self._do_sync() if self.sync_gradients: context = contextl

Introduce automatic gradient accumulation wrapper + fix a few test issues (#484) * Have accelerator handle gradient accumulation Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

accumulate

86ce737d7fc94f8000dbd5e13021d0411bb4204a

accelerate

accelerator.py

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

Python

def accumulate(self, model): self._do_sync() if self.sync_gradients: context = contextlib.nullcontext else: context = self.no_sync with context(model): yield

python3.10.4/Lib/email/_header_value_parser.py

def get_attribute(value): attribute = Attribute() if value and value[0] in CFWS_LEADER: token, value = get_cfws(value) attribute.append(token) if value and value[0] in ATTRIBUTE_ENDS: raise errors.H

add python 3.10.4 for windows

get_attribute

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

_header_value_parser.py

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

Python

def get_attribute(value): attribute = Attribute() if value and value[0] in CFWS_LEADER: token, value = get_cfws(value) attribute.append(token) if value and value[0] in ATTRIBUTE_ENDS: raise errors.HeaderParseError( "expected token but found '{}'".format(value)) token, value = get_attrtext(value) attribute.append(token) if value and value[0] in CFWS_LEADER: token, value = get_cfws(value) attribute.append(token) return attribute, value

@pytest.fixture

modin/pandas/test/test_io.py

def eval_to_file(modin_obj, pandas_obj, fn, extension, **fn_kwargs): with ensure_clean_dir() as dirname: unique_filename_modin = get_unique_filename( extension=extension, data_dir=dirname ) unique_filename_pandas = get_unique_filename( extension=extension, data_dir=dirname ) # parameter `max_retries=0` is set for `to_csv` function on Ray engine, # in order to increase the stability of tests, we repeat the call of # the entire function manually last_exception = None for _ in range(3): try: getattr(modin_obj, fn)(unique_filename_modin, **fn_kwargs) except EXCEPTIONS as exc: last_exception = exc continue break else: raise last_exception getattr(pandas_obj, fn)(unique_

TEST-#4879: Use pandas `ensure_clean()` in place of `io_tests_data` (#4881) Signed-off-by: Karthik Velayutham <vkarthik@ponder.io>

eval_to_file

5086a9ea37bc37e6e58da0ceaf5864b16cc8e0ed

modin

test_io.py

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

Python

def eval_to_file(modin_obj, pandas_obj, fn, extension, **fn_kwargs): with ensure_clean_dir() as dirname: unique_filename_modin = get_unique_filename( extension=extension, data_dir=dirname ) unique_filename_pandas = get_unique_filename( extension=extension, data_dir=dirname ) # parameter `max_retries=0` is set for `to_csv` function on Ray engine, # in order to increase the stability of tests, we repeat the call of # the entire function manually last_exception = None for _ in range(3): try: getattr(modin_obj, fn)(unique_filename_modin, **fn_kwargs) except EXCEPTIONS as exc: last_exception = exc continue break else: raise last_exception getattr(pandas_obj, fn)(unique_filename_pandas, **fn_kwargs) assert assert_files_eq(unique_filename_modin, unique_filename_pandas) @pytest.fixture

django/contrib/contenttypes/fields.py

def _is_matching_generic_foreign_key(self, field): return ( isinstance(field, GenericForeignKey) and field.ct_field == self.content_type_field_name and field.fk_field == self.object_id_field_name )

Refs #33476 -- Reformatted code with Black.

_is_matching_generic_foreign_key

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

fields.py

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

Python

def _is_matching_generic_foreign_key(self, field): return ( isinstance(field, GenericForeignKey) and field.ct_field == self.content_type_field_name and field.fk_field == self.object_id_field_name )

lib/align/alignments.py

def hashes_to_frame(self): if not self._hashes_to_frame: logger.debug("Generating hashes to frame") for frame_name, val in self._data.items(): for idx, face in enumerate(val["faces"]): sel

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

hashes_to_frame

5e73437be47f2410439a3c6716de96354e6a0c94

faceswap

alignments.py

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

Python

def hashes_to_frame(self): if not self._hashes_to_frame: logger.debug("Generating hashes to frame") for frame_name, val in self._data.items(): for idx, face in enumerate(val["faces"]): self._hashes_to_frame.setdefault(face["hash"], {})[frame_name] = idx return self._hashes_to_frame

qutebrowser/completion/completiondelegate.py

def _get_textdoc(self, index): assert self._opt is not None # FIXME we probably should do eliding here. See # qcommonstyle.cpp:viewItemDrawText # https://github.com/qutebrowser/qutebrowser/issues/118 text_option = QTextOption() if self._opt.features & QStyleOptionViewItem.WrapText: text_option.setWrapMode(QTextOption.WordWrap) else: text_option.setWrapMode(QTextOption.ManualWrap) text_option.setTextDirection(self._opt.direction) text_option.setAlignment(QStyle.visualAlignment( self._opt.direction, self._opt.displayAlignment)) if self._doc is not None: self._doc.deleteLater() self._doc = QTextDocument(self) self._doc.setDefaultFont(self._opt.font) self._doc.setDefaultTextOption(text_option) self._doc.setDocumentMargin(2) if index.parent().isValid(): view = self.parent() assert isinstance(view, completionwidget.CompletionView), view pattern = view.pattern columns_to_filter = index.model().columns_to_filter(index) if index.column() in columns_to_filter and pattern: if self._opt.state & QStyle.State_Selected: color = config.val.colors.completion.item.selected.match.fg else: color = config.val.colors.completion.match.fg _Highlighter(self._doc, pattern, color) self._doc.setPlainText(self._opt.text) else: self._doc.setHtml( '<span style="font: {};">{}</span>'.

mypy: Upgrade to PyQt5-stubs 5.15.6.0 For some unknown reason, those new stubs cause a *lot* of things now to be checked by mypy which formerly probably got skipped due to Any being implied somewhere. The stubs themselves mainly improved, with a couple of regressions too. In total, there were some 337 (!) new mypy errors. This commit fixes almost all of them, and the next commit improves a fix to get things down to 0 errors again. Overview of the changes: ==== qutebrowser/app.py - Drop type ignore due to improved stubs. ==== qutebrowser/browser/browsertab.py - Specify the type of _widget members more closely than just QWidget. This is debatable: I suppose the abstract stuff shouldn't need to know anything about the concrete backends at all. But it seems like we cut some corners when initially implementing things, and put some code in browsertab.py just because the APIs of both backends happened to be compatible. Perhaps something to reconsider once we drop QtWebKit and hopefully implement a dummy backend. - Add an additional assertion in AbstractAction.run_string. This is already covered by the isinstance(member, self.action_base) above it, but that's too dynamic for mypy to understand. - Fix the return type of AbstractScroller.pos_px, which is a QPoint (with x and y components), not a single int. - Fix the return type of AbstractScroller.pos_perc, which is a Tuple (with x and y components), not a single int. - Fix the argument types of AbstractScroller.to_perc, as it's possible to pass fractional percentages too. - Specify the type for AbstractHistoryPrivate._history. See above (_widget) re this being debatable. - Fix the return type of AbstractTabPrivate.event_target(), which can be None (see #3888). - Fix the return type of AbstractTabPrivate.run_js_sync, which is Any (the JS return value), not None. - Fix the argument type for AbstractTabPrivate.toggle_inspector: position can be None to use the last used position. - Declare the type of sub-objects of AbstractTab. - Fix the return value of AbstractTab.icon(), which is the QIcon, not None. ==== qutebrowser/browser/commands.py - Make sure the active window is a MainWindow (with a .win_id attribute). ==== qutebrowser/browser/downloadview.py - Add _model() which makes sure that self.model() is a DownloadModel, not None or any other model. This is needed because other methods access a variety of custom attributes on it, e.g. last_index(). ==== qutebrowser/browser/greasemonkey.py - Add an ignore for AbstractDownload.requested_url which we patch onto the downloads. Probably would be nicer to add it as a proper attribute which always gets set by the DownloadManager. ==== qutebrowser/browser/hints.py - Remove type ignores for QUrl.toString(). - Add a new type ignore for combining different URL flags (which works, but is not exactly type safe... still probably a regression in the stubs). - Make sure the things we get back from self._get_keyparser are what we actually expect. Probably should introduce a TypedDict (and/or overloads for _get_keyparser with typing.Literal) to teach mypy about the exact return value. See #7098. This is needed because we access Hint/NormalKeyParser-specific attributes such as .set_inhibited_timout() or .update_bindings(). ==== qutebrowser/browser/inspector.py - Similar changes than in browsertab.py to make some types where we share API (e.g. .setPage()) more concrete. Didn't work out unfortunately, see next commit. ==== qutebrowser/browser/network/pac.py - Remove now unneeded type ignore for signal. ==== qutebrowser/browser/qtnetworkdownloads.py - Make sure that downloads is a qtnetworkdownloads.DownloadItem (rather than an AbstractDownload), so that we can call ._uses_nam() on it. ==== qutebrowser/browser/qutescheme.py - Remove now unneeded type ignore for QUrl flags. ==== qutebrowser/browser/urlmarks.py - Specify the type of UrlMarkManager._lineparser, as those only get initialized in _init_lineparser of subclasses, so mypy doesn't know it's supposed to exist. ==== qutebrowser/browser/webelem.py - New casts to turn single KeyboardModifier (enum) entries into KeyboardModifiers (flags). Might not be needed anymore with Qt 6. - With that, casting the final value is now unneeded. ==== qutebrowser/browser/webengine/notification.py - Remove now unneeded type ignore for signal. - Make sure the self.sender() we get in HerbeNotificationAdapter._on_finished() is a QProcess, not just any QObject. ==== qutebrowser/browser/webengine/webenginedownloads.py - Remove now unneeded type ignores for signals. ==== qutebrowser/browser/webengine/webengineelem.py - Specify the type of WebEngineElement._tab. - Remove now unneeded type ignore for mixed flags. ==== qutebrowser/browser/webengine/webengineinspector.py - See changes to inspector.py and next commit. - Remove now unneeded type ignore for signal. ==== qutebrowser/browser/webengine/webenginequtescheme.py - Remove now unneeded type ignore for mixed flags. ==== qutebrowser/browser/webengine/webenginesettings.py - Ignore access of .setter attribute which we patch onto QWebEngineProfile. Would be nice to have a subclass or wrapper-class instead. ==== qutebrowser/browser/webengine/webenginetab.py - Specified the type of _widget members more closely than just QWidget. See browsertab.py changes for details. - Remove some now-unneeded type ignores for creating FindFlags. - Specify more concrete types for WebEngineTab members where we actually need to access WebEngine-specific attributes. - Make sure the page we get is our custom WebEnginePage subclass, not just any QWebEnginePage. This is needed because we access custom attributes on it. ==== qutebrowser/browser/webengine/webview.py - Make sure the page we get is our custom WebEnginePage subclass, not just any QWebEnginePage. This is needed because we access custom attributes on it. ==== qutebrowser/browser/webkit/network/networkreply.py - Remove now unneeded type ignores for signals. ==== qutebrowser/browser/webkit/webkitinspector.py - See changes to inspector.py and next commit. ==== qutebrowser/browser/webkit/webkittab.py - Specify the type of _widget members more closely than just QWidget. See browsertab.py changes for details. - Add a type ignore for WebKitAction because our workaround needs to treat them as ints (which is allowed by PyQt, even if not type-safe). - Add new ignores for findText calls: The text is a QString and can be None; the flags are valid despite mypy thinking they aren't (stubs regression?). - Specify the type for WebKitHistoryPrivate._history, because we access WebKit-specific attributes. See above (_widget) re this being debatable. - Make mypy aware that .currentFrame() and .frameAt() can return None (stubs regression?). - Make sure the .page() and .page().networkAccessManager() are our subclasses rather than the more generic QtWebKit objects, as we use custom attributes. - Add new type ignores for signals (stubs regression!) ==== qutebrowser/browser/webkit/webpage.py - Make sure the .networkAccessManager() is our subclass rather than the more generic QtWebKit object, as we use custom attributes. - Replace a cast by a type ignore. The cast didn't work anymore. ==== qutebrowser/browser/webkit/webview.py - Make sure the .page() is our subclass rather than the more generic QtWebKit object, as we use custom attributes. ==== qutebrowser/commands/userscripts.py - Remove now unneeded type ignore for signal. ==== qutebrowser/completion/completer.py - Add a new _completion() getter (which ensures it actually gets the completion view) rather than accessing the .parent() directly (which could be any QObject). ==== qutebrowser/completion/completiondelegate.py - Make sure self.parent() is a CompletionView (no helper method as there is only one instance). - Remove a now-unneeded type ignore for adding QSizes. ==== qutebrowser/completion/completionwidget.py - Add a ._model() getter which ensures that we get a CompletionModel (with custom attributes) rather than Qt's .model() which can be any QAbstractItemModel (or None). - Removed a now-unneeded type ignore for OR-ing flags. ==== qutebrowser/completion/models/completionmodel.py - Remove now unneeded type ignores for signals. - Ignore a complaint about .set_pattern() not being defined. Completion categories don't share any common parent class, so it would be good to introduce a typing.Protocol for this. See #7098. ==== qutebrowser/components/misccommands.py - Removed a now-unneeded type ignore for OR-ing flags. ==== qutebrowser/components/readlinecommands.py - Make sure QApplication.instance() is a QApplication (and not just a QCoreApplication). This includes the former "not None" check. ==== qutebrowser/components/scrollcommands.py - Add basic annotation for "funcs" dict. Could have a callable protocol to specify it needs a count kwarg, see #7098. ==== qutebrowser/config/stylesheet.py - Correctly specify that stylesheet apply to QWidgets, not any QObject. - Ignore an attr-defined for obj.STYLESHEET. Perhaps could somehow teach mypy about this with overloads and protocols (stylesheet for set_register being None => STYLESHEET needs to be defined, otherwise anything goes), but perhaps not worth the troble. See #7098. ==== qutebrowser/keyinput/keyutils.py - Remove some now-unneeded type ignores and add a cast for using a single enum value as flags. Might need to look at this again with Qt 6 support. ==== qutebrowser/keyinput/modeman.py - Add a FIXME for using a TypedDict, see comments for hints.py above. ==== qutebrowser/mainwindow/mainwindow.py - Remove now-unneeded type ignores for calling with OR-ed flags. - Improve where we cast from WindowType to WindowFlags, no int needed - Use new .tab_bar() getter, see below. ==== qutebrowser/mainwindow/prompt.py - Remove now-unneeded type ignores for calling with OR-ed flags. ==== qutebrowser/mainwindow/statusbar/bar.py - Adjust type ignores around @pyqtProperty. The fact one is still needed seems like a stub regression. ==== qutebrowser/mainwindow/statusbar/command.py - Fix type for setText() override (from QLineEdit): text can be None (QString in C++). ==== qutebrowser/mainwindow/statusbar/url.py - Adjust type ignores around @pyqtProperty. The fact one is still needed seems like a stub regression. ==== qutebrowser/mainwindow/tabbedbrowser.py - Specify that TabDeque manages browser tabs, not any QWidgets. It accesses AbstractTab-specific attributes. - Make sure that the .tabBar() we get is a tabwidget.TabBar, as we access .maybe_hide. - Fix the annotations for stored marks: Scroll positions are a QPoint, not int. - Add _current_tab() and _tab_by_idx() wrappers for .currentWidget() and .widget(), which ensures that the return values are valid AbstractTabs (or None for _tab_by_idx). This is needed because we access AbstractTab-specific attributes. - For some places, where the tab can be None, continue using .currentTab() but add asserts. - Remove some now-unneeded [unreachable] ignores, as mypy knows about the None possibility now. ==== qutebrowser/mainwindow/tabwidget.py - Add new tab_bar() and _tab_by_idx() helpers which check that the .tabBar() and .widget() are of type TabBar and AbstractTab, respectively. - Add additional assertions where we expect ._tab_by_idx() to never be None. - Remove dead code in get_tab_fields for handling a None y scroll position. I was unable to find any place in the code where this could be set to None. - Remove some now-unneeded type ignores and casts, as mypy now knows that _type_by_idx() could be None. - Work around a strange instance where mypy complains about not being able to find the type of TabBar.drag_in_progress from TabWidget._toggle_visibility, despite it clearly being shown as a bool *inside* that class without any annotation. - Add a ._tab_widget() getter in TabBar which ensures that the .parent() is in fact a TabWidget. ==== qutebrowser/misc/crashsignal.py - Remove now unneeded type ignores for signals. ==== qutebrowser/misc/editor.py - Remove now unneeded type ignores for signals. ==== qutebrowser/misc/ipc.py - Remove now unneeded type ignores for signals. - Add new type ignores for .error() which is both a signal and a getter (stub regression?). Won't be relevant for Qt 6 anymore, as the signal was renamed to errorOccurred in 5.15. ==== qutebrowser/misc/objects.py - Make sure mypy knows that objects.app is our custom Application (with custom attributes) rather than any QApplication. ==== qutebrowser/utils/objreg.py - Ignore attr-defined for .win_id attributes. Maybe could add a typing.Protocol, but ideally, the whole objreg stuff should die one day anyways. ==== tests/unit/completion/test_completer.py - Make CompletionWidgetStub inherit from CompletionView so that it passes the new isinstance() asserts in completer.py (see above).

_get_textdoc

a20bb67a878b2e68abf8268c1b0a27f018d01352

qutebrowser

completiondelegate.py

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

Python

def _get_textdoc(self, index): assert self._opt is not None # FIXME we probably should do eliding here. See # qcommonstyle.cpp:viewItemDrawText # https://github.com/qutebrowser/qutebrowser/issues/118 text_option = QTextOption() if self._opt.features & QStyleOptionViewItem.WrapText: text_option.setWrapMode(QTextOption.WordWrap) else: text_option.setWrapMode(QTextOption.ManualWrap) text_option.setTextDirection(self._opt.direction) text_option.setAlignment(QStyle.visualAlignment( self._opt.direction, self._opt.displayAlignment)) if self._doc is not None: self._doc.deleteLater() self._doc = QTextDocument(self) self._doc.setDefaultFont(self._opt.font) self._doc.setDefaultTextOption(text_option) self._doc.setDocumentMargin(2) if index.parent().isValid(): view = self.parent() assert isinstance(view, completionwidget.CompletionView), view pattern = view.pattern columns_to_filter = index.model().columns_to_filter(index) if index.column() in columns_to_filter and pattern: if self._opt.state & QStyle.State_Selected: color = config.val.colors.completion.item.selected.match.fg else: color = config.val.colors.completion.match.fg _Highlighter(self._doc, pattern, color) self._doc.setPlainText(self._opt.text) else: self._doc.setHtml( '<span style="font: {};">{}</span>'.format( html.escape(config.val.fonts.completion.category), html.escape(self._opt.text)))

reconstruction/ostec/external/face_detector/detect_face.py

def feed(self, *args): assert len(args) != 0 self.terminals = []

initialize ostec

feed

7375ee364e0df2a417f92593e09557f1b2a3575a

insightface

detect_face.py

https://github.com/deepinsight/insightface.git

Python

def feed(self, *args): assert len(args) != 0 self.terminals = [] for fed_layer in args: if isinstance(fed_layer, str): try: fed_layer = self.layers[fed_layer] except KeyError: raise KeyError('Unknown layer name fed: %s' % fed_layer) self.terminals.append(fed_layer) return self

keras/engine/training_utils_v1.py

def unpack_iterator_input(iterator): try: next_element = iterator.get_next() except tf.errors.OutOfRangeError: raise RuntimeError( "Your dataset iterator ran out of data; " "Make sure that your dataset can generate " "required number of samples." ) if isinstance(next_element, (list, tuple)): if len(next_element) not in [2, 3]: raise ValueError( "Please pr

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

unpack_iterator_input

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training_utils_v1.py

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

Python

def unpack_iterator_input(iterator): try: next_element = iterator.get_next() except tf.errors.OutOfRangeError: raise RuntimeError( "Your dataset iterator ran out of data; " "Make sure that your dataset can generate " "required number of samples." ) if isinstance(next_element, (list, tuple)): if len(next_element) not in [2, 3]: raise ValueError( "Please provide model inputs as a list or tuple of 2 or 3 " "elements: (input, target) or (input, target, sample_weights) " "Received %s" % next_element ) if len(next_element) == 2: x, y = next_element weights = None else: x, y, weights = next_element else: x = next_element y = None weights = None return x, y, weights

tests/mesh_utils_test.py

def mock_2x2x4_devices(one_device_per_chip): return mock_devices(2, 2, 4, 'TPU v4', one_device_pe

[mesh_utils] Support creating device meshes for hybrid networks Also makes some NFCs to other mesh_utils code. PiperOrigin-RevId: 442581767

mock_2x2x4_devices

3f9e45e0c5b035de27b14588cd3b4cfd5f3c1f04

jax

mesh_utils_test.py

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

Python

def mock_2x2x4_devices(one_device_per_chip): return mock_devices(2, 2, 4, 'TPU v4', one_device_per_chip)

ludwig/export.py

def export_triton(model_path, output_path="model_repository", model_name="ludwig_model", model_version=1, **kwargs): logger.info(f"Model path: {model_path}") logger.info(f"Output path: {output_path}"

Adding new export for Triton (#2078) * Adding new export for triton. Fixes for load model for neuropod export, add output dict format * Adding test for triton. Fix to cast int to string for os.path.join. Added annotation for neurpod * Minor tweaks to config.pbtxt output * Remove logger that is not being used * Restrict torchmetrics<0.9 and whylogs<1.0 until compatibility fixed * Update export_triton to return model path, and added docstrings * Update api to return both model path and config path Co-authored-by: Travis Addair <tgaddair@gmail.com>

export_triton

698a0e0f1ed95d20116dc51aa9c6a7ed48446deb

ludwig

export.py

https://github.com/ludwig-ai/ludwig.git

Python

def export_triton(model_path, output_path="model_repository", model_name="ludwig_model", model_version=1, **kwargs): logger.info(f"Model path: {model_path}") logger.info(f"Output path: {output_path}") logger.info(f"Model name: {model_name}") logger.info(f"Model version: {model_version}") logger.info("\n") model = LudwigModel.load(model_path) os.makedirs(output_path, exist_ok=True) utils_export_triton(model, output_path, model_name, model_version) logger.info(f"Saved to: {output_path}")

src/sentry/eventstore/models.py

def tags(self) -> Sequence[Tuple[str, str]]: tags_key_column = self._get_column_name(Columns.TAGS_KEY) tags_value_column = self._get_column_name(Columns.TAGS_VALUE) if tags_key_column in self._snuba_data and tags_value_column in self._snuba_data: keys = self._snuba_data[tags_key_column] values = self._snuba_data[tags_value_column] if keys and values and len(keys) == len(values): return sorted(zip(keys, values)) else: return [] # Nodestore implementation try: rv = sorted( (t, v) for t, v in get_path(self.data, "tags", filter=True) or () if t is not None and v is not None ) return rv except ValueError: # at one point Sentry allowed inva

feat(perf_issues): Add `GroupEvent` and split some functionality in `Event` into a base class. (#38143) Since we can now have events with multiple groups, we can no longer rely on the `Event.group` property. This pr adds in a `GroupEvent` subclass that should be passed around wherever we expect an event to have a single `Group` associated with it. `Event` has been split up into `BaseEvent` and `Event`. We will deprecate and remove uses of `group_id` and `group` in the `Event` class going forward. If we need an event with a `Group`, we can use `build_group_events` to fetch all `GroupEvents` associated with the `Event`, or `for_group` if we just need a specific `Event`/`Group` pairing. Going forward, the plan is to store all groups in the `groups` property. This means that error events being sent via eventstream will have their group included in `groups` as well. We'll need to update the errors processor in snuba to look there instead of `group_id`. This seems cleaner long term, instead of having both `group_id` and `group_ids` passed through. To figure out where we need to use `build_group_events` and `for_group` we can do a mix of searching the codebase and commenting out the `group_id` and `group` properties and see how CI goes.

tags

6aaaf5089b2c39757883179df5a8512db3b0c716

sentry

models.py

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

Python

def tags(self) -> Sequence[Tuple[str, str]]: tags_key_column = self._get_column_name(Columns.TAGS_KEY) tags_value_column = self._get_column_name(Columns.TAGS_VALUE) if tags_key_column in self._snuba_data and tags_value_column in self._snuba_data: keys = self._snuba_data[tags_key_column] values = self._snuba_data[tags_value_column] if keys and values and len(keys) == len(values): return sorted(zip(keys, values)) else: return [] # Nodestore implementation try: rv = sorted( (t, v) for t, v in get_path(self.data, "tags", filter=True) or () if t is not None and v is not None ) return rv except ValueError: # at one point Sentry allowed invalid tag sets such as (foo, bar) # vs ((tag, foo), (tag, bar)) return []

netbox/dcim/tests/test_models.py

def test_cable_validates_compatible_types(self): # An interface cannot be connected to a power port cable = Cable(a_terminations=[self.interface1, self.interface2], b_terminations=[self.interface3]) with self.assertRaises(ValidationError): cable.clean() # TODO: Remove this? # def

Clean up tests

test_cable_validates_compatible_types

6280398bc17211bbc5b321039144c1eb0461f4a9

netbox

test_models.py

https://github.com/netbox-community/netbox.git

Python

def test_cable_validates_compatible_types(self): # An interface cannot be connected to a power port cable = Cable(a_terminations=[self.interface1, self.interface2], b_terminations=[self.interface3]) with self.assertRaises(ValidationError): cable.clean() # TODO: Remove this? # def test_cable_front_port_cannot_connect_to_corresponding_rear_port(self): # # cable = Cable(a_terminations=[self.front_port1], b_terminations=[self.rear_port1]) # with self.assertRaises(ValidationError): # cable.clean()

keras/saving/hdf5_format.py

def save_model_to_hdf5(model, filepath, overwrite=True, include_optimizer=True): if h5py is None: raise ImportError( "`save_model()` using h5 format requires h5py. Could not " "import h5py." ) # TODO(psv) Add warning when we save models that contain non-serializable # entities like metrics added using `add_metric` and losses added using # `add_loss.` if len(model.weights) != len(model._undeduplicated_weights): logging.warning( "Found duplicated `Variable`s in Model's `weights`. " "This is usually caused by `Variable`s being shared by " "Layers in the Model. These `Variable`s will be treated " "as separate `Variable`s when the Model is restored. To " 'avoid this, please save with `save_format="tf"`.' ) if not isinstance(filepath, h5py.File): # If file exists and should not be overwritten. if not overwrite and os.path.isfile(filepath): proceed = ask_to_proceed_with_overwrite(filepath) if not proceed: return # Try creating dir if not exist dirpath = os.path.dirname(filepath) if not os.path.exists(dirpath): tf.io.gfile.makedirs(dirpath) f = h5py.File(filepath, mode="w") opened_new_file = True else: f = filepath opened_new_file = False try: model_metadata = saving_utils.model_metadata(model, include_optimiz

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

save_model_to_hdf5

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

hdf5_format.py

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

Python

def save_model_to_hdf5(model, filepath, overwrite=True, include_optimizer=True): if h5py is None: raise ImportError( "`save_model()` using h5 format requires h5py. Could not " "import h5py." ) # TODO(psv) Add warning when we save models that contain non-serializable # entities like metrics added using `add_metric` and losses added using # `add_loss.` if len(model.weights) != len(model._undeduplicated_weights): logging.warning( "Found duplicated `Variable`s in Model's `weights`. " "This is usually caused by `Variable`s being shared by " "Layers in the Model. These `Variable`s will be treated " "as separate `Variable`s when the Model is restored. To " 'avoid this, please save with `save_format="tf"`.' ) if not isinstance(filepath, h5py.File): # If file exists and should not be overwritten. if not overwrite and os.path.isfile(filepath): proceed = ask_to_proceed_with_overwrite(filepath) if not proceed: return # Try creating dir if not exist dirpath = os.path.dirname(filepath) if not os.path.exists(dirpath): tf.io.gfile.makedirs(dirpath) f = h5py.File(filepath, mode="w") opened_new_file = True else: f = filepath opened_new_file = False try: model_metadata = saving_utils.model_metadata(model, include_optimizer) for k, v in model_metadata.items(): if isinstance(v, (dict, list, tuple)): f.attrs[k] = json.dumps( v, default=json_utils.get_json_type ).encode("utf8") else: f.attrs[k] = v model_weights_group = f.create_group("model_weights") save_weights_to_hdf5_group(model_weights_group, model) # TODO(b/128683857): Add integration tests between tf.keras and external # Keras, to avoid breaking TF.js users. if isinstance(model.optimizer, optimizer_experimental.Optimizer): logging.warning( "HDF5 format does not save weights of" " `optimizer_experimental.Optimizer`, your optimizer will" " be recompiled at loading time." ) elif ( include_optimizer and model.optimizer and not isinstance(model.optimizer, optimizer_v1.TFOptimizer) ): save_optimizer_weights_to_hdf5_group(f, model.optimizer) f.flush() finally: if opened_new_file: f.close()

packages/syft/src/syft/core/node/common/node_manager/user_manager.py

def set(self, **kwargs) -> None: # nosec attributes = {} user_id = kwargs["u

replaced all methods of usermanager class, working login Co-Authored By: Ionesio

set

066545e8a88e842aa7d0a5d57bac88716001bced

PySyft

user_manager.py

https://github.com/OpenMined/PySyft.git

Python

def set(self, **kwargs) -> None: # nosec attributes = {} user_id = kwargs["user_id"] user = self.first(id_int=int(user_id)) if not user: raise UserNotFoundError for k, v in kwargs.items(): if k in user.__attr_searchable__: attributes[k] = v if kwargs.get("email", None): user.email = kwargs["email"] elif kwargs.get("role", None): user.role = kwargs["role"] elif kwargs.get("name", None): user.name = kwargs["name"] elif kwargs.get("budget", None): user.budget = kwargs["budget"] elif kwargs.get("website", None): user.website = kwargs["website"] elif kwargs.get("institution", None): user.institution = kwargs["institution"] else: raise Exception attributes["__blob__"] = user.to_bytes() self.update_one({"id_int": int(user_id)}, {"$set": attributes})

tests/sentry/snuba/metrics/fields/test_base.py

def test_get_entity_and_validate_dependency_tree_of_a_single_entity_derived_metric(self): use_case_id = UseCaseKey.RELEASE_HEALTH expected_derived_metrics_entities = { SessionMRI.ALL.value: "metrics_counters", SessionMRI.ALL_USER.value: "metrics_sets", SessionMRI.CRASHED.value: "metrics_counters", SessionMRI.CRASHED_USER.value: "metrics_sets", SessionMRI.ABNORMAL.value: "metrics_counters", SessionMRI.ABNORMAL_USER.value: "metrics_sets", SessionMRI.CRASH_FREE_RATE.value: "metrics_counters", SessionMRI.CRASH_FREE_USER_RATE.value: "metrics_sets", SessionMRI.ERRORED_PREAGGREGATED.value: "metrics_counters", SessionMRI.ERRORED_SET.value: "metrics_sets", SessionMRI.ERRORED_USER_ALL.value: "metrics_sets", SessionMRI.CRASHED_AND_ABNORMAL_USER.value: "metrics_sets", SessionMRI.ERRORED_USER.value: "metrics_sets", } for key, value in expected_derived_metrics_entities.items(): assert ( MOCKED_DERIVED_METRICS[key].get_entity( projects=[self.project], use_case_id=use_case_id ) ) == value # Incorrectly setup SingularEntityDerivedMetric with metrics spanning multiple entities with pytest.raises(DerivedMetricParseException): self.crash_free_fake.get_entity(projects=[self.proje

fix(snuba): Add appropriate `UseCaseKey` for indexer [TET-146] (#36308) * fix(snuba): Add appropriate `UseCaseKey` for indexer Update indexer invocation call to have the appropriate `UseCaseKey` depending on use case. In `src/sentry/sentry_metrics/indexer/base.py::StringIndexer` when using `resolve` and `reverse_resolve` callers should not rely on the default use_case_id. Important changes: - Add required parameter `use_case_id: UseCaseKey` to `get_series` from `src/sentry/snuba/metrics/datasource.py#L612`; - Add required parameter to `get_metrics` in `src/sentry/snuba/metrics/datasource.py` - Add required parameter to `get_tags` in `src/sentry/snuba/metrics/datasource.py` - Add required parameter to `get_tag_values` in `src/sentry/snuba/metrics/datasource.py`

test_get_entity_and_validate_dependency_tree_of_a_single_entity_derived_metric

cd803d173c72b64d06c0687170bf9a945d0b503c

sentry

test_base.py

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

Python

def test_get_entity_and_validate_dependency_tree_of_a_single_entity_derived_metric(self): use_case_id = UseCaseKey.RELEASE_HEALTH expected_derived_metrics_entities = { SessionMRI.ALL.value: "metrics_counters", SessionMRI.ALL_USER.value: "metrics_sets", SessionMRI.CRASHED.value: "metrics_counters", SessionMRI.CRASHED_USER.value: "metrics_sets", SessionMRI.ABNORMAL.value: "metrics_counters", SessionMRI.ABNORMAL_USER.value: "metrics_sets", SessionMRI.CRASH_FREE_RATE.value: "metrics_counters", SessionMRI.CRASH_FREE_USER_RATE.value: "metrics_sets", SessionMRI.ERRORED_PREAGGREGATED.value: "metrics_counters", SessionMRI.ERRORED_SET.value: "metrics_sets", SessionMRI.ERRORED_USER_ALL.value: "metrics_sets", SessionMRI.CRASHED_AND_ABNORMAL_USER.value: "metrics_sets", SessionMRI.ERRORED_USER.value: "metrics_sets", } for key, value in expected_derived_metrics_entities.items(): assert ( MOCKED_DERIVED_METRICS[key].get_entity( projects=[self.project], use_case_id=use_case_id ) ) == value # Incorrectly setup SingularEntityDerivedMetric with metrics spanning multiple entities with pytest.raises(DerivedMetricParseException): self.crash_free_fake.get_entity(projects=[self.project], use_case_id=use_case_id)

sklearn/decomposition/_sparse_pca.py

def transform(self, X): check_is_fitted(self) X = self._validate_data(X, reset=False) X = X - self.mean_ U = ridge_regression( self.components_.T, X.T, self.ridge_alpha, solver="cholesky" ) return U

MAINT Use _validate_params in SparsePCA and MiniBatchSparsePCA (#23710) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>

transform

db6123fe40400828918037f3fae949bfcc4d9d05

scikit-learn

_sparse_pca.py

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

Python

def transform(self, X): check_is_fitted(self) X = self._validate_data(X, reset=False) X = X - self.mean_ U = ridge_regression( self.components_.T, X.T, self.ridge_alpha, solver="cholesky" ) return U

netbox/utilities/utils.py

def serialize_object(obj, extra=None): json_str = serialize('json', [obj]) print(json_str) data = json.loads(json_str)[0]['fields'] # Exclude any MPTTModel fields if issubclass(obj.__class__, MPTTModel): for field in ['level', 'lft', 'rght', 'tree_id']: data.pop

Extend Cable model to support multiple A/B terminations

serialize_object

4bb9b6ee2639db683b70d6ddbee055497e0a3647

netbox

utils.py

https://github.com/netbox-community/netbox.git

Python

def serialize_object(obj, extra=None): json_str = serialize('json', [obj]) print(json_str) data = json.loads(json_str)[0]['fields'] # Exclude any MPTTModel fields if issubclass(obj.__class__, MPTTModel): for field in ['level', 'lft', 'rght', 'tree_id']: data.pop(field) # Include custom_field_data as "custom_fields" if hasattr(obj, 'custom_field_data'): data['custom_fields'] = data.pop('custom_field_data') # Include any tags. Check for tags cached on the instance; fall back to using the manager. if is_taggable(obj): tags = getattr(obj, '_tags', None) or obj.tags.all() data['tags'] = [tag.name for tag in tags] # Append any extra data if extra is not None: data.update(extra) # Copy keys to list to avoid 'dictionary changed size during iteration' exception for key in list(data): # Private fields shouldn't be logged in the object change if isinstance(key, str) and key.startswith('_'): data.pop(key) return data

tests/components/history/test_init.py

async def test_statistics_during_period(recorder_mock, hass, hass_ws_client, caplog): now = dt_util.utcnow() await async_setup_component(hass, "history", {}) client = await hass_ws_client() # Test the WS API works and issues a warning await client.send_json( { "id": 1, "type": "history/statistics_during_period", "start_time": now.isoformat(), "end_time": now.isoformat(), "statistic_ids": ["sensor.test"], "period": "hour", } ) response = await client.receive_json() assert response["success"] assert response["result"] == {} assert ( "WS API 'history/statistics_during_period' is deprecated and will be removed in " "Home Assistant Core 2022.12. Use 'recorder/statistics_during_period' instead" ) in caplog.text # Test the WS API forwards to recorder with patch( "homeassistant.components.history.recorder_ws.ws_handle_get_statistics_during_period", wraps=ws_handle_get_statist

Ensure recorder test fixture is setup before hass fixture (#80528) * Ensure recorder test fixture is setup before hass fixture * Adjust more tests

test_statistics_during_period

31a787558fd312331b55e5c2c4b33341fc3601fc

core

test_init.py

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

Python

async def test_statistics_during_period(recorder_mock, hass, hass_ws_client, caplog): now = dt_util.utcnow() await async_setup_component(hass, "history", {}) client = await hass_ws_client() # Test the WS API works and issues a warning await client.send_json( { "id": 1, "type": "history/statistics_during_period", "start_time": now.isoformat(), "end_time": now.isoformat(), "statistic_ids": ["sensor.test"], "period": "hour", } ) response = await client.receive_json() assert response["success"] assert response["result"] == {} assert ( "WS API 'history/statistics_during_period' is deprecated and will be removed in " "Home Assistant Core 2022.12. Use 'recorder/statistics_during_period' instead" ) in caplog.text # Test the WS API forwards to recorder with patch( "homeassistant.components.history.recorder_ws.ws_handle_get_statistics_during_period", wraps=ws_handle_get_statistics_during_period, ) as ws_mock: await client.send_json( { "id": 2, "type": "history/statistics_during_period", "start_time": now.isoformat(), "end_time": now.isoformat(), "statistic_ids": ["sensor.test"], "period": "hour", } ) await client.receive_json() ws_mock.assert_awaited_once()

wagtail/search/management/commands/update_index.py

def queryset_chunks(self, qs, chunk_size=DEFAULT_CHUNK_SIZE): i = 0

Reformat with black

queryset_chunks

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

update_index.py

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

Python

def queryset_chunks(self, qs, chunk_size=DEFAULT_CHUNK_SIZE): i = 0 while True: items = list(qs[i * chunk_size :][:chunk_size]) if not items: break yield items i += 1

src/sentry/search/events/builder.py

def flattened_having(self) -> List[Condition]: flattened: List[Condition] = [] boolean_conditions: List[BooleanCondition] = [] for condition in self.having: if isinstance(condition, Condition): flattened.append(condition) elif isinstance(condition, BooleanCondition): boolean_conditions.append(condition) while len(boolean_conditions) > 0: boolean_condition = boolean_conditions.pop() for condition in boolean_condition.conditions: if isinstance(condition, Condition): flattened.append(condition)

fix(snql): Add aggregations to select in auto_aggregation (#31061) - This is to fix an issue for queries that have the uniq aggregation in the HAVING clause, and is not selected. - Previously we would not add the aggregation to the select clause in these cases - Now anything in the having clause will get added to the select clause as well if auto_aggregation is enabled - if its disabled we raise an invalid search query error - This also fixes a bug where this having validation wasn't working correctly for boolean conditions

flattened_having

2a4da479b2d4a2faa901701f4c73ff823236e9e8

sentry

builder.py

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

Python

def flattened_having(self) -> List[Condition]: flattened: List[Condition] = [] boolean_conditions: List[BooleanCondition] = [] for condition in self.having: if isinstance(condition, Condition): flattened.append(condition) elif isinstance(condition, BooleanCondition): boolean_conditions.append(condition) while len(boolean_conditions) > 0: boolean_condition = boolean_conditions.pop() for condition in boolean_condition.conditions: if isinstance(condition, Condition): flattened.append(condition) elif isinstance(condition, BooleanCondition): boolean_conditions.append(condition) return flattened

pandas/core/arrays/datetimelike.py

def _add_timedelta_arraylike(self, other): # overridden by PeriodArray if len(self) != len(other): raise ValueError("cannot add indices of unequal length") if isinstance(other, np.ndarray):

EA interface: rename ExtensionArray._hasnans to ._hasna (#45519)

_add_timedelta_arraylike

a0b40c0f2ad73420a54e48ec4f564b9667e3f452

pandas

datetimelike.py

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

Python

def _add_timedelta_arraylike(self, other): # overridden by PeriodArray if len(self) != len(other): raise ValueError("cannot add indices of unequal length") if isinstance(other, np.ndarray): # ndarray[timedelta64]; wrap in TimedeltaIndex for op from pandas.core.arrays import TimedeltaArray other = TimedeltaArray._from_sequence(other) self_i8 = self.asi8 other_i8 = other.asi8 new_values = checked_add_with_arr( self_i8, other_i8, arr_mask=self._isnan, b_mask=other._isnan ) if self._hasna or other._hasna: mask = self._isnan | other._isnan np.putmask(new_values, mask, iNaT) return type(self)(new_values, dtype=self.dtype)

keras/engine/functional.py

def _map_graph_network(inputs, outputs): # "depth" is number of layers between output Node and the Node. # Nodes are ordered from inputs -> outputs. nodes_in_decreasing_depth, layer_indices = _build_map(outputs) network_nodes = { _make_node_key(node.layer.name, node.layer._inbound_nodes.index(node)) for node in nodes_in_decreasing_depth } nodes_depths = {} # dict {node: depth value} layers_depths = {} # dict {layer: depth value} for node in reversed(nodes_in_decreasing_depth): # If the depth is not set, the node has no outbound nodes (depth 0). depth = nodes_depths.setdefault(node, 0) # Update the depth of the corresponding layer previous_depth = layers_depths.get(node.layer, 0) # If we've seen this layer before at a higher depth, # we should use that depth instead of the node depth. # This is necessary for shared layers that have inputs at different # depth levels in the graph. depth = max(depth, previous_depth) layers_depths[node.layer] = depth nodes_depths[node] = depth # Update the depth of inbound nodes. # The "depth" of a node is the max of the depths # of all nodes it is connected to + 1. for node_dep in node.parent_nodes: previous_depth = nodes_depths.get(node_dep, 0) nodes_depths[node_dep] = max(depth + 1, previous_depth) # Handle inputs that are not connected to outputs. # We do not error out here because the inputs may be used to compute losses # and metrics. for input_t in inputs: input_layer = input_t._keras_history[0] if input_layer not in layers_depths: layers_depths[input_layer] = 0 layer_indices[input_layer] = -1 nodes_depths[input_layer._inbound_nodes[0]] = 0 network_nodes.add(_make_node_key(input_layer.name, 0)) # Build a dict {depth: list of nodes with this depth} nodes_by_depth = collections.defaultdict(list) for node, depth in nodes_depths.items(): nodes_by_depth[depth].append(node) # Build a dict {depth: list of layers with this depth} layers_by_depth = collections.defaultdict(list) for layer, depth in layers_depths.items(): layers_by_depth[depth].append(layer) # Get sorted list of layer depths. depth_keys = list(layers_by_depth.keys()) depth_keys.sort(reverse=True) # Set self.layers ordered by depth. layers = [] for depth in depth_keys: layers_for_depth = layers_by_depth[depth] # Network.layers needs to have a deterministic order: # here we order them by traversal order. layers_for_depth.sort(key=lambda x: layer_indices[x]) layers.extend(layers_for_depth) # Get sorted list of node depths. depth_keys = list(nodes_by_depth.keys()) depth_keys.sort(reverse=True) # Check that all tensors required are computable. # computable_tensors: all tensors in the graph # that can be computed from the inputs provided. computable_tensors = set() for x in inputs: computable_tensors.add(id(x)) layers_with_complete_input = [] # To provide a better error msg. for depth in depth_keys: for node in nodes_by_depth[depth]: layer = node.layer if layer and not node.is_input: for x in tf.nest.flatten(node.keras_inputs): if id(x) not in computable_tensors:

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

_map_graph_network

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

functional.py

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

Python

def _map_graph_network(inputs, outputs): # "depth" is number of layers between output Node and the Node. # Nodes are ordered from inputs -> outputs. nodes_in_decreasing_depth, layer_indices = _build_map(outputs) network_nodes = { _make_node_key(node.layer.name, node.layer._inbound_nodes.index(node)) for node in nodes_in_decreasing_depth } nodes_depths = {} # dict {node: depth value} layers_depths = {} # dict {layer: depth value} for node in reversed(nodes_in_decreasing_depth): # If the depth is not set, the node has no outbound nodes (depth 0). depth = nodes_depths.setdefault(node, 0) # Update the depth of the corresponding layer previous_depth = layers_depths.get(node.layer, 0) # If we've seen this layer before at a higher depth, # we should use that depth instead of the node depth. # This is necessary for shared layers that have inputs at different # depth levels in the graph. depth = max(depth, previous_depth) layers_depths[node.layer] = depth nodes_depths[node] = depth # Update the depth of inbound nodes. # The "depth" of a node is the max of the depths # of all nodes it is connected to + 1. for node_dep in node.parent_nodes: previous_depth = nodes_depths.get(node_dep, 0) nodes_depths[node_dep] = max(depth + 1, previous_depth) # Handle inputs that are not connected to outputs. # We do not error out here because the inputs may be used to compute losses # and metrics. for input_t in inputs: input_layer = input_t._keras_history[0] if input_layer not in layers_depths: layers_depths[input_layer] = 0 layer_indices[input_layer] = -1 nodes_depths[input_layer._inbound_nodes[0]] = 0 network_nodes.add(_make_node_key(input_layer.name, 0)) # Build a dict {depth: list of nodes with this depth} nodes_by_depth = collections.defaultdict(list) for node, depth in nodes_depths.items(): nodes_by_depth[depth].append(node) # Build a dict {depth: list of layers with this depth} layers_by_depth = collections.defaultdict(list) for layer, depth in layers_depths.items(): layers_by_depth[depth].append(layer) # Get sorted list of layer depths. depth_keys = list(layers_by_depth.keys()) depth_keys.sort(reverse=True) # Set self.layers ordered by depth. layers = [] for depth in depth_keys: layers_for_depth = layers_by_depth[depth] # Network.layers needs to have a deterministic order: # here we order them by traversal order. layers_for_depth.sort(key=lambda x: layer_indices[x]) layers.extend(layers_for_depth) # Get sorted list of node depths. depth_keys = list(nodes_by_depth.keys()) depth_keys.sort(reverse=True) # Check that all tensors required are computable. # computable_tensors: all tensors in the graph # that can be computed from the inputs provided. computable_tensors = set() for x in inputs: computable_tensors.add(id(x)) layers_with_complete_input = [] # To provide a better error msg. for depth in depth_keys: for node in nodes_by_depth[depth]: layer = node.layer if layer and not node.is_input: for x in tf.nest.flatten(node.keras_inputs): if id(x) not in computable_tensors: raise ValueError( f"Graph disconnected: cannot obtain value for tensor {x} " f'at layer "{layer.name}". The following previous layers ' f"were accessed without issue: {layers_with_complete_input}" ) for x in tf.nest.flatten(node.outputs): computable_tensors.add(id(x)) layers_with_complete_input.append(layer.name) # Ensure name unicity, which will be crucial for serialization # (since serialized nodes refer to layers by their name). all_names = [layer.name for layer in layers] for name in all_names: if all_names.count(name) != 1: raise ValueError( f'The name "{name}" is used {all_names.count(name)} ' "times in the model. All layer names should be unique." ) return network_nodes, nodes_by_depth, layers, layers_by_depth

python/ray/data/dataset.py

def default_batch_format(self) -> Type: # noqa: E501 import pandas as pd import pyarrow as pa schema = self.schema() assert isinstance(schema,

[Datasets] Add `Dataset.default_batch_format` (#28434) Participants in the PyTorch UX study couldn't understand how the "native" batch format works. This PR introduces a method Dataset.native_batch_format that tells users exactly what the native batch format is, so users don't have to guess.

default_batch_format

206e847694cba414dc4664e4ae02b20e10e3f25d

ray

dataset.py

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

Python

def default_batch_format(self) -> Type: # noqa: E501 import pandas as pd import pyarrow as pa schema = self.schema() assert isinstance(schema, (type, PandasBlockSchema, pa.Schema)) if isinstance(schema, type): return list if isinstance(schema, (PandasBlockSchema, pa.Schema)): if schema.names == [VALUE_COL_NAME]: return np.ndarray return pd.DataFrame

@pytest.mark.parametrize("Tree", REG_TREES.values()) @pytest.mark.parametrize( "old_criterion, new_criterion", [ ("mse", "squared_error"), ("mae", "absolute_error"), ], )

sklearn/tree/tests/test_tree.py

def test_decision_tree_regressor_sample_weight_consistency(criterion): tree_params = dict(criterion=criterion) tree = DecisionTreeRegressor(**tree_params, random_state=42) for kind in ["zeros", "ones"]: check_sample_weights_invariance( "DecisionTreeRegressor_" + criterion, tree, kind="zeros" ) rng = np.random.RandomState(0) n_samples, n_features = 10, 5 X = rng.rand(n_samples, n_features) y = np.mean(X, axis=1) + rng.rand(n_samples) # make it positive in order to work also for poisson criterion y += np.min(y) + 0.1 # check that multiplying sample_weight by 2 is equivalent # to repeating corresponding samples twice X2 = np.concatenate([X, X[: n_samples // 2]], axis=0) y2 = np.concatenate([y, y[: n_samples // 2]]) sample_weight_1 = np.ones(len(y)) sample_weight_1[: n_samples // 2] = 2 tree1 = DecisionTreeRegressor(**tree_params).fit( X, y, sample_weight=sample_weight_1 ) tree2 = DecisionTreeRegressor(**tree_params).fit(X2, y2, sample_weight=None) assert tree1.tree_.node_count == tree2.tree_.node_count # Thresholds, tree.tree_.threshold, and values, tree.tree_.value, are not # exactly the same, but on the training set, those differences do not # matter and thus predictions are the same. assert_allclose(tree1.predict(X), tree2.predict(X)) # TODO: Remove in v1.2 @pytest.mark.parametrize("Tree", REG_TREES.values()) @pytest.mark.parametrize( "old_criterion, new_criterion", [ ("mse", "squared_error"),

MNT fix typo in tree test name (#22943)

test_decision_tree_regressor_sample_weight_consistency

f89a40bd92004368dee38ea76a1b9eaddaff4d7a

scikit-learn

test_tree.py

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

Python

def test_decision_tree_regressor_sample_weight_consistency(criterion): tree_params = dict(criterion=criterion) tree = DecisionTreeRegressor(**tree_params, random_state=42) for kind in ["zeros", "ones"]: check_sample_weights_invariance( "DecisionTreeRegressor_" + criterion, tree, kind="zeros" ) rng = np.random.RandomState(0) n_samples, n_features = 10, 5 X = rng.rand(n_samples, n_features) y = np.mean(X, axis=1) + rng.rand(n_samples) # make it positive in order to work also for poisson criterion y += np.min(y) + 0.1 # check that multiplying sample_weight by 2 is equivalent # to repeating corresponding samples twice X2 = np.concatenate([X, X[: n_samples // 2]], axis=0) y2 = np.concatenate([y, y[: n_samples // 2]]) sample_weight_1 = np.ones(len(y)) sample_weight_1[: n_samples // 2] = 2 tree1 = DecisionTreeRegressor(**tree_params).fit( X, y, sample_weight=sample_weight_1 ) tree2 = DecisionTreeRegressor(**tree_params).fit(X2, y2, sample_weight=None) assert tree1.tree_.node_count == tree2.tree_.node_count # Thresholds, tree.tree_.threshold, and values, tree.tree_.value, are not # exactly the same, but on the training set, those differences do not # matter and thus predictions are the same. assert_allclose(tree1.predict(X), tree2.predict(X)) # TODO: Remove in v1.2 @pytest.mark.parametrize("Tree", REG_TREES.values()) @pytest.mark.parametrize( "old_criterion, new_criterion", [ ("mse", "squared_error"), ("mae", "absolute_error"), ], )

tests/admin_views/tests.py

def test_with_fk_to_field(self): response = self.client.get( reverse("admin:auth_user_changelist") + "?q=joe&%s=id" % TO_FIELD_V

Refs #33476 -- Reformatted code with Black.

test_with_fk_to_field

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_with_fk_to_field(self): response = self.client.get( reverse("admin:auth_user_changelist") + "?q=joe&%s=id" % TO_FIELD_VAR ) self.assertContains(response, "\n1 user\n") self.assertContains( response, '<input type="hidden" name="%s" value="id">' % TO_FIELD_VAR, html=True, )

fastai/torch_core.py

def remove_module_load(state_dict): new_state_dict = OrderedDict() fo

Upgrading to support latest Pytorch version

remove_module_load

4fc3616712edb19179b17dd270ad6cf63abf99c2

DeOldify

torch_core.py

https://github.com/jantic/DeOldify.git

Python

def remove_module_load(state_dict): new_state_dict = OrderedDict() for k, v in state_dict.items(): new_state_dict[k[7:]] = v return new_state_dict

@add_start_docstrings( "The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.", LAYOUTLMV3_START_DOCSTRING, )

src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py

def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) LAYOUTLMV3_START_DOCSTRING = r LAYOUTLMV3_INPUTS_DOCSTRING = r @add_start_docstrings( "The bare LayoutLMv3 Model transformer outputting raw hidden-states w

[LayoutLMv3] Add TensorFlow implementation (#18678) Co-authored-by: Esben Toke Christensen <esben.christensen@visma.com> Co-authored-by: Lasse Reedtz <lasse.reedtz@visma.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

serving

de8548ebf3242305d0f9792dacb6f86b196a3a33

transformers

modeling_tf_layoutlmv3.py

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

Python

def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) LAYOUTLMV3_START_DOCSTRING = r LAYOUTLMV3_INPUTS_DOCSTRING = r @add_start_docstrings( "The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.", LAYOUTLMV3_START_DOCSTRING, )

homeassistant/components/sisyphus/media_player.py

def media_image_url(self): if self._table.active_track: return self._table.active_track.get_th

Sisyphus: Fix bad super call (#63327) Co-authored-by: Franck Nijhof <git@frenck.dev>

media_image_url

9f0805f51293851096d7ece48f48a041e4a809e0

core

media_player.py

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

Python

def media_image_url(self): if self._table.active_track: return self._table.active_track.get_thumbnail_url(Track.ThumbnailSize.LARGE) return super().media_image_url

@pytest.mark.parametrize( "ongoing_requests", [[7, 1, 8, 4], [8, 1, 8, 4], [6, 1, 8, 4], [0, 1, 8, 4]])

python/ray/serve/tests/test_autoscaling_policy.py

def test_fluctuating_ongoing_requests(delay_s): config = AutoscalingConfig( min_replicas=1, max_replicas=10, target_num_ongoing_requests_per_replica=50, upscale_delay_s=delay_s, downscale_delay_s=delay_s) policy = BasicAutoscalingPolicy(config) if delay_s > 0: wait_periods = int(delay_s / CONTROL_LOOP_PERIOD_S) assert wait_periods > 1 underload_requests, overload_requests = [20, 20], [100] trials = 1000 new_num_replicas = None for trial in range(trials): if trial % 2 == 0: new_num_replicas = policy.get_decision_num_replicas( current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1) if delay_s > 0: assert new_num_replicas == 1, trial else: assert new_num_replicas == 2, trial else: new_num_replicas = policy.get_decision_num_replicas( current_num_ongoing_requests=underload_requests, curr_target_num_replicas=2) if delay_s > 0: assert new_num_replicas == 2, trial else:

[Serve] Serve Autoscaling Release tests (#21208)

test_fluctuating_ongoing_requests

75b3080834bceb184e9ba19e21511eb0ea19955b

ray

test_autoscaling_policy.py

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

Python

def test_fluctuating_ongoing_requests(delay_s): config = AutoscalingConfig( min_replicas=1, max_replicas=10, target_num_ongoing_requests_per_replica=50, upscale_delay_s=delay_s, downscale_delay_s=delay_s) policy = BasicAutoscalingPolicy(config) if delay_s > 0: wait_periods = int(delay_s / CONTROL_LOOP_PERIOD_S) assert wait_periods > 1 underload_requests, overload_requests = [20, 20], [100] trials = 1000 new_num_replicas = None for trial in range(trials): if trial % 2 == 0: new_num_replicas = policy.get_decision_num_replicas( current_num_ongoing_requests=overload_requests, curr_target_num_replicas=1) if delay_s > 0: assert new_num_replicas == 1, trial else: assert new_num_replicas == 2, trial else: new_num_replicas = policy.get_decision_num_replicas( current_num_ongoing_requests=underload_requests, curr_target_num_replicas=2) if delay_s > 0: assert new_num_replicas == 2, trial else: assert new_num_replicas == 1, trial @pytest.mark.parametrize( "ongoing_requests", [[7, 1, 8, 4], [8, 1, 8, 4], [6, 1, 8, 4], [0, 1, 8, 4]])

modules/audio/svs/diffsinger/utils/audio.py

def librosa_pad_lr(x, fsize, fshift, pad_sides=1):

Add Diffsinger Module (#2120) * add diffsinger * update README * update README

librosa_pad_lr

7eef3bfde63d03acbd1fc9a15a5e56bef47c0ef7

PaddleHub

audio.py

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

Python

def librosa_pad_lr(x, fsize, fshift, pad_sides=1): assert pad_sides in (1, 2) # return int(fsize // 2) pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0] if pad_sides == 1: return 0, pad else: return pad // 2, pad // 2 + pad % 2 # Conversions

jax/tools/colab_tpu.py

def setup_tpu(tpu_driver_version='tpu_driver-0.2'): global TPU_DRIVER_MODE if not TPU_DRIVER_MODE: colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0] url = f

Pin default jax.tools.colab_tpu.setup_tpu driver version. Prior to this change, we were defaulting to the TPU nightly driver version. We should instead pin to the version associated with the default jaxlib version that Colab uses.

setup_tpu

0cc4066bb7bf758a5ba8c5def9c2c32a1c98fb89

jax

colab_tpu.py

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

Python

def setup_tpu(tpu_driver_version='tpu_driver-0.2'): global TPU_DRIVER_MODE if not TPU_DRIVER_MODE: colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0] url = f'http://{colab_tpu_addr}:8475/requestversion/{tpu_driver_version}' requests.post(url) TPU_DRIVER_MODE = 1 # The following is required to use TPU Driver as JAX's backend. config.FLAGS.jax_xla_backend = "tpu_driver" config.FLAGS.jax_backend_target = "grpc://" + os.environ['COLAB_TPU_ADDR']

tests/storage/databases/main/test_lock.py

def test_timeout_lock(self): lock = self.get_success(self.store.try_acquire_lock("name", "key")) assert lock is not None self.get_success(lock.__aenter__()) # We simulate the process getting stuck by cancelling the looping call # that keeps the lock active.

Add type hints for `tests/unittest.py`. (#12347) In particular, add type hints for get_success and friends, which are then helpful in a bunch of places.

test_timeout_lock

f0b03186d96305fd44d74a89bf4230beec0c5c31

synapse

test_lock.py

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

Python

def test_timeout_lock(self): lock = self.get_success(self.store.try_acquire_lock("name", "key")) assert lock is not None self.get_success(lock.__aenter__()) # We simulate the process getting stuck by cancelling the looping call # that keeps the lock active. lock._looping_call.stop() # Wait for the lock to timeout. self.reactor.advance(2 * _LOCK_TIMEOUT_MS / 1000) lock2 = self.get_success(self.store.try_acquire_lock("name", "key")) self.assertIsNotNone(lock2) self.assertFalse(self.get_success(lock.is_still_valid()))

keras/preprocessing/image.py

def random_brightness(x, brightness_range, scale=True): if len(brightness_range) != 2: raise ValueError( "`brightness_range should be tuple or list of two floats. " "Received: %s" % (brightness_range,) ) u = np.random.uniform(brightness_range[0], bri

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

random_brightness

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

image.py

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

Python

def random_brightness(x, brightness_range, scale=True): if len(brightness_range) != 2: raise ValueError( "`brightness_range should be tuple or list of two floats. " "Received: %s" % (brightness_range,) ) u = np.random.uniform(brightness_range[0], brightness_range[1]) return apply_brightness_shift(x, u, scale)

tests/util/test_async.py

def test_check_loop_sync(caplog): hasync.check_loop() assert "Detected block

Warn on`time.sleep` in event loop (#63766) Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

test_check_loop_sync

dc58bc375ae203e3d394225f9c3a5a14d43cb2f3

core

test_async.py

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

Python

def test_check_loop_sync(caplog): hasync.check_loop() assert "Detected blocking call inside the event loop" not in caplog.text

python/ray/serve/_private/deployment_state.py

def update(self) -> bool: try: # Add or remove DeploymentReplica instances in self._replicas. # This should be the only place we adjust total number of replicas # we manage. running_replicas_changed = self._scale_deployment_replicas() # Check the state of existing replicas and transition if necessary. running_replicas_changed |= self._check_and_update_replicas() if running_replicas_changed: self._notify_running_replicas_changed() deleted = self._check_curr_status() except Exception: self._curr_status_info = DeploymentStatusInfo( name=self._name, status=DeploymentStatus.UNHEALTHY,

[Serve] add alpha gRPC support (#28175)

update

65d0c0aa48be8f9f7faae857d3ab71444997755a

ray

deployment_state.py

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

Python

def update(self) -> bool: try: # Add or remove DeploymentReplica instances in self._replicas. # This should be the only place we adjust total number of replicas # we manage. running_replicas_changed = self._scale_deployment_replicas() # Check the state of existing replicas and transition if necessary. running_replicas_changed |= self._check_and_update_replicas() if running_replicas_changed: self._notify_running_replicas_changed() deleted = self._check_curr_status() except Exception: self._curr_status_info = DeploymentStatusInfo( name=self._name, status=DeploymentStatus.UNHEALTHY, message="Failed to update deployment:" f"\n{traceback.format_exc()}", ) deleted = False return deleted

tests/csrf_tests/tests.py

def test_https_malformed_host(self): req = self._get_request(method="POST") req._is_secure_override = True req.META["HTTP_HOST"] = "@malformed" req.META["HTTP_REFERER"] = "https://www.evil.org/somepage" req.META["S

Refs #33476 -- Reformatted code with Black.

test_https_malformed_host

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_https_malformed_host(self): req = self._get_request(method="POST") req._is_secure_override = True req.META["HTTP_HOST"] = "@malformed" req.META["HTTP_REFERER"] = "https://www.evil.org/somepage" req.META["SERVER_PORT"] = "443" mw = CsrfViewMiddleware(token_view) expected = ( "Referer checking failed - https://www.evil.org/somepage does not " "match any trusted origins." ) with self.assertRaisesMessage(RejectRequest, expected): mw._check_referer(req) response = mw.process_view(req, token_view, (), {}) self.assertEqual(response.status_code, 403)

airbyte-integrations/connectors/source-orb/source_orb/source.py

def stream_slices(self, **kwargs) -> Iterable[Optional[Mapping[str, Any]]]: # TODO: self.authenticator should optionally pull from sel

🎉 New Source: Orb (#9985) * V1 of source_orb connector * add boostrap.md file * add clause on Pagination to bootstrap.md * add SUMMARY documentation * add lookback_window_days connector parameter * Add support for start_date parameter * Add ability to transform record in order to un-nest IDs * Add support for extracting event properties based on connector configuration

stream_slices

1e0ac30ebdcfce55a5644bcd486044da45c93dd6

airbyte

source.py

https://github.com/airbytehq/airbyte.git

Python

def stream_slices(self, **kwargs) -> Iterable[Optional[Mapping[str, Any]]]: # TODO: self.authenticator should optionally pull from self._session.auth customers_stream = Customers(authenticator=self._session.auth) for customer in customers_stream.read_records(sync_mode=SyncMode.full_refresh): yield {"customer_id": customer["id"]}

keras/utils/metrics_utils.py

def ragged_assert_compatible_and_get_flat_values(values, mask=None): if isinstance(values, list): is_all_ragged = all(isinstance(rt, tf.RaggedTensor) for rt in values) is_any_ragged = any(isinstance(rt, tf.RaggedTensor) for rt in values) else: is_all_ragged = isinstance(values, tf.RaggedTensor) is_any_ragged = is_all_ragged if is_all_ragged and ((mask is None) or isinstance(mask, tf.RaggedTensor)): to_be_stri

resolve line-too-long in utils

ragged_assert_compatible_and_get_flat_values

80ee2fa4e1db2dda14370110830db82be3eb97b7

keras

metrics_utils.py

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

Python

def ragged_assert_compatible_and_get_flat_values(values, mask=None): if isinstance(values, list): is_all_ragged = all(isinstance(rt, tf.RaggedTensor) for rt in values) is_any_ragged = any(isinstance(rt, tf.RaggedTensor) for rt in values) else: is_all_ragged = isinstance(values, tf.RaggedTensor) is_any_ragged = is_all_ragged if is_all_ragged and ((mask is None) or isinstance(mask, tf.RaggedTensor)): to_be_stripped = False if not isinstance(values, list): values = [values] to_be_stripped = True # NOTE: we leave the flat_values compatibility to # tf.TensorShape `assert_is_compatible_with` check if both dynamic # dimensions are equal and then use the flat_values. nested_row_split_list = [rt.nested_row_splits for rt in values] assertion_list = _assert_splits_match(nested_row_split_list) # if both are ragged sample_weights also should be ragged with same # dims. if isinstance(mask, tf.RaggedTensor): assertion_list_for_mask = _assert_splits_match( [nested_row_split_list[0], mask.nested_row_splits] ) with tf.control_dependencies(assertion_list_for_mask): mask = tf.expand_dims(mask.flat_values, -1) # values has at least 1 element. flat_values = [] for value in values: with tf.control_dependencies(assertion_list): flat_values.append(tf.expand_dims(value.flat_values, -1)) values = flat_values[0] if to_be_stripped else flat_values elif is_any_ragged: raise TypeError( "Some of the inputs are not tf.RaggedTensor. " f"Input received: {values}" ) # values are empty or value are not ragged and mask is ragged. elif isinstance(mask, tf.RaggedTensor): raise TypeError( "Ragged mask is not allowed with non-ragged inputs. " f"Input received: {values}, mask received: {mask}" ) return values, mask

jina/orchestrate/deployments/__init__.py

def update_sandbox_args(self): if self.is_sandbox: host, port = HubIO.deploy_public_sandbox(self.args) self._sandbox_deployed = True self.first_pod_args.host = host self.first_pod_args.port = port if self.head_args: self.pod_args['head'].host = h

fix: do not deploy sandbox on init (#4844)

update_sandbox_args

7c4c39a9d82c58ef2493c21a288c755901a9594e

jina

__init__.py

https://github.com/jina-ai/jina.git

Python

def update_sandbox_args(self): if self.is_sandbox: host, port = HubIO.deploy_public_sandbox(self.args) self._sandbox_deployed = True self.first_pod_args.host = host self.first_pod_args.port = port if self.head_args: self.pod_args['head'].host = host self.pod_args['head'].port = port

d2l/mxnet.py

def download_all(): for name in DATA_HUB: download(name) DATA_HUB['kaggle_house_train'] = ( DATA_URL + 'kaggle_house_pred_train.csv', '585e9cc93e70b39160e7921475f9bcd7d31219ce') DATA_HUB['kaggle_house_test'] = ( DATA_URL + 'kaggle_house

[PaddlePaddle] Merge master into Paddle branch (#1186) * change 15.2 title in chinese version (#1109) change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘ * 修改部分语义表述 (#1105) * Update r0.17.5 (#1120) * Bump versions in installation * 94行typo: （“bert.mall”）->（“bert.small”） (#1129) * line 313: "bert.mall" -> "bert.small" (#1130) * fix: update language as native reader (#1114) * Fix the translation of "stride" (#1115) * Update index.md (#1118) 修改部分语义表述 * Update self-attention-and-positional-encoding.md (#1133) 依照本书的翻译习惯，将pooling翻译成汇聚 * maybe a comment false (#1149) * maybe a little false * maybe a little false * A minor bug in the rcnn section (Chinese edition) (#1148) * Update bert.md (#1137) 一个笔误 # 假设batch_size=2，num_pred_positions=3 # 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1] * Update calculus.md (#1135) * fix typo in git documentation (#1106) * fix: Update the Chinese translation in lr-scheduler.md (#1136) * Update lr-scheduler.md * Update chapter_optimization/lr-scheduler.md Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * fix translation for kaggle-house-price.md (#1107) * fix translation for kaggle-house-price.md * fix translation for kaggle-house-price.md Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com> * Update weight-decay.md (#1150) * Update weight-decay.md 关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解 关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。 并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释 解释为何会增加复杂性以及为何需要细粒度工具。 * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Fix a spelling error (#1161) * Update gru.md (#1152) The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state. 翻译错误 * Unify the function naming (#1113) Unify naming of the function 'init_xavier()'. * Update mlp-concise.md (#1166) * Update mlp-concise.md 语句不通顺 * Update environment.md 语序异常 * Update config.ini * fix the imprecise description (#1168) Co-authored-by: yuande <yuande> * fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175) * Fix some typos. (#1163) * Update batch-norm.md (#1170) fixing typos u->x in article * Update linear-regression.md (#1090) We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that 原译文把who也直接翻译出来了。 * Update mlp.md (#1117) * Update mlp.md 修改部分语义表述 * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Correct a translation error. (#1091) * Correct a translation error. * Update chapter_computer-vision/image-augmentation.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update aws.md (#1121) * Update aws.md * Update chapter_appendix-tools-for-deep-learning/aws.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update image-augmentation.md (#1093) * Update anchor.md (#1088) fix a minor issue in code * Update anchor.md * Update image-augmentation.md * fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087) * Avoid `torch.meshgrid` user warning (#1174) Avoids the following user warning: ```python ~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] ``` * bump to 2.0.0-beta1 * Update sequence.md * bump beta1 on readme * Add latex code block background to config * BLD: Bump python support version 3.9 (#1183) * BLD: Bump python support version 3.9 * Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4 * BLD: Bump torch and tensorflow * Update Jenkinsfile * Update chapter_installation/index.md * Update chapter_installation/index.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update config.ini * Update INFO.md * Update INFO.md * Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187) * resolve the conflicts * revise from publisher (#1089) * revise from publisher * d2l api * post_latex * revise from publisher * revise ch11 * Delete d2l-Copy1.bib * clear cache * rm d2lbook clear * debug anchor * keep original d2l doc Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> * 重复语句 (#1188) Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Improve expression for chapter_preliminaries/pandas.md (#1184) * Update pandas.md * Improve expression * Improve expression * Update chapter_preliminaries/pandas.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Improce expression for chapter_preliminaries/linear-algebra.md (#1185) * Improce expression * Improve code comments * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Fix multibox_detection bugs * Update d2l to 0.17.5 version * restore older version * Upgrade pandas * change to python3.8 * Test warning log * relocate warning log * test logs filtering * Update gru.md * Add DeprecationWarning filter * Test warning log * Update attention mechanisms & computational performance * Update multilayer perceptron& linear & convolution networks & computer vision * Update recurrent&optimition&nlp pretraining & nlp applications * ignore warnings * Update index.md * Update linear networks * Update multilayer perceptrons&deep learning computation * Update preliminaries * Check and Add warning filter * Update kaggle-cifar10.md * Update object-detection-dataset.md * Update ssd.md fcn.md * Update hybridize.md * Update hybridize.md Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com> Co-authored-by: zhou201505013 <39976863+zhou201505013@users.noreply.github.com> Co-authored-by: Xinwei Liu <xinzone@outlook.com> Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: hugo_han <57249629+HugoHann@users.noreply.github.com> Co-authored-by: gyro永不抽风 <1247006353@qq.com> Co-authored-by: CanChengZheng <zcc550169544@163.com> Co-authored-by: linlin <jajupmochi@gmail.com> Co-authored-by: iuk <liukun0104@gmail.com> Co-authored-by: yoos <49556860+liyunlongaaa@users.noreply.github.com> Co-authored-by: Mr. Justice Lawrence John Wargrave <65226618+RUCWargrave@users.noreply.github.com> Co-authored-by: Chiyuan Fu <fuchiyuan2019@outlook.com> Co-authored-by: Sunhuashan <48636870+Sunhuashan@users.noreply.github.com> Co-authored-by: Haiker Sun <haizhou.uestc2011@gmail.com> Co-authored-by: Ming Liu <akira.liu@njnu.edu.cn> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: silenceZheng66 <13754430639@163.com> Co-authored-by: Wenchao Yan <56541797+YWonchall@users.noreply.github.com> Co-authored-by: Kiki2049 <55939997+Kiki2049@users.noreply.github.com> Co-authored-by: Krahets <krahets@163.com> Co-authored-by: friedmainfunction <73703265+friedmainfunction@users.noreply.github.com> Co-authored-by: Jameson <miraclecome@gmail.com> Co-authored-by: P. Yao <12227516+YaoPengCN@users.noreply.github.com> Co-authored-by: Yulv-git <34329208+Yulv-git@users.noreply.github.com> Co-authored-by: Liu,Xiao <45966993+liuxiao916@users.noreply.github.com> Co-authored-by: YIN, Gang <1246410+yingang@users.noreply.github.com> Co-authored-by: Joe-HZ <58297431+Joe-HZ@users.noreply.github.com> Co-authored-by: lybloveyou <102609904+lybloveyou@users.noreply.github.com> Co-authored-by: VigourJiang <jiangfuqiang154@163.com> Co-authored-by: zxhd863943427 <74853597+zxhd863943427@users.noreply.github.com> Co-authored-by: LYF <27893441+liyufan@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> Co-authored-by: xiaotinghe <xiaotih@amazon.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Holly-Max <60691735+Holly-Max@users.noreply.github.com> Co-authored-by: HinGwenWoong <peterhuang0323@qq.com> Co-authored-by: Shuai Zhang <cheungdaven@gmail.com>

download_all

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

d2l-zh

mxnet.py

https://github.com/d2l-ai/d2l-zh.git