Datasets:

semeru

/

Galeras-3k_deduped

like 0

.venv/lib/python3.8/site-packages/pip/_vendor/toml/decoder.py

upd; format

transferlearning

Python

decoder.py

def _getpath(p): if (3, 6) <= sys.version_info: import os return os.fspath(p) if _detect_pathlib_path(p): return str(p) return p try: FNFError = FileNotFoundError except NameError: FNFError = IOError TIME_RE = re.compile(r"([0-9]{2}):([0-9]{2}):([0-9]{2})(\.([0-9]{3,6}))?")

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def _getpath(p): if (3, 6) <= sys.version_info: import os return os.fspath(p) if _detect_pathlib_path(p): return str(p) return p try: FNFError = FileNotFoundError except NameError:

_getpath

wagtail/core/tests/test_blocks.py

Reformat with black

wagtail

Python

test_blocks.py

def test_validate_non_required_choice_block(self): block = blocks.ChoiceBlock( choices=[("tea", "Tea"), ("coffee", "Coffee")], required=False ) self.assertEqual(block.clean("coffee"), "coffee") with self.assertRaises(ValidationError): block.clean("whisky") self.assertEqual(block.clean(""), "") self.assertEqual(block.clean(None), "")

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def test_validate_non_required_choice_block(self): block = blocks.ChoiceBlock( choices=[("tea", "Tea"), ("coffee", "Coffee")], required=False ) self.assertEqual(block.clean("coffee"), "coffee") with self.a

test_validate_non_required_choice_block

seaborn/tests/_core/test_subplots.py

Refactor figure setup and subplot metadata tracking into Subplots class Squashed commit of the following: commit e6f99078d46947eab678b9dd0303657a3129f9fc Author: Michael Waskom <mwaskom@nyu.edu> Date: Sun Aug 1 17:56:49 2021 -0400 Address a couple TODOs commit c48ba3af8095973b7dca9554934a695751f58726 Author: Michael Waskom <mwaskom@nyu.edu> Date: Mon Jul 26 06:42:29 2021 -0400 Add docstrings in Subplots commit 97e6465b0f998f541b445b189682fbf134869391 Author: Michael Waskom <mwaskom@nyu.edu> Date: Sun Jul 25 17:53:22 2021 -0400 Fix unshared label visibility test commit e2d93a28313c2cb9170e56b2e4b373987993be7c Author: Michael Waskom <mwaskom@nyu.edu> Date: Sun Jul 25 17:16:41 2021 -0400 Add more label visibility tests commit 698ee72b5d5f9f3939c50cde9e2baacdf5487807 Author: Michael Waskom <mwaskom@nyu.edu> Date: Sat Jul 24 11:08:32 2021 -0400 Begin adding label visibility tests commit 97167b4701532eeccadaa899520d57e38c26dd43 Author: Michael Waskom <mwaskom@nyu.edu> Date: Mon Jul 19 06:55:48 2021 -0400 Fix interior tick labels with unshared axes commit 9331d5d91a7861aebfe03fa86ee122902c0d1d8a Author: Michael Waskom <mwaskom@nyu.edu> Date: Sat Jul 17 17:03:48 2021 -0400 Fix interior labels for wrapped plots commit 38f2efa7e732958430c006f24827c6ac69640ef3 Author: Michael Waskom <mwaskom@nyu.edu> Date: Sat Jul 17 16:03:34 2021 -0400 Fix non-cartesian interior labels commit 3c07f981110890d38aee19b38c43080863132122 Author: Michael Waskom <mwaskom@nyu.edu> Date: Sat Jul 17 15:44:48 2021 -0400 Integrate Subplots into Plot commit 841a3c998eae8f8cc85fd65af7ea8e6f32fc5510 Author: Michael Waskom <mwaskom@nyu.edu> Date: Sat Jul 17 13:00:09 2021 -0400 Complete subplots tests commit 8ceb7e6c35ea0cbcd014067035d7ea219204f464 Author: Michael Waskom <mwaskom@nyu.edu> Date: Fri Jul 16 19:45:29 2021 -0400 Continue building out subplot tests commit b0ce0e7a9e3534fdad04ef9e287e4c6bb19fe684 Author: Michael Waskom <mwaskom@nyu.edu> Date: Thu Jul 15 21:35:21 2021 -0400 Continue building out subplots tests commit 5f4b67d4d90cde7d0d899527b1fd8607348a5f5b Author: Michael Waskom <mwaskom@nyu.edu> Date: Wed Jul 14 20:57:35 2021 -0400 Add some tests for Subplots functionality commit 58fbf8e3f349174f4d1d29f71fa867ad4b49d264 Author: Michael Waskom <mwaskom@nyu.edu> Date: Sun Jul 11 20:49:29 2021 -0400 Begin refactoring figure setup into Subplots class commit 6bb853e20ad3b42b2728d212a51ed8de2ff47bde Author: Michael Waskom <mwaskom@nyu.edu> Date: Sun Jul 11 16:02:26 2021 -0400 Fix overlooked lint and test

seaborn

Python

test_subplots.py

def test_col_facet_wrapped(self, long_df): key = "b" wrap = 3 data = PlotData(long_df, {"col": key}) s = Subplots({}, {"wrap": wrap}, {}, data) n_levels = len(categorical_order(long_df[key])) assert s.n_subplots == n_levels assert s.subplot_spec["ncols"] == wrap assert s.subplot_spec["nrows"] == n_levels // wrap + 1 assert s.subplot_spec["sharex"] is True assert s.subplot_spec["sharey"] is True

c16180493bd44fd76092fdd9ea0060bac91e47fe

https://github.com/mwaskom/seaborn.git

def test_col_facet_wrapped(self, long_df): key = "b" wrap = 3 data = PlotData(long_df, {"col": key}) s = Subplots({}, {"wrap": wrap}, {}, data) n_levels = len(categorical_order(long_df[key])) assert s.n_subplots == n_levels assert s.subplot_spec["ncols"] == wrap assert s.subplot_spec["nrows"] == n_levels // wrap + 1 assert s.subplot_spec["sharex"] is True as

test_col_facet_wrapped

homeassistant/components/command_line/switch.py

Improve code quality command_line (#65333)

core

Python

switch.py

def _query_state(self) -> str | int | None: if self._command_state: if self._value_template: return self._query_state_value(self._command_state) return self._query_state_code(self._command_state) if TYPE_CHECKING: return None

3771c154fa0ea8e0b49d41ece55a7a18c444ee6a

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

def _query_state(self) -> str | int | None:

_query_state

mkdocs/tests/config/config_options_tests.py

MarkdownExtensions' default is an empty list

mkdocs

Python

config_options_tests.py

def test_missing_default(self): option = config_options.MarkdownExtensions() config = {} config['markdown_extensions'] = option.validate(None) option.post_validation(config, 'markdown_extensions') self.assertEqual( { 'markdown_extensions': [], 'mdx_configs': {}, }, config, )

2c986996d041f0059b4d3c2ff4bd647cadeb68de

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

def test_missing_default(self): option = config_options.MarkdownExtensions() config = {} config['markdown_extensions'] = option.validate(None) option.post_validation(config, 'markdown_extensions') self.assertEqual( {

test_missing_default

tests/components/number/test_init.py

Align number and sensor device classes (#81909) * Align number and sensor device classes * Add tests * Tweak tests

core

Python

test_init.py

def test_device_classes_aligned(): non_numeric_device_classes = { SensorDeviceClass.DATE, SensorDeviceClass.DURATION, SensorDeviceClass.TIMESTAMP, } for device_class in SensorDeviceClass: if device_class in non_numeric_device_classes: continue assert hasattr(NumberDeviceClass, device_class.name) assert getattr(NumberDeviceClass, device_class.name).value == device_class.value

b6586d5c34bf7ea5c30fbb1b62c438078ea14f39

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

def test_device_classes_aligned(): non_numeric_device_classes = { SensorDeviceClass.DATE, SensorDeviceClass.DURATION, SensorDeviceClass.TIMESTAMP, } for device_class in SensorDeviceClass: if device_class in non_numeric_device_classes: continue assert hasattr(NumberDeviceClass, device_class.name) assert getattr(NumberDeviceClass, device_class.name).va

test_device_classes_aligned

python/ray/internal/internal_api.py

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

ray

Python

internal_api.py

def store_stats_summary(reply): store_summary = "--- Aggregate object store stats across all nodes ---\n" # TODO(ekl) it would be nice if we could provide a full memory usage # breakdown by type (e.g., pinned by worker, primary, etc.) store_summary += ( "Plasma memory usage {} MiB, {} objects, {}% full, {}% " "needed\n".format( int(reply.store_stats.object_store_bytes_used / (1024 * 1024)), reply.store_stats.num_local_objects, round( 100 * reply.store_stats.object_store_bytes_used / reply.store_stats.object_store_bytes_avail, 2, ), round( 100 * reply.store_stats.object_store_bytes_primary_copy / reply.store_stats.object_store_bytes_avail, 2, ), ) ) if reply.store_stats.object_store_bytes_fallback > 0: store_summary += "Plasma filesystem mmap usage: {} MiB\n".format( int(reply.store_stats.object_store_bytes_fallback / (1024 * 1024)) ) if reply.store_stats.spill_time_total_s > 0: store_summary += ( "Spilled {} MiB, {} objects, avg write throughput {} MiB/s\n".format( int(reply.store_stats.spilled_bytes_total / (1024 * 1024)), reply.store_stats.spilled_objects_total, int( reply.store_stats.spilled_bytes_total / (1024 * 1024) / reply.store_stats.spill_time_total_s ), ) ) if reply.store_stats.restore_time_total_s > 0: store_summary += ( "Restored {} MiB, {} objects, avg read throughput {} MiB/s\n".format( int(reply.store_stats.restored_bytes_total / (1024 * 1024)), reply.store_stats.restored_objects_total, int( reply.store_stats.restored_bytes_total / (1024 * 1024) / reply.store_stats.restore_time_total_s ), ) ) if reply.store_stats.consumed_bytes > 0: store_summary += "Objects consumed by Ray tasks: {} MiB.\n".format( int(reply.store_stats.consumed_bytes / (1024 * 1024)) ) if reply.store_stats.object_pulls_queued: store_summary += "Object fetches queued, waiting for available memory." return store_summary

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def store_stats_summary(reply): store_summary = "--- Aggregate object store stats across all nodes ---\n" # TODO(ekl) it would be nice if we could provide a full memory usage # breakdown by type (e.g., pinned by worker, primary, etc.) store_summary += ( "Plasma memory usage {} MiB, {} objects, {}% full, {}% " "needed\n".format( int(reply.store_stats.object_store_bytes_used / (1024 * 1024)), reply.store_stats.num_local_objects, round( 100 * reply.store_stats.object_store_bytes_used / reply.store_stats.object_store_bytes_avail, 2, ), round( 100 * reply.store_stats.object_store_bytes_primary_copy / reply.store_stats.object_store_bytes_avail, 2, ), ) ) if reply.store_stats.object_store_bytes_fallback > 0: store_summary += "Plasma filesystem mmap usage: {} MiB\n".format( int(reply.store_stats.object_store_bytes_fallback / (1024 * 1024)) ) if reply.store_stats.spill_time_total_s > 0: store_summary += ( "Spilled {} MiB, {} objects, avg write throughput {} MiB/s\n".format( int(reply.store_stats.spilled_bytes_total / (1024 * 1024)), reply.store_stats.spilled_objects_total, int( reply.store_stats.spilled_bytes_total / (1024 * 1024) / reply.store_stats.spill_time_total_s ), )

store_stats_summary

mitmproxy/contrib/kaitaistruct/png.py

update kaitai definitions

mitmproxy

Python

png.py

def _read(self): self.magic = self._io.read_bytes(8) if not self.magic == b"\x89\x50\x4E\x47\x0D\x0A\x1A\x0A": raise kaitaistruct.ValidationNotEqualError(b"\x89\x50\x4E\x47\x0D\x0A\x1A\x0A", self.magic, self._io, u"/seq/0") self.ihdr_len = self._io.read_u4be() if not self.ihdr_len == 13: raise kaitaistruct.ValidationNotEqualError(13, self.ihdr_len, self._io, u"/seq/1") self.ihdr_type = self._io.read_bytes(4) if not self.ihdr_type == b"\x49\x48\x44\x52": raise kaitaistruct.ValidationNotEqualError(b"\x49\x48\x44\x52", self.ihdr_type, self._io, u"/seq/2") self.ihdr = Png.IhdrChunk(self._io, self, self._root) self.ihdr_crc = self._io.read_bytes(4) self.chunks = [] i = 0 while True: _ = Png.Chunk(self._io, self, self._root) self.chunks.append(_) if ((_.type == u"IEND") or (self._io.is_eof())) : break i += 1

002f919dda5f01d067c2e786426c68751551d15c

https://github.com/mitmproxy/mitmproxy.git

def _read(self): self.magic = self._io.read_bytes(8) if not self.magic == b"\x89\x50\x4E\x47\x0D\x0A\x1A\x0A": raise kaitaistruct.ValidationNotEqualError(b"\x89\x50\x4E\x47\x0D\x0A\x1A\x0A", self.magic, self._io, u"/seq/0") self.ihdr_len = self._io.read_u4be() if not self.ihdr_len == 13: raise kaitaistruct.ValidationNotEqualError(13, self.ihdr_len, self._io, u"/seq/1") self.ihdr_type = self._io.read_bytes(4) if not self.ihdr_type == b"\x49\x48\x44\x52": raise kaitaistruct.ValidationNotEqualError(b"\x49\x48\x44\x52", self.ihdr_type, self._io, u"/seq/2") self.ihdr = Png.IhdrChunk(self._io, self, self._root) self.ihdr_crc = self._io.read_bytes(4) self.chunks = [] i = 0 while True: _ = Png.Chunk(self._io, self, self._root) self.chunks.append(_) if ((_.type == u"IEND") or (self._io.is_eof())) : break i += 1

_read

keras/optimizers/optimizer_v2/adamax_test.py

resolve line-too-long in optimizer

keras

Python

adamax_test.py

def testSlotsUniqueEager(self): v1 = tf.Variable(1.0) v2 = tf.Variable(1.0) opt = adamax.Adamax(1.0) opt.minimize(lambda: v1 + v2, var_list=[v1, v2]) # There should be iteration, and two unique slot variables for v1 and # v2. self.assertLen({id(v) for v in opt.variables()}, 5)

406774b60ac6b505ae9bf7e8728b00a1523ad4a3

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

def testSlotsUniqueEager(self): v1 = tf.Var

testSlotsUniqueEager

ivy/backends/jax/core/general.py

renamed dev_str arg to dev for all methods.

ivy

Python

general.py

def identity(n, dtype='float32', batch_shape=None, dev=None): dtype = _jnp.__dict__[dtype] mat = _jnp.identity(n, dtype=dtype) if batch_shape is None: return_mat = mat else: reshape_dims = [1]*len(batch_shape) + [n, n] tile_dims = list(batch_shape) + [1, 1] return_mat = _jnp.tile(_jnp.reshape(mat, reshape_dims), tile_dims) return to_dev(return_mat, default_device(dev)) meshgrid = lambda *xs, indexing='ij': _jnp.meshgrid(*xs, indexing=indexing)

d743336b1f3654cd0315f380f43eed4116997c1d

https://github.com/unifyai/ivy.git

def identity(n, dtype='float32', batch_shape=None, dev=None): dtype = _jnp.__dict__[dtype] mat = _jnp.iden

identity

jax/experimental/sparse/bcoo.py

[sparse] Move broadcasting_vmap to sparse util. PiperOrigin-RevId: 478566197

jax

Python

bcoo.py

def _bcoo_multiply_dense(data, indices, v, *, spinfo): # TODO(jakevdp): the logic here is similar to bcoo_extract... can we reuse that? shape = spinfo.shape if v.ndim == 0: return lax.mul(data, v) if shape == v.shape: # Note: due to distributive property, no deduplication necessary! return lax.mul(data, bcoo_extract(indices, v)) if lax.broadcast_shapes(v.shape, shape) != shape: raise NotImplementedError( "multiplication between sparse and dense is only implemented for cases " "where the output shape matches the sparse matrix shape. Got " f"shape={shape}, v.shape={v.shape}") v = lax.expand_dims(v, range(len(shape) - v.ndim)) props = _validate_bcoo(data, indices, shape) def _mul(data, indices, v): assert indices.shape[1] == v.ndim - props.n_dense ind = tuple(indices[:, i] for i in range(indices.shape[1])) ind = tuple(i if s != 1 else 0 for i, s in zip(ind, v.shape)) return data * v[ind] for _ in range(props.n_batch): _mul = _broadcasting_vmap(_mul) return _mul(data, indices, v) @tree_util.register_pytree_node_class

58a2abe1b5496acb177a5fd10394e001c381bff9

@tree_util.register_pytree_node_class

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

def _bcoo_multiply_dense(data, indices, v, *, spinfo): # TODO(jakevdp): the logic here is similar to bcoo_extract... can we reuse that? shape = spinfo.shape if v.ndim == 0: return lax.mul(data, v) if shape == v.shape: # Note: due to distributive property, no deduplication necessary! return lax.mul(data, bcoo_extract(indices, v)) if lax.broadcast_shapes(v.shape, shape) != shape: raise NotImplementedError( "multiplication between sparse and dense is only implemented for cases " "where the output shape matches the sparse matrix shape. Got " f"shape={shape}, v.shape={v.shape}") v = lax.expand_dims(v, range(len(shape) - v.ndim)) props = _validate_bcoo(data, indices, shape) def _mul(data, indices, v): assert indices.shape[1] == v.ndim - props.n_dense ind = tuple(indices[:, i] for i in range(indices.shape[1])) ind = tuple(i if s != 1 else 0 for i, s in zip(ind, v.shape)) return data * v[ind] for _ in range(props.n_batch): _mul = _broadcasting_vmap(_mul) return _m

_bcoo_multiply_dense

rllib/evaluation/tests/test_env_runner_v2.py

[RLlib] Make sure we step() after adding init_obs. (#26827)

ray

Python

test_env_runner_v2.py

def test_sample_batch_rollout_single_agent_env(self): config = ( PPOConfig() .framework("torch") .training( # Specifically ask for a batch of 200 samples. train_batch_size=200, ) .rollouts( num_envs_per_worker=1, horizon=4, num_rollout_workers=0, # Enable EnvRunnerV2. enable_connectors=True, ) ) algo = PPO(config, env=DebugCounterEnv) rollout_worker = algo.workers.local_worker() sample_batch = rollout_worker.sample() self.assertEqual(sample_batch.env_steps(), 200) self.assertEqual(sample_batch.agent_steps(), 200)

0bc560bd541c320b0699464e8d23134c07899c18

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

def test_sample_batch_rollout_single_agent_env(self): config = ( PPOConfig() .framework("torch") .training( # Specifically ask for a batch of 200 samples. train_batch_size=200, ) .rollouts( num_envs_per_worker=1, horizon=4,

test_sample_batch_rollout_single_agent_env

freqtrade/wallets.py

Add dry-run position wallet calculation

freqtrade

Python

wallets.py

def _update_dry(self) -> None: # Recreate _wallets to reset closed trade balances _wallets = {} _positions = {} open_trades = Trade.get_trades_proxy(is_open=True) # If not backtesting... # TODO: potentially remove the ._log workaround to determine backtest mode. if self._log: tot_profit = Trade.get_total_closed_profit() else: tot_profit = LocalTrade.total_profit tot_in_trades = sum(trade.stake_amount for trade in open_trades) used_stake = 0.0 if self._config.get('trading_mode', 'spot') != TradingMode.FUTURES: current_stake = self.start_cap + tot_profit - tot_in_trades total_stake = current_stake for trade in open_trades: curr = self._exchange.get_pair_base_currency(trade.pair) _wallets[curr] = Wallet( curr, trade.amount, 0, trade.amount ) else: tot_in_trades = 0 for position in open_trades: # size = self._exchange._contracts_to_amount(position.pair, position['contracts']) size = position.amount # TODO-lev: stake_amount in real trades does not include the leverage ... collateral = position.stake_amount / position.leverage leverage = position.leverage tot_in_trades -= collateral _positions[position.pair] = PositionWallet( position.pair, position=size, leverage=leverage, collateral=collateral, side=position.trade_direction ) current_stake = self.start_cap + tot_profit used_stake = tot_in_trades total_stake = current_stake - tot_in_trades _wallets[self._config['stake_currency']] = Wallet( currency=self._config['stake_currency'], free=current_stake, used=used_stake, total=total_stake ) self._wallets = _wallets self._positions = _positions

13e74c5693e68ddb6b7afa4559ac23d2ec8ee26c

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

def _update_dry(self) -> None: # Recreate _wallets to reset closed trade balances _wallets = {} _positions = {} open_trades = Trade.get_trades_proxy(is_open=True) # If not backtesting... # TODO: potentially remove the ._log workaround to determine backtest mode. if self._log: tot_profit = Trade.get_total_closed_profit() else: tot_profit = LocalTrade.total_profit tot_in_trades = sum(trade.stake_amount for trade in open_trades) used_stake = 0.0 if self._config.get('trading_mode', 'spot') != TradingMode.FUTURES: current_stake = self.start_cap + tot_profit - tot_in_trades total_stake = current_stake for trade in open_trades: curr = self._exchange.get_pair_base_currency(trade.pair) _wallets[curr] = Wallet( curr, trade.amount, 0, trade.amount ) else: tot_in_trades = 0 for position in open_trades: # size = self._exchange._contracts_to_amount(position.pair, position['contracts']) size = position.amount # TODO-lev: stake_amount in real trades does not include the leverage ... collateral = position.stake_amount / position.leverage leverage = position.leverage tot_in_trades -= collateral _positions[position.pair] = PositionWallet( position.pair, position=size, leverage=leverage, collateral=collateral, side=position.trade_direction ) current_stake = self.start_cap + tot_profit used_stak

_update_dry

jina/orchestrate/deployments/__init__.py

feat: distributed replicas across different hosts (#5217)

jina

Python

__init__.py

def update_pod_args(self): if self.args.runtime_cls == 'GatewayRuntime': _set_gateway_uses(self.args) if isinstance(self.args, Dict): # This is used when a Deployment is created in a remote context, where pods & their connections are already given. self.pod_args = self.args else: self.pod_args = self._parse_args(self.args) if self.external: for pod, port, host, scheme, tls in zip( self.pod_args['pods'][0], self.ext_repl_ports, self.ext_repl_hosts, self.ext_repl_schemes, self.ext_repl_tls, ): pod.port = port pod.host = host pod.scheme = scheme pod.tls = tls

82960f105149c478e4fc88e8b4fef8bbe2454429

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

def update_pod_args(self): if self.args.runtime_cls == 'GatewayRuntime': _set_gateway_uses(self.args) if isinstance(self.args, Dict): # This is used when a Deployment is created in a remote context, where pods & their connections are already given. self.pod_args = self.args else: self.pod_args = self._parse

update_pod_args

python/ray/tune/trial.py

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

ray

Python

trial.py

def get_json_state(self) -> str: if not self._state_json or not self._state_valid: json_state = json.dumps( self.__getstate__(), indent=2, cls=TuneFunctionEncoder ) self._state_json = json_state self._state_valid = True return self._state_json

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def get_json_state(self) -> str:

get_json_state

mkdocs/tests/plugin_tests.py

Format code with `black -l100 --skip-string-normalization`

mkdocs

Python

plugin_tests.py

def test_plugin_config_multivalue_dict(self, mock_class): cfg = { 'plugins': [ { 'sample': { 'foo': 'foo value', 'bar': 42, }, 'extra_key': 'baz', } ], } option = config.config_options.Plugins() with self.assertRaises(config.base.ValidationError): option.validate(cfg['plugins'])

dca7cbb43fcd6ea7c677c98ba585395b070d387b

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

def test_plugin_config_multivalue_dict(self, mock_class): cfg = { 'plugins': [ { 'sample': { 'foo': 'foo value', 'bar': 42, }, 'extra_key':

test_plugin_config_multivalue_dict

homeassistant/components/justnimbus/entity.py

Fix Just Nimbus error codes (#83856)

core

Python

entity.py

def available(self) -> bool: return super().available and self.coordinator.data is not None

cc5d3193698c107d6b56f6001ffb7707fb77bdef

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

def available(self) -> bool: retu

available

pandas/tests/util/test_assert_almost_equal.py

DEPR: Remove check_less_precise in asserters (#49461)

pandas

Python

test_assert_almost_equal.py

def test_assert_almost_equal_numbers_atol(a, b): # Equivalent to the deprecated check_less_precise=True, enforced in 2.0 _assert_almost_equal_both(a, b, rtol=0.5e-3, atol=0.5e-3) @pytest.mark.parametrize("a,b", [(1.1, 1.11), (0.1, 0.101), (0.000011, 0.001012)])

490c5d049890d8ea71ec5e2dc4ffa6196c10cc63

@pytest.mark.parametrize("a,b", [(1.1, 1.11), (0.1, 0.101), (0.000011, 0.001012)])

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

def test_assert_almost_equal_numbers_atol(a, b): # Equivalent to the deprecated check_less_precise=True, enfor

test_assert_almost_equal_numbers_atol

pandas/tests/io/parser/test_converters.py

BUG: read_csv not respecting converter in all cases for index col (#46053)

pandas

Python

test_converters.py

def test_converter_identity_object(all_parsers): # GH#40589 parser = all_parsers data = "A,B\n1,2\n3,4" rs = parser.read_csv(StringIO(data), converters={"A": lambda x: x}) xp = DataFrame({"A": ["1", "3"], "B": [2, 4]}) tm.assert_frame_equal(rs, xp)

7ee8ab07e538de55bd02f1ed5c2d211c7e342ddc

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

def test_converter_identity_object(all_parsers): # GH#40589 parser = all_parsers data = "A,B\n1,2\n3,4" rs = parser.re

test_converter_identity_object

keras/layers/preprocessing/discretization_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

discretization_test.py

def test_one_hot_output(self): input_data = np.array([-1.5, 1.0, 3.4, 3.5]) expected_output = [ [1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 1.0], ] expected_output_shape = [None, 4] inputs = keras.Input(shape=(1,)) layer = discretization.Discretization( bin_boundaries=[0.0, 1.0, 2.0], output_mode="one_hot" ) outputs = layer(inputs) self.assertAllEqual(expected_output_shape, outputs.shape.as_list()) model = keras.Model(inputs, outputs) output_data = model(input_data) self.assertAllEqual(expected_output, output_data)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def test_one_hot_output(self): input_data = np.array([-1.5, 1.0, 3.4, 3.5]) expected_output = [ [1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 1.0], ] expected_output_shape = [None, 4] inputs = keras.Input(shape=(1,)) layer = discretization.Discretization( bin_boundaries=[0.0, 1.0, 2.0], output_mode="one_hot" ) outputs = layer(inputs) self.assertAllEqual(expected_output_shape, outputs.shape.as_list()) model = keras.Model(inputs, outputs) output_data = model(input_data) self.assertAllEqual(expect

test_one_hot_output

python/ray/tune/tests/test_trial_scheduler.py

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

ray

Python

test_trial_scheduler.py

def testMedianStoppingSoftStop(self): rule = MedianStoppingRule( metric="episode_reward_mean", mode="max", grace_period=0, min_samples_required=1, hard_stop=False, ) t1, t2 = self.basicSetup(rule) runner = mock_trial_runner() rule.on_trial_complete(runner, t1, result(10, 1000)) rule.on_trial_complete(runner, t2, result(10, 1000)) t3 = Trial("PPO") self.assertEqual( rule.on_trial_result(runner, t3, result(1, 260)), TrialScheduler.CONTINUE ) self.assertEqual( rule.on_trial_result(runner, t3, result(2, 260)), TrialScheduler.PAUSE )

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def testMedianStoppingSoftStop(self): rule = MedianStoppingRule( metric="episode_reward_mean", mode="max", grace_period=0, min_samples_required=1, hard_stop=False, ) t1, t2 = self.basicSetup(rule) runner = mock_trial_runner() rule.on_trial_complete(runner, t1, result(10, 1000)) rule.on_trial_complete(runner, t2, result(10, 1000)) t3 = Trial("PPO") self.assertEqual( rule.on_trial_result(runner, t3, result(1, 260)), TrialScheduler.CONTINUE ) self.assertEqual( rule.on_trial_result(runner, t3, result(2, 260)), TrialScheduler.

testMedianStoppingSoftStop

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

upd; format

transferlearning

Python

pyparsing.py

def copy(self): ret = ParseResults(self.__toklist) ret.__tokdict = dict(self.__tokdict.items()) ret.__parent = self.__parent ret.__accumNames.update(self.__accumNames) ret.__name = self.__name return ret

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def copy(self): ret = ParseResults(self.__toklist) ret.__tokdict = dict(self.__tokdict.items()) ret.__parent = self.__parent ret.

copy

d2l/mxnet.py

sync lib

d2l-zh

Python

mxnet.py

def read_csv_labels(fname): with open(fname, 'r') as f: # 跳过文件头行(列名) lines = f.readlines()[1:] tokens = [l.rstrip().split(',') for l in lines] return dict(((name, label) for name, label in tokens))

1c2e25a557db446b5691c18e595e5664cc254730

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

def read_csv_labels(fname): with open(fname, 'r') as f:

read_csv_labels

tests/unit/config/test_configexc.py

Display close matches for invalid settings

qutebrowser

Python

test_configexc.py

def test_no_option_error(deleted, renamed, all_names, expected): e = configexc.NoOptionError( 'opt', deleted=deleted, renamed=renamed, all_names=all_names, ) assert e.option == 'opt' assert str(e) == expected

c9380605a1240748769c012403520323b4d2c3be

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

def test_no_option_error(deleted, renamed, all_names, expected): e = configexc.NoOptionError( 'opt', deleted=deleted, renamed=renamed, all_names=all_names, ) assert e.option == 'opt' assert str(e) == expected

test_no_option_error

tests/pytests/functional/utils/win_dacl/test_file.py

Add changelong

salt

Python

test_file.py

def test_has_permission_missing(test_file): result = win_dacl.set_permissions( obj_name=str(test_file), principal="Backup Operators", permissions="read_execute", access_mode="grant", obj_type="file", reset_perms=False, protected=None, ) assert result is True # Test has_permission not exact result = win_dacl.has_permission( obj_name=str(test_file), principal="Backup Operators", permission="write", access_mode="grant", obj_type="file", exact=False, ) assert result is False

5550d1823e9cb571740ae9e57b25424cfe6a919e

https://github.com/saltstack/salt.git

def test_has_permission_missing(test_file):

test_has_permission_missing

modin/pandas/test/test_series.py

FIX-#4411: Fix binary_op between datetime64 Series and pandas timedelta (#4592) Signed-off-by: Karthik Velayutham <vkarthik@ponder.io>

modin

Python

test_series.py

def test_add_series_to_timedeltaindex(): # Make a pandas.core.indexes.timedeltas.TimedeltaIndex deltas = pd.to_timedelta([1], unit="h") test_series = create_test_series(np.datetime64("2000-12-12")) eval_general(*test_series, lambda s: s + deltas) eval_general(*test_series, lambda s: s - deltas) @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)

af7f4ed8ff0033a9a4e7d35a948f2057033bd826

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)

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

def test_add_series_to_timedeltaindex(): # Make a pandas.core.indexes.timedeltas.TimedeltaIndex deltas = pd.to_timedelta([1], unit="h") test_series = create_test_series(np.datetim

test_add_series_to_timedeltaindex

pandas/core/indexing.py

TYP: Ignore numpy related issues (#45244)

pandas

Python

indexing.py

def _ensure_iterable_column_indexer(self, column_indexer): ilocs: Sequence[int] if is_integer(column_indexer): ilocs = [column_indexer] elif isinstance(column_indexer, slice): ilocs = np.arange(len(self.obj.columns))[ # type: ignore[assignment] column_indexer ] elif isinstance(column_indexer, np.ndarray) and is_bool_dtype( column_indexer.dtype ): ilocs = np.arange(len(column_indexer))[column_indexer] else: ilocs = column_indexer return ilocs

d603d43df2057ecdf74010d9dadc735e37f8f7b5

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

def _ensure_iterable_column_indexer(self, column_indexer): ilocs: Sequence[int] if is_integer(column_indexer): ilocs = [column_indexer] elif isinstance(column_indexer, slice): ilocs = np.arange(len(self.obj.columns))[ # type: ignore[assignment] column_

_ensure_iterable_column_indexer

test/nodes/test_prompt_node.py

feat: Expand LLM support with PromptModel, PromptNode, and PromptTemplate (#3667) Co-authored-by: ZanSara <sarazanzo94@gmail.com>

haystack

Python

test_prompt_node.py

def test_run_invalid_template(prompt_node): with pytest.raises(ValueError, match="invalid-task not supported"): prompt_node.prompt("invalid-task", {})

9ebf164cfdfb320503b7161493420c1b0ec577a3

https://github.com/deepset-ai/haystack.git

def test_run_invalid_template(prompt_node): with pytest.raises(

test_run_invalid_template

tests/unit/bokeh/test_objects.py

Redesign serialization protocol (#11960) * Redesign serialization in bokeh * Redesign deserialization in bokehjs * Resolve type issues and test failures * Make 'bytes' serialization work in bokeh * Partially update bokeh's serialization tests * Resolve issues with cyclic references * Don't limit StaticGraphProvider to tuples * Make FixedTicker.ticks' type more flexible * Use np.array instead of np.ndarray * Remove references to BokehJSONEncoder * Resolve sphinx warnings related to JSON * Implement hybrid serialization for map/dict * Use === or !== with unset symbol * Finalize deserialization of refs * Remove 'old' attribute from ModelChangedEvent * Make ButtonClick.__init__ less restrictive * Use Map<number, ...> in StaticLayoutProvider.graph_layout * Start using Map<K, V> for non-string keys * Fix plotting/file/line_on_off example * Don't denormalize specs in bokehjs * Hack around issues with resolving figure model * Remove special cases from defaults' tests * Temporarily update unit/bokeh/test_objects * Promote streaming/patching events and remove hints * Allow to stream/patch any property in bokehjs * Drop unneeded Property.serializable_value() * Set callback_invoker on hinted events * Simplify unit/bokeh/test_objects.py * Always preserve ndarrays even for dtype="object" * Refine and normalize naming conventions * Remove unused functions * Move Model.to_json() to sphinxext.bokeh_model * Include references in serialized values * Actually encode data when streaming/patching * Robustify differential serialization * Allow bokehjs to send binary buffers * Add dtype=object code path to ColorSpec * Simplify definitions of data specs * Remove meaningless code comments * Introduce Bytes and replace Base64String * Add support for serialization of slices * Remove obsolete comment from property/dataspec.py * Add a comment regarding ndarray.tobytes() * Try serializing pandas' types last * Standardize error reporting * Resturucture bokehjs serialization code * Redesign default model resolution * Refactor 'kind' in document events * Don't depend on Document in Deserializer * Make Deserializer.encode() re-entrant * Move *Buffer to serialization/buffer * Finalize differential serialization * Serialize vectorized values as structures * Rename Event.{decode_json->from_serializable} * Don't use has_ref() in Model.to_serializable() * Handle circular object references in bokehjs * Reorganize serialization unit tests * Redesign model registry and qualified names * Remove the need for StaticSerializer * Make 'attributes' optional in type reps * Allow to serialize typed arrays as binary * Finalize handling of binary buffers * Use memoryview to further defer encoding * Test dict serialization and ordering * Downcast ndarrays {u}int{64->32} if possible * Add preliminary release/migration notes * Robustify encoding of objects and object refs * Remove support for serialization of relativedelta * Import pandas only if really necessary * Statically type bokeh.core.serialization * Add preliminary serialization's documentation * Add Deserializer.deserialize() for symmetric APIs * Handle streaming/patching/data events in io.notebook * Update handling of buffers in io.notebook * Properly serialize MessageSent event * Add a regression test for issue #11694 * Preserve order of inherited properties * Add support for serialization of symbols * Update defaults' tests to use type="object" * Move DocJson.version to the first entry * Add a preliminary regression test for #11930 * Fix integration/glyphs/rect_log_axis.py * Fix value detection in dataspecs involving String * Remove an unnecessary type assertion

bokeh

Python

test_objects.py

def test_get_class(self) -> None: from bokeh.model import get_class self.mkclass() tclass = get_class('test_objects.TestModelCls.mkclass.Test_Class') assert hasattr(tclass, 'foo') with pytest.raises(KeyError): get_class('Imaginary_Class')

fca16442ae90afcd2ac61f4e554e538776730831

https://github.com/bokeh/bokeh.git

def test_get_class(self) -> None: from bokeh.model import get_class self.mkclass() tclass = get_class('test_objects.TestModelCls.mkclass.Test_Class') assert hasattr(tclass, 'foo') with pytest.raises

test_get_class

src/transformers/models/xglm/modeling_tf_xglm.py

Add TF implementation of `XGLMModel` (#16543) * Add TFXGLM models * Add todo: self.supports_xla_generation = False Co-authored-by: Daniel Stancl <stancld@Daniels-MacBook-Pro.local> Co-authored-by: Daniel Stancl <stancld@daniels-mbp.home> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by: Daniel <daniel.stancl@rossum.ai> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

transformers

Python

modeling_tf_xglm.py

def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past),) return reordered_past

c72d7d91bf4899760725793421eff9da640c8527

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

def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(tf.gather(past_sta

_reorder_cache

nni/compression/pytorch/quantization/observer_quantizer.py

[Compression] remove pruning v1 & refactor directory (#5228)

nni

Python

observer_quantizer.py

def quantize_input(self, inputs, wrapper, **kwargs): if self.compressed: module = wrapper.module inputs = self._quantize(inputs, module.input_scale, module.input_zero_point, module.input_qmin, module.input_qmax) else: self.record(wrapper, 'input', inputs) return inputs

d68c786ff81bad19c04619d6a999ff34aaa724e7

https://github.com/microsoft/nni.git

def quantize_input(self, inputs, wrapper, **kwargs): if self.compressed: module = wrapper.module

quantize_input

django/db/backends/postgresql/base.py

Refs #33476 -- Reformatted code with Black.

django

Python

base.py

def psycopg2_version(): version = psycopg2.__version__.split(" ", 1)[0] return get_version_tuple(version) PSYCOPG2_VERSION = psycopg2_version() if PSYCOPG2_VERSION < (2, 8, 4): raise ImproperlyConfigured( "psycopg2 version 2.8.4 or newer is required; you have %s" % psycopg2.__version__ ) # Some of these import psycopg2, so import them after checking if it's installed. from .client import DatabaseClient # NOQA from .creation import DatabaseCreation # NOQA from .features import DatabaseFeatures # NOQA from .introspection import DatabaseIntrospection # NOQA from .operations import DatabaseOperations # NOQA from .schema import DatabaseSchemaEditor # NOQA psycopg2.extensions.register_adapter(SafeString, psycopg2.extensions.QuotedString) psycopg2.extras.register_uuid() # Register support for inet[] manually so we don't have to handle the Inet() # object on load all the time. INETARRAY_OID = 1041 INETARRAY = psycopg2.extensions.new_array_type( (INETARRAY_OID,), "INETARRAY", psycopg2.extensions.UNICODE, ) psycopg2.extensions.register_type(INETARRAY)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def psycopg2_version(): version = psycopg2.__version__.split(" ", 1)[0] return get_version_tuple(version) PSYCOPG2_VERSION = psycopg2_version() if PSYCOPG2_VERSION < (2, 8, 4): raise ImproperlyConfigured( "psycopg2 version 2.8.4 or newer is required; you have %s" % psycopg2.__version__ ) # Some of these import psycopg2, so import them after checking if it's installed. from .client import DatabaseClient # NOQA from .creation import DatabaseCreation # NOQA from .features import DatabaseFeatures # NOQA from .introspection import DatabaseIntrospection # NOQA from .operations import DatabaseOperations # NOQA from .schema import DatabaseSchemaEditor # NOQA psycopg2.extensions.register_adapter(SafeString, psycopg2.extensions.QuotedString) psycopg2.extras.register_uuid() # Register sup

psycopg2_version

netbox/utilities/forms/fields.py

Fixes #8317: Fix CSV import of multi-select custom field values

netbox

Python

fields.py

def to_python(self, value): if not value: return [] if not isinstance(value, str): raise forms.ValidationError(f"Invalid value for a multiple choice field: {value}") return value.split(',')

7421e5f7d7e579ed1a0acf840c39ae61fd851504

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

def to_python(self, value): if not value: return [] if not isinstance(value, str): raise forms.Valid

to_python

keras/distribute/dataset_creator_model_fit_test.py

resolve line-too-long in distribute

keras

Python

dataset_creator_model_fit_test.py

def testModelPredict(self, strategy): _, predictions = self._model_predict(strategy, steps=3) # Check the first (0th index), fourth (3rd index) and the last # predictions because the first, fourth and the last input are the same # in `model.predict` so there predictions should match. self.assertTrue( all(predictions[0] == predictions[i] for i in [0, 3, 5]) ) self.assertFalse( all(predictions[0] == predictions[i] for i in [0, 1, 2, 4]) )

b1105dca17670dcac229271e63d5073fe445b84c

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

def testModelPredict(self, strategy): _, predictions = self._model_predict(strategy, steps=3) # Check the first (0th index), fourth (3rd index) and the l

testModelPredict

youtube_dl/extractor/neteasemusic.py

[netease] Get netease music download url through player api (#31235) * remove unplayable song from test * compatible with python 2 * using standard User_Agent, fix imports * use hash instead of long description * fix lint * fix hash

youtube-dl

Python

neteasemusic.py

def extract_formats(self, info): formats = [] song_id = info['id'] for song_format in self._FORMATS: details = info.get(song_format) if not details: continue bitrate = int_or_none(details.get('bitrate')) or 999000 data = self._call_player_api(song_id, bitrate) for song in try_get(data, lambda x: x['data'], list) or []: song_url = try_get(song, lambda x: x['url']) if self._is_valid_url(song_url, info['id'], 'song'): formats.append({ 'url': song_url, 'ext': details.get('extension'), 'abr': float_or_none(song.get('br'), scale=1000), 'format_id': song_format, 'filesize': int_or_none(song.get('size')), 'asr': int_or_none(details.get('sr')), }) return formats

c91cbf60729af93c4677864aa6c8b74b576146ca

https://github.com/ytdl-org/youtube-dl.git

def extract_formats(self, info): formats = [] song_id = info['id'] for song_format in self._FORMATS: details = info.get(song_format) if not details: continue bitrate = int_or_none(details.get('bitrate')) or 999000 data = self._call_player_api(song_id, bitrate) for song in try_get(data, lambda x: x['data'], list) or []: song_url = try_get(song, lambda x: x['url']) if self._is_valid_url(song_url, info['id'], 'song'): formats.append({ 'url': song_url, 'ext': details.get('extension'), 'abr': float_or_none(song.get('br'), scale=1000),

extract_formats

test/lib/ansible_test/_util/controller/sanity/validate-modules/validate_modules/main.py

Extend validate-modules to also validate plugins (#71734) * Let validate-modules also validate plugins. * Support 'option' in 'cli'. * Use DOCUMENTABLE_PLUGINS instead of UNDOCUMENTED_PLUGIN_TYPES. * Support 'keyword', clean up error codes. * Call settings.process_errors only once; remove __version__. * Add changelog fragment.

ansible

Python

main.py

def _check_for_new_args(self, doc): if not self.base_branch or self._is_new_module(): return with CaptureStd(): try: existing_doc, dummy_examples, dummy_return, existing_metadata = get_docstring( self.base_module, fragment_loader, verbose=True, collection_name=self.collection_name, is_module=self.plugin_type == 'module') existing_options = existing_doc.get('options', {}) or {} except AssertionError: fragment = doc['extends_documentation_fragment'] self.reporter.warning( path=self.object_path, code='missing-existing-doc-fragment', msg='Pre-existing DOCUMENTATION fragment missing: %s' % fragment ) return except Exception as e: self.reporter.warning_trace( path=self.object_path, tracebk=e ) self.reporter.warning( path=self.object_path, code='unknown-doc-fragment', msg=('Unknown pre-existing DOCUMENTATION error, see TRACE. Submodule refs may need updated') ) return try: mod_collection_name = existing_doc.get('version_added_collection') mod_version_added = self._create_strict_version( str(existing_doc.get('version_added', '0.0')), collection_name=mod_collection_name) except ValueError: mod_collection_name = self.collection_name mod_version_added = self._create_strict_version('0.0') options = doc.get('options', {}) or {} should_be = '.'.join(ansible_version.split('.')[:2]) strict_ansible_version = self._create_strict_version(should_be, collection_name='ansible.builtin') for option, details in options.items(): try: names = [option] + details.get('aliases', []) except (TypeError, AttributeError): # Reporting of this syntax error will be handled by schema validation. continue if any(name in existing_options for name in names): # The option already existed. Make sure version_added didn't change. for name in names: existing_collection_name = existing_options.get(name, {}).get('version_added_collection') existing_version = existing_options.get(name, {}).get('version_added') if existing_version: break current_collection_name = details.get('version_added_collection') current_version = details.get('version_added') if current_collection_name != existing_collection_name: self.reporter.error( path=self.object_path, code='option-incorrect-version-added-collection', msg=('version_added for existing option (%s) should ' 'belong to collection %r. Currently belongs to %r' % (option, current_collection_name, existing_collection_name)) ) elif str(current_version) != str(existing_version): self.reporter.error( path=self.object_path, code='option-incorrect-version-added', msg=('version_added for existing option (%s) should ' 'be %r. Currently %r' % (option, existing_version, current_version)) ) continue try: collection_name = details.get('version_added_collection') version_added = self._create_strict_version( str(details.get('version_added', '0.0')), collection_name=collection_name) except ValueError as e: # already reported during schema validation continue if collection_name != self.collection_name: continue if (strict_ansible_version != mod_version_added and (version_added < strict_ansible_version or strict_ansible_version < version_added)): self.reporter.error( path=self.object_path, code='option-incorrect-version-added', msg=('version_added for new option (%s) should ' 'be %r. Currently %r' % (option, should_be, version_added)) ) return existing_doc

0990c4ca7cb1b239a76e8cdb78af01ca9601731e

https://github.com/ansible/ansible.git

def _check_for_new_args(self, doc): if not self.base_branch or self._is_new_module(): return with CaptureStd(): try: existing_doc, dummy_examples, dummy_return, existing_metadata = get_docstring( self.base_module, fragment_loader, verbose=True, collection_name=self.collection_name, is_module=self.plugin_type == 'module') existing_options = existing_doc.get('options', {}) or {} except AssertionError: fragment = doc['extends_documentation_fragment'] self.reporter.warning( path=self.object_path, code='missing-existing-doc-fragment', msg='Pre-existing DOCUMENTATION fragment missing: %s' % fragment ) return except Exception as e: self.reporter.warning_trace( path=self.object_path, tracebk=e ) self.reporter.warning( path=self.object_path, code='unknown-doc-fragment', msg=('Unknown pre-existing DOCUMENTATION error, see TRACE. Submodule refs may need updated') ) return try: mod_collection_name = existing_doc.get('version_added_collection') mod_version_added = self._create_strict_version( str(existing_doc.get('version_added', '0.0')), collection_name=mod_collection_name) except ValueError: mod_collection_name = self.collection_name mod_version_added = self._create_strict_version('0.0') options = doc.get('options', {}) or {} should_be = '.'.join(ansible_version.split('.')[:2]) strict_ansible_version = self._create_strict_version(should_be, collection_name='ansible.builtin') for option, details in options.items(): try: names = [option] + details.get('aliases', []) except (TypeError, AttributeError): # Reporting of this syntax error will be handled by schema validation. continue if any(name in existing_options for name in names): # The option already existed. Make sure versio

_check_for_new_args

release/ray_release/tests/test_cluster_manager.py

[ci/release] Refactor release test e2e into package (#22351) Adds a unit-tested and restructured ray_release package for running release tests. Relevant changes in behavior: Per default, Buildkite will wait for the wheels of the current commit to be available. Alternatively, users can a) specify a different commit hash, b) a wheels URL (which we will also wait for to be available) or c) specify a branch (or user/branch combination), in which case the latest available wheels will be used (e.g. if master is passed, behavior matches old default behavior). The main subpackages are: Cluster manager: Creates cluster envs/computes, starts cluster, terminates cluster Command runner: Runs commands, e.g. as client command or sdk command File manager: Uploads/downloads files to/from session Reporter: Reports results (e.g. to database) Much of the code base is unit tested, but there are probably some pieces missing. Example build (waited for wheels to be built): https://buildkite.com/ray-project/kf-dev/builds/51#_ Wheel build: https://buildkite.com/ray-project/ray-builders-branch/builds/6023

ray

Python

test_cluster_manager.py

def testFindCreateClusterEnvExisting(self): # Find existing env and succeed self.cluster_manager.set_cluster_env(self.cluster_env) self.assertTrue(self.cluster_manager.cluster_env_name) self.assertFalse(self.cluster_manager.cluster_env_id) self.sdk.returns["search_cluster_environments"] = APIDict( metadata=APIDict( next_paging_token=None, ), results=[ APIDict( name="no_match", id="wrong", ), APIDict(name=self.cluster_manager.cluster_env_name, id="correct"), ], ) self.cluster_manager.create_cluster_env() self.assertEqual(self.cluster_manager.cluster_env_id, "correct") self.assertEqual(self.sdk.call_counter["search_cluster_environments"], 1) self.assertEqual(len(self.sdk.call_counter), 1)

331b71ea8dfee20bd71f6529fa372fd9d91c9ff4

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

def testFindCreateClusterEnvExisting(self): # Find existing env and succeed self.cluster_manager.set_cluster_env(self.cluster_env) self.assertTrue(self.cluster_manager.cluster_env_name) self.assertFalse(self.cluster_manager.cluster_env_id) self.sdk.returns["search_cluster_environments"] = APIDict( metadata=APIDict( next_paging_token=None, ), results=[ APIDict( name="no_match", id="wrong", ), APIDict(name=self.cluster_manager.cluster_env_name, id="correct"),

testFindCreateClusterEnvExisting

sympy/algebras/tests/test_quaternion.py

minor edit

sympy

Python

test_quaternion.py

def test_to_euler(): q = Quaternion(w, x, y, z) norm_of_q = Quaternion(q.norm()) # Extrinsic rotations for seq_tuple in permutations('xyz'): # asymmetric sequences seq = ''.join(seq_tuple) euler_from_q = q.to_euler(seq) q_back = Quaternion.from_euler(euler_from_q, seq) q_diff = simplify(q * q_back.conjugate()) assert q_diff == norm_of_q # symmetric sequences seq = ''.join([seq_tuple[0], seq_tuple[1], seq_tuple[0]]) euler_from_q = q.to_euler(seq) q_back = Quaternion.from_euler(euler_from_q, seq) q_diff = simplify(q * q_back.conjugate()) assert q_diff == norm_of_q # Intrinsic rotations for seq_tuple in permutations('XYZ'): # asymmetric sequences seq = ''.join(seq_tuple) euler_from_q = q.to_euler(seq) q_back = Quaternion.from_euler(euler_from_q, seq) q_diff = simplify(q * q_back.conjugate()) assert q_diff == norm_of_q # symmetric sequences seq = ''.join([seq_tuple[0], seq_tuple[1], seq_tuple[0]]) euler_from_q = q.to_euler(seq) q_back = Quaternion.from_euler(euler_from_q, seq) q_diff = simplify(q * q_back.conjugate()) assert q_diff == norm_of_q

6fe28f68866ac6fb1aea564dbde99190cec9c1ff

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

def test_to_euler(): q = Quaternion(w, x, y, z) norm_of_q = Quaternion(q.norm()) # Extrinsic rotations for seq_tuple in permutations('xyz'): # asymmetric sequences seq = ''.join(seq_tuple) euler_from_q = q.to_euler(seq) q_back = Quaternion.from_euler(euler_from_q, seq) q_diff = simplify(q * q_back.conjugate()) assert q_diff == norm_of_q # symmetric sequences seq = ''.join([seq_tup

test_to_euler

tests/handlers/test_auth.py

Add some type hints to the tests.handlers module. (#12207)

synapse

Python

test_auth.py

def test_short_term_login_token_gives_user_id(self) -> None: token = self.macaroon_generator.generate_short_term_login_token( self.user1, "", duration_in_ms=5000 ) res = self.get_success(self.auth_handler.validate_short_term_login_token(token)) self.assertEqual(self.user1, res.user_id) self.assertEqual("", res.auth_provider_id) # when we advance the clock, the token should be rejected self.reactor.advance(6) self.get_failure( self.auth_handler.validate_short_term_login_token(token), AuthError, )

e10a2fe0c28ec9206c0e2275df492f61ff5025f2

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

def test_short_term_login_token_gives_user_id(self) -> None: token = self.macaroon_generator.generate_short_term_login_token( self.user1, "", duration_in_ms=5000 ) res = self.get_success(self.auth_handler.validate_short_term_login_token(token)) self.assertEqual(self.user1, res.user_id) self.assertEqual("", res.auth_provider_id) # when we advance the clock, the token should be rejected self.reactor.advance(6) self.get_failure( self.auth_handler.validate_short_term_login_token(token), AuthError, )

test_short_term_login_token_gives_user_id

tests/unit/serve/runtimes/worker/test_worker_runtime.py

fix: list-like args passed as string (#5464) Co-authored-by: Alaeddine Abdessalem <alaeddine-13@live.fr>

jina

Python

test_worker_runtime.py

async def test_worker_runtime_reflection(): args = _generate_pod_args() cancel_event = multiprocessing.Event()

87912a37ce7ab3c3b63c12b48d6cdfe31f81742c

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

async def test_worker_runtime_reflection(): args = _generate_pod_args() cancel_event = multiprocessing.E

test_worker_runtime_reflection

nuitka/utils/Download.py

Windows: Updated MinGW64 compiler to be used

Nuitka

Python

Download.py

def getCachedDownloadedMinGW64(target_arch, assume_yes_for_downloads): # Large URLs, pylint: disable=line-too-long if target_arch == "x86_64": url = "https://github.com/brechtsanders/winlibs_mingw/releases/download/11.2.0-14.0.0-9.0.0-msvcrt-r7/winlibs-x86_64-posix-seh-gcc-11.2.0-llvm-14.0.0-mingw-w64msvcrt-9.0.0-r7.zip" binary = r"mingw64\bin\gcc.exe" else: url = "https://github.com/brechtsanders/winlibs_mingw/releases/download/11.2.0-14.0.0-9.0.0-msvcrt-r7/winlibs-i686-posix-dwarf-gcc-11.2.0-llvm-14.0.0-mingw-w64msvcrt-9.0.0-r7.zip" binary = r"mingw32\bin\gcc.exe" gcc_binary = getCachedDownload( url=url, is_arch_specific=target_arch, specificity=url.rsplit("/", 2)[1], binary=binary, flatten=False, message="Nuitka will use gcc from MinGW64 of winlibs to compile on Windows.", reject="Only this specific gcc is supported with Nuitka.", assume_yes_for_downloads=assume_yes_for_downloads, ) return gcc_binary

c6b19fa56bbd6d14728f152e92b9001dc76dd550

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

def getCachedDownloadedMinGW64(target_arch, assume_yes_for_downloads): # Large URLs, pylint: disable=line-too-long if target_arch == "x86_64": url = "https://github.com/brechtsanders/winlibs_mingw/releases/download/11.2.0-14.0.0-9.0.0-msvcrt-r7/winlibs-x86_64-posix-seh-gcc-11.2.0-llvm-14.0.0-mingw-w64msvcrt-9.0.0-r7.zip" binary = r"mingw64\bin\gcc.exe" else: url = "https://github.com/brechtsanders/winlibs_mingw/releases/download/11.2.0-14.0.0-9.0.0-msvcrt-r7/winlibs-i686-posix-dwarf-gcc-11.2.0-llvm-14.0.0-mingw-w64msvcrt-9.0.0-r7.zip" binary = r"mingw32\bin\gcc.exe" gcc_binary = getCachedDownload( url=url, is_arch_specific=target_arch,

getCachedDownloadedMinGW64

tests/sentry/incidents/test_subscription_processor.py

fix(metric_alerts): Make sure critical triggers resolve properly when no action is set on a warning trigger (#31883) ### Problem If we have an alert set up like: - Warning: 50. Action: None - Critical: 100. Action: Slack Then if we go from critical -> warning state the slack resolve action will fail to fire. ### Cause The reason this happens is related to a previous fix. For an alert like - Warning: 50. Action: Slack - Critical: 100. Action: Slack When going from critical -> warning the critical action would be marked as resolved. This would cause a slack notification with `Resolved` to be sent to the channel. This is misleading, because the alert is still active, just in the warning state. What we want here is to fire a warning notification instead. The initial fix for this was that when we resolved a critical trigger, we’d check and see whether there was an active warning trigger. If so, we’d send a warning trigger fire to our actions, rather than a critical trigger resolve. This works ok for many cases, but fails when the actions on the warning trigger are different to those on the critical trigger. ### Fix Substituting the warning trigger for the critical trigger causes us subtle bugs. So, instead of this, when triggering fires/resolves on our action handlers we will also pass along the incident state change that the trigger/resolve caused the incident to go into. So if a critical trigger resolves, we check what state it would have put the incident in. If there’s a warning trigger, then the state is warning. If no warning trigger, the state is closed. This state is then used to appropriately generate the messages that we send to users via our various actions. So now, If we have an alert set up like: - Warning: 50. Action: None - Critical: 100. Action: Slack If this goes from - critical -> warning OR critical -> resolved we will send `IncidentStatus.WARNING` to any actions related to the critical trigger. - warning -> resolved We do nothing since there are no actions on the warning trigger If we have an alert set up like: - Warning: 50. Action: Slack - Critical: 100. Action: Slack If this goes from: - critical -> warning: critical trigger, `IncidentStatus.Warning` - warning -> resolved: warning trigger, `IncidentStatus.Closed` - critical -> resolved: Since we de-dupe triggers to avoid spamming the user, we will select the warning trigger here, and send `IncidentStatus.closed` If we have an alert set up like: - Warning: 50. Action: Slack - Critical: 100. Action: Pagerduty If this goes from: - critical -> warning: critical trigger, `IncidentStatus.Warning` sent to Pagerduty. Nothing sent to Slack - warning -> resolved: warning trigger, `IncidentStatus.Closed` sent to Slack. Nothing sent to Pagerduty - critical -> resolved: Critical trigger, `IncidentStatus.Warning` sent to Pagerduty. Warning trigger, `IncidentStatus.Closed` sent to Slack. We don’t de-dupe here since the actions are different.

sentry

Python

test_subscription_processor.py

def test_multiple_triggers(self): rule = self.rule rule.update(threshold_period=1) trigger = self.trigger warning_trigger = create_alert_rule_trigger( self.rule, WARNING_TRIGGER_LABEL, trigger.alert_threshold - 20 ) warning_action = create_alert_rule_trigger_action( warning_trigger, AlertRuleTriggerAction.Type.EMAIL, AlertRuleTriggerAction.TargetType.USER, str(self.user.id), ) processor = self.send_update( rule, warning_trigger.alert_threshold + 1, timedelta(minutes=-10), subscription=self.sub ) self.assert_trigger_counts(processor, warning_trigger, 0, 0) self.assert_trigger_counts(processor, trigger, 0, 0) incident = self.assert_active_incident(rule, self.sub) self.assert_trigger_exists_with_status(incident, warning_trigger, TriggerStatus.ACTIVE) self.assert_trigger_does_not_exist(trigger) self.assert_actions_fired_for_incident( incident, [warning_action], [(warning_trigger.alert_threshold + 1, IncidentStatus.WARNING)], ) processor = self.send_update( rule, trigger.alert_threshold + 1, timedelta(minutes=-9), subscription=self.sub ) self.assert_trigger_counts(processor, trigger, 0, 0) self.assert_trigger_counts(processor, warning_trigger, 0, 0) incident = self.assert_active_incident(rule, self.sub) self.assert_trigger_exists_with_status(incident, warning_trigger, TriggerStatus.ACTIVE) self.assert_trigger_exists_with_status(incident, trigger, TriggerStatus.ACTIVE) self.assert_actions_fired_for_incident( incident, [self.action], [(trigger.alert_threshold + 1, IncidentStatus.CRITICAL)] ) processor = self.send_update( rule, trigger.alert_threshold - 1, timedelta(minutes=-7), subscription=self.sub ) self.assert_trigger_counts(processor, trigger, 0, 0) self.assert_trigger_counts(processor, warning_trigger, 0, 0) incident = self.assert_active_incident(rule, self.sub) self.assert_trigger_exists_with_status(incident, trigger, TriggerStatus.RESOLVED) self.assert_trigger_exists_with_status(incident, warning_trigger, TriggerStatus.ACTIVE) self.assert_actions_resolved_for_incident( incident, [self.action], [(trigger.alert_threshold - 1, IncidentStatus.WARNING)] ) processor = self.send_update( rule, rule.resolve_threshold - 1, timedelta(minutes=-6), subscription=self.sub ) self.assert_trigger_counts(processor, trigger, 0, 0) self.assert_trigger_counts(processor, warning_trigger, 0, 0) self.assert_no_active_incident(rule, self.sub) self.assert_trigger_exists_with_status(incident, trigger, TriggerStatus.RESOLVED) self.assert_trigger_exists_with_status(incident, warning_trigger, TriggerStatus.RESOLVED) self.assert_actions_resolved_for_incident( incident, [warning_action], [(rule.resolve_threshold - 1, IncidentStatus.CLOSED)] )

146fba432a32568be7d0b884dae0c39a6c33a11f

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

def test_multiple_triggers(self): rule = self.rule rule.update(threshold_period=1) trigger = self.trigger warning_trigger = create_alert_rule_trigger( self.rule, WARNING_TRIGGER_LABEL, trigger.alert_threshold - 20 ) warning_action = create_alert_rule_trigger_action( warning_trigger, AlertRuleTriggerAction.Type.EMAIL, AlertRuleTriggerAction.TargetType.USER, str(self.user.id), ) processor = self.send_update( rule, warning_trigger.alert_threshold + 1, timedelta(minutes=-10), subscription=self.sub ) self.assert_trigger_counts(processor, warning_trigger, 0, 0) self.assert_trigger_counts(processor, trigger, 0, 0) incident = self.assert_active_incident(rule, self.sub) self.assert_trigger_exists_with_status(incident, warning_trigger, TriggerStatus.ACTIVE) self.assert_trigger_does_not_exist(trigger) self.assert_actions_fired_for_incident( incident, [warning_action], [(warning_trigger.alert_threshold + 1, IncidentStatus.WARNING)], ) processor = self.send_update( rule, trigger.alert_threshold + 1, timedelta(minutes=-9), subscription=self.sub ) self.assert_trigger_counts(processor, trigger, 0, 0) self.assert_trigger_counts(processor, warning_trigger, 0, 0) incident = self.assert_active_incident(rule, self.sub) self.assert_trigger_exists_with_status(incident,

test_multiple_triggers

tests/test_builder.py

Multiprocessed dataset builder [WIP] (#5107) * multiprocessing-compatible naming scheme and refactor * multiprocessed shard writing for GeneratorBasedBuilder * multiprocessed shard writing for ArrowBasedBuilder * style * multiprocessed dataset loading * compatibility with non-sharded datasets * bugfix * bugfix * removed unused import * fixed bad ordering * less misleading tqdm * fix gen_kwargs distribution + read shards * minor * minor2 * support beam datasets * docstrings + minor * add iflatmap_unordered for parallel write & progress updates * use 1 tqdm bar receiving updates from subprocesses * docs * add test_iflatmap_unordered * style * test arrow_reader.py * fix test_iflatmap_unordered * add Beam test_download_and_prepare_sharded * test gen_kwargs distribution * test download_and_prepare with num_proc * style * improve test * don't close the pool * fix multiprocessing on windows * keep multiprocessing disabled by default * again + docs * more docs * more docs * some var renaming * style * Apply suggestions from code review Co-authored-by: Mario Šaško <mariosasko777@gmail.com> * Apply suggestions from code review Co-authored-by: Mario Šaško <mariosasko777@gmail.com> * added utils/sharding.py * style * style Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com> Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> Co-authored-by: Mario Šaško <mariosasko777@gmail.com>

datasets

Python

test_builder.py

def test_generator_based_builder_download_and_prepare_as_parquet(tmp_path): builder = DummyGeneratorBasedBuilder(cache_dir=tmp_path) builder.download_and_prepare(file_format="parquet") assert builder.info.splits["train"].num_examples == 100 parquet_path = os.path.join(tmp_path, builder.name, "default", "0.0.0", f"{builder.name}-train.parquet") assert os.path.exists(parquet_path) assert pq.ParquetFile(parquet_path) is not None

2945690ea731f85a356220a71cdc630281c676f4

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

def test_generator_based_builder_download_and_prepare_as_parquet(tmp_path): builder = DummyGeneratorBasedBuilder(cache_dir=tmp_path) builder.download_and_prepare(file_format="parquet") assert builder.info.splits["train"].num_examples == 100 parquet_path = os.path.join(tmp_path, builder.name, "default", "0.0.0", f"{builder.name}-train.parquet")

test_generator_based_builder_download_and_prepare_as_parquet

keras/mixed_precision/autocast_variable.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

autocast_variable.py

def scatter_sub(self, sparse_delta, use_locking=False, name=None): return self._apply_update( self._variable.scatter_sub, sparse_delta, use_locking, name )

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def scatter_sub(self, sparse_delta, use_locking=False, name=None): return self._apply_update( self

scatter_sub

test/test_linalg.py

Remove random_fullrank_matrix_distinc_singular_value (#68183) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68183 We do so in favour of `make_fullrank_matrices_with_distinct_singular_values` as this latter one not only has an even longer name, but also generates inputs correctly for them to work with the PR that tests noncontig inputs latter in this stack. We also heavily simplified the generation of samples for the SVD, as it was fairly convoluted and it was not generating the inputs correclty for the noncontiguous test. To do the transition, we also needed to fix the following issue, as it was popping up in the tests: Fixes https://github.com/pytorch/pytorch/issues/66856 cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D32684853 Pulled By: mruberry fbshipit-source-id: e88189c8b67dbf592eccdabaf2aa6d2e2f7b95a4

pytorch

Python

test_linalg.py

def test_inverse(self, device, dtype): make_fullrank = make_fullrank_matrices_with_distinct_singular_values make_arg = partial(make_fullrank, device=device, dtype=dtype)

baeca11a21e285d66ec3e4103c29dfd0b0245b85

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

def test_inverse(self, device, dtype): make_fullrank = make_fullrank_matrices_with_distinct_singular_value

test_inverse

netbox/dcim/models/cables.py

Migrate CablePath to use two-dimensional array

netbox

Python

cables.py

def save(self, *args, **kwargs): super().save(*args, **kwargs) # Save the flattened nodes list self._nodes = flatten_path(self.path) # TODO # Record a direct reference to this CablePath on its originating object # model = self.origin._meta.model # model.objects.filter(pk=self.origin.pk).update(_path=self.pk)

82706eb3a68e963d7ac089478788b87892d4ee79

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

def save(self, *args, **kwargs): super().save(*args, **kwargs) # Save the flattened nodes list self._nodes = flatten_path(self.path)

save

homeassistant/components/wallbox/number.py

Migrate NumberEntity u-z to native_value (#73488)

core

Python

number.py

def native_max_value(self) -> float: return cast(float, self._coordinator.data[CHARGER_MAX_AVAILABLE_POWER_KEY])

576de9ac4052c90b8737e41110d05f06f41d000e

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

def native_max_value(self) -> float: return cast(float,

max_value

python/ray/util/collective/collective_group/nccl_util.py

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

ray

Python

nccl_util.py

def get_tensor_n_elements(tensor): if isinstance(tensor, cupy.ndarray) or isinstance(tensor, numpy.ndarray): return tensor.size if torch_available(): if isinstance(tensor, torch.Tensor): return torch.numel(tensor) raise ValueError( "Unsupported tensor type. Got: {}. Supported " "GPU tensor types are: torch.Tensor, " "cupy.ndarray.".format(type(tensor)) )

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def get_tensor_n_elements(tensor): if isinstance(tensor, cupy.ndarray) or isinstance(tensor, numpy.ndarray): return tensor.size if torch_available(): if isinstance(tensor, torch.Tensor): return torch.numel(tensor) raise ValueError( "Unsupported tensor type. Got: {}. Supported " "GPU tensor types are: torch.Tensor, "

get_tensor_n_elements

examples/compose/plot_transformed_target.py

FEA add PredictionErrorDisplay (#18020) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>

scikit-learn

Python

plot_transformed_target.py

def compute_score(y_true, y_pred): return { "R2": f"{r2_score(y_true, y_pred):.3f}", "MedAE": f"{median_absolute_error(y_true, y_pred):.3f}", } # %% from sklearn.compose import TransformedTargetRegressor from sklearn.linear_model import RidgeCV from sklearn.metrics import PredictionErrorDisplay f, (ax0, ax1) = plt.subplots(1, 2, sharey=True) ridge_cv = RidgeCV().fit(X_train, y_train) y_pred_ridge = ridge_cv.predict(X_test) ridge_cv_with_trans_target = TransformedTargetRegressor( regressor=RidgeCV(), func=np.log1p, inverse_func=np.expm1 ).fit(X_train, y_train) y_pred_ridge_with_trans_target = ridge_cv_with_trans_target.predict(X_test) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge, kind="actual_vs_predicted", ax=ax0, scatter_kwargs={"alpha": 0.5}, ) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge_with_trans_target, kind="actual_vs_predicted", ax=ax1, scatter_kwargs={"alpha": 0.5}, ) # Add the score in the legend of each axis for ax, y_pred in zip([ax0, ax1], [y_pred_ridge, y_pred_ridge_with_trans_target]): for name, score in compute_score(y_test, y_pred).items(): ax.plot([], [], " ", label=f"{name}={score}") ax.legend(loc="upper left") ax0.set_title("Ridge regression \n without target transformation") ax1.set_title("Ridge regression \n with target transformation") f.suptitle("Synthetic data", y=1.05) plt.tight_layout() # %% # Real-world data set ##################### # # In a similar manner, the Ames housing data set is used to show the impact # of transforming the targets before learning a model. In this example, the # target to be predicted is the selling price of each house. from sklearn.datasets import fetch_openml from sklearn.preprocessing import quantile_transform ames = fetch_openml(name="house_prices", as_frame=True, parser="pandas") # Keep only numeric columns X = ames.data.select_dtypes(np.number) # Remove columns with NaN or Inf values X = X.drop(columns=["LotFrontage", "GarageYrBlt", "MasVnrArea"]) # Let the price be in k$ y = ames.target / 1000 y_trans = quantile_transform( y.to_frame(), n_quantiles=900, output_distribution="normal", copy=True ).squeeze() # %% # A :class:`~sklearn.preprocessing.QuantileTransformer` is used to normalize # the target distribution before applying a # :class:`~sklearn.linear_model.RidgeCV` model. f, (ax0, ax1) = plt.subplots(1, 2) ax0.hist(y, bins=100, density=True) ax0.set_ylabel("Probability") ax0.set_xlabel("Target") ax0.set_title("Target distribution") ax1.hist(y_trans, bins=100, density=True) ax1.set_ylabel("Probability") ax1.set_xlabel("Target") ax1.set_title("Transformed target distribution") f.suptitle("Ames housing data: selling price", y=1.05) plt.tight_layout() # %% X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) # %% # The effect of the transformer is weaker than on the synthetic data. However, # the transformation results in an increase in :math:`R^2` and large decrease # of the MedAE. The residual plot (predicted target - true target vs predicted # target) without target transformation takes on a curved, 'reverse smile' # shape due to residual values that vary depending on the value of predicted # target. With target transformation, the shape is more linear indicating # better model fit. from sklearn.preprocessing import QuantileTransformer f, (ax0, ax1) = plt.subplots(2, 2, sharey="row", figsize=(6.5, 8)) ridge_cv = RidgeCV().fit(X_train, y_train) y_pred_ridge = ridge_cv.predict(X_test) ridge_cv_with_trans_target = TransformedTargetRegressor( regressor=RidgeCV(), transformer=QuantileTransformer(n_quantiles=900, output_distribution="normal"), ).fit(X_train, y_train) y_pred_ridge_with_trans_target = ridge_cv_with_trans_target.predict(X_test) # plot the actual vs predicted values PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge, kind="actual_vs_predicted", ax=ax0[0], scatter_kwargs={"alpha": 0.5}, ) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge_with_trans_target, kind="actual_vs_predicted", ax=ax0[1], scatter_kwargs={"alpha": 0.5}, ) # Add the score in the legend of each axis for ax, y_pred in zip([ax0[0], ax0[1]], [y_pred_ridge, y_pred_ridge_with_trans_target]): for name, score in compute_score(y_test, y_pred).items(): ax.plot([], [], " ", label=f"{name}={score}") ax.legend(loc="upper left") ax0[0].set_title("Ridge regression \n without target transformation") ax0[1].set_title("Ridge regression \n with target transformation") # plot the residuals vs the predicted values PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge, kind="residual_vs_predicted", ax=ax1[0], scatter_kwargs={"alpha": 0.5}, ) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge_with_trans_target, kind="residual_vs_predicted", ax=ax1[1], scatter_kwargs={"alpha": 0.5}, ) ax1[0].set_title("Ridge regression \n without target transformation") ax1[1].set_title("Ridge regression \n with target transformation") f.suptitle("Ames housing data: selling price", y=1.05) plt.tight_layout() plt.show()

40d7d880eddaf3a9a5e37ba2a8206caf22744926

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

def compute_score(y_true, y_pred): return { "R2": f"{r2_score(y_true, y_pred):.3f}", "MedAE": f"{median_absolute_error(y_true, y_pred):.3f}", } # %% from sklearn.compose import TransformedTargetRegressor from sklearn.linear_model import RidgeCV from sklearn.metrics import PredictionErrorDisplay f, (ax0, ax1) = plt.subplots(1, 2, sharey=True) ridge_cv = RidgeCV().fit(X_train, y_train) y_pred_ridge = ridge_cv.predict(X_test) ridge_cv_with_trans_target = TransformedTargetRegressor( regressor=RidgeCV(), func=np.log1p, inverse_func=np.expm1 ).fit(X_train, y_train) y_pred_ridge_with_trans_target = ridge_cv_with_trans_target.predict(X_test) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge, kind="actual_vs_predicted", ax=ax0, scatter_kwargs={"alpha": 0.5}, ) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge_with_trans_target, kind="actual_vs_predicted", ax=ax1, scatter_kwargs={"alpha": 0.5}, ) # Add the score in the legend of each axis for ax, y_pred in zip([ax0, ax1], [y_pred_ridge, y_pred_ridge_with_trans_target]): for name, score in compute_score(y_test, y_pred).items(): ax.plot([], [], " ", label=f"{name}={score}") ax.legend(loc="upper left") ax0.set_title("Ridge regression \n without target transformation") ax1.set_title("Ridge regression \n with target transformation") f.suptitle("Synthetic data", y=1.05) plt.tight_layout() # %% # Real-world data set ##################### # # In a similar manner, the Ames housing data set is used to show the impact # of transforming the targets before learning a model. In this example, the # target to be predicted is the selling price of each house. from sklearn.datasets import fetch_openml from sklearn.preprocessing import quantile_transform ames = fetch_openml(name="house_prices", as_frame=True, parser="pandas") # Keep only numeric columns X = ames.data.select_dtypes(np.number) # Remove columns with NaN or Inf values X = X.drop(columns=["LotFrontage", "GarageYrBlt", "MasVnrArea"]) # Let the price be in k$ y = ames.target / 1000 y_trans = quantile_transform( y.to_frame(), n_quantiles=900, output_distribution="normal", copy=True ).squeeze() # %% # A :class:`~sklearn.preprocessing.QuantileTransformer` is used to normalize # the target distribution before applying a # :class:`~sklearn.linear_model.RidgeCV` model. f, (ax0, ax1) = plt.subplots(1, 2) ax0.hist(y, bins=100, density=True) ax0.set_ylabel("Probability") ax0.set_xlabel("Target") ax0.set_title("Target distribution") ax1.hist(y_trans, bins=100, density=True) ax1.set_ylabel("Probability") ax1.set_xlabel("Targ

compute_score

datasets/xtreme/xtreme.py

Support streaming xtreme dataset for PAWS-X config (#4132) * Support streaming xtreme dataset for PAWS-X config * Align tasks in dataset card

datasets

Python

xtreme.py

def generate_examples(config=None, filepath=None, filename=None): lang = config.name.split(".")[1] for path, file in filepath: if f"/{lang}/" in path and path.endswith(filename): lines = (line.decode("utf-8") for line in file) data = csv.reader(lines, delimiter="\t") next(data) # skip header for id_, row in enumerate(data): if len(row) == 4: yield id_, { "sentence1": row[1], "sentence2": row[2], "label": row[3], }

8caed0c1e7b9658f08c10c8b90eb203b2cedc8e4

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

def generate_examples(config=None, filepath=None, filename=None): lang = config.name.split(".")[1] for path, file in filepath: if f"/{lang}/" in path and path.endswith(filename): lines = (line.decode("utf-8") for line in file) data = csv.reader(lines, delimiter="\t") next(data) # skip header for id_, row in enumerate(data): if len(row) == 4: yield id_, { "sentence1": row[1], "sentence2": row[2], "label": row[3], }

generate_examples

src/transformers/models/donut/feature_extraction_donut.py

Add Donut (#18488) * First draft * Improve script * Update script * Make conversion work * Add final_layer_norm attribute to Swin's config * Add DonutProcessor * Convert more models * Improve feature extractor and convert base models * Fix bug * Improve integration tests * Improve integration tests and add model to README * Add doc test * Add feature extractor to docs * Fix integration tests * Remove register_buffer * Fix toctree and add missing attribute * Add DonutSwin * Make conversion script work * Improve conversion script * Address comment * Fix bug * Fix another bug * Remove deprecated method from docs * Make Swin and Swinv2 untouched * Fix code examples * Fix processor * Update model_type to donut-swin * Add feature extractor tests, add token2json method, improve feature extractor * Fix failing tests, remove integration test * Add do_thumbnail for consistency * Improve code examples * Add code example for document parsing * Add DonutSwin to MODEL_NAMES_MAPPING * Add model to appropriate place in toctree * Update namespace to appropriate organization Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

transformers

Python

feature_extraction_donut.py

def rotate_image(self, image, size): if not isinstance(image, Image.Image): image = self.to_pil_image(image) if (size[1] > size[0] and image.width > image.height) or (size[1] < size[0] and image.width < image.height): image = self.rotate(image, angle=-90, expand=True) return image

2ab790e82d0759b667cd848a4d49e6ad65e15d59

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

def rotate_image(self, image, size): if not isin

rotate_image

keras/losses_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

losses_test.py

def test_ragged_tensors_3d(self): # shape [2, 1, None] y_true = tf.ragged.constant([[[1, 1]], [[0]]]) # shape [2, 1, None, 2] y_pred = tf.ragged.constant( [[[[0.1, 0.9], [0.1, 0.9]]], [[[0.9, 0.1]]]] ) cce_obj = losses.SparseCategoricalCrossentropy() loss = cce_obj(y_true, y_pred) self.assertAlmostEqual(self.evaluate(loss), 0.1054, 3) @test_combinations.generate(test_combinations.combine(mode=["graph", "eager"]))

84afc5193d38057e2e2badf9c889ea87d80d8fbf

@test_combinations.generate(test_combinations.combine(mode=["graph", "eager"]))

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

def test_ragged_tensors_3d(self): # shape [2, 1, None] y_true = tf.ragged.constant([[[1, 1]], [[0]]]) # shape [2, 1, None, 2] y_pred = tf.ragged.constant( [[[[0.1, 0.9], [0.1, 0.9]]],

test_ragged_tensors_3d

python/ccxt/deribit.py

1.66.37 [ci skip]

ccxt

Python

deribit.py

def fetch_markets(self, params={}): currenciesResponse = self.publicGetGetCurrencies(params) # # { # jsonrpc: '2.0', # result: [ # { # withdrawal_priorities: [ # {value: 0.15, name: 'very_low'}, # {value: 1.5, name: 'very_high'}, # ], # withdrawal_fee: 0.0005, # min_withdrawal_fee: 0.0005, # min_confirmations: 1, # fee_precision: 4, # currency_long: 'Bitcoin', # currency: 'BTC', # coin_type: 'BITCOIN' # } # ], # usIn: 1583761588590479, # usOut: 1583761588590544, # usDiff: 65, # testnet: False # } # currenciesResult = self.safe_value(currenciesResponse, 'result', []) result = [] for i in range(0, len(currenciesResult)): currencyId = self.safe_string(currenciesResult[i], 'currency') request = { 'currency': currencyId, } instrumentsResponse = self.publicGetGetInstruments(self.extend(request, params)) # # { # jsonrpc: '2.0', # result: [ # { # tick_size: 0.0005, # taker_commission: 0.0004, # strike: 300, # settlement_period: 'week', # quote_currency: 'USD', # option_type: 'call', # min_trade_amount: 1, # maker_commission: 0.0004, # kind: 'option', # is_active: True, # instrument_name: 'ETH-13MAR20-300-C', # expiration_timestamp: 1584086400000, # creation_timestamp: 1582790403000, # contract_size: 1, # base_currency: 'ETH' # }, # ], # usIn: 1583761889500586, # usOut: 1583761889505066, # usDiff: 4480, # testnet: False # } # instrumentsResult = self.safe_value(instrumentsResponse, 'result', []) for k in range(0, len(instrumentsResult)): market = instrumentsResult[k] id = self.safe_string(market, 'instrument_name') baseId = self.safe_string(market, 'base_currency') quoteId = self.safe_string(market, 'quote_currency') settleId = quoteId base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) settle = self.safe_currency_code(settleId) kind = self.safe_string(market, 'kind') settlementPeriod = self.safe_value(market, 'settlement_period') swap = (settlementPeriod == 'perpetual') future = not swap and (kind == 'future') option = (kind == 'option') symbol = quote + '/' + base + ':' + settle expiry = self.safe_integer(market, 'expiration_timestamp') strike = None optionType = None type = 'swap' if option or future: symbol = symbol + '-' + self.yymmdd(expiry, '') if option: type = 'option' strike = self.safe_number(market, 'strike') optionType = self.safe_string(market, 'option_type') symbol = symbol + ':' + self.number_to_string(strike) + ':' + optionType else: type = 'future' minTradeAmount = self.safe_number(market, 'min_trade_amount') tickSize = self.safe_number(market, 'tick_size') result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'settle': settle, 'baseId': baseId, 'quoteId': quoteId, 'settleId': settleId, 'type': type, 'spot': False, 'margin': False, 'swap': swap, 'future': future, 'option': option, 'contract': True, 'linear': False, 'inverse': True, 'taker': self.safe_number(market, 'taker_commission'), 'maker': self.safe_number(market, 'maker_commission'), 'contractSize': self.safe_number(market, 'contract_size'), 'active': self.safe_value(market, 'is_active'), 'expiry': expiry, 'expiryDatetime': self.iso8601(expiry), 'strike': strike, 'optionType': optionType, 'precision': { 'amount': minTradeAmount, 'price': tickSize, }, 'limits': { 'leverage': { 'min': None, 'max': None, }, 'amount': { 'min': minTradeAmount, 'max': None, }, 'price': { 'min': tickSize, 'max': None, }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, }) return result

09b439be4c7b8d1ef31ad1cbb3688f9ac48dcdcd

https://github.com/ccxt/ccxt.git

def fetch_markets(self, params={}): currenciesResponse = self.publicGetGetCurrencies(params) # # { # jsonrpc: '2.0', # result: [ # { # withdrawal_priorities: [ # {value: 0.15, name: 'very_low'}, # {value: 1.5, name: 'very_high'}, # ], # withdrawal_fee: 0.0005, # min_withdrawal_fee: 0.0005, # min_confirmations: 1, # fee_precision: 4, # currency_long: 'Bitcoin', # currency: 'BTC', # coin_type: 'BITCOIN' # } # ], # usIn: 1583761588590479, # usOut: 1583761588590544, # usDiff: 65, # testnet: False # } # currenciesResult = self.safe_value(currenciesResponse, 'result', []) result = [] for i in range(0, len(currenciesResult)): currencyId = self.safe_string(currenciesResult[i], 'currency') request = { 'currency': currencyId, } instrumentsResponse = self.publicGetGetInstruments(self.extend(request, params)) # # { # jsonrpc: '2.0', # result: [ # { # tick_size: 0.0005, # taker_commission: 0.0004, # strike: 300, # settlement_period: 'week', # quote_currency: 'USD', #

fetch_markets

src/sentry/search/events/builder.py

feat(mep): Validate orderby for mep (#32943) - This validates the orderby for mep queries to check that we aren't ordering by something that cannot be ordered

sentry

Python

builder.py

def validate_orderby_clause(self) -> None: for orderby in self.orderby: if isinstance(orderby.exp, Column) and orderby.exp.subscriptable == "tags": raise IncompatibleMetricsQuery("Can't orderby tags")

f2e775086eb653cf8c4680a2bdd90ee707e30ae0

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

def validate_orderby_clause(self) -> None: for orderby in self.orderby: if isinstance(orderby.exp, Column) and orderby.exp.subscriptable == "tags": raise IncompatibleMetricsQuery("Can't orderby tags")

validate_orderby_clause

ludwig/utils/data_utils.py

Use pandas instead of dask to read excel (#2005) https://github.com/ludwig-ai/ludwig/pull/2005

ludwig

Python

data_utils.py

def read_spss(data_fp, df_lib): # https://github.com/dask/dask/issues/9055 if df_lib.__name__ == DASK_MODULE_NAME: logger.warning("Falling back to pd.read_spss() since dask backend does not support it") return pd.read_spss(data_fp) @spread

5c3b4475a02aaa340a6e11d4302d29d4b7eccedf

@spread

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

def read_spss(data_fp, df_lib): # https://github.com/dask/dask/issues/9055 if df_lib.__name__ == DASK_MODULE_NAME: logger.warning("Falling back to pd.read_spss() since dask backend does not support it") return pd.read_spss(data_fp) @spread

read_spss

python3.10.4/Lib/ctypes/test/test_loading.py

add python 3.10.4 for windows

XX-Net

Python

test_loading.py

def test_find(self): for name in ("c", "m"): lib = find_library(name) if lib: cdll.LoadLibrary(lib) CDLL(lib)

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def test_find(self): for name

test_find

tests/sentry/incidents/endpoints/test_project_alert_rule_index.py

ref(tests): Remove `get_valid_response()` (#34822)

sentry

Python

test_project_alert_rule_index.py

def test_simple_crash_rate_alerts_for_users(self): self.valid_alert_rule.update( { "aggregate": "percentage(users_crashed, users) AS _crash_rate_alert_aggregate", } ) with self.feature(["organizations:incidents", "organizations:performance-view"]): resp = self.get_success_response( self.organization.slug, self.project.slug, status_code=201, **self.valid_alert_rule ) assert "id" in resp.data alert_rule = AlertRule.objects.get(id=resp.data["id"]) assert resp.data == serialize(alert_rule, self.user)

096b5511e244eecd8799b2a0324655207ce8985e

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

def test_simple_crash_rate_alerts_for_users(self): self.valid_alert_rule.update( { "aggregate": "percentage(users_crashed, users) AS _crash_rate_alert_aggregate", } ) with self.feature(["organizations:incidents", "organizations:performance-view"]): resp = self.get_success_response( self.organization.slug, self.project.slug, status_code=201, **self.valid_alert_rule ) assert "id" in resp.data

test_simple_crash_rate_alerts_for_users

tests/sentry/utils/test_committers.py

ref: replace self.assertRaises with pytest.raises (#35685) * add flake8 plugin to detect assertRaises * ref: replace self.assertRaises with pytest.raises * non-sed fixes

sentry

Python

test_committers.py

def test_no_commits(self): event = self.store_event( data={ "timestamp": iso_format(before_now(seconds=1)), "message": "Kaboom!", "stacktrace": { "frames": [ { "function": "handle_set_commits", "abs_path": "/usr/src/sentry/src/sentry/tasks.py", "module": "sentry.tasks", "in_app": True, "lineno": 30, "filename": "sentry/tasks.py", }, { "function": "set_commits", "abs_path": "/usr/src/sentry/src/sentry/models/release.py", "module": "sentry.models.release", "in_app": True, "lineno": 39, "filename": "sentry/models/release.py", }, ] }, "tags": {"sentry:release": self.release.version}, }, project_id=self.project.id, ) GroupRelease.objects.create( group_id=event.group.id, project_id=self.project.id, release_id=self.release.id ) with pytest.raises(Commit.DoesNotExist): get_serialized_event_file_committers(self.project, event)

284e980df0018f8baee659999268bdd4c7d08255

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

def test_no_commits(self): event = self.store_event( data={ "timestamp": iso_format(before_now(seconds=1)), "message": "Kaboom!", "stacktrace": { "frames": [ { "function": "handle_set_commits", "abs_path": "/usr/src/sentry/src/sentry/tasks.py", "module": "sentry.tasks", "in_app": True, "lineno": 30, "filename": "sentry/tasks.py", }, { "function": "set_commits", "abs_path": "/usr/src/sentry/src/sentry/models/release.py", "module": "sentry.models.release", "in_a

test_no_commits

ivy_tests/test_core/test_random.py

renamed dtype_str arg to dtype for all methods.

ivy

Python

test_random.py

def test_seed(seed_val, dtype, tensor_fn, dev_str, call): # smoke test ivy.seed(seed_val) # compilation test if call in [helpers.torch_call]: # pytorch scripting does not support functions with None return return if not ivy.wrapped_mode(): helpers.assert_compilable(ivy.seed) # shuffle @pytest.mark.parametrize( "x", [[1, 2, 3], [[1., 4.], [2., 5.], [3., 6.]]]) @pytest.mark.parametrize( "dtype", ['float32']) @pytest.mark.parametrize( "tensor_fn", [ivy.array, helpers.var_fn])

562846b6dce660054181cae7b05bbadd75489795

@pytest.mark.parametrize( "x", [[1, 2, 3], [[1., 4.], [2., 5.], [3., 6.]]]) @pytest.mark.parametrize( "dtype", ['float32']) @pytest.mark.parametrize( "tensor_fn", [ivy.array, helpers.var_fn])

https://github.com/unifyai/ivy.git

def test_seed(seed_val, dtype, tensor_fn, dev_str, call): # smoke test ivy.seed(seed_val) # compilation test if call in [helpers.torch_call]: # pytorch scripting does not support functions with None return return if not ivy.wrapped_mode(): helpers.assert_compilable(ivy.seed) # shuffl

test_seed

src/transformers/testing_utils.py

Update all require decorators to use skipUnless when possible (#16999)

transformers

Python

testing_utils.py

def require_tf(test_case): return unittest.skipUnless(is_tf_available(), "test requires TensorFlow")(test_case)

57e6464ac9a31156f1c93e59107323e6ec01309e

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

def require_tf(test_case): return unittest.skipUnless(is_tf_available()

require_tf

python/ccxt/async_support/vcc.py

1.71.83 [ci skip]

ccxt

Python

vcc.py

def describe(self): return self.deep_extend(super(vcc, self).describe(), { 'id': 'vcc', 'name': 'VCC Exchange', 'countries': ['VN'], # Vietnam 'rateLimit': 1000, 'version': 'v3', 'has': { 'CORS': None, 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'addMargin': False, 'cancelAllOrders': True, 'cancelOrder': True, 'createOrder': True, 'createReduceOnlyOrder': False, 'editOrder': None, 'fetchBalance': True, 'fetchBorrowRate': False, 'fetchBorrowRateHistories': False, 'fetchBorrowRateHistory': False, 'fetchBorrowRates': False, 'fetchBorrowRatesPerSymbol': False, 'fetchClosedOrders': True, 'fetchCurrencies': True, 'fetchDepositAddress': True, 'fetchDeposits': True, 'fetchFundingHistory': False, 'fetchFundingRate': False, 'fetchFundingRateHistory': False, 'fetchFundingRates': False, 'fetchIndexOHLCV': False, 'fetchIsolatedPositions': False, 'fetchLeverage': False, 'fetchMarkets': True, 'fetchMarkOHLCV': False, 'fetchMyTrades': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchOrders': None, 'fetchPosition': False, 'fetchPositions': False, 'fetchPositionsRisk': False, 'fetchPremiumIndexOHLCV': False, 'fetchTicker': 'emulated', 'fetchTickers': True, 'fetchTrades': True, 'fetchTradingFee': True, 'fetchTradingFees': None, 'fetchTransactions': True, 'fetchWithdrawals': True, 'reduceMargin': False, 'setLeverage': False, 'setMarginMode': False, 'setPositionMode': False, }, 'timeframes': { '1m': '60000', '5m': '300000', '15m': '900000', '30m': '1800000', '1h': '3600000', '2h': '7200000', '4h': '14400000', '6h': '21600000', '12h': '43200000', '1d': '86400000', '1w': '604800000', }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/100545356-8427f500-326c-11eb-9539-7d338242d61b.jpg', 'api': { 'public': 'https://api.vcc.exchange', 'private': 'https://api.vcc.exchange', }, 'www': 'https://vcc.exchange', 'doc': [ 'https://vcc.exchange/api', ], 'fees': 'https://support.vcc.exchange/hc/en-us/articles/360016401754', 'referral': 'https://vcc.exchange?ref=l4xhrH', }, 'api': { 'public': { 'get': [ 'summary', 'exchange_info', 'assets', # Available Currencies 'ticker', # Ticker list for all symbols 'trades/{market_pair}', # Recent trades 'orderbook/{market_pair}', # Orderbook 'chart/bars', # Candles 'tick_sizes', ], }, 'private': { 'get': [ 'user', 'balance', # Get trading balance 'orders/{order_id}', # Get a single order by order_id 'orders/open', # Get open orders 'orders', # Get closed orders 'orders/trades', # Get trades history 'deposit-address', # Generate or get deposit address 'transactions', # Get deposit/withdrawal history ], 'post': [ 'orders', # Create new order ], 'put': [ 'orders/{order_id}/cancel', # Cancel order 'orders/cancel-by-type', 'orders/cancel-all', ], }, }, 'fees': { 'trading': { 'tierBased': False, 'percentage': True, 'maker': self.parse_number('0.002'), 'taker': self.parse_number('0.002'), }, }, 'exceptions': { 'exact': {}, 'broad': { 'limit may not be greater than': BadRequest, # {"message":"The given data was invalid.","errors":{"limit":["The limit may not be greater than 1000."]}} 'Insufficient balance': InsufficientFunds, # {"message":"Insufficient balance."} 'Unauthenticated': AuthenticationError, # {"message":"Unauthenticated."} # wrong api key 'signature is invalid': AuthenticationError, # {"message":"The given data was invalid.","errors":{"signature":["HMAC signature is invalid"]}} 'Timeout': RequestTimeout, # {"code":504,"message":"Gateway Timeout","description":""} 'Too many requests': RateLimitExceeded, # {"code":429,"message":"Too many requests","description":"Too many requests"} 'quantity field is required': InvalidOrder, # {"message":"The given data was invalid.","errors":{"quantity":["The quantity field is required when type is market."]}} 'price field is required': InvalidOrder, # {"message":"The given data was invalid.","errors":{"price":["The price field is required when type is limit."]}} 'error_security_level': PermissionDenied, # {"message":"error_security_level"} 'pair is invalid': BadSymbol, # {"message":"The given data was invalid.","errors":{"coin":["Trading pair is invalid","Trading pair is offline"]}} # {"message":"The given data was invalid.","errors":{"type":["The selected type is invalid."]}} # {"message":"The given data was invalid.","errors":{"trade_type":["The selected trade type is invalid."]}} 'type is invalid': InvalidOrder, 'Data not found': OrderNotFound, # {"message":"Data not found"} }, }, })

ff158ebe7e1ed14772139737d13bb5edfd6d9430

https://github.com/ccxt/ccxt.git

def describe(self): return self.deep_extend(super(vcc, self).describe(), { 'id': 'vcc', 'name': 'VCC Exchange', 'countries': ['VN'], # Vietnam 'rateLimit': 1000, 'version': 'v3', 'has': { 'CORS': None, 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'addMargin': False, 'cancelAllOrders': True, 'cancelOrder': True, 'createOrder': True, 'createReduceOnlyOrder': False, 'editOrder': None, 'fetchBalance': True, 'fetchBorrowRate': False, 'fetchBorrowRateHistories': False, 'fetchBorrowRateHistory': False, 'fetchBorrowRates': False, 'fetchBorrowRatesPerSymbol': False, 'fetchClosedOrders': True, 'fetchCurrencies': True, 'fetchDepositAddress': True, 'fetchDeposits': True, 'fetchFundingHistory': False, 'fetchFundingRate': False, 'fetchFundingRateHistory': False, 'fetchFundingRates': False, 'fetchIndexOHLCV': False, 'fetchIsolatedPositions': False, 'fetchLeverage': False, 'fetchMarkets': True, 'fetchMarkOHLCV': False, 'fetchMyTrades': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchOrders': None, 'fetchPosition': False, 'fetchPositions': False, 'fetchPositionsRisk': False, 'fetchPremiumIndexOHLCV': False, 'fetchTicker': 'emulated', 'fetchTickers': True, 'fetchTrades': True, 'fetchTradingFee': True, 'fetchTradingFees': None, 'fetchTransactions': True, 'fetchWithdrawals': True, 'reduceMargin': False, 'setLeverage': False, 'setMarginMode': False, 'setPositionMode': False, }, 'timeframes': { '1m': '60000', '5m': '300000', '15m': '900000', '30m': '1800000', '1h': '3600000', '2h': '7200000', '4h': '14400000', '6h': '21600000', '12h': '43200000', '1d': '86400000', '1w': '604800000', }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/100545356-8427f500-326c-11eb-9539-7d338242d61b.jpg', 'api': { 'public': 'https://api.vcc.exchange', 'private': 'https://api.vcc.exchange', }, 'www': 'https://vcc.exchange', 'doc': [ 'https://vcc.exchange/api', ], 'fees': 'https://support.vcc.exchange/hc/en-us/articles/360016401754', 'referral': 'https://vcc.exchange?ref=l4xhrH', }, 'api': { 'public': { 'get': [ 'summary', 'exchange_info', 'assets', # Available Currencies 'ticker', # Ticker list for all symbols 'trades/{market_pair}', # Recent trades 'orderbook/{market_pair}', # Orderbook 'chart/bars', # Candles 'tick_sizes', ], }, 'private': { 'get': [ 'user', 'balance', # Get trading balance 'orders/{order_id}', # Get a single order by order_id 'orders/open', # Get open orders 'orders', # Get closed orders 'orders/trades', # Get trades history 'deposit-address', # Generate or get deposit address 'transactions', # Get deposit/withdrawal history ], 'post': [ 'orders', # Create new order ], 'put': [ 'orders/{order_id}/cancel', # Cancel order 'orders/cancel-by-type', 'orders/cancel-all', ], }, }, 'fees': { 'trading': { 'tierBased': False, 'percentage': True, 'maker': self.parse_number('0.002'), 'taker': self.parse_number('0.002'), }, }, 'exceptions': { 'exact': {}, 'broad': { 'limit may not be greater than': BadRequest, # {"message":"The given data was invalid.","errors":{"limit":["The limit may not be greater than 1000."]}} 'Insufficient balance': InsufficientFunds, # {"message":"Insufficient balance."} 'Unauthenticated': AuthenticationError, # {"message":"Unauthenticated."} # wrong api key 'signature is invalid': AuthenticationError, # {"message":"The given data was invalid.","errors":{"signature":["HMAC signature is invalid"]}} 'Timeout': RequestTimeout, # {"code":504,"message":"Gateway Timeout","description":""} 'Too many requests': RateLimitExceeded, # {"code":429,"message":"Too many requests","description":"Too many requests"} 'quantity field is required': InvalidOrder, # {"message":"The given data was invalid.","errors":{"quantity":["The quantity field is required when type is market."]}} 'price field is required': InvalidOrder, # {"message":"The given data was invalid.","errors":{"price":["The price field is required when type is limit."]}} 'error_security_level': PermissionDenied, # {"message":"error_security_level"} 'pair is invalid': BadSymbol, # {"message":"The given data was invalid.","errors":{"coin":["Trading pair is invalid","Trading pa

describe

src/documents/tests/test_matchables.py

Reduces number of warnings from testing from 165 to 128. In doing so, fixes a few minor things in the decrypt and export commands

paperless-ngx

Python

test_matchables.py

def test_tach_invalid_regex(self): self._test_matching("[", "MATCH_REGEX", [], ["Don't match this"])

85b210ebf61d4525cae3311eaae91012c8986cf7

https://github.com/paperless-ngx/paperless-ngx.git

def test_tach_invalid_regex(self): self._test_matching("[", "MATCH_REGEX", [], ["Don't match this"])

test_tach_invalid_regex

.venv/lib/python3.8/site-packages/pip/_vendor/pkg_resources/__init__.py

upd; format

transferlearning

Python

__init__.py

def ensure_directory(path): dirname = os.path.dirname(path) py31compat.makedirs(dirname, exist_ok=True)

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def ensure_directory(path): dirname = os.path.dirname(path) py31compat.makedirs(dirname, exist_ok=True)

ensure_directory

tests/models/groupvit/test_modeling_groupvit.py

Adding GroupViT Models (#17313) * add group vit and fixed test (except slow) * passing slow test * addressed some comments * fixed test * fixed style * fixed copy * fixed segmentation output * fixed test * fixed relative path * fixed copy * add ignore non auto configured * fixed docstring, add doc * fixed copies * Apply suggestions from code review merge suggestions Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * resolve comment, renaming model * delete unused attr * use fix copies * resolve comments * fixed attn * remove unused vars * refactor tests * resolve final comments * add demo notebook * fixed inconsitent default * Apply suggestions from code review Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * rename stage->stages * Create single GroupViTEncoderLayer class * Update conversion script * Simplify conversion script * Remove cross-attention class in favor of GroupViTAttention * Convert other model as well, add processor to conversion script * addressing final comment * fixed args * Update src/transformers/models/groupvit/modeling_groupvit.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

transformers

Python

test_modeling_groupvit.py

def setUp(self): self.model_tester = GroupViTVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=GroupViTVisionConfig, has_text_modality=False, hidden_size=37 )

6c8f4c9a938a09749ea1b19a5fa2a8dd27e99a29

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

def setUp(self): self.model_tester = GroupViTVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=GroupViTVisionConfig,

setUp

test/mitmproxy/addons/test_dns_resolver.py

[dns] offline dns_resolve tests at 100% coverage

mitmproxy

Python

test_dns_resolver.py

async def test_simple(monkeypatch): monkeypatch.setattr(dns_resolver, "resolve_message", lambda _, __: asyncio.sleep(0, "resp")) dr = dns_resolver.DnsResolver() with taddons.context(dr, proxyserver.Proxyserver()) as tctx: f = tflow.tdnsflow() await dr.dns_request(f) assert f.response tctx.options.dns_mode = "reverse:8.8.8.8" f = tflow.tdnsflow() await dr.dns_request(f) assert not f.response

dd61b21ce37c112c3b1e35774396da9ad0d51b76

https://github.com/mitmproxy/mitmproxy.git

async def test_simple(monkeypatch): monkeypatch.setattr(dns_resolver, "resolve_message", lambda _, __: asyncio.sleep(0, "resp")) dr = dns_resolver.DnsResolver() with taddons.context(dr, proxyserver.Proxyserver()) as tctx: f = tflow.tdnsflow() await dr.dns_request(f) assert f.response tctx.options.dns_mode = "reverse:8.8.8.8" f

test_simple

.venv/lib/python3.8/site-packages/pip/_internal/operations/freeze.py

upd; format

transferlearning

Python

freeze.py

def __str__(self): # type: () -> str req = self.req if self.editable: req = f'-e {req}' return '\n'.join(list(self.comments) + [str(req)]) + '\n'

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def __str__(self): # type: () -> str req = self.req if self.editable: req = f'-e {req}' return '\n'.join(list(self.comments)

__str__

tests/components/alexa/test_capabilities.py

Exclude hidden entities from alexa (#68555)

core

Python

test_capabilities.py

async def test_api_set_color_rgb(hass): request = get_new_request("Alexa.ColorController", "SetColor", "light#test") # add payload request["directive"]["payload"]["color"] = { "hue": "120", "saturation": "0.612", "brightness": "0.342", } # setup test devices hass.states.async_set( "light.test", "off", {"friendly_name": "Test light", "supported_features": 16} ) call_light = async_mock_service(hass, "light", "turn_on") msg = await smart_home.async_handle_message(hass, get_default_config(hass), request) await hass.async_block_till_done() assert "event" in msg msg = msg["event"] assert len(call_light) == 1 assert call_light[0].data["entity_id"] == "light.test" assert call_light[0].data["rgb_color"] == (33, 87, 33) assert msg["header"]["name"] == "Response"

dc8e87a6f70439f9830d93d03c53d6ff098a4861

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

async def test_api_set_color_rgb(hass): request = get_new_request("Alexa.ColorController", "SetColor", "light#test") # add payload request["directive"]["payload"]["color"] = {

test_api_set_color_rgb

packages/syft/tests/syft/core/tensor/adp/private_method_test.py

Remove autograd, old Mechanism, continued renaming entities to datasubject

PySyft

Python

private_method_test.py

def test_string_entity() -> None: x = sy.Tensor(np.array([1, 2, 3, 4], dtype=DEFAULT_INT_NUMPY_TYPE)) out = x.private(min_val=0, max_val=5, data_subjects="bob") assert out.child.entity.name == "bob"

859b728f41b728447b88b54479e1600a4996dc09

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

def test_string_entity() -> None: x = sy.Tensor(np.array([1, 2, 3, 4], dtype=DEFAULT_INT_NUMPY_TYPE)) out = x.private(min_val=0, max_val=5, data_subjects="bob")

test_string_entity

deepspeed/runtime/utils.py

MoE inference + PR-MoE model support (#1705) Co-authored-by: Reza Yazdani <reyazda@microsoft.com> Co-authored-by: Zhewei Yao <zheweiy@berkeley.edu> Co-authored-by: Ammar Ahmad Awan <ammar.awan@microsoft.com> Co-authored-by: Jeff Rasley <jerasley@microsoft.com> Co-authored-by: Samyam Rajbhandari <samyamr@microsoft.com>

DeepSpeed

Python

utils.py

def has_overflow(self, params, has_moe_params=None): if has_moe_params is None: has_moe_params = self.has_moe_params overflow = self.has_overflow_serial(params) # Since each model parallel GPU carries only part of the model, # make sure overflow flag is synced across all the model parallel GPUs overflow_gpu = torch.cuda.ByteTensor([overflow]) # torch.distributed.all_reduce(overflow_gpu, # op=torch.distributed.ReduceOp.MAX, # group=mpu.get_model_parallel_group()) if has_moe_params: # All reduce this across expert_parallel_group, so that if an expert # overflows, we detect it here dist.all_reduce(overflow_gpu, op=dist.ReduceOp.MAX, group=groups.get_max_expert_parallel_group()) if self.zero_reduce_scatter: torch.distributed.all_reduce(overflow_gpu, op=torch.distributed.ReduceOp.MAX, group=torch.distributed.group.WORLD) elif self.mpu is not None: if self.deepspeed is not None: using_pipeline = hasattr(self.deepspeed, 'pipeline_enable_backward_allreduce') if (using_pipeline and self.deepspeed.pipeline_enable_backward_allreduce is False ) or (not using_pipeline and self.deepspeed.enable_backward_allreduce is False): torch.distributed.all_reduce( overflow_gpu, op=torch.distributed.ReduceOp.MAX, group=self.mpu.get_data_parallel_group()) torch.distributed.all_reduce(overflow_gpu, op=torch.distributed.ReduceOp.MAX, group=self.mpu.get_model_parallel_group()) elif self.deepspeed is not None and self.deepspeed.enable_backward_allreduce is False: torch.distributed.all_reduce(overflow_gpu, op=torch.distributed.ReduceOp.MAX, group=torch.distributed.group.WORLD) overflow = overflow_gpu[0].item() return bool(overflow) # `x` is a torch.Tensor

e46d808a1b6cb7e04cb2806e38547b1e3e50c25a

https://github.com/microsoft/DeepSpeed.git

def has_overflow(self, params, has_moe_params=None): if has_moe_params is None: has_moe_params = self.has_moe_params overflow = self.has_overflow_serial(params) # Since each model parallel GPU carries only part of the model, # make sure overflow flag is synced across all the model parallel GPUs overflow_gpu = torch.cuda.ByteTensor([overflow]) # torch.distributed.all_reduce(overflow_gpu, # op=torch.distributed.ReduceOp.MAX, # group=mpu.get_model_parallel_group()) if has_moe_params: # All reduce this across expert_parallel_group, so that if an expert # overflows, we detect it here dist.all_reduce(overflow_gpu, op=dist.ReduceOp.MAX, group=groups.get_max_expert_parallel_group()) if self.zero

has_overflow

jina/parsers/create.py

docs: adapt to 3.0 (#4254) * docs: comparing alternatives (#4249) * docs: fix conflict * docs: remove line * docs: add docarray logos * docs: proper link to docarray * docs: change index * docs: change reference types ecosystem * docs: change comparing * docs: update docs/get-started/comparing-to-alternatives.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/get-started/comparing-to-alternatives.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/get-started/comparing-to-alternatives.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/get-started/comparing-to-alternatives.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/get-started/comparing-to-alternatives.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/get-started/comparing-to-alternatives.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/get-started/comparing-to-alternatives.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/index.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: fix kubernetes docs (#4259) * docs: fix kubernetes docs * docs: add caution * docs: executor documentation refactoring (#4256) * fix: fix link to share executors * docs: adjust install section for 3.0 (#4265) * docs: adjust readme (#4270) * docs: async in executors tuto (#4264) * docs: move things to how-to (#4271) * docs: updating docker-compose docs (#4252) * docs: move docker compose * docs: caution in kubernetes and docker compose (#4272) * docs: update gpu guide for jina 3 (#4255) * docs: move gpu to how-to (#4273) * docs: migration guide to jina 3 (#4263) * docs: change index link to how-ot * docs: move migrate to get-started (#4274) * docs: adapt some kubernetes content (#4275) * docs: add architecture overview (#4280) * docs: add proto back to API reference (#4281) * docs: external executors tutorial (#4267) * docs: move external executor how-to (#4283) * docs: rephrase comparing to alternatives (#4282) * docs: update docs/fundamentals/concepts.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/concepts.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: fix architeceture map legend * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: index with readme content (#4285) * docs(executor): fix grammatical errors (#4284) * docs: update docs/fundamentals/executor/index.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/index.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/index.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/index.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/index.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs. update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: Update docs/fundamentals/executor/index.md * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: add containerize executor section (#4288) * docs: update docs/fundamentals/architecture-overview.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/how-to/kubernetes.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/how-to/sandbox.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/how-to/sandbox.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/how-to/sandbox.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/fundamentals/executor/hub/index.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: apply suggestions from code review Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: update docs/how-to/kubernetes.md * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/how-to/sandbox.md * docs: apply suggestions from code review Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: add scale tutorial (#4287) * docs: refactor scale how-to (#4289) * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-in-flow.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: Update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: update docs/fundamentals/executor/executor-api.md Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: rewrite flow section (#4266) * docs: refactor flow docs * docs: update flow index * docs: refactor create a flow section * docs: add flow api section * docs: some minor polishing * docs: add more flow info * docs: address comments * docs: small refactor flow docs (#4293) * docs: fix examples (#4294) * docs: small changes to flow (#4297) * chore: remove the eah announcement (#4295) * docs: polish sandbox tutorial (#4286) * docs: add Hub to ecosys (#4300) * docs: minor clean up on 3.0 branch (#4301) * docs: apply suggestions from code review Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: migration attributes (#4299) * docs: use post and not search (#4302) * docs: very small change (#4304) * docs: add section for extending the http api (#4303) * docs: update docs/how-to/sandbox.md Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: restructure content and layout (#4305) * docs: why to use flow (#4308) * docs: unify docarray import (#4310) * docs(readme): polish (#4307) * docs: fix snippets (#4311) * docs: apply suggestions from code review Co-authored-by: CatStark <susana.guzman@jina.ai> * docs: add jina new (#4313) * docs: add jina new * docs: add jina new * style: fix overload and cli autocomplete * docs: rephrase dockerize section (#4298) * docs: fix create flow images (#4314) * docs: restructure 2 (#4315) * docs: keep clean code (#4316) * docs: restructure 2 * docs: restructure 2 * docs: restructure 2 * docs: update docs/fundamentals/flow/index.md * docs: restructure 2 * docs: restructure 2 * docs: restructure 2 * docs: restructure 2 * docs: restructure 2 * docs: add minimum working example (#4321) * docs: create landing page for how-to's (#4312) * docs(how-to): create landing page * docs: add links to executor how-tos * docs: add links to deployment how-tos * docs: shorten scaling-out description * docs: add info box * docs(sandbox): optimize pic (#4324) * docs: fix inconsistent definition for executor and flow (#4322) * docs: fix inconsistent definitions * docs: fix inconsistent definitions * docs: restructure 2 * fix(docs): yaml formating (#4327) * docs: restructure 2 * docs: fix formatting (#4329) * chore: update banner for docs (#4330) * docs: review readme 2 (#4323) * docs(sandbox): optimize sandbox pic (#4331) * docs: remove jinad from install section (#4333) * docs: apply suggestions from code review Co-authored-by: CatStark <susana.guzman@jina.ai> * docs: restructure 2 * docs: restructure 2 * docs: add what is jina (#4332) * docs: add what is jina * docs: remove comparing to alternatives document * docs: update docs/get-started/what-is-jina.md Co-authored-by: cristian <cristianmtr@users.noreply.github.com> * docs: update docs/get-started/what-is-jina.md Co-authored-by: cristian <cristianmtr@users.noreply.github.com> * docs: apply suggestions from code review Co-authored-by: cristian <cristianmtr@users.noreply.github.com> * docs: add link to docarray * docs: apply suggestions from code review Co-authored-by: Nan Wang <nan.wang@jina.ai> Co-authored-by: Han Xiao <artex.xh@gmail.com> Co-authored-by: cristian <cristianmtr@users.noreply.github.com> Co-authored-by: Nan Wang <nan.wang@jina.ai> Co-authored-by: Han Xiao <artex.xh@gmail.com> * fix(docs): apply black automatically (#4337) * docs: fix executor api snippet (#4339) Co-authored-by: Sami Jaghouar <sami.jaghouar@jina.ai> * docs: fix quote * fix: blackifiy readme + single quote (#4340) * docs: fix quote * docs: fix quote * docs: fix quote * docs: fix quote * docs: replace png with svg (#4334) * docs: apply suggestions from code review Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> * docs: fix quote * docs: add highlighting and more positive phrasing (#4338) * docs: fix quote * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: fix typo Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * chore: apply suggestions from code review Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> * docs: apply suggestions from code review Co-authored-by: CatStark <susana.guzman@jina.ai> * docs: fix quote * chore: fix typo Co-authored-by: CatStark <susana.guzman@jina.ai> Co-authored-by: Alex Cureton-Griffiths <alexcg1@users.noreply.github.com> Co-authored-by: AlaeddineAbdessalem <alaeddine-13@live.fr> Co-authored-by: Tobias Jacobowitz <tobias.jacobowitz@posteo.de> Co-authored-by: samsja <55492238+samsja@users.noreply.github.com> Co-authored-by: Johannes Messner <44071807+JohannesMessner@users.noreply.github.com> Co-authored-by: Johannes Messner <messnerjo@gmail.com> Co-authored-by: Roshan Jossy <roshan.jossy@jina.ai> Co-authored-by: Wang Bo <bo.wang@jina.ai> Co-authored-by: Nan Wang <nan.wang@jina.ai> Co-authored-by: Zhaofeng Miao <522856232@qq.com> Co-authored-by: Han Xiao <han.xiao@jina.ai> Co-authored-by: CatStark <susana.guzman@jina.ai> Co-authored-by: Jina Dev Bot <dev-bot@jina.ai> Co-authored-by: cristian <cristianmtr@users.noreply.github.com> Co-authored-by: Han Xiao <artex.xh@gmail.com> Co-authored-by: Sami Jaghouar <sami.jaghouar@jina.ai>

jina

Python

create.py

def set_new_project_parser(parser=None): if not parser: parser = set_base_parser() parser.add_argument( 'name', type=str, help='The name of the project', default='hello-jina' ) return parser

07e2ef0a5cd2baf90a0e30c32e5898d1fdfc4d48

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

def set_new_project_parser(parser=None): if not parser: parser = set_base_parser() parser.add_argument( 'nam

set_new_project_parser

ludwig/combiners/combiners.py

fix: Naming scheme cleanup that includes: renaming `ludwig.marshmallow` module to `ludwig.validation` to avoid implicit import errors, and moving `ludwig.utils.schema` into this new module. (#1936) * Rename marshmallow/ folder to marshmallow_schema_utils/, marshmallow_schema_utils.py to utils.py (under folder), update all refs. * Rename marshmallow/ folder to marshmallow_schema_utils/, marshmallow_schema_utils.py to utils.py (under folder), update all refs. * update extract_schema * update generated files. * update manifest * rename using validation/schema_utils naming * update generated files * new naming scheme * fix imports. * rerun extract_schema

ludwig

Python

combiners.py

def get_combiner_conds(): combiner_types = sorted(list(combiner_registry.keys())) conds = [] for combiner_type in combiner_types: combiner_cls = combiner_registry[combiner_type] schema_cls = combiner_cls.get_schema_cls() combiner_schema = marshmallow_utils.get_custom_schema_from_marshmallow_class(schema_cls) combiner_props = combiner_schema["properties"] combiner_cond = marshmallow_utils.create_cond({"type": combiner_type}, combiner_props) conds.append(combiner_cond) return conds # super class to house common properties

a95f611d582a724740af772ead1fa439b3713124

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

def get_combiner_conds(): combiner_types = sorted(list(combiner_registry.keys())) conds = [] for combiner_type in combiner_types:

get_combiner_conds

keras/saving/experimental/saving_lib_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

saving_lib_test.py

def train_step(self, data): tf.print(train_step_message) x, y = data with tf.GradientTape() as tape: y_pred = self(x) loss = self.compiled_loss(y, y_pred) gradients = tape.gradient(loss, self.trainable_variables) self.optimizer.apply_gradients(zip(gradients, self.trainable_variables)) return {}

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def train_step(self, data): tf.print(train_step_message) x, y = data with tf.GradientTape() as tape: y_pred = s

train_step

src/transformers/models/longt5/modeling_flax_longt5.py

Add `LongT5` model (#16792) * Initial commit * Make some fixes * Make PT model full forward pass * Drop TF & Flax implementation, fix copies etc * Add Flax model and update some corresponding stuff * Drop some TF things * Update config and flax local attn * Add encoder_attention_type to config * . * Update docs * Do some cleansing * Fix some issues -> make style; add some docs * Fix position_bias + mask addition + Update tests * Fix repo consistency * Fix model consistency by removing flax operation over attn_mask * [WIP] Add PT TGlobal LongT5 * . * [WIP] Add flax tglobal model * [WIP] Update flax model to use the right attention type in the encoder * Fix flax tglobal model forward pass * Make the use of global_relative_attention_bias * Add test suites for TGlobal model * Fix minor bugs, clean code * Fix pt-flax equivalence though not convinced with correctness * Fix LocalAttn implementation to match the original impl. + update READMEs * Few updates * Update: [Flax] improve large model init and loading #16148 * Add ckpt conversion script accoring to #16853 + handle torch device placement * Minor updates to conversion script. * Typo: AutoModelForSeq2SeqLM -> FlaxAutoModelForSeq2SeqLM * gpu support + dtype fix * Apply some suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * * Remove (de)parallelize stuff * Edit shape comments * Update README.md * make fix-copies * Remove caching logic for local & tglobal attention * Apply another batch of suggestions from code review * Add missing checkpoints * Format converting scripts * Drop (de)parallelize links from longT5 mdx * Fix converting script + revert config file change * Revert "Remove caching logic for local & tglobal attention" This reverts commit 2a619828f6ddc3e65bd9bb1725a12b77fa883a46. * Stash caching logic in Flax model * Make side relative bias used always * Drop caching logic in PT model * Return side bias as it was * Drop all remaining model parallel logic * Remove clamp statements * Move test files to the proper place * Update docs with new version of hf-doc-builder * Fix test imports * Make some minor improvements * Add missing checkpoints to docs * Make TGlobal model compatible with torch.onnx.export * Replace some np.ndarray with jnp.ndarray * Fix TGlobal for ONNX conversion + update docs * fix _make_global_fixed_block_ids and masked neg value * update flax model * style and quality * fix imports * remove load_tf_weights_in_longt5 from init and fix copies * add slow test for TGlobal model * typo fix * Drop obsolete is_parallelizable and one warning * Update __init__ files to fix repo-consistency * fix pipeline test * Fix some device placements * [wip]: Update tests -- need to generate summaries to update expected_summary * Fix quality * Update LongT5 model card * Update (slow) summarization tests * make style * rename checkpoitns * finish * fix flax tests Co-authored-by: phungvanduy <pvduy23@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: patil-suraj <surajp815@gmail.com>

transformers

Python

modeling_flax_longt5.py

def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values return model_kwargs FLAX_LONGT5_CONDITIONAL_GENERATION_DOCSTRING = overwrite_call_docstring( FlaxLongT5ForConditionalGeneration, LONGT5_INPUTS_DOCSTRING + FLAX_LONGT5_CONDITIONAL_GENERATION_DOCSTRING ) append_replace_return_docstrings( FlaxLongT5ForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC )

a72f1c9f5b907f96cbb7de3bbb02a1d431d34071

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

def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values return model_kwargs FLAX_LONGT5_CONDITIONAL_GENERATION_DOCSTRING = overwrite_call_docstring( FlaxLongT5ForConditionalGeneration, LONGT5_INPUTS_DOCSTRING + FLAX_LONGT5_CONDITIONAL_GENERATION_DOCSTRING ) append_replace_return_docstrings( FlaxLongT5ForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC )

update_inputs_for_generation

tests/test_visibility.py

Rename storage classes (#12913)

synapse

Python

test_visibility.py

def _inject_outlier(self) -> EventBase: builder = self.event_builder_factory.for_room_version( RoomVersions.V1, { "type": "m.room.member", "sender": "@test:user", "state_key": "@test:user", "room_id": TEST_ROOM_ID, "content": {"membership": "join"}, }, ) event = self.get_success(builder.build(prev_event_ids=[], auth_event_ids=[])) event.internal_metadata.outlier = True self.get_success( self._storage_controllers.persistence.persist_event( event, EventContext.for_outlier(self._storage_controllers) ) ) return event

1e453053cb12ff084fdcdc2f75c08ced274dff21

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

def _inject_outlier(self) -> EventBase: builder = self.event_builder_factory.for_room_version( RoomVersions.V1, {

_inject_outlier

tests/packaging/test_file_packager.py

Fix packager flow collisions

prefect

Python

test_file_packager.py

async def test_file_packager_by_serializer(serializer): packager = FilePackager(serializer=serializer) manifest = await packager.package(howdy) assert isinstance(manifest, FilePackageManifest) unpackaged_howdy = await manifest.unpackage() assert unpackaged_howdy("bro").result() == "howdy bro"

32d4fb18769d663292fb059eda1e15a8628af689

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

async def test_file_packager_by_serializer(serializer): packager = FileP

test_file_packager_by_serializer

homeassistant/components/homekit_controller/config_flow.py

Fix some homekit_controller pylint warnings and (local only) test failures (#76122)

core

Python

config_flow.py

async def _async_setup_controller(self) -> None: self.controller = await async_get_controller(self.hass)

d5695a2d8656d2f9cb4d549c80cad331c914af1f

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

async def _async_setup_controller(self) -> None: self.controller = await async_get_controller(self.hass)

_async_setup_controller

test/test_preprocessor.py

Change return types of indexing pipeline nodes (#2342) * Change return types of file converters * Change return types of preprocessor * Change return types of crawler * Adapt utils to functions to new return types * Adapt __init__.py to new method names * Prevent circular imports * Update Documentation & Code Style * Let DocStores' run method accept Documents * Adapt tests to new return types * Update Documentation & Code Style * Put "# type: ignore" to right place * Remove id_hash_keys property from Document primitive * Update Documentation & Code Style * Adapt tests to new return types and missing id_hash_keys property * Fix mypy * Fix mypy * Adapt PDFToTextOCRConverter * Remove id_hash_keys from RestAPI tests * Update Documentation & Code Style * Rename tests * Remove redundant setting of content_type="text" * Add DeprecationWarning * Add id_hash_keys to elasticsearch_index_to_document_store * Change document type from dict to Docuemnt in PreProcessor test * Fix file path in Tutorial 5 * Remove added output in Tutorial 5 * Update Documentation & Code Style * Fix file_paths in Tutorial 9 + fix gz files in fetch_archive_from_http * Adapt tutorials to new return types * Adapt tutorial 14 to new return types * Update Documentation & Code Style * Change assertions to HaystackErrors * Import HaystackError correctly Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

haystack

Python

test_preprocessor.py

def test_remove_substrings(): document = Document(content="This is a header. Some additional text. wiki. Some emoji ✨ 🪲 Weird whitespace\b\b\b.") # check that the file contains the substrings we are about to remove assert "This is a header." in document.content assert "wiki" in document.content assert "🪲" in document.content assert "whitespace" in document.content assert "✨" in document.content preprocessor = PreProcessor(remove_substrings=["This is a header.", "wiki", "🪲"]) documents = preprocessor.process(document) assert "This is a header." not in documents[0].content assert "wiki" not in documents[0].content assert "🪲" not in documents[0].content assert "whitespace" in documents[0].content assert "✨" in documents[0].content

834f8c49024063ce17a63e50a9d7cff12f1c4f91

https://github.com/deepset-ai/haystack.git

def test_remove_substrings(): document = Document(content="This is a header. Some additional text. wiki. Some emoji ✨ 🪲 Weird whitespace\b\b\b.") # check that the file contains the substrings we are about to remove assert "This is a header." in document.content assert "wiki" in document.content assert "🪲" in document.content assert "whitespace" in document.content assert "✨" in document.content preprocessor = PreProcessor(remove_substrings=["This is a header.", "wiki", "🪲"]) documents = preprocessor.process(document) assert "This is a header." not in documents[0].content assert "wiki" not in documents[0].content assert "🪲" n

test_remove_substrings

homeassistant/components/fivem/__init__.py

Fivem integration (#65089) * Initial fivem integration setup * Use licenseKey for unique ID * Create FiveMServer class * Create FiveMStatusBinarySensor * Fix platform loading * Create sensor platform * Remove config flow tests * Update manifest.json * Use attr_ instead or properties in sensors.py * Use entry_id as unique_id * Move device info to _attr instead of property * Register callback in FiveMEntity * Create config flow tests * Add loggin to fivem * Use FiveM in config_flow * Use update_coordinator instead of dispatcher * Bump fivem-api to 0.1.2 * Remove leftovers * More tests for config flow * Add component files to .coveragerc * Fix simple comments * Add gamename check to config flow * Use entity descriptions for sensors * Move extra attributes to init * Use [] instead of get() for server info * Fix error in gamename test

core

Python

__init__.py

async def _async_update_data(self) -> dict[str, Any]: was_online = self.online try: server = await self._fivem.get_server() self.online = True except FiveMServerOfflineError: self.online = False if was_online and not self.online: _LOGGER.warning("Connection to '%s:%s' lost", self.host, self.port) elif not was_online and self.online: _LOGGER.info("Connection to '%s:%s' (re-)established", self.host, self.port) if self.online: players_list: list[str] = [] for player in server.players: players_list.append(player.name) players_list.sort() resources_list = server.resources resources_list.sort() return { NAME_PLAYERS_ONLINE: len(players_list), NAME_PLAYERS_MAX: server.max_players, NAME_RESOURCES: len(resources_list), NAME_STATUS: self.online, ATTR_PLAYERS_LIST: players_list, ATTR_RESOURCES_LIST: resources_list, } raise UpdateFailed @dataclass

0ea82bdbfb0d58b1af273e39da65cbb9e4af1015

@dataclass

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

async def _async_update_data(self) -> dict[str, Any]: was_online = self.online try: server = await self._fivem.get_server() self.online = True except FiveMServerOfflineError: self.online = False if was_online and not self.online: _LOGGER.warning("Connection to '%s:%s' lost", self.host, self.port) elif not was_online and self.online: _LOGGER.info("Connection to '%s:%s' (re-)established", self.host, self.port) if self.online: players_list: list[str] = [] for player in server.players: players_list.append(player.name) players_list.sort() resources_list = server.resources resources_list.sort() return { NAME_PLAYERS_ONLINE: len(players_list), NAME_PLAYERS_MAX: server.max_players, NAME_RESOURCES: len(resources_list), NAME_STATUS: self.online, ATTR_PLAYERS

_async_update_data

modin/pandas/base.py

REFACTOR-#5092: Fix future warning for `set_axis` function (#5093) Co-authored-by: Vasily Litvinov <fam1ly.n4me@yandex.ru> Signed-off-by: Myachev <anatoly.myachev@intel.com>

modin

Python

base.py

def swaplevel(self, i=-2, j=-1, axis=0): # noqa: PR01, RT01, D200 axis = self._get_axis_number(axis) idx = self.index if axis == 0 else self.columns return self.set_axis(idx.swaplevel(i, j), axis=axis)

9013f54283eb6776920ee3bf527e208a516d086d

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

def swaplevel(self, i=-2, j=-1, axis=0): # noqa: PR01, RT01, D200

swaplevel

tests/sentry/models/test_projectownership.py

feat(issues): Store assignee integration in group activity (#38526) - When a user is assigned via slack or ms teams, add the integration to activity data - When assigned via codeowners, add the integration and rule as a string

sentry

Python

test_projectownership.py

def test_get_autoassign_owners_no_codeowners_or_issueowners(self): assert ProjectOwnership.get_autoassign_owners(self.project.id, {}) == ( False, [], False, None, )

f1c3fa1660fa8144b5965f0375f5abec122243bf

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

def test_get_autoassign_owners_no_codeowners_or_issueowners(self): assert ProjectOw

test_get_autoassign_owners_no_codeowners_or_issueowners

src/sentry/search/events/datasets/metrics.py

chore(metrics): Remove tag values are strings option (#41092) - This removes the tag value option since we're now fully on using tag values as strings instead of indexed integers - This is needed so we can start on wildcard searching

sentry

Python

metrics.py

def function_converter(self) -> Mapping[str, fields.MetricsFunction]: resolve_metric_id = { "name": "metric_id", "fn": lambda args: self.resolve_metric(args["column"]), } function_converter = { function.name: function for function in [ # Note while the discover version of apdex, count_miserable, user_misery # accepts arguments, because this is precomputed with tags no parameters # are available fields.MetricsFunction( "apdex", optional_args=[fields.NullableNumberRange("satisfaction", 0, None)], snql_distribution=self._resolve_apdex_function, default_result_type="number", ), fields.MetricsFunction( "avg", required_args=[ fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS, ) ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: Function( "avgIf", [ Column("value"), Function( "equals", [ Column("metric_id"), args["metric_id"], ], ), ], alias, ), result_type_fn=self.reflective_result_type(), default_result_type="integer", ), fields.MetricsFunction( "count_miserable", required_args=[ fields.MetricArg( "column", allowed_columns=["user"], allow_custom_measurements=False ) ], optional_args=[fields.NullableNumberRange("satisfaction", 0, None)], calculated_args=[resolve_metric_id], snql_set=self._resolve_count_miserable_function, default_result_type="integer", ), fields.MetricsFunction( "count_unparameterized_transactions", snql_distribution=lambda args, alias: Function( "countIf", [ Column("value"), Function( "and", [ Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), Function( "equals", [ self.builder.column("transaction"), self.builder.resolve_tag_value("<< unparameterized >>"), ], ), ], ), ], alias, ), # Not yet exposed, need to add far more validation around tag&value private=True, default_result_type="integer", ), fields.MetricsFunction( "count_null_transactions", snql_distribution=lambda args, alias: Function( "countIf", [ Column("value"), Function( "and", [ Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), Function( "equals", [ self.builder.column("transaction"), "", ], ), ], ), ], alias, ), private=True, ), fields.MetricsFunction( "count_has_transaction_name", snql_distribution=lambda args, alias: Function( "countIf", [ Column("value"), Function( "and", [ Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), Function( "and", [ Function( "notEquals", [ self.builder.column("transaction"), "", ], ), Function( "notEquals", [ self.builder.column("transaction"), self.builder.resolve_tag_value( "<< unparameterized >>" ), ], ), ], ), ], ), ], alias, ), private=True, default_result_type="integer", ), fields.MetricsFunction( "user_misery", optional_args=[ fields.NullableNumberRange("satisfaction", 0, None), fields.with_default( constants.MISERY_ALPHA, fields.NumberRange("alpha", 0, None) ), fields.with_default( constants.MISERY_BETA, fields.NumberRange("beta", 0, None) ), ], calculated_args=[], snql_set=self._resolve_user_misery_function, default_result_type="number", ), fields.MetricsFunction( "p50", optional_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: self._resolve_percentile( args, alias, 0.5 ), result_type_fn=self.reflective_result_type(), default_result_type="duration", ), fields.MetricsFunction( "p75", optional_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: self._resolve_percentile( args, alias, 0.75 ), result_type_fn=self.reflective_result_type(), default_result_type="duration", ), fields.MetricsFunction( "p90", optional_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: self._resolve_percentile( args, alias, 0.90 ), result_type_fn=self.reflective_result_type(), default_result_type="duration", ), fields.MetricsFunction( "p95", optional_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: self._resolve_percentile( args, alias, 0.95 ), result_type_fn=self.reflective_result_type(), default_result_type="duration", ), fields.MetricsFunction( "p99", optional_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: self._resolve_percentile( args, alias, 0.99 ), result_type_fn=self.reflective_result_type(), default_result_type="duration", ), fields.MetricsFunction( "p100", optional_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: self._resolve_percentile(args, alias, 1), result_type_fn=self.reflective_result_type(), default_result_type="duration", ), fields.MetricsFunction( "max", required_args=[ fields.MetricArg("column"), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: Function( "maxIf", [ Column("value"), Function("equals", [Column("metric_id"), args["metric_id"]]), ], alias, ), result_type_fn=self.reflective_result_type(), ), fields.MetricsFunction( "min", required_args=[ fields.MetricArg("column"), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: Function( "minIf", [ Column("value"), Function("equals", [Column("metric_id"), args["metric_id"]]), ], alias, ), result_type_fn=self.reflective_result_type(), ), fields.MetricsFunction( "sum", required_args=[ fields.MetricArg("column"), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: Function( "sumIf", [ Column("value"), Function("equals", [Column("metric_id"), args["metric_id"]]), ], alias, ), result_type_fn=self.reflective_result_type(), ), fields.MetricsFunction( "sumIf", required_args=[ fields.ColumnTagArg("if_col"), fields.FunctionArg("if_val"), ], calculated_args=[ { "name": "resolved_val", "fn": lambda args: self.builder.resolve_tag_value(args["if_val"]), } ], snql_counter=lambda args, alias: Function( "sumIf", [ Column("value"), Function("equals", [args["if_col"], args["resolved_val"]]), ], alias, ), default_result_type="integer", ), fields.MetricsFunction( "percentile", required_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), fields.NumberRange("percentile", 0, 1), ], calculated_args=[resolve_metric_id], snql_distribution=self._resolve_percentile, result_type_fn=self.reflective_result_type(), default_result_type="duration", ), fields.MetricsFunction( "count_unique", required_args=[ fields.MetricArg( "column", allowed_columns=["user"], allow_custom_measurements=False ) ], calculated_args=[resolve_metric_id], snql_set=lambda args, alias: Function( "uniqIf", [ Column("value"), Function("equals", [Column("metric_id"), args["metric_id"]]), ], alias, ), default_result_type="integer", ), fields.MetricsFunction( "uniq", snql_set=lambda args, alias: Function( "uniq", [Column("value")], alias, ), ), fields.MetricsFunction( "uniqIf", required_args=[ fields.ColumnTagArg("if_col"), fields.FunctionArg("if_val"), ], calculated_args=[ { "name": "resolved_val", "fn": lambda args: self.builder.resolve_tag_value(args["if_val"]), } ], snql_set=lambda args, alias: Function( "uniqIf", [ Column("value"), Function("equals", [args["if_col"], args["resolved_val"]]), ], alias, ), default_result_type="integer", ), fields.MetricsFunction( "count", snql_distribution=lambda args, alias: Function( "countIf", [ Column("value"), Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), ], alias, ), default_result_type="integer", ), fields.MetricsFunction( "count_web_vitals", required_args=[ fields.MetricArg( "column", allowed_columns=[ "measurements.fp", "measurements.fcp", "measurements.lcp", "measurements.fid", "measurements.cls", ], allow_custom_measurements=False, ), fields.SnQLStringArg( "quality", allowed_strings=["good", "meh", "poor", "any"] ), ], calculated_args=[resolve_metric_id], snql_distribution=self._resolve_web_vital_function, default_result_type="integer", ), fields.MetricsFunction( "epm", snql_distribution=lambda args, alias: Function( "divide", [ Function( "countIf", [ Column("value"), Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), ], ), Function("divide", [args["interval"], 60]), ], alias, ), optional_args=[fields.IntervalDefault("interval", 1, None)], default_result_type="number", ), fields.MetricsFunction( "eps", snql_distribution=lambda args, alias: Function( "divide", [ Function( "countIf", [ Column("value"), Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), ], ), args["interval"], ], alias, ), optional_args=[fields.IntervalDefault("interval", 1, None)], default_result_type="number", ), fields.MetricsFunction( "failure_count", snql_distribution=self._resolve_failure_count, default_result_type="integer", ), fields.MetricsFunction( "failure_rate", snql_distribution=lambda args, alias: Function( "divide", [ self._resolve_failure_count(args), Function( "countIf", [ Column("value"), Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), ], ), ], alias, ), default_result_type="percentage", ), fields.MetricsFunction( "histogram", required_args=[fields.MetricArg("column")], calculated_args=[resolve_metric_id], snql_distribution=self._resolve_histogram_function, default_result_type="number", private=True, ), ] } for alias, name in constants.FUNCTION_ALIASES.items(): if name in function_converter: function_converter[alias] = function_converter[name].alias_as(alias) return function_converter

4c9c03f8a9416b53bf74f2d77df43499973ecf89

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

def function_converter(self) -> Mapping[str, fields.MetricsFunction]: resolve_metric_id = { "name": "metric_id", "fn": lambda args: self.resolve_metric(args["column"]), } function_converter = { function.name: function for function in [ # Note while the discover version of apdex, count_miserable, user_misery # accepts arguments, because this is precomputed with tags no parameters # are available fields.MetricsFunction( "apdex", optional_args=[fields.NullableNumberRange("satisfaction", 0, None)], snql_distribution=self._resolve_apdex_function, default_result_type="number", ), fields.MetricsFunction( "avg", required_args=[ fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS, ) ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: Function( "avgIf", [ Column("value"), Function( "equals", [ Column("metric_id"), args["metric_id"], ], ), ], alias, ), result_type_fn=self.reflective_result_type(), default_result_type="integer", ), fields.MetricsFunction( "count_miserable", required_args=[ fields.MetricArg( "column", allowed_columns=["user"], allow_custom_measurements=False ) ], optional_args=[fields.NullableNumberRange("satisfaction", 0, None)], calculated_args=[resolve_metric_id], snql_set=self._resolve_count_miserable_function, default_result_type="integer", ), fields.MetricsFunction( "count_unparameterized_transactions", snql_distribution=lambda args, alias: Function( "countIf", [ Column("value"), Function( "and", [ Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), Function( "equals", [ self.builder.column("transaction"), self.builder.resolve_tag_value("<< unparameterized >>"), ], ), ], ), ], alias, ), # Not yet exposed, need to add far more validation around tag&value private=True, default_result_type="integer", ), fields.MetricsFunction( "count_null_transactions", snql_distribution=lambda args, alias: Function( "countIf", [ Column("value"), Function( "and", [ Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), Function( "equals", [ self.builder.column("transaction"), "", ], ), ], ), ], alias, ), private=True, ), fields.MetricsFunction( "count_has_transaction_name", snql_distribution=lambda args, alias: Function( "countIf", [ Column("value"), Function( "and", [ Function( "equals", [ Column("metric_id"), self.resolve_metric("transaction.duration"), ], ), Function( "and", [ Function( "notEquals", [ self.builder.column("transaction"), "", ], ), Function( "notEquals", [ self.builder.column("transaction"), self.builder.resolve_tag_value( "<< unparameterized >>" ), ], ), ], ), ], ), ], alias, ), private=True, default_result_type="integer", ), fields.MetricsFunction( "user_misery", optional_args=[ fields.NullableNumberRange("satisfaction", 0, None), fields.with_default( constants.MISERY_ALPHA, fields.NumberRange("alpha", 0, None) ), fields.with_default( constants.MISERY_BETA, fields.NumberRange("beta", 0, None) ), ], calculated_args=[], snql_set=self._resolve_user_misery_function, default_result_type="number", ), fields.MetricsFunction( "p50", optional_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: self._resolve_percentile( args, alias, 0.5 ), result_type_fn=self.reflective_result_type(), default_result_type="duration", ), fields.MetricsFunction( "p75", optional_args=[ fields.with_default( "transaction.duration", fields.MetricArg( "column", allowed_columns=constants.METRIC_DURATION_COLUMNS ), ), ], calculated_args=[resolve_metric_id], snql_distribution=lambda args, alias: self._re

function_converter

tests/exchange/test_ccxt_compat.py

Okx - conditional candle-length

freqtrade

Python

test_ccxt_compat.py

def test_ccxt__async_get_candle_history(self, exchange): exchange, exchangename = exchange # For some weired reason, this test returns random lengths for bittrex. if not exchange._ft_has['ohlcv_has_history'] or exchangename == 'bittrex': return pair = EXCHANGES[exchangename]['pair'] timeframe = EXCHANGES[exchangename]['timeframe'] candle_type = CandleType.SPOT timeframe_ms = timeframe_to_msecs(timeframe) now = timeframe_to_prev_date( timeframe, datetime.now(timezone.utc)) for offset in (360, 120, 30, 10, 5, 2): since = now - timedelta(days=offset) since_ms = int(since.timestamp() * 1000) res = exchange.loop.run_until_complete(exchange._async_get_candle_history( pair=pair, timeframe=timeframe, since_ms=since_ms, candle_type=candle_type ) ) assert res assert res[0] == pair assert res[1] == timeframe assert res[2] == candle_type candles = res[3] candle_count = exchange.ohlcv_candle_limit(timeframe, candle_type, since_ms) * 0.9 candle_count1 = (now.timestamp() * 1000 - since_ms) // timeframe_ms assert len(candles) >= min(candle_count, candle_count1) assert candles[0][0] == since_ms or (since_ms + timeframe_ms)

111b04c9e65668067646265e614326f81aa1bf1c

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

def test_ccxt__async_get_candle_history(self, exchange): exchange, exchangename = exchange # For some weired reason, this test returns random lengths for bittrex. if not exchange._ft_has['ohlcv_has_history'] or exchangename == 'bittrex': return pair = EXCHANGES[exchangename]['pair'] timeframe = EXCHANGES[exchangename]['timeframe'] candle_type = CandleType.SPOT timeframe_ms = timeframe_to_msecs(timeframe) now = timeframe_to_prev_date( timeframe, datetime.now(timezone.utc)) for offset in (360, 120, 30, 10, 5, 2): since = now - timedelta(days=offset) since_ms = int(since.timestamp() * 1000) res = exchange.loop.run_until_complete(exchange._async_get_candle_history( pair=pair, timeframe=timeframe, since_ms=since_ms, candle_type=candle_type ) ) assert res assert res[0] == pair assert res[1] == timeframe assert res[2] == candle_type candles = res[3] candle_count = exchange.ohlcv_candle_limit(timeframe, candle_type, since_ms) * 0.9 candle_count1 = (now.timestamp() * 1000 - since_ms) //

test_ccxt__async_get_candle_history

python/ray/_private/runtime_env/_clonevirtualenv.py

Update license for MLflow's conda utils and virtualenv-clone (#22402) When we vendor third-party code, we should update LICENSE file. Previously we vendored two pieces of code: - conda utilities from MLflow - virtualenv-clone But we only included the attribution in the relevant source files, not in our LICENSE file. This PR adds the necessary info to our LICENSE file.

ray

Python

_clonevirtualenv.py

def fix_symlink_if_necessary(src_dir, dst_dir): # sometimes the source virtual environment has symlinks that point to itself # one example is $OLD_VIRTUAL_ENV/local/lib points to $OLD_VIRTUAL_ENV/lib # this function makes sure # $NEW_VIRTUAL_ENV/local/lib will point to $NEW_VIRTUAL_ENV/lib # usually this goes unnoticed unless one tries to upgrade a package though pip, so this bug is hard to find. logger.info("scanning for internal symlinks that point to the original virtual env") for dirpath, dirnames, filenames in os.walk(dst_dir): for a_file in itertools.chain(filenames, dirnames): full_file_path = os.path.join(dirpath, a_file) if os.path.islink(full_file_path): target = os.path.realpath(full_file_path) if target.startswith(src_dir): new_target = target.replace(src_dir, dst_dir) logger.debug("fixing symlink in %s" % (full_file_path,)) os.remove(full_file_path) os.symlink(new_target, full_file_path)

606e2b2cde89a4869129dbca907bc14a7a9d1197

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

def fix_symlink_if_necessary(src_dir, dst_dir): # sometimes the source virtual environment has symlinks that point to itself # one example is $OLD_VIRTUAL_ENV/local/lib points to $OLD_VIRTUAL_ENV/lib # this function makes sure # $NEW_VIRTUAL_ENV/local/lib will point to $NEW_VIRTUAL_ENV/lib # usually this goes unnoticed unless one tries to upgrade a package though pip, so this bug is hard to find. logger.inf

fix_symlink_if_necessary

tests/snuba/api/endpoints/test_organization_events.py

fix(tests): Discover backend test flakes (#41057) - `MetricsQueryBuilder` wasn't sorting environment tags - Consistent timestamps on test_organization_events - Updated `apply_feature_flag_on_cls` to only apply decorator on the run method

sentry

Python

test_organization_events.py

def test_issue_in_columns(self): project1 = self.create_project() project2 = self.create_project() event1 = self.store_event( data={ "event_id": "a" * 32, "transaction": "/example", "message": "how to make fast", "timestamp": self.ten_mins_ago_iso, "fingerprint": ["group_1"], }, project_id=project1.id, ) event2 = self.store_event( data={ "event_id": "b" * 32, "transaction": "/example", "message": "how to make fast", "timestamp": self.ten_mins_ago_iso, "fingerprint": ["group_1"], }, project_id=project2.id, ) features = {"organizations:discover-basic": True, "organizations:global-views": True} query = {"field": ["id", "issue"], "orderby": ["id"]} response = self.do_request(query, features=features) assert response.status_code == 200, response.content data = response.data["data"] assert len(data) == 2 assert data[0]["id"] == event1.event_id assert data[0]["issue.id"] == event1.group_id assert data[0]["issue"] == event1.group.qualified_short_id assert data[1]["id"] == event2.event_id assert data[1]["issue.id"] == event2.group_id assert data[1]["issue"] == event2.group.qualified_short_id

618ae63cf2ba419e44e79ce578d88e8b062d7dd9

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

def test_issue_in_columns(self): project1 = self.create_project() project2 = self.create_project() event1 = self.store_event( data={ "event_id": "a" * 32, "transaction": "/example", "message": "how to ma

test_issue_in_columns

dask/typing.py

Collection Protocol (#8674) [PEP 544](https://www.python.org/dev/peps/pep-0544/) introduces the `Protocol` class to the `typing` module in Python 3.8 (the soon be the minimum supported version, https://github.com/dask/community/issues/213). Writing new Dask collections for [dask-awkward](https://github.com/ContinuumIO/dask-awkward/) has had me thinking about working on a `DaskCollection` protocol. I imagine the benefits to be: - usage with static type checkers - other activity in this area at - #8295 - #8706 - #8854 - Python supporting IDEs take advantage of typing - self-documenting; some improvements to [the custom collections page](https://docs.dask.org/en/latest/custom-collections.html) of the docs. The protocol docs can be autogenerated and added to that page. - purely opt-in feature The `typing.runtime_checkable` decorator allows use of `isinstance(x, DaskCollection)` in any code base that uses Dask collections; for example: ```python >>> from dask.typing import DaskCollection >>> import dask.array as da >>> x = da.zeros((10, 3)) >>> isinstance(x, DaskCollection) True ``` (though this is an order of magnitude slower than `dask.base.is_dask_collection` which only checks for `x.__dask_graph__() is not None`; static typing checking & built-in interface documentation are the core benefits IMO) Something else that came up in the brief discussion on a call last week was having `{Scheduler,Worker,Nanny}Plugin` protocols in `distributed`; and perhaps those are better places to start introducing protocols to Dask since on the user side typically more folks would write plugins than new collections.

dask

Python

typing.py

def __dask_graph__(self) -> Mapping: raise NotImplementedError("Inheriting class must implement this method.")

1e783d9a714160e968936cb22d54d085959ab09e

https://github.com/dask/dask.git

def __dask_graph__(self) -> Mapping:

__dask_graph__

mindsdb/integrations/handlers/databend_handler/tests/test_databend_handler.py

added the unit tests for the handler

mindsdb

Python

test_databend_handler.py

def test_1_native_query_show_dbs(self): result = self.handler.native_query("SHOW DATABASES;") assert result.type is not RESPONSE_TYPE.ERROR # def test_2_wrong_native_query_returns_error(self): # result = self.handler.native_query("SHOW DATABASE1S;") # assert result.type is RESPONSE_TYPE.ERROR

add8253659f2a16152fa513ae310b4b6b5242e1e

https://github.com/mindsdb/mindsdb.git

def test_1_native_query_show_dbs(self): result = self.handler.native_query("SHOW DATABASES;") assert result.type is not RESPONSE_TYPE.ERROR # def test_2_wrong_native_query_returns_error(self): # result = self.handler.native_query("SHOW DATABASE1S;") # assert result.type is RESPONSE_TY

test_1_native_query_show_dbs

test/lib/ansible_test/_internal/host_profiles.py

ansible-test - Improve container management. (#78550) See changelogs/fragments/ansible-test-container-management.yml for details.

ansible

Python

host_profiles.py

def setup(self) -> None: bootstrapper = BootstrapDocker( controller=self.controller, python_versions=[self.python.version], ssh_key=SshKey(self.args), ) setup_sh = bootstrapper.get_script() shell = setup_sh.splitlines()[0][2:] try: docker_exec(self.args, self.container_name, [shell], data=setup_sh, capture=False) except SubprocessError: display.info(f'Checking container "{self.container_name}" logs...') docker_logs(self.args, self.container_name) raise

cda16cc5e9aa8703fb4e1ac0a0be6b631d9076cc

https://github.com/ansible/ansible.git

def setup(self) -> None: bootstrapper = BootstrapDocker( controller=self.controller, python_versions=[self.python.version], ssh_key=SshKey(self.args), ) setup_sh = bootstrapper.get_script()

setup

wagtail/snippets/tests.py

Use ReportView for Snippets HistoryView and use filterset

wagtail

Python

tests.py

def get_url(self, snippet, url_name, args=None): app_label = snippet._meta.app_label model_name = snippet._meta.model_name view_name = f"wagtailsnippets_{app_label}_{model_name}:{url_name}" if args is None: args = [quote(snippet.pk)] return reverse(view_name, args=args)

7b9531f9910ec8624ee66772805438e9f3084d3d

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

def get_url(self, snippet, url_name, args=None): app_label = snippet._meta.app_label model_name = snippet._meta.model_name view_name = f"wagtailsnippets_{app_label}_{model_name}:{url_name}" if args is None: args = [quote(snippet.pk)] return reverse(view_name, args=args)

get_url

erpnext/accounts/report/cash_flow/custom_cash_flow.py

refactor: convert raw sql to frappe.qb

erpnext

Python

custom_cash_flow.py

def get_accounts_in_mappers(mapping_names): cfm = frappe.qb.DocType('Cash Flow Mapping') cfma = frappe.qb.DocType('Cash Flow Mapping Accounts') result = ( frappe.qb .select( cfma.name, cfm.label, cfm.is_working_capital, cfm.is_income_tax_liability, cfm.is_income_tax_expense, cfm.is_finance_cost, cfm.is_finance_cost_adjustment, cfma.account ) .from_(cfm) .join(cfma) .on(cfm.name == cfma.parent) .where(cfma.parent.isin(mapping_names)) ).run() return result

00bfee97c766e771a1ab0b57d223ba9e87b70e9a

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

def get_accounts_in_mappers(mapping_names): cfm = frappe.qb.DocType('Cash Flow Mapping') cfma = frappe.qb.DocType('Cash Flow Mapping Accounts') result = ( frappe.qb .select( cfma.name, cfm.label, cfm.is_working_cap

get_accounts_in_mappers

tests/sentry/search/events/test_builder.py

ref(mep): Some cleanup to the metric query builder (#32139) - This adds metric_id to the search conditions based on the aggregates added so that there's a top level filter instead of just the aggregate -if combinator filters. This should help with query performance - This also removes the combinator&merge from query construction since snuba can handle this for us, which makes the functions a bit cleaner

sentry

Python

test_builder.py

def _metric_conditions(metrics) -> List[Condition]: return [ Condition( Column("metric_id"), Op.IN, sorted(indexer.resolve(constants.METRICS_MAP[metric]) for metric in metrics), ) ]

5e1cb0e215c061e13ec1262a814450a33d49a398

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

def _metric_conditions(metrics) -> List[Condition]: return [ Condition( Column("metr

_metric_conditions

tests/test_main.py

Switching to `pydantic_core` (#4516) * working on core schema generation * adapting main.py * getting tests to run * fix tests * disable pyright, fix mypy * moving to class-based model generation * working on validators * change how models are created * start fixing test_main.py * fixing mypy * SelfType * recursive models working, more tests fixed * fix tests on <3.10 * get docs build to pass * starting to cleanup types.py * starting works on custom types * working on using annotated-types * using annoated types for constraints * lots of cleanup, fixing network tests * network tests passing :tada: * working on types * working on types and cleanup * fixing UUID type, restructing again * more types and newer pydantic-core * working on Iterable * more test_types tests * support newer pydantic-core, fixing more test_types.py * working through more test_types.py * test_types.py at last passing locally :tada: * fixing more tests in test_types.py * fix datetime_parse tests and linting * get tests running again, rename to test_datetime.py * renaming internal modules * working through mypy errors * fixing mypy * refactoring _generate_schema.py * test_main.py passing * uprev deps * fix conftest and linting? * importing Annotated * ltining * import Annotated from typing_extensions * fixing 3.7 compatibility * fixing tests on 3.9 * fix linting * fixing SecretField and 3.9 tests * customising get_type_hints * ignore warnings on 3.11 * spliting repr out of utils * removing unused bits of _repr, fix tests for 3.7 * more cleanup, removing many type aliases * clean up repr * support namedtuples and typeddicts * test is_union * removing errors, uprev pydantic-core * fix tests on 3.8 * fixing private attributes and model_post_init * renaming and cleanup * remove unnecessary PydanticMetadata inheritance * fixing forward refs and mypy tests * fix signatures, change how xfail works * revert mypy tests to 3.7 syntax * correct model title * try to fix tests * fixing ClassVar forward refs * uprev pydantic-core, new error format * add "force" argument to model_rebuild * Apply suggestions from code review Suggestions from @tiangolo and @hramezani :pray: Co-authored-by: Hasan Ramezani <hasan.r67@gmail.com> Co-authored-by: Sebastián Ramírez <tiangolo@gmail.com> * more suggestions from @tiangolo * extra -> json_schema_extra on Field Co-authored-by: Hasan Ramezani <hasan.r67@gmail.com> Co-authored-by: Sebastián Ramírez <tiangolo@gmail.com>

pydantic

Python

test_main.py

def test_nullable_strings_fails(NoneCheckModel): with pytest.raises(ValidationError) as exc_info: NoneCheckModel( required_str_value=None, required_str_none_value=None, required_bytes_value=None, required_bytes_none_value=None, ) assert exc_info.value.errors() == [ { 'type': 'string_type', 'loc': ('required_str_value',), 'msg': 'Input should be a valid string', 'input': None, }, { 'type': 'bytes_type', 'loc': ('required_bytes_value',), 'msg': 'Input should be a valid bytes', 'input': None, }, ] @pytest.fixture(name='ParentModel', scope='session')

594effa279668bd955e98f1cd5c036b37d3bbd40

@pytest.fixture(name='ParentModel', scope='session')

https://github.com/pydantic/pydantic.git

def test_nullable_strings_fails(NoneCheckModel): with pytest.raises(ValidationError) as exc_info: NoneCheckModel( required_str_value=None, required_str_none_value=None, required_bytes_value=None, required_bytes_none_value=None, ) assert exc_info.value.errors() == [ { 'type': 'string_type', 'loc': ('required_str_value',), 'msg': 'Input should be a valid string', 'input': None, }, { 'type': 'bytes_type', 'loc': ('required_bytes_value',), 'msg': 'Input should be a valid bytes', 'input': None, }, ] @pytest.fixture(name='Pare

test_nullable_strings_fails

test/lib/ansible_test/_internal/host_configs.py

ansible-test - Code cleanup and refactoring. (#77169) * Remove unnecessary PyCharm ignores. * Ignore intentional undefined attribute usage. * Add missing type hints. Fix existing type hints. * Fix docstrings and comments. * Use function to register completion handler. * Pass strings to display functions. * Fix CompositeAction handling of dest argument. * Use consistent types in expressions/assignments. * Use custom function to keep linters happy. * Add missing raise for custom exception. * Clean up key/value type handling in cloud plugins. * Use dataclass instead of dict for results. * Add custom type_guard function to check lists. * Ignore return type that can't be checked (yet). * Avoid changing types on local variables.

ansible

Python

host_configs.py

def apply_defaults(self, context, defaults): # type: (HostContext, CompletionConfig) -> None assert isinstance(defaults, PosixCompletionConfig) super().apply_defaults(context, defaults) self.python = self.python or NativePythonConfig() self.python.apply_defaults(context, defaults) @dataclasses.dataclass

a06fa496d3f837cca3c437ab6e9858525633d147

@dataclasses.dataclass

https://github.com/ansible/ansible.git

def apply_defaults(self, context, defaults): # type: (HostContext, CompletionConfig) -> None assert isinstance(defaults, PosixCompletionConfig) super().apply_defaults(context, defaults) self.python = self.python or NativePythonConfig() self.python.apply_defaults(context, defaults) @dataclasses.data

apply_defaults

tests/test_logging.py

Implement `flush(block: bool ...)` Previously, this always blocked. The new implementaiton is non-blocking, but we need to block in tests so the data is present for assertions

prefect

Python

test_logging.py

def test_flush_event_is_cleared(self, worker): worker._flush_event = MagicMock(return_val=False) with temporary_settings(PREFECT_LOGGING_ORION_BATCH_INTERVAL="5"): worker.start() worker.flush(block=True) worker._flush_event.wait.assert_called_with(5) worker._flush_event.clear.assert_called()

fa64dff0102537b3d249af16c7ea7821982195dd

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

def test_flush_event_is_cleared(self, worker): worker._flush_event = MagicMock(return_val=False) with temporary_settings(PREFECT_LOGGING_ORION_BATCH_INTERVAL="5"): worker.start() worker.flush(block=True) worker._flush_event.wait.assert_called_with(5) worker._

test_flush_event_is_cleared

awx/main/tests/unit/api/test_views.py

Add new flak8 rules to do some meaningful corrections

awx

Python

test_views.py

def test_get_endpoints(self, mocker): endpoints = [ 'ping', 'config', # 'settings', 'me', 'dashboard', 'organizations', 'users', 'projects', 'teams', 'credentials', 'inventory', 'inventory_sources', 'groups', 'hosts', 'job_templates', 'jobs', 'ad_hoc_commands', 'system_job_templates', 'system_jobs', 'schedules', 'notification_templates', 'notifications', 'labels', 'unified_job_templates', 'unified_jobs', 'activity_stream', 'workflow_job_templates', 'workflow_jobs', ] view = ApiVersionRootView() ret = view.get(mocker.MagicMock()) assert ret.status_code == 200 for endpoint in endpoints: assert endpoint in ret.data

d3eb2c197595c29c4a3f7b38cd609ce953009623

https://github.com/ansible/awx.git

def test_get_endpoints(self, mocker): endpoints = [ 'ping', 'config', # 'settings', 'me', 'dashboard', 'organizations', 'users', 'projects', 'teams', 'credentials', 'inventory', 'inventory_sources', 'groups', 'hosts', 'job_templates', 'jobs', 'ad_hoc_commands', 'system_job_templates', 'system_jobs', 'schedules', 'notification_templates',

test_get_endpoints

modin/pandas/test/test_series.py

FEAT-#4035: Upgrade pandas support to 1.4 (#4036) Co-authored-by: Igoshev, Yaroslav <yaroslav.igoshev@intel.com> Co-authored-by: Alexey Prutskov <alexey.prutskov@intel.com> Co-authored-by: Rehan Durrani <rehan@ponder.io> Co-authored-by: ienkovich <ilya.enkovich@intel.com> Co-authored-by: Vasily Litvinov <vasilij.n.litvinov@intel.com> Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru> Signed-off-by: Devin Petersohn <devin.petersohn@gmail.com>

modin

Python

test_series.py

def test_var(data, skipna, ddof): modin_series, pandas_series = create_test_series(data) try: pandas_result = pandas_series.var(skipna=skipna, ddof=ddof) except Exception as e: with pytest.raises(type(e)): modin_series.var(skipna=skipna, ddof=ddof) else: modin_result = modin_series.var(skipna=skipna, ddof=ddof) df_equals(modin_result, pandas_result)

39fbc57e809c2422b250f0be58d076a22bd45031

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

def test_var(data, skipna, ddof): modin_series, pandas_series = create_test_series(data) try: pandas_result = pandas_series

test_var

keras/regularizers_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

regularizers_test.py

def test_zero_regularization(self): # Verifies that training with zero regularization works. x, y = np.ones((10, 10)), np.ones((10, 3)) model = test_utils.get_model_from_layers( [ keras.layers.Dense( 3, kernel_regularizer=keras.regularizers.l2(0) ) ], input_shape=(10,), ) model.compile("sgd", "mse", run_eagerly=test_utils.should_run_eagerly()) model.fit(x, y, batch_size=5, epochs=1)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def test_zero_regularization(self): # Verifies that training with zero regularization works. x, y = np.ones((10, 10)), np.ones((10, 3)) model = t

test_zero_regularization

tests/trainer/test_trainer.py

Raise a warning if evaulation is triggered with best ckpt in case of multiple checkpoint callbacks (#11274) Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

lightning

Python

test_trainer.py

def test_best_ckpt_evaluate_raises_warning_with_multiple_ckpt_callbacks(): ckpt_callback1 = ModelCheckpoint() ckpt_callback1.best_model_path = "foo_best_model.ckpt" ckpt_callback2 = ModelCheckpoint() ckpt_callback2.best_model_path = "bar_best_model.ckpt" trainer = Trainer(callbacks=[ckpt_callback1, ckpt_callback2]) trainer.state.fn = TrainerFn.TESTING with pytest.warns(UserWarning, match="best checkpoint path from first checkpoint callback"): trainer._Trainer__set_ckpt_path(ckpt_path="best", model_provided=False, model_connected=True)

7eab379da2fdca542849ed4ad313d0851c2271e3

https://github.com/Lightning-AI/lightning.git

def test_best_ckpt_evaluate_raises_warning_with_multiple_ckpt_callbacks(): ckpt_callback1 = ModelCheckpoint() ckpt_callback1.best_model_path = "foo_best_model.ckpt" ckpt_callback2 = ModelCheckpoint() ckpt_callback2.best_model_path = "bar_best_model.ckpt" train

test_best_ckpt_evaluate_raises_warning_with_multiple_ckpt_callbacks

test/mitmproxy/net/test_server_spec.py

make it black!

mitmproxy

Python

test_server_spec.py

def test_parse_with_mode(): assert server_spec.parse_with_mode("m:example.com") == ( "m", ("https", ("example.com", 443)), ) with pytest.raises(ValueError): server_spec.parse_with_mode("moo")

b3587b52b25077f68116b9852b041d33e7fc6601

https://github.com/mitmproxy/mitmproxy.git

def test_parse_with_mode(): assert server_spec.parse_with_mode("m:example.com") == ( "m", ("https", ("example.com", 443)), ) with pyte

test_parse_with_mode

tests/components/matter/test_config_flow.py

Add matter integration BETA (#83064) * Add matter base (#79372) Co-authored-by: Marcel van der Veldt <m.vanderveldt@outlook.com> * Add matter server add-on flow (#82698) * Add matter server add-on flow * Fix stale error argument * Clean docstrings * Use localhost as default address * Add matter websocket api foundation (#82848) * Add matter config entry add-on management (#82865) * Use matter refactored server/client library (#83003) Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Bump python-matter-server to 1.0.6 (#83059) * Extend matter websocket api (#82948) * Extend matter websocket api * Finish docstring * Fix pin type * Adjust api after new client * Adjust api to frontend for now Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

core

Python

test_config_flow.py

def setup_entry_fixture() -> Generator[AsyncMock, None, None]: with patch( "homeassistant.components.matter.async_setup_entry", return_value=True ) as mock_setup_entry: yield mock_setup_entry @pytest.fixture(name="client_connect", autouse=True)

e2308fd15cec4dfdd25d843b72cd3071657fd5b8

@pytest.fixture(name="client_connect", autouse=True)

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

def setup_entry_fixture() -> Generator[AsyncMock, None, None]: with patch( "homeassistant.components.matter.async_setu

setup_entry_fixture

lib/matplotlib/backends/backend_wx.py

Separately track modifier keys for mouse events. Whether the event modifiers are directly available on enter/leave events depends on the backend, but all are handled here (except possibly for macos, which I haven't checked).

matplotlib

Python

backend_wx.py

def _on_motion(self, event): event.Skip() MouseEvent("motion_notify_event", self, *self._mpl_coords(event), modifiers=self._mpl_modifiers(event), guiEvent=event)._process()

b4e9e3131cdd7f1ad33ea06e21e7d3e51762af91

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

def _on_motion(self, event): event.Skip() MouseEvent("motion_notify_event", self, *self._mpl_coords(event), modifiers=self._mpl_modifiers(event), gui

_on_motion