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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34,462 | 149,618 | 590 | freqtrade/freqtradebot.py | 170 | 31 | def enter_positions(self) -> int:
trades_created = 0
whitelist = copy.deepcopy(self.active_pair_whitelist)
if not whitelist:
logger.info("Active pair whitelist is empty.")
return trades_created
# Remove pairs for currently opened trades from the whitelist
for trade in Trade.get_open_trades():
if | Use "side" parameter when calling Pairlocks | enter_positions | 737bdfe844e575bdbbc9cd9d2a84291fe2e58300 | freqtrade | freqtradebot.py | 17 | 34 | https://github.com/freqtrade/freqtrade.git | 10 | 172 | 0 | 111 | 327 | Python | {
"docstring": "\n Tries to execute entry orders for new trades (positions)\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 9
} | def enter_positions(self) -> int:
trades_created = 0
whitelist = copy.deepcopy(self.active_pair_whitelist)
if not whitelist:
logger.info("Active pair whitelist is empty.")
return trades_created
# Remove pairs for currently opened trades from the whitelist
for trade in Trade.get_open_trades():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
logger.info("No currency pair in active pair whitelist, "
"but checking to exit open trades.")
return trades_created
if PairLocks.is_global_lock(side='*'):
# This only checks for total locks (both sides).
# per-side locks will be evaluated by `is_pair_locked` within create_trade,
# once the direction for the trade is clear.
lock = PairLocks.get_pair_longest_lock('*')
if lock:
self.log_once(f"Global pairlock active until "
f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}. "
f"Not creating new trades, reason: {lock.reason}.", logger.info)
else:
self.log_once("Global pairlock active. Not creating new trades.", logger.info)
return trades_created
# Create entity and execute trade for each pair from whitelist
for pair in whitelist:
try:
trades_created += self.create_trade(pair)
except DependencyException as exception:
logger.warning('Unable to create trade for %s: %s', pair, exception)
if not trades_created:
logger.debug("Found no enter signals for whitelisted currencies. Trying again...")
return trades_created
|
|
7,335 | 40,171 | 77 | dash/_validate.py | 22 | 8 | def validate_js_path(registered_paths, package_name, path_in_package_dist):
if package_name not in registered_paths:
raise exceptions.DependencyException(
f
)
if path_in_package_dist not in registered_paths[package_name]:
raise exceptions.DependencyException(
f
) | f-strings everywhere! fffff | validate_js_path | c3c84b9ecf16bcc61ed80ec39d511af92fe07f2c | dash | _validate.py | 15 | 17 | https://github.com/plotly/dash.git | 3 | 40 | 0 | 15 | 93 | Python | {
"docstring": "\n Error loading dependency. \"{package_name}\" is not a registered library.\n Registered libraries are:\n {list(registered_paths.keys())}\n \n \"{package_name}\" is registered but the path requested is not valid.\n The path requested: \"{path_in_package_dist}\"\n List of registered paths: {registered_paths}\n ",
"language": "en",
"n_whitespaces": 122,
"n_words": 32,
"vocab_size": 25
} | def validate_js_path(registered_paths, package_name, path_in_package_dist):
if package_name not in registered_paths:
raise exceptions.DependencyException(
f
)
if path_in_package_dist not in registered_paths[package_name]:
raise exceptions.DependencyException(
f
)
|
|
27,706 | 124,874 | 76 | python/ray/serve/utils.py | 36 | 12 | def get_all_node_ids() -> List[Tuple[str, str]]:
node_ids = []
# Sort on NodeID to ensure the ordering is deterministic across the cluster.
for node in sorted(ray.nodes(), key=lambda entry: entry["NodeID"]):
# print(node)
if node["Alive"]:
node_ids.append((node["NodeID"], node["NodeName"]))
return node_ids
| Revert "Revert "[serve] Use soft constraint for pinning controller on head node (#25091)" (#25857)" (#25858) | get_all_node_ids | 0ecc7dad74d77a24705e44da2ba80892177377bc | ray | utils.py | 14 | 11 | https://github.com/ray-project/ray.git | 3 | 65 | 0 | 33 | 110 | Python | {
"docstring": "Get IDs for all live nodes in the cluster.\n\n Returns a list of (node_id: str, ip_address: str). The node_id can be\n passed into the Ray SchedulingPolicy API.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 27,
"vocab_size": 26
} | def get_all_node_ids() -> List[Tuple[str, str]]:
node_ids = []
# Sort on NodeID to ensure the ordering is deterministic across the cluster.
for node in sorted(ray.nodes(), key=lambda entry: entry["NodeID"]):
# print(node)
if node["Alive"]:
node_ids.append((node["NodeID"], node["NodeName"]))
return node_ids
|
|
69,876 | 242,540 | 267 | src/PIL/PpmImagePlugin.py | 70 | 8 | def _ignore_comments(self, block):
comment_spans = False
while True:
comment_start = block.find(b"#") # look for next comment
if comment_start == -1: # no comment found
break
comment_end = self._find_comment_end(block, comment_start)
if comment_end != -1: # comment ends in this block
block = (
block[:comment_start] + block[comment_end + 1 :]
) # delete comment
else: # last comment continues to next block(s)
block = block[:com | Implement bitonal decoder | _ignore_comments | ea7e108ca3c6fcd00014de370075ed0361a08138 | Pillow | PpmImagePlugin.py | 15 | 16 | https://github.com/python-pillow/Pillow.git | 4 | 80 | 0 | 45 | 136 | Python | {
"docstring": "\n Deletes comments from block. If comment does not end in this\n block, raises a flag.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 15,
"vocab_size": 15
} | def _ignore_comments(self, block):
comment_spans = False
while True:
comment_start = block.find(b"#") # look for next comment
if comment_start == -1: # no comment found
break
comment_end = self._find_comment_end(block, comment_start)
if comment_end != -1: # comment ends in this block
block = (
block[:comment_start] + block[comment_end + 1 :]
) # delete comment
else: # last comment continues to next block(s)
block = block[:comment_start]
comment_spans = True
break
return block, comment_spans
|
|
28,826 | 128,859 | 18 | python/ray/train/tests/test_gpu.py | 9 | 5 | def test_torch_auto_gpu_to_cpu(ray_start_4_cpus_2_gpus):
num_workers = 2
assert os.environ["CUDA_VISIBLE_DEVICES"] == ""
| [AIR] Hard deprecate old Trainer, old callbacks (#29015)
Hard deprecations for ray.train.Trainer, ray.train.callbacks and ray.train.checkpoint.CheckpointStrategy. Restart-on-failure logic from BackendExecutor has also been removed as it is superseded by Tune.
Some tests have been refactored to use the new API. Tests that are no longer applicable have been removed.
Signed-off-by: Antoni Baum <[email protected]>
Signed-off-by: Amog Kamsetty <[email protected]>
Co-authored-by: Amog Kamsetty <[email protected]> | test_torch_auto_gpu_to_cpu | d99eff919bf785f911e4eebc87ddc4960344a139 | ray | test_gpu.py | 8 | 23 | https://github.com/ray-project/ray.git | 3 | 163 | 0 | 9 | 35 | Python | {
"docstring": "Tests if GPU tensors are auto converted to CPU on driver.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def test_torch_auto_gpu_to_cpu(ray_start_4_cpus_2_gpus):
num_workers = 2
assert os.environ["CUDA_VISIBLE_DEVICES"] == ""
|
|
56,301 | 221,262 | 250 | python3.10.4/Lib/calendar.py | 60 | 19 | def formatyear(self, theyear, width=3):
v = []
a = v.append
width = max(width, 1)
a('<table border="0" cellpadding="0" cellspacing="0" class="%s">' %
self.cssclass_year)
a('\n')
a('<tr><th colspan="%d" class="%s">%s</th></tr>' % (
width, self.cssclass_year_head, theyear))
for i in range(January, January+12, width):
# months in this row
months = range(i, min(i+width, 13))
a('<tr>')
for m in months:
a('<td>')
a(self.formatmon | add python 3.10.4 for windows | formatyear | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | calendar.py | 14 | 19 | https://github.com/XX-net/XX-Net.git | 3 | 131 | 0 | 52 | 223 | Python | {
"docstring": "\n Return a formatted year as a table of tables.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 8
} | def formatyear(self, theyear, width=3):
v = []
a = v.append
width = max(width, 1)
a('<table border="0" cellpadding="0" cellspacing="0" class="%s">' %
self.cssclass_year)
a('\n')
a('<tr><th colspan="%d" class="%s">%s</th></tr>' % (
width, self.cssclass_year_head, theyear))
for i in range(January, January+12, width):
# months in this row
months = range(i, min(i+width, 13))
a('<tr>')
for m in months:
a('<td>')
a(self.formatmonth(theyear, m, withyear=False))
a('</td>')
a('</tr>')
a('</table>')
return ''.join(v)
|
|
7,106 | 39,093 | 522 | recommenders/models/sasrec/ssept.py | 198 | 40 | def predict(self, inputs):
training = False
user = inputs["user"]
input_seq = inputs["input_seq"]
candidate = inputs["candidate"]
mask = tf.expand_dims(tf.cast(tf.not_equal(input_seq, 0), tf.float32), -1)
seq_embeddings, positional_embeddings = self.embedding(input_seq) # (1, s, h)
u0_latent = self.user_embedding_layer(user)
u0_latent = u0_latent * (self.user_embedding_dim ** 0.5) # (1, 1, h)
u0_latent = tf.squeeze(u0_latent, axis=0) # (1, h)
test_user_emb = tf.tile(u0_latent, [1 + self.num_neg_test, 1]) # (101, h)
u_latent = self.user_embedding_layer(user)
u_latent = u_latent * (self.user_embedding_dim ** 0.5) # (b, 1, h)
u_latent = tf.tile(u_latent, [1, tf.shape(input_seq)[1], 1]) # (b, s, h)
seq_embeddings = tf.reshape(
tf.concat([seq_embeddings, u_latent], 2),
[tf.shape(input_seq)[0], -1, self.hidden_units],
)
seq_embeddings | cleanup-1 | predict | f15d8b347b601069aba950a53f879e9659bd7c91 | recommenders | ssept.py | 13 | 40 | https://github.com/microsoft/recommenders.git | 1 | 378 | 0 | 95 | 578 | Python | {
"docstring": "\n Model prediction for candidate (negative) items\n\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 6,
"vocab_size": 6
} | def predict(self, inputs):
training = False
user = inputs["user"]
input_seq = inputs["input_seq"]
candidate = inputs["candidate"]
mask = tf.expand_dims(tf.cast(tf.not_equal(input_seq, 0), tf.float32), -1)
seq_embeddings, positional_embeddings = self.embedding(input_seq) # (1, s, h)
u0_latent = self.user_embedding_layer(user)
u0_latent = u0_latent * (self.user_embedding_dim ** 0.5) # (1, 1, h)
u0_latent = tf.squeeze(u0_latent, axis=0) # (1, h)
test_user_emb = tf.tile(u0_latent, [1 + self.num_neg_test, 1]) # (101, h)
u_latent = self.user_embedding_layer(user)
u_latent = u_latent * (self.user_embedding_dim ** 0.5) # (b, 1, h)
u_latent = tf.tile(u_latent, [1, tf.shape(input_seq)[1], 1]) # (b, s, h)
seq_embeddings = tf.reshape(
tf.concat([seq_embeddings, u_latent], 2),
[tf.shape(input_seq)[0], -1, self.hidden_units],
)
seq_embeddings += positional_embeddings # (b, s, h1 + h2)
seq_embeddings *= mask
seq_attention = seq_embeddings
seq_attention = self.encoder(seq_attention, training, mask)
seq_attention = self.layer_normalization(seq_attention) # (b, s, h1+h2)
seq_emb = tf.reshape(
seq_attention,
[tf.shape(input_seq)[0] * self.seq_max_len, self.hidden_units],
) # (b*s1, h1+h2)
candidate_emb = self.item_embedding_layer(candidate) # (b, s2, h2)
candidate_emb = tf.squeeze(candidate_emb, axis=0) # (s2, h2)
candidate_emb = tf.reshape(
tf.concat([candidate_emb, test_user_emb], 1), [-1, self.hidden_units]
) # (b*s2, h1+h2)
candidate_emb = tf.transpose(candidate_emb, perm=[1, 0]) # (h1+h2, b*s2)
test_logits = tf.matmul(seq_emb, candidate_emb) # (b*s1, b*s2)
test_logits = tf.reshape(
test_logits,
[tf.shape(input_seq)[0], self.seq_max_len, 1 + self.num_neg_test],
) # (1, s, 101)
test_logits = test_logits[:, -1, :] # (1, 101)
return test_logits
|
|
70,075 | 243,702 | 197 | src/PIL/Image.py | 36 | 12 | def tobitmap(self, name="image"):
self.load()
if self.mode != "1":
msg = "not a bitmap"
raise ValueError(msg)
data = self.tobytes("xbm")
return b"".join(
[
f"#define {name}_width {self.size[0]}\n".encode("ascii"),
f"#define {name}_height {self.size[1]}\n".encode("ascii"),
f"static char {name}_bits[] = {{\n".encode("ascii"),
data,
b"};",
]
)
| Improve exception traceback readability | tobitmap | 2ae55ccbdad9c842929fb238ea1eb81d1f999024 | Pillow | Image.py | 14 | 15 | https://github.com/python-pillow/Pillow.git | 2 | 76 | 0 | 33 | 173 | Python | {
"docstring": "\n Returns the image converted to an X11 bitmap.\n\n .. note:: This method only works for mode \"1\" images.\n\n :param name: The name prefix to use for the bitmap variables.\n :returns: A string containing an X11 bitmap.\n :raises ValueError: If the mode is not \"1\"\n ",
"language": "en",
"n_whitespaces": 87,
"n_words": 44,
"vocab_size": 35
} | def tobitmap(self, name="image"):
self.load()
if self.mode != "1":
msg = "not a bitmap"
raise ValueError(msg)
data = self.tobytes("xbm")
return b"".join(
[
f"#define {name}_width {self.size[0]}\n".encode("ascii"),
f"#define {name}_height {self.size[1]}\n".encode("ascii"),
f"static char {name}_bits[] = {{\n".encode("ascii"),
data,
b"};",
]
)
|
|
42,422 | 177,528 | 455 | networkx/classes/digraph.py | 102 | 22 | def add_edges_from(self, ebunch_to_add, **attr):
for e in ebunch_to_add:
ne = len(e)
if ne == 3:
u, v, dd = e
elif ne == 2:
u, v = e
dd = {}
else:
raise NetworkXError(f"Edge tuple {e} must be a 2-tuple or 3-tuple.")
if u not in self._succ:
if u is None:
raise ValueError("None cannot be a node")
self._succ[u] = self.adjlist_inner_dict_factory()
self._pred[u] = self.adjlist_inner_dict_factory()
self._node[u] = self.node_attr_dict_factory()
if v not in self._succ:
if v is None:
raise ValueError("None cannot be a node")
self._succ[v] = self.adjlist_inner_dict_factory()
self._pred[v] = self.a | doc: update documentation when providing an iterator over current graph to add/remove_edges_from. (#6268)
* doc for add_edges_from
* doc for digraph
* doc for multigraph
* multigraph.add_nodes_from returns keylist
* update docs for graph - edges
* doc update: graph.add_nodes_from
* doc update: graph.remove_nodes_from
* doc update: graph.add_edges_from
* doc update: rewording for graph.add_edges_from
* doc update: graph.add_weighted_edges_from rewording
* doc update: digraph updated as graph
* doc update: digraph minor sync
* doc update: multigraph same as graph
* Update graph.py
* Update digraph.py
* Update multigraph.py | add_edges_from | 979d54acba7c3d372c93d44c6c149700608ce8b0 | networkx | digraph.py | 14 | 27 | https://github.com/networkx/networkx.git | 8 | 217 | 0 | 55 | 350 | Python | {
"docstring": "Add all the edges in ebunch_to_add.\n\n Parameters\n ----------\n ebunch_to_add : container of edges\n Each edge given in the container will be added to the\n graph. The edges must be given as 2-tuples (u, v) or\n 3-tuples (u, v, d) where d is a dictionary containing edge data.\n attr : keyword arguments, optional\n Edge data (or labels or objects) can be assigned using\n keyword arguments.\n\n See Also\n --------\n add_edge : add a single edge\n add_weighted_edges_from : convenient way to add weighted edges\n\n Notes\n -----\n Adding the same edge twice has no effect but any edge data\n will be updated when each duplicate edge is added.\n\n Edge attributes specified in an ebunch take precedence over\n attributes specified via keyword arguments.\n\n When adding edges from an iterator over the graph you are changing,\n a `RuntimeError` can be raised with message:\n `RuntimeError: dictionary changed size during iteration`. This\n happens when the graph's underlying dictionary is modified during\n iteration. To avoid this error, evaluate the iterator into a separate\n object, e.g. by using `list(iterator_of_edges)`, and pass this\n object to `G.add_edges_from`.\n\n Examples\n --------\n >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc\n >>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples\n >>> e = zip(range(0, 3), range(1, 4))\n >>> G.add_edges_from(e) # Add the path graph 0-1-2-3\n\n Associate data to edges\n\n >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)\n >>> G.add_edges_from([(3, 4), (1, 4)], label=\"WN2898\")\n\n Evaluate an iterator over a graph if using it to modify the same graph\n\n >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])\n >>> # Grow graph by one new node, adding edges to all existing nodes.\n >>> # wrong way - will raise RuntimeError\n >>> # G.add_edges_from(((5, n) for n in G.nodes))\n >>> # right way - note that there will be no self-edge for node 5\n >>> G.add_edges_from(list((5, n) for n in G.nodes))\n ",
"language": "en",
"n_whitespaces": 629,
"n_words": 305,
"vocab_size": 185
} | def add_edges_from(self, ebunch_to_add, **attr):
for e in ebunch_to_add:
ne = len(e)
if ne == 3:
u, v, dd = e
elif ne == 2:
u, v = e
dd = {}
else:
raise NetworkXError(f"Edge tuple {e} must be a 2-tuple or 3-tuple.")
if u not in self._succ:
if u is None:
raise ValueError("None cannot be a node")
self._succ[u] = self.adjlist_inner_dict_factory()
self._pred[u] = self.adjlist_inner_dict_factory()
self._node[u] = self.node_attr_dict_factory()
if v not in self._succ:
if v is None:
raise ValueError("None cannot be a node")
self._succ[v] = self.adjlist_inner_dict_factory()
self._pred[v] = self.adjlist_inner_dict_factory()
self._node[v] = self.node_attr_dict_factory()
datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
datadict.update(attr)
datadict.update(dd)
self._succ[u][v] = datadict
self._pred[v][u] = datadict
|
|
20,022 | 100,558 | 60 | lib/gpu_stats/amd.py | 16 | 10 | def _select_device(self) -> None:
if os.path.exists(plaidml.settings.user_settings): # pylint:disable=no-member
self._log("debug", "Setting PlaidML devices from user_setting | Refactor lib.gpu_stats (#1218)
* inital gpu_stats refactor
* Add dummy CPU Backend
* Update Sphinx documentation | _select_device | bdbbad4d310fb606b6f412aa81e9f57ccd994e97 | faceswap | amd.py | 10 | 8 | https://github.com/deepfakes/faceswap.git | 2 | 37 | 0 | 16 | 68 | Python | {
"docstring": "\n If the plaidml user configuration settings exist, then set the default GPU from the\n settings file, Otherwise set the GPU to be the one with most VRAM. ",
"language": "en",
"n_whitespaces": 42,
"n_words": 27,
"vocab_size": 20
} | def _select_device(self) -> None:
if os.path.exists(plaidml.settings.user_settings): # pylint:disable=no-member
self._log("debug", "Setting PlaidML devices from user_settings")
else:
self._select_largest_gpu()
|
|
54,179 | 215,789 | 75 | tests/pytests/functional/modules/file/test_readlink.py | 26 | 12 | def test_readlink_non_canonical(file, source):
int | Add some funtional tests
Add functional tests for the following:
- file.readlink
- file.replace
- file.symlink
Remove unit tests for file.replace as they are duplicated in the added
functional test | test_readlink_non_canonical | a35b29b2651bf33c5d5b45e64bc7765ffde4aff4 | salt | test_readlink.py | 11 | 11 | https://github.com/saltstack/salt.git | 2 | 65 | 0 | 21 | 114 | Python | {
"docstring": "\n Test readlink where there are nested symlinks and canonicalize=False\n Should resolve to the first symlink\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 15,
"vocab_size": 15
} | def test_readlink_non_canonical(file, source):
intermediate = source.parent / "intermediate.lnk"
intermediate.symlink_to(source)
target = source.parent / "symlink.lnk"
target.symlink_to(intermediate)
try:
result = file.readlink(path=target)
assert result == str(intermediate)
finally:
intermediate.unlink()
target.unlink()
|
|
4,229 | 22,159 | 140 | pipenv/patched/pip/_vendor/requests/utils.py | 49 | 11 | def select_proxy(url, proxies):
proxies = proxies or {}
urlparts = urlparse(url)
if urlparts.hostname is None:
return proxies.get(urlparts.scheme, proxies.get("al | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | select_proxy | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | pipenv | utils.py | 12 | 17 | https://github.com/pypa/pipenv.git | 5 | 91 | 0 | 35 | 148 | Python | {
"docstring": "Select a proxy for the url, if applicable.\n\n :param url: The url being for the request\n :param proxies: A dictionary of schemes or schemes and hosts to proxy URLs\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 29,
"vocab_size": 24
} | def select_proxy(url, proxies):
proxies = proxies or {}
urlparts = urlparse(url)
if urlparts.hostname is None:
return proxies.get(urlparts.scheme, proxies.get("all"))
proxy_keys = [
urlparts.scheme + "://" + urlparts.hostname,
urlparts.scheme,
"all://" + urlparts.hostname,
"all",
]
proxy = None
for proxy_key in proxy_keys:
if proxy_key in proxies:
proxy = proxies[proxy_key]
break
return proxy
|
|
56,085 | 220,693 | 194 | python3.10.4/Lib/asyncio/sslproto.py | 31 | 14 | def eof_received(self):
try:
| add python 3.10.4 for windows | eof_received | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | sslproto.py | 15 | 12 | https://github.com/XX-net/XX-Net.git | 5 | 65 | 0 | 29 | 118 | Python | {
"docstring": "Called when the other end of the low-level stream\n is half-closed.\n\n If this returns a false value (including None), the transport\n will close itself. If it returns a true value, closing the\n transport is up to the protocol.\n ",
"language": "en",
"n_whitespaces": 74,
"n_words": 38,
"vocab_size": 29
} | def eof_received(self):
try:
if self._loop.get_debug():
logger.debug("%r received EOF", self)
self._wakeup_waiter(ConnectionResetError)
if not self._in_handshake:
keep_open = self._app_protocol.eof_received()
if keep_open:
logger.warning('returning true from eof_received() '
'has no effect when using ssl')
finally:
self._transport.close()
|
|
39,190 | 162,332 | 157 | yt_dlp/extractor/common.py | 49 | 10 | def url_result(url, ie=None, video_id=None, video_title=None, *, url_transparent=False, **kwargs):
if ie is not None:
kwargs['ie_key'] = ie if isinstance(ie, str) else ie.ie_key()
if video_id is not None:
kwargs['id'] = video_id
if video_title is not None:
kwargs['title'] = video_title
return {
**kwargs,
'_type': 'url_transparent' if url_transparent else 'url',
'url': url,
}
| [extractor] Improve `url_result` and related | url_result | 311b6615d85d3530f2709c50e4223ff3b6b14361 | yt-dlp | common.py | 11 | 12 | https://github.com/yt-dlp/yt-dlp.git | 6 | 94 | 0 | 33 | 151 | Python | {
"docstring": "Returns a URL that points to a page that should be processed",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 10
} | def url_result(url, ie=None, video_id=None, video_title=None, *, url_transparent=False, **kwargs):
if ie is not None:
kwargs['ie_key'] = ie if isinstance(ie, str) else ie.ie_key()
if video_id is not None:
kwargs['id'] = video_id
if video_title is not None:
kwargs['title'] = video_title
return {
**kwargs,
'_type': 'url_transparent' if url_transparent else 'url',
'url': url,
}
|
|
23,904 | 110,064 | 121 | lib/mpl_toolkits/mplot3d/art3d.py | 48 | 24 | def _shade_colors(color, normals, lightsource=None):
if lightsource is None:
# chosen for backwards-compatibility
lightsource = mcolors.LightSource(azdeg=225, altdeg=19.4712)
with np.errstate(invalid="ignore"):
shade = ((normals / np.linalg.norm(normals, axis=1, keepdims=True))
@ lightsource.direction)
mask = ~np.isnan(shade)
| Refactor shading | _shade_colors | d9d75f2bbf340034a93bdf8cd913fa83a71ece9c | matplotlib | art3d.py | 15 | 19 | https://github.com/matplotlib/matplotlib.git | 3 | 176 | 0 | 41 | 174 | Python | {
"docstring": "\n Shade *color* using normal vectors given by *normals*,\n assuming a *lightsource* (using default position if not given).\n *color* can also be an array of the same length as *normals*.\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 29,
"vocab_size": 28
} | def _shade_colors(color, normals, lightsource=None):
if lightsource is None:
# chosen for backwards-compatibility
lightsource = mcolors.LightSource(azdeg=225, altdeg=19.4712)
with np.errstate(invalid="ignore"):
shade = ((normals / np.linalg.norm(normals, axis=1, keepdims=True))
@ lightsource.direction)
mask = ~np.isnan(shade)
if mask.any():
# convert dot product to allowed shading fractions
in_norm = mcolors.Normalize(-1, 1)
out_norm = mcolors.Normalize(0.3, 1).inverse
|
|
76,391 | 260,641 | 31 | sklearn/feature_selection/_rfe.py | 10 | 8 | def score(self, X, y, **fit_params):
check_is_fitted(self)
return self.estimator_.score(self.transform(X), y, **fit_params)
| MAINT solve long line reported by flake8 (#24065) | score | 6e5ef2e9b8c64e6788428610ae884b9bf3d298a2 | scikit-learn | _rfe.py | 9 | 3 | https://github.com/scikit-learn/scikit-learn.git | 1 | 36 | 0 | 9 | 56 | Python | {
"docstring": "Reduce X to the selected features and return the score of the estimator.\n\n Parameters\n ----------\n X : array of shape [n_samples, n_features]\n The input samples.\n\n y : array of shape [n_samples]\n The target values.\n\n **fit_params : dict\n Parameters to pass to the `score` method of the underlying\n estimator.\n\n .. versionadded:: 1.0\n\n Returns\n -------\n score : float\n Score of the underlying base estimator computed with the selected\n features returned by `rfe.transform(X)` and `y`.\n ",
"language": "en",
"n_whitespaces": 212,
"n_words": 72,
"vocab_size": 46
} | def score(self, X, y, **fit_params):
check_is_fitted(self)
return self.estimator_.score(self.transform(X), y, **fit_params)
|
|
46,959 | 194,423 | 68 | kivy/effects/scroll.py | 18 | 8 | def reset(self, pos):
self.value = pos
self.velocity = 0
if self.history:
| ScrollEffect: Fix layout when ScrollView gets resized | reset | b046b560ef3cebbe2573327017793bc2c348aecd | kivy | scroll.py | 12 | 6 | https://github.com/kivy/kivy.git | 2 | 48 | 0 | 15 | 77 | Python | {
"docstring": "(internal) Reset the value and the velocity to the `pos`.\n Mostly used when the bounds are checked.\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 17,
"vocab_size": 14
} | def reset(self, pos):
self.value = pos
self.velocity = 0
if self.history:
val = self.history[-1][1]
self.history = [(time(), val)]
|
|
@pytest.mark.parametrize(
"percentage, expected_result",
[
(1, 2),
(100, 50),
(50, 26),
],
) | 108,182 | 309,482 | 53 | tests/components/tradfri/test_util.py | 19 | 7 | def test_from_fan_speed(fan_speed, expected_result):
assert _from_fan_speed(fan_speed) == expected_result
@pytes | Bump pytradfri to 8.0.1 and fix fan preset mode "Auto" bug (#63920)
* Move util functions
* Fix errors
* Revert changes
* Fix tests
* Use self.async_set_percentage()
* Fix calculation functions and associated tests
* Handle case of 0
* Update tests/components/tradfri/test_util.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Update tests/components/tradfri/test_util.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Update tests/components/tradfri/test_util.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Handle case of 0
* Update homeassistant/components/tradfri/fan.py
Co-authored-by: Martin Hjelmare <[email protected]>
Co-authored-by: Martin Hjelmare <[email protected]> | test_from_fan_speed | b52a8ba37a5e5e05b80beddff06b116371941d86 | core | test_util.py | 8 | 2 | https://github.com/home-assistant/core.git | 1 | 15 | 1 | 19 | 69 | Python | {
"docstring": "Test that we can convert fan speed to percentage value.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def test_from_fan_speed(fan_speed, expected_result):
assert _from_fan_speed(fan_speed) == expected_result
@pytest.mark.parametrize(
"percentage, expected_result",
[
(1, 2),
(100, 50),
(50, 26),
],
) |
@pytest.fixture | 18,735 | 91,199 | 17 | src/sentry/utils/pytest/fixtures.py | 9 | 7 | def task_runner():
from sentry.testutils.helpers.task_runner import TaskRunner
return Task | ref(proj-config): Introduce new tasks (#35238) | task_runner | 2058dd477767e47c9fce603766a45e1fbe29c33d | sentry | fixtures.py | 6 | 3 | https://github.com/getsentry/sentry.git | 1 | 17 | 1 | 8 | 35 | Python | {
"docstring": "Context manager that ensures Celery tasks run directly inline where invoked.\n\n While this context manager is active any Celery tasks created will run immediately at\n the callsite rather than being sent to RabbitMQ and handled by a worker.\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 38,
"vocab_size": 34
} | def task_runner():
from sentry.testutils.helpers.task_runner import TaskRunner
return TaskRunner
@pytest.fixture |
51,579 | 206,586 | 15 | django/utils/crypto.py | 9 | 9 | def get_random_string(length, allowed_chars=RANDOM_STRING_CHARS):
return "".join(secrets.choice(allo | Refs #33476 -- Reformatted code with Black. | get_random_string | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | crypto.py | 10 | 2 | https://github.com/django/django.git | 2 | 29 | 0 | 9 | 49 | Python | {
"docstring": "\n Return a securely generated random string.\n\n The bit length of the returned value can be calculated with the formula:\n log_2(len(allowed_chars)^length)\n\n For example, with default `allowed_chars` (26+26+10), this gives:\n * length: 12, bit length =~ 71 bits\n * length: 22, bit length =~ 131 bits\n ",
"language": "en",
"n_whitespaces": 74,
"n_words": 44,
"vocab_size": 34
} | def get_random_string(length, allowed_chars=RANDOM_STRING_CHARS):
return "".join(secrets.choice(allowed_chars) for i in range(length))
|
|
73,901 | 251,953 | 35 | test/mitmproxy/proxy/test_tutils.py | 17 | 8 | def test_command_reply(tplaybook):
tplaybook >> TEvent()
tplaybook << TCommand()
tplaybook >> tutils.reply()
assert tplaybook
assert tplaybook.actual[1] == tplaybook.actual[2].command
| make it black! | test_command_reply | b3587b52b25077f68116b9852b041d33e7fc6601 | mitmproxy | test_tutils.py | 9 | 6 | https://github.com/mitmproxy/mitmproxy.git | 1 | 44 | 0 | 12 | 69 | Python | {
"docstring": "CommandReplies can use relative offsets to point to the matching command.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | def test_command_reply(tplaybook):
tplaybook >> TEvent()
tplaybook << TCommand()
tplaybook >> tutils.reply()
assert tplaybook
assert tplaybook.actual[1] == tplaybook.actual[2].command
|
|
80,259 | 269,762 | 152 | keras/benchmarks/distribution_util.py | 47 | 11 | def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
if all_reduce_alg is None:
return None
mirrored_all_reduce_options = {
"nccl": tf.distribute.NcclAllReduce,
"hierarchical_copy": tf.distribute.HierarchicalCopyAllReduce,
}
if al | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | _mirrored_cross_device_ops | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | distribution_util.py | 13 | 16 | https://github.com/keras-team/keras.git | 3 | 65 | 0 | 40 | 110 | Python | {
"docstring": "Return a CrossDeviceOps based on all_reduce_alg and num_packs.\n\n Args:\n all_reduce_alg: a string specifying which cross device op to pick, or None.\n num_packs: an integer specifying number of packs for the cross device op.\n\n Returns:\n tf.distribute.CrossDeviceOps object or None.\n\n Raises:\n ValueError: if `all_reduce_alg` not in [None, \"nccl\", \"hierarchical_copy\"].\n ",
"language": "en",
"n_whitespaces": 79,
"n_words": 47,
"vocab_size": 41
} | def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
if all_reduce_alg is None:
return None
mirrored_all_reduce_options = {
"nccl": tf.distribute.NcclAllReduce,
"hierarchical_copy": tf.distribute.HierarchicalCopyAllReduce,
}
if all_reduce_alg not in mirrored_all_reduce_options:
raise ValueError(
"When used with `mirrored`, valid values for all_reduce_alg are "
"[`nccl`, `hierarchical_copy`]. Supplied value: {}".format(
all_reduce_alg
)
)
cross_device_ops_class = mirrored_all_reduce_options[all_reduce_alg]
return cross_device_ops_class(num_packs=num_packs)
|
|
7,966 | 43,461 | 46 | tests/providers/microsoft/azure/hooks/test_asb.py | 11 | 14 | def test_delete_queue(self, mock_sb_admin_client):
hook = AdminClientHook(azure_service_bus_conn_id=sel | Implement Azure Service Bus Queue Operators (#24038)
Implemented Azure Service Bus Queue based Operator's to create queue, send message to the queue and receive message(list of message or batch message) and delete queue in azure service
- Added `AzureServiceBusCreateQueueOperator`
- Added `AzureServiceBusSendMessageOperator`
- Added `AzureServiceBusReceiveMessageOperator`
- Added `AzureServiceBusDeleteQueueOperator`
- Added Example DAG
- Added Documentation
- Added hooks and connection type in - provider yaml file
- Added unit Test case, doc strings | test_delete_queue | 09f38ad3f6872bae5059a1de226362eb358c4a7a | airflow | test_asb.py | 13 | 5 | https://github.com/apache/airflow.git | 1 | 52 | 0 | 10 | 87 | Python | {
"docstring": "\n Test Delete queue functionality by passing queue name, assert the function with values,\n mock the azure service bus function `delete_queue`\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 20,
"vocab_size": 17
} | def test_delete_queue(self, mock_sb_admin_client):
hook = AdminClientHook(azure_service_bus_conn_id=self.conn_id)
hook.delete_queue(self.queue_name)
expected_calls = [mock.call().__enter__().delete_queue(self.queue_name)]
mock_sb_admin_client.assert_has_calls(expected_calls)
|
|
57,056 | 223,772 | 77 | python3.10.4/Lib/email/message.py | 25 | 10 | def get_content_disposition(self):
value = self.get('content-disposition')
if value is None:
return None
c_d = _splitparam(value)[0].lower()
retu | add python 3.10.4 for windows | get_content_disposition | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | message.py | 11 | 6 | https://github.com/XX-net/XX-Net.git | 2 | 36 | 0 | 20 | 73 | Python | {
"docstring": "Return the message's content-disposition if it exists, or None.\n\n The return values can be either 'inline', 'attachment' or None\n according to the rfc2183.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 23,
"vocab_size": 21
} | def get_content_disposition(self):
value = self.get('content-disposition')
if value is None:
return None
c_d = _splitparam(value)[0].lower()
return c_d
# I.e. def walk(self): ...
from email.iterators import walk
|
|
49,056 | 198,878 | 165 | sympy/printing/aesaracode.py | 51 | 13 | def _get_or_create(self, s, name=None, dtype=None, broadcastable=None):
# Defaults
if name is None:
name = s.name
if dtype is None:
dtype = 'floatX'
if broadcastable is None:
broadcastable = ()
key = self._get_key(s, name, dtype=dtype, broadcastable=broadcastable)
if key in self.cache:
return self.cache[key]
value = aet.tensor(name=name, dtype=dtype, shape=broadcastable)
self.cache[key] = value
| fix(printing): change Aesara argument broadcastable to shape | _get_or_create | 68bd82de645a61f4bbc0b6246e70959373c9cba2 | sympy | aesaracode.py | 9 | 13 | https://github.com/sympy/sympy.git | 5 | 107 | 0 | 30 | 164 | Python | {
"docstring": "\n Get the Aesara variable for a SymPy symbol from the cache, or create it\n if it does not exist.\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 19,
"vocab_size": 17
} | def _get_or_create(self, s, name=None, dtype=None, broadcastable=None):
# Defaults
if name is None:
name = s.name
if dtype is None:
dtype = 'floatX'
if broadcastable is None:
broadcastable = ()
key = self._get_key(s, name, dtype=dtype, broadcastable=broadcastable)
if key in self.cache:
return self.cache[key]
value = aet.tensor(name=name, dtype=dtype, shape=broadcastable)
self.cache[key] = value
return value
|
|
51,203 | 205,769 | 111 | django/db/models/query.py | 29 | 11 | def defer(self, *fields):
self._not_support_combined_queries("defer")
if self._fields is not None:
raise TypeError("Cannot call defer() after .values() or .values_list()")
clone = self._chain()
if fields == (None,):
| Refs #33476 -- Reformatted code with Black. | defer | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | query.py | 11 | 10 | https://github.com/django/django.git | 3 | 62 | 0 | 27 | 107 | Python | {
"docstring": "\n Defer the loading of data for certain fields until they are accessed.\n Add the set of deferred fields to any existing set of deferred fields.\n The only exception to this is if None is passed in as the only\n parameter, in which case removal all deferrals.\n ",
"language": "en",
"n_whitespaces": 82,
"n_words": 46,
"vocab_size": 35
} | def defer(self, *fields):
self._not_support_combined_queries("defer")
if self._fields is not None:
raise TypeError("Cannot call defer() after .values() or .values_list()")
clone = self._chain()
if fields == (None,):
clone.query.clear_deferred_loading()
else:
clone.query.add_deferred_loading(fields)
return clone
|
|
55,500 | 218,848 | 45 | python3.10.4/Lib/lib2to3/pytree.py | 13 | 6 | def match_seq(self, nodes, results=None):
if len(nodes) != 1:
| add python 3.10.4 for windows | match_seq | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | pytree.py | 8 | 4 | https://github.com/XX-net/XX-Net.git | 2 | 34 | 0 | 12 | 53 | Python | {
"docstring": "\n Does this pattern exactly match a sequence of nodes?\n\n Default implementation for non-wildcard patterns.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 14
} | def match_seq(self, nodes, results=None):
if len(nodes) != 1:
return False
return self.match(nodes[0], results)
|
|
18,336 | 87,837 | 24 | src/sentry/auth/access.py | 10 | 9 | def team_ids_with_membership(self) -> FrozenSet[int]:
return frozenset(team.id for team in self._ | ref(access): Remove models from Access fields (#40940)
Anticipating changes for Hybrid Cloud silo boundaries, change the public
interface of the `Access` class to not expose any ORM models as
dataclass fields. As a first step, replace all such objects with their
raw IDs. (Credit to @corps for the underlying idea. Future steps:
replace models as method parameters; replace raw IDs with API object
representations.) | team_ids_with_membership | b3ce25d7c3ce85a9b7195f97c6d3d76c764e1808 | sentry | access.py | 11 | 11 | https://github.com/getsentry/sentry.git | 2 | 28 | 0 | 10 | 46 | Python | {
"docstring": "Return the IDs of teams in which the user has actual membership.\n\n This represents the set of all teams for which `has_team_membership` returns\n true. Use that method where possible and use this property only when you need\n to iterate or query for all such teams.\n\n Compare to accessible_team_ids, which is equal to this property in the\n typical case but represents a superset of IDs in case of superuser access.\n ",
"language": "en",
"n_whitespaces": 111,
"n_words": 69,
"vocab_size": 49
} | def team_ids_with_membership(self) -> FrozenSet[int]:
return frozenset(team.id for team in self._team_memberships.keys())
|
|
30,547 | 135,117 | 337 | rllib/models/tests/test_distributions.py | 99 | 32 | def test_gumbel_softmax(self):
for fw, sess in framework_iterator(frameworks=("tf2", "tf"), session=True):
batch_size = 1000
num_categories = 5
input_space = Box(-1.0, 1.0, shape=(batch_size, num_categories))
input_space.seed(42)
# Batch of size=n and deterministic.
inputs = input_space.sample()
gumbel_softmax = GumbelSoftmax(inputs, {}, temperature=1.0)
expected = softmax(inputs)
# Sample n times, expect always mean value (deterministic draw).
out = gumbel_softmax.deterministic_sample()
check(out, expected)
| [RLlib] Deprecate `AlgorithmConfig.framework("tfe")`: Use `tf2` instead. (#29755) | test_gumbel_softmax | 432f023642731bf53aac9b6c778f9dd7b1d82a57 | ray | test_distributions.py | 15 | 18 | https://github.com/ray-project/ray.git | 3 | 188 | 0 | 71 | 286 | Python | {
"docstring": "Tests the GumbelSoftmax ActionDistribution (tf + eager only).",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | def test_gumbel_softmax(self):
for fw, sess in framework_iterator(frameworks=("tf2", "tf"), session=True):
batch_size = 1000
num_categories = 5
input_space = Box(-1.0, 1.0, shape=(batch_size, num_categories))
input_space.seed(42)
# Batch of size=n and deterministic.
inputs = input_space.sample()
gumbel_softmax = GumbelSoftmax(inputs, {}, temperature=1.0)
expected = softmax(inputs)
# Sample n times, expect always mean value (deterministic draw).
out = gumbel_softmax.deterministic_sample()
check(out, expected)
# Batch of size=n and non-deterministic -> expect roughly that
# the max-likelihood (argmax) ints are output (most of the time).
inputs = input_space.sample()
gumbel_softmax = GumbelSoftmax(inputs, {}, temperature=1.0)
expected_mean = np.mean(np.argmax(inputs, -1)).astype(np.float32)
outs = gumbel_softmax.sample()
if sess:
outs = sess.run(outs)
check(np.mean(np.argmax(outs, -1)), expected_mean, rtol=0.08)
|
|
1,562 | 9,140 | 105 | parsing/dml_csr/utils/miou.py | 43 | 16 | def get_confusion_matrix(gt_label, pred_label, num_classes):
index = (gt_label * num_classes + pred_label).astype('int32')
label_count = np.bincount(index)
confusion_matrix = np.zeros((num_classes, num_classes))
for i_label in range(num_classes):
for i_pred_label in range(num_classes):
cur_index = i_label * num_classes + i_pred_label
if cur_index < len(label_count):
confusion_matrix[i_label, i_pred_label] = | Create miou.py | get_confusion_matrix | 995b44897fe6158bb70ad03a3e79f517f65f9034 | insightface | miou.py | 13 | 10 | https://github.com/deepinsight/insightface.git | 4 | 88 | 0 | 29 | 138 | Python | {
"docstring": "\n Calcute the confusion matrix by given label and pred\n :param gt_label: the ground truth label\n :param pred_label: the pred label\n :param num_classes: the nunber of class\n :return: the confusion matrix\n ",
"language": "en",
"n_whitespaces": 49,
"n_words": 30,
"vocab_size": 19
} | def get_confusion_matrix(gt_label, pred_label, num_classes):
index = (gt_label * num_classes + pred_label).astype('int32')
label_count = np.bincount(index)
confusion_matrix = np.zeros((num_classes, num_classes))
for i_label in range(num_classes):
for i_pred_label in range(num_classes):
cur_index = i_label * num_classes + i_pred_label
if cur_index < len(label_count):
confusion_matrix[i_label, i_pred_label] = label_count[cur_index]
return confusion_matrix
|
|
38,875 | 161,059 | 232 | ppg2mel/utils/nets_utils.py | 103 | 35 | def make_pad_mask(lengths, xs=None, length_dim=-1):
if length_dim == 0:
raise ValueError('length_dim cannot be 0: {}'.format(length_dim))
if not isinstance(lengths, list):
lengths = lengths.tolist()
bs = int(len(lengths))
if xs is None:
maxlen = int(max(lengths))
else:
maxlen = xs.size(length_dim)
seq_range = torch.arange(0, maxlen, dtype=torch.int64)
seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
mask = seq_range_expand >= seq_length_expand
if xs is not None:
assert xs.size(0) == bs, (xs.size(0), bs)
if length_dim < 0:
length_dim = xs.dim() + length_dim
# ind = (:, None, ..., None, :, , None, ..., None)
ind = | Init ppg extractor and ppg2mel (#375)
* Init ppg extractor and ppg2mel
* add preprocess and training
* FIx known issues
* Update __init__.py
Allow to gen audio
* Fix length issue
* Fix bug of preparing fid
* Fix sample issues
* Add UI usage of PPG-vc | make_pad_mask | b617a87ee40ab384767a27335313c2c65ee094ec | MockingBird | nets_utils.py | 15 | 22 | https://github.com/babysor/MockingBird.git | 8 | 219 | 0 | 66 | 347 | Python | {
"docstring": "Make mask tensor containing indices of padded part.\n\n Args:\n lengths (LongTensor or List): Batch of lengths (B,).\n xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor.\n length_dim (int, optional): Dimension indicator of the above tensor. See the example.\n\n Returns:\n Tensor: Mask tensor containing indices of padded part.\n dtype=torch.uint8 in PyTorch 1.2-\n dtype=torch.bool in PyTorch 1.2+ (including 1.2)\n\n Examples:\n With only lengths.\n\n >>> lengths = [5, 3, 2]\n >>> make_non_pad_mask(lengths)\n masks = [[0, 0, 0, 0 ,0],\n [0, 0, 0, 1, 1],\n [0, 0, 1, 1, 1]]\n\n With the reference tensor.\n\n >>> xs = torch.zeros((3, 2, 4))\n >>> make_pad_mask(lengths, xs)\n tensor([[[0, 0, 0, 0],\n [0, 0, 0, 0]],\n [[0, 0, 0, 1],\n [0, 0, 0, 1]],\n [[0, 0, 1, 1],\n [0, 0, 1, 1]]], dtype=torch.uint8)\n >>> xs = torch.zeros((3, 2, 6))\n >>> make_pad_mask(lengths, xs)\n tensor([[[0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1]],\n [[0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1]],\n [[0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)\n\n With the reference tensor and dimension indicator.\n\n >>> xs = torch.zeros((3, 6, 6))\n >>> make_pad_mask(lengths, xs, 1)\n tensor([[[0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1]],\n [[0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1]],\n [[0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8)\n >>> make_pad_mask(lengths, xs, 2)\n tensor([[[0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1]],\n [[0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1]],\n [[0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)\n\n ",
"language": "en",
"n_whitespaces": 1334,
"n_words": 417,
"vocab_size": 87
} | def make_pad_mask(lengths, xs=None, length_dim=-1):
if length_dim == 0:
raise ValueError('length_dim cannot be 0: {}'.format(length_dim))
if not isinstance(lengths, list):
lengths = lengths.tolist()
bs = int(len(lengths))
if xs is None:
maxlen = int(max(lengths))
else:
maxlen = xs.size(length_dim)
seq_range = torch.arange(0, maxlen, dtype=torch.int64)
seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
mask = seq_range_expand >= seq_length_expand
if xs is not None:
assert xs.size(0) == bs, (xs.size(0), bs)
if length_dim < 0:
length_dim = xs.dim() + length_dim
# ind = (:, None, ..., None, :, , None, ..., None)
ind = tuple(slice(None) if i in (0, length_dim) else None
for i in range(xs.dim()))
mask = mask[ind].expand_as(xs).to(xs.device)
return mask
|
|
17,843 | 84,491 | 149 | zerver/tests/test_upload.py | 58 | 9 | def test_guess_content_type_from_filename(self) -> None:
data, content_type = encode_multipart_formdata({"file": ("somefile" | upload: Remove `mimetype` url parameter in `get_file_info`.
This `mimetype` parameter was introduced in c4fa29a and its last
usage removed in 5bab2a3. This parameter was undocumented in the
OpenAPI endpoint documentation for `/user_uploads`, therefore
there shouldn't be client implementations that rely on it's
presence.
Removes the `request.GET` call for the `mimetype` parameter and
replaces it by getting the `content_type` value from the file,
which is an instance of Django's `UploadedFile` class and stores
that file metadata as a property.
If that returns `None` or an empty string, then we try to guess
the `content_type` from the filename, which is the same as the
previous behaviour when `mimetype` was `None` (which we assume
has been true since it's usage was removed; see above).
If unable to guess the `content_type` from the filename, we now
fallback to "application/octet-stream", instead of an empty string
or `None` value.
Also, removes the specific test written for having `mimetype` as
a url parameter in the request, and replaces it with a test that
covers when we try to guess `content_type` from the filename. | test_guess_content_type_from_filename | aa796af0a8b665ee730a059bc2594ae21cb1e828 | zulip | test_upload.py | 12 | 15 | https://github.com/zulip/zulip.git | 1 | 100 | 0 | 40 | 170 | Python | {
"docstring": "\n Test coverage for files without content-type in the metadata;\n in which case we try to guess the content-type from the filename.\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 21,
"vocab_size": 17
} | def test_guess_content_type_from_filename(self) -> None:
data, content_type = encode_multipart_formdata({"file": ("somefile", b"zulip!", None)})
result = self.api_post(
self.example_user("hamlet"), "/api/v1/user_uploads", data, content_type=content_type
)
self.assert_json_success(result)
data, content_type = encode_multipart_formdata({"file": ("somefile.txt", b"zulip!", None)})
result = self.api_post(
self.example_user("hamlet"), "/api/v1/user_uploads", data, content_type=content_type
)
self.assert_json_success(result)
# This test will go through the code path for uploading files onto LOCAL storage
# when Zulip is in DEVELOPMENT mode. |
|
56,518 | 221,801 | 166 | python3.10.4/Lib/ctypes/_aix.py | 85 | 9 | def get_member(name, members):
# look first for a generic match - prepend lib and append .so
expr = rf'lib{name}\.so'
member = get_one_match(expr, members)
if member:
return member
elif AIX_ABI == 64:
expr = rf'lib{name | add python 3.10.4 for windows | get_member | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | _aix.py | 11 | 15 | https://github.com/XX-net/XX-Net.git | 5 | 67 | 0 | 49 | 121 | Python | {
"docstring": "\n Return an archive member matching the request in name.\n Name is the library name without any prefix like lib, suffix like .so,\n or version number.\n Given a list of members find and return the most appropriate result\n Priority is given to generic libXXX.so, then a versioned libXXX.so.a.b.c\n and finally, legacy AIX naming scheme.\n ",
"language": "en",
"n_whitespaces": 75,
"n_words": 53,
"vocab_size": 47
} | def get_member(name, members):
# look first for a generic match - prepend lib and append .so
expr = rf'lib{name}\.so'
member = get_one_match(expr, members)
if member:
return member
elif AIX_ABI == 64:
expr = rf'lib{name}64\.so'
member = get_one_match(expr, members)
if member:
return member
# since an exact match with .so as suffix was not found
# look for a versioned name
# If a versioned name is not found, look for AIX legacy member name
member = get_version(name, members)
if member:
return member
else:
return get_legacy(members)
|
|
8,176 | 44,137 | 40 | tests/core/test_impersonation_tests.py | 12 | 8 | def check_original_docker_image():
if not os.path.isfile('/.dockerenv') or os.environ.get('PYTHON_BASE_IMAG | Fixed tests failing on Python 3.8 (#21022)
The change #21003 broke TestDeprecation class tests by removing
TestCase and leaving self.skipTest.
This change replaces self.skipTest with pytest.skipTest everywhere. | check_original_docker_image | b96e4992b5df24898e169c01fe23e4cb7d32dd94 | airflow | test_impersonation_tests.py | 10 | 9 | https://github.com/apache/airflow.git | 3 | 33 | 0 | 12 | 60 | Python | {
"docstring": "Adding/removing a user as part of a test is very bad for host os\n(especially if the user already existed to begin with on the OS), therefore we check if we run inside a\nthe official docker container and only allow to run the test there. This is done by checking /.dockerenv\nfile (always present inside container) and checking for PYTHON_BASE_IMAGE variable.\n",
"language": "en",
"n_whitespaces": 58,
"n_words": 62,
"vocab_size": 46
} | def check_original_docker_image():
if not os.path.isfile('/.dockerenv') or os.environ.get('PYTHON_BASE_IMAGE') is None:
raise pytest.skip(
)
|
|
27,392 | 123,487 | 631 | lib/core/option.py | 253 | 37 | def _useWizardInterface():
if not conf.wizard:
return
logger.info("starting wizard interface")
while not conf.url:
message = "Please enter full target URL (-u): "
conf.url = readInput(message, default=None)
message = "%s data (--data) [Enter for None]: " % ((conf.method if conf.method != HTTPMETHOD.GET else None) or HTTPMETHOD.POST)
conf.data = readInput(message, default=None)
if not (any('=' in _ for _ in (conf.url, conf.data)) or '*' in conf.url):
warnMsg = "no GET and/or %s parameter(s) found for testing " % ((conf.method if conf.method != HTTPMETHOD.GET else None) or HTTPMETHOD.POST)
warnMsg += "(e.g. GET parameter 'id' in 'http://www.site.com/vuln.php?id=1'). "
if not conf.crawlDepth and not conf.forms:
warnMsg += "Will search for forms"
conf.forms = True
logger.warning(warnMsg)
choice = None
while choice is None or choice not in ("", "1", "2", "3"):
message = "Injection difficulty (--level/--risk). Please choose:\n"
message += "[1] Normal (default)\n[2] Medium\n[3] Hard"
choice = readInput(message, default='1')
if choice == '2':
conf.risk = 2
conf.level = 3
elif choice == '3':
conf.risk = 3
conf.level = 5
else:
conf.risk = 1
conf.level = 1
if not conf.getAll:
choice = None
while choice is None or choice not in ("", "1", "2", "3"):
message = "Enumeration (--banner/--current-user/etc). Please choose:\n"
message += "[1] Basic (default)\n[2] Intermediate\n[3] All"
choice = readInput(message, default='1')
if choice == '2':
options = WIZARD.INTERMEDIATE
elif choice == '3':
options = WIZARD.ALL
else:
options = WIZARD.BASIC
for _ in options:
conf.__setitem__(_, Tru | Fixing DeprecationWarning (logger.warn) | _useWizardInterface | df4293473d2fb6e887e31522cab5aff95e201581 | sqlmap | option.py | 15 | 50 | https://github.com/sqlmapproject/sqlmap.git | 22 | 350 | 0 | 128 | 611 | Python | {
"docstring": "\n Presents simple wizard interface for beginner users\n ",
"language": "en",
"n_whitespaces": 14,
"n_words": 7,
"vocab_size": 7
} | def _useWizardInterface():
if not conf.wizard:
return
logger.info("starting wizard interface")
while not conf.url:
message = "Please enter full target URL (-u): "
conf.url = readInput(message, default=None)
message = "%s data (--data) [Enter for None]: " % ((conf.method if conf.method != HTTPMETHOD.GET else None) or HTTPMETHOD.POST)
conf.data = readInput(message, default=None)
if not (any('=' in _ for _ in (conf.url, conf.data)) or '*' in conf.url):
warnMsg = "no GET and/or %s parameter(s) found for testing " % ((conf.method if conf.method != HTTPMETHOD.GET else None) or HTTPMETHOD.POST)
warnMsg += "(e.g. GET parameter 'id' in 'http://www.site.com/vuln.php?id=1'). "
if not conf.crawlDepth and not conf.forms:
warnMsg += "Will search for forms"
conf.forms = True
logger.warning(warnMsg)
choice = None
while choice is None or choice not in ("", "1", "2", "3"):
message = "Injection difficulty (--level/--risk). Please choose:\n"
message += "[1] Normal (default)\n[2] Medium\n[3] Hard"
choice = readInput(message, default='1')
if choice == '2':
conf.risk = 2
conf.level = 3
elif choice == '3':
conf.risk = 3
conf.level = 5
else:
conf.risk = 1
conf.level = 1
if not conf.getAll:
choice = None
while choice is None or choice not in ("", "1", "2", "3"):
message = "Enumeration (--banner/--current-user/etc). Please choose:\n"
message += "[1] Basic (default)\n[2] Intermediate\n[3] All"
choice = readInput(message, default='1')
if choice == '2':
options = WIZARD.INTERMEDIATE
elif choice == '3':
options = WIZARD.ALL
else:
options = WIZARD.BASIC
for _ in options:
conf.__setitem__(_, True)
logger.debug("muting sqlmap.. it will do the magic for you")
conf.verbose = 0
conf.batch = True
conf.threads = 4
dataToStdout("\nsqlmap is running, please wait..\n\n")
kb.wizardMode = True
|
|
10,737 | 53,209 | 146 | src/prefect/orion/alembic/env.py | 63 | 16 | async def run_migrations_online() -> None:
engine = await db_interface.engine()
versi | initial commit | run_migrations_online | 88a2e91018e5efe2970ba86238d69d1031350593 | prefect | env.py | 16 | 20 | https://github.com/PrefectHQ/prefect.git | 4 | 117 | 0 | 44 | 183 | Python | {
"docstring": "\n Run migrations in 'online' mode.\n\n In this scenario we need to create an Engine\n and associate a connection with the context.\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 21,
"vocab_size": 21
} | async def run_migrations_online() -> None:
engine = await db_interface.engine()
versions_dir = context.get_x_argument(as_dictionary=True).get("versions_dir", None)
if versions_dir is None:
# if version dir is not explicitly provided determine versions location from dialect
dialect = get_dialect(engine=engine)
if dialect.name == "postgresql":
versions_dir = Path(context.script.dir / "postgresql")
elif dialect.name == "sqlite":
versions_dir = Path(context.script.dir / "sqlite")
else:
raise ValueError(f"No versions dir exists for dialect: {dialect.name}")
context.script.version_locations = [versions_dir]
|
|
@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs | 13,751 | 64,915 | 16 | erpnext/accounts/doctype/payment_order/payment_order.py | 23 | 13 | def get_mop_query(doctype, txt, searchfield, start, page_len, filters):
return frappe.db.sql(
,
{"parent": filters.get("parent"), "start": start, "page_len": page_len, "txt": "%%%s%%" % txt},
)
@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs | style: format code with black | get_mop_query | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | payment_order.py | 12 | 7 | https://github.com/frappe/erpnext.git | 1 | 50 | 1 | 21 | 99 | Python | {
"docstring": " select mode_of_payment from `tabPayment Order Reference`\n\t\twhere parent = %(parent)s and mode_of_payment like %(txt)s\n\t\tlimit %(start)s, %(page_len)s",
"language": "en",
"n_whitespaces": 15,
"n_words": 17,
"vocab_size": 16
} | def get_mop_query(doctype, txt, searchfield, start, page_len, filters):
return frappe.db.sql(
,
{"parent": filters.get("parent"), "start": start, "page_len": page_len, "txt": "%%%s%%" % txt},
)
@frappe.whitelist()
@frappe.validate_and_sanitize_search_inputs |
23,246 | 108,535 | 208 | lib/matplotlib/tests/test_pyplot.py | 88 | 27 | def test_doc_pyplot_summary():
pyplot_docs = Path(__file__).parent / '../../../doc/api/pyplot_summary.rst'
if not pyplot_docs.exists():
pytest.skip("Documentation sources not available")
lines = pyplot_docs.read_text()
m = re.search(r':nosignatures:\n\n(.*?)\n\n', lines, re.DOTALL)
doc_functions = set(line.strip() for line in m.group(1).split('\n'))
plot_commands = set(plt.get_pl | Cleanup documentation generation for pyplot
- remove the awkward `pyplot.plotting()` function, which only served
as a namespace to take up the docs for pyplot and output them via
`.. autofunction`
- Instead generate the same information using `.. autosummary::`. We
have to list the desired methods here explicitly. I've added a test
that these are the same as previously auto-generated in the
`plotting()` docstring. If we change anything in pyplot, we'll be
notified through the test failure that we have to adapt the
autosummary list.
- Removed the docstring generation logic
`_setup_pyplot_info_docstrings()`. Apart from generating the
`plotting()` docstring, this added docstrings to the pyplot colormap
setters. Instead, we now add these docstrings directly via
boilerplate.py
Co-authored-by: Elliott Sales de Andrade <[email protected]> | test_doc_pyplot_summary | 032316bc6c7798fca6c82de24167c975f237687f | matplotlib | test_pyplot.py | 13 | 20 | https://github.com/matplotlib/matplotlib.git | 5 | 127 | 0 | 60 | 228 | Python | {
"docstring": "Test that pyplot_summary lists all the plot functions.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | def test_doc_pyplot_summary():
pyplot_docs = Path(__file__).parent / '../../../doc/api/pyplot_summary.rst'
if not pyplot_docs.exists():
pytest.skip("Documentation sources not available")
lines = pyplot_docs.read_text()
m = re.search(r':nosignatures:\n\n(.*?)\n\n', lines, re.DOTALL)
doc_functions = set(line.strip() for line in m.group(1).split('\n'))
plot_commands = set(plt.get_plot_commands())
missing = plot_commands.difference(doc_functions)
if missing:
raise AssertionError(
f"The following pyplot functions are not listed in the "
f"documentation. Please add them to doc/api/pyplot_summary.rst: "
f"{missing!r}")
extra = doc_functions.difference(plot_commands)
if extra:
raise AssertionError(
f"The following functions are listed in the pyplot documentation, "
f"but they do not exist in pyplot. "
f"Please remove them from doc/api/pyplot_summary.rst: {extra!r}")
|
|
13,956 | 65,618 | 75 | erpnext/controllers/accounts_controller.py | 107 | 16 | def validate_child_on_delete(row, parent):
if parent.doctype == "Sales Order":
if flt(row.delivered_qty):
frappe.throw(
_("Row #{0}: Cannot delete item {1} which has already been delivered").format(
row.idx, row.item_code
)
)
if flt(row.work_order_qty):
frappe.throw(
_("Row #{0}: Cannot delete item {1} which has work order assigned to it.").format(
row.idx, row.item_code
)
)
if flt(row.ordered_qty):
frappe.throw(
_("Row #{0}: Ca | style: format code with black | validate_child_on_delete | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | accounts_controller.py | 16 | 32 | https://github.com/frappe/erpnext.git | 8 | 161 | 0 | 42 | 269 | Python | {
"docstring": "Check if partially transacted item (row) is being deleted.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def validate_child_on_delete(row, parent):
if parent.doctype == "Sales Order":
if flt(row.delivered_qty):
frappe.throw(
_("Row #{0}: Cannot delete item {1} which has already been delivered").format(
row.idx, row.item_code
)
)
if flt(row.work_order_qty):
frappe.throw(
_("Row #{0}: Cannot delete item {1} which has work order assigned to it.").format(
row.idx, row.item_code
)
)
if flt(row.ordered_qty):
frappe.throw(
_("Row #{0}: Cannot delete item {1} which is assigned to customer's purchase order.").format(
row.idx, row.item_code
)
)
if parent.doctype == "Purchase Order" and flt(row.received_qty):
frappe.throw(
_("Row #{0}: Cannot delete item {1} which has already been received").format(
row.idx, row.item_code
)
)
if flt(row.billed_amt):
frappe.throw(
_("Row #{0}: Cannot delete item {1} which has already been billed.").format(
row.idx, row.item_code
)
)
|
|
20,730 | 101,311 | 496 | scripts/fsmedia.py | 108 | 23 | def _get_items(self):
postprocess_items = {}
# Debug Landmarks
if (hasattr(self._args, 'debug_landmarks') and self._args.debug_landmarks):
postprocess_items["DebugLandmarks"] = None
# Face Filter post processing
if ((hasattr(self._args, "filter") and self._args.filter is not None) or
(hasattr(self._args, "nfilter") and
self._args.nfilter is not None)):
if hasattr(self._args, "detector"):
detector = self._args.detector.replace("-", "_").lower()
else:
detector = "cv2_dnn"
if hasattr(self._args, "aligner"):
aligner = self._args.aligner.replace("-", "_").lower()
else:
aligner = "cv2_dnn"
face_filter = dict(detector=detector,
aligner=aligner,
multiprocess=not self._args.singleprocess)
filter_lists = {}
if hasattr(self._args, "ref_threshold" | bugfix: debug landmarks | _get_items | 9e503bdaa2bfe2baaea50ad2e4bf742f309d9d10 | faceswap | fsmedia.py | 16 | 29 | https://github.com/deepfakes/faceswap.git | 12 | 249 | 0 | 67 | 422 | Python | {
"docstring": " Check the passed in command line arguments for requested actions,\n\n For any requested actions, add the item to the actions list along with\n any relevant arguments and keyword arguments.\n\n Returns\n -------\n dict\n The name of the action to be performed as the key. Any action specific\n arguments and keyword arguments as the value.\n ",
"language": "en",
"n_whitespaces": 118,
"n_words": 53,
"vocab_size": 37
} | def _get_items(self):
postprocess_items = {}
# Debug Landmarks
if (hasattr(self._args, 'debug_landmarks') and self._args.debug_landmarks):
postprocess_items["DebugLandmarks"] = None
# Face Filter post processing
if ((hasattr(self._args, "filter") and self._args.filter is not None) or
(hasattr(self._args, "nfilter") and
self._args.nfilter is not None)):
if hasattr(self._args, "detector"):
detector = self._args.detector.replace("-", "_").lower()
else:
detector = "cv2_dnn"
if hasattr(self._args, "aligner"):
aligner = self._args.aligner.replace("-", "_").lower()
else:
aligner = "cv2_dnn"
face_filter = dict(detector=detector,
aligner=aligner,
multiprocess=not self._args.singleprocess)
filter_lists = {}
if hasattr(self._args, "ref_threshold"):
face_filter["ref_threshold"] = self._args.ref_threshold
for filter_type in ('filter', 'nfilter'):
filter_args = getattr(self._args, filter_type, None)
filter_args = None if not filter_args else filter_args
filter_lists[filter_type] = filter_args
face_filter["filter_lists"] = filter_lists
postprocess_items["FaceFilter"] = {"kwargs": face_filter}
logger.debug("Postprocess Items: %s", postprocess_items)
return postprocess_items
|
|
55,370 | 218,532 | 255 | python3.10.4/Lib/ipaddress.py | 83 | 15 | def _collapse_addresses_internal(addresses):
# First merge
to_merge = list(addresses)
subnets = {}
while to_merge:
net = to_merge.pop()
supernet = net.supernet()
existing = subnets.get(supernet)
if existing is None:
subnets[supernet] = net
elif existing != net:
# Merge consecutive subnets
del subnets[supernet]
to_merge.append(supernet)
# Then iterate over resulting networks, skipping subsumed subnets
last = None
for net in sorted(subnets.values()):
if last is not None:
# Since they are | add python 3.10.4 for windows | _collapse_addresses_internal | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | ipaddress.py | 12 | 19 | https://github.com/XX-net/XX-Net.git | 7 | 104 | 0 | 56 | 177 | Python | {
"docstring": "Loops through the addresses, collapsing concurrent netblocks.\n\n Example:\n\n ip1 = IPv4Network('192.0.2.0/26')\n ip2 = IPv4Network('192.0.2.64/26')\n ip3 = IPv4Network('192.0.2.128/26')\n ip4 = IPv4Network('192.0.2.192/26')\n\n _collapse_addresses_internal([ip1, ip2, ip3, ip4]) ->\n [IPv4Network('192.0.2.0/24')]\n\n This shouldn't be called directly; it is called via\n collapse_addresses([]).\n\n Args:\n addresses: A list of IPv4Network's or IPv6Network's\n\n Returns:\n A list of IPv4Network's or IPv6Network's depending on what we were\n passed.\n\n ",
"language": "en",
"n_whitespaces": 150,
"n_words": 57,
"vocab_size": 47
} | def _collapse_addresses_internal(addresses):
# First merge
to_merge = list(addresses)
subnets = {}
while to_merge:
net = to_merge.pop()
supernet = net.supernet()
existing = subnets.get(supernet)
if existing is None:
subnets[supernet] = net
elif existing != net:
# Merge consecutive subnets
del subnets[supernet]
to_merge.append(supernet)
# Then iterate over resulting networks, skipping subsumed subnets
last = None
for net in sorted(subnets.values()):
if last is not None:
# Since they are sorted, last.network_address <= net.network_address
# is a given.
if last.broadcast_address >= net.broadcast_address:
continue
yield net
last = net
|
|
22,780 | 107,491 | 102 | lib/matplotlib/backend_bases.py | 25 | 13 | def inaxes(self, xy):
axes_list = [a for a in self.figure.get_axes()
if a.patch.contains_point(xy) and a.get_visible()]
if axes_list:
axes = cbook._topmost_artist(axes_list) | DOC: More cleanup axes -> Axes | inaxes | f156db08eee54d285ab0fb4e031e48d078ba6aa3 | matplotlib | backend_bases.py | 12 | 8 | https://github.com/matplotlib/matplotlib.git | 5 | 56 | 0 | 20 | 92 | Python | {
"docstring": "\n Return the topmost visible `~.axes.Axes` containing the point *xy*.\n\n Parameters\n ----------\n xy : (float, float)\n (x, y) pixel positions from left/bottom of the canvas.\n\n Returns\n -------\n `~matplotlib.axes.Axes` or None\n The topmost visible Axes containing the point, or None if there\n is no Axes at the point.\n ",
"language": "en",
"n_whitespaces": 136,
"n_words": 46,
"vocab_size": 36
} | def inaxes(self, xy):
axes_list = [a for a in self.figure.get_axes()
if a.patch.contains_point(xy) and a.get_visible()]
if axes_list:
axes = cbook._topmost_artist(axes_list)
else:
axes = None
return axes
|
|
8,611 | 45,484 | 300 | airflow/migrations/versions/98271e7606e2_add_scheduling_decision_to_dagrun_and_.py | 135 | 32 | def upgrade():
conn = op.get_bind()
is_sqlite = bool(conn.dialect.name == "sqlite")
is_mssql = bool(conn.dialect.name == "mssql")
if is_sqlite:
op.execute("PRAGMA foreign_keys=off")
with op.batch_alter_table('dag_run', schema=None) as batch_op:
batch_op.add_column(sa.Column('last_scheduling_decision', TIMESTAMP, nullable=True))
batch_op.create_index('idx_last_scheduling_decision', ['last_scheduling_decision'], unique=False)
batch_op.add_column(sa.Column('dag_hash', sa.String(32), nullable=True))
with op.batch_alter_table('dag', schema=None) as batch_op:
batc | Autogenerate migration reference doc (#21601)
* document airflow version in each alembic migration module and use this to autogen the doc
* update each migration module to have the same description used in migration ref (so it can be used in autogen) | upgrade | 69f6f9e01b6df76c3c8fa266d460324163957887 | airflow | 98271e7606e2_add_scheduling_decision_to_dagrun_and_.py | 13 | 34 | https://github.com/apache/airflow.git | 4 | 309 | 0 | 94 | 553 | Python | {
"docstring": "Apply Add ``scheduling_decision`` to ``DagRun`` and ``DAG``\n UPDATE dag SET\n concurrency={concurrency},\n has_task_concurrency_limits={1 if is_sqlite or is_mssql else sa.true()}\n where concurrency IS NULL\n ",
"language": "en",
"n_whitespaces": 65,
"n_words": 22,
"vocab_size": 22
} | def upgrade():
conn = op.get_bind()
is_sqlite = bool(conn.dialect.name == "sqlite")
is_mssql = bool(conn.dialect.name == "mssql")
if is_sqlite:
op.execute("PRAGMA foreign_keys=off")
with op.batch_alter_table('dag_run', schema=None) as batch_op:
batch_op.add_column(sa.Column('last_scheduling_decision', TIMESTAMP, nullable=True))
batch_op.create_index('idx_last_scheduling_decision', ['last_scheduling_decision'], unique=False)
batch_op.add_column(sa.Column('dag_hash', sa.String(32), nullable=True))
with op.batch_alter_table('dag', schema=None) as batch_op:
batch_op.add_column(sa.Column('next_dagrun', TIMESTAMP, nullable=True))
batch_op.add_column(sa.Column('next_dagrun_create_after', TIMESTAMP, nullable=True))
# Create with nullable and no default, then ALTER to set values, to avoid table level lock
batch_op.add_column(sa.Column('concurrency', sa.Integer(), nullable=True))
batch_op.add_column(sa.Column('has_task_concurrency_limits', sa.Boolean(), nullable=True))
batch_op.create_index('idx_next_dagrun_create_after', ['next_dagrun_create_after'], unique=False)
try:
from airflow.configuration import conf
concurrency = conf.getint('core', 'dag_concurrency', fallback=16)
except: # noqa
concurrency = 16
# Set it to true here as it makes us take the slow/more complete path, and when it's next parsed by the
# DagParser it will get set to correct value.
op.execute(
f
)
with op.batch_alter_table('dag', schema=None) as batch_op:
batch_op.alter_column('concurrency', type_=sa.Integer(), nullable=False)
batch_op.alter_column('has_task_concurrency_limits', type_=sa.Boolean(), nullable=False)
if is_sqlite:
op.execute("PRAGMA foreign_keys=on")
|
|
591 | 3,889 | 147 | airbyte-integrations/connectors/source-orb/source_orb/source.py | 50 | 10 | def enrich_ledger_entries_with_event_data(self, ledger_entries):
# Build up a list of the subset of ledger entries we are expected
# to enrich with event metadata.
event_id_to_ledger_entry = {}
for entry in ledger_entries:
maybe_event_id: Optional[str] = entry.get("event_id")
if maybe_event_id:
event_id_to_ledger_entry[maybe_event_id] = entry
# Nothing to enrich; short-circuit
if len(event_id_to_ledger_entry) == 0:
return ledger_entries
| 🎉 New Source: Orb (#9985)
* V1 of source_orb connector
* add boostrap.md file
* add clause on Pagination to bootstrap.md
* add SUMMARY documentation
* add lookback_window_days connector parameter
* Add support for start_date parameter
* Add ability to transform record in order to un-nest IDs
* Add support for extracting event properties based on connector configuration | enrich_ledger_entries_with_event_data | 1e0ac30ebdcfce55a5644bcd486044da45c93dd6 | airbyte | source.py | 11 | 35 | https://github.com/airbytehq/airbyte.git | 12 | 261 | 0 | 41 | 84 | Python | {
"docstring": "\n Enriches a list of ledger entries with event metadata (applies only to decrements that\n have an event_id property set, i.e. automated decrements to the ledger applied by Orb).\n ",
"language": "en",
"n_whitespaces": 50,
"n_words": 28,
"vocab_size": 25
} | def enrich_ledger_entries_with_event_data(self, ledger_entries):
# Build up a list of the subset of ledger entries we are expected
# to enrich with event metadata.
event_id_to_ledger_entry = {}
for entry in ledger_entries:
maybe_event_id: Optional[str] = entry.get("event_id")
if maybe_event_id:
event_id_to_ledger_entry[maybe_event_id] = entry
# Nothing to enrich; short-circuit
if len(event_id_to_ledger_entry) == 0:
return ledger_entries
|
|
413 | 3,245 | 164 | packages/syft/tests/syft/core/adp/data_subject_ledger_test.py | 81 | 16 | def test_cache() -> None:
ledger_store = DictLedgerStore()
user_key = b"1322"
ledger = DataSubjectLedger.get_or_create(store=ledger_store, user_key=user_key)
assert (
ledger._cache_constant2epsilon[0] == 0.05372712063485988
), "The first value in the cache is incorrect"
assert (
ledger._cache_constant2epsilon[1] == 0.07773597369831031
), "Has the DP cache been changed?"
rdp_700k = convert_constants_to_indices(np.array([700_000]))
assert (
ledger._cache_constant2epsilon.take(rdp_700k)[0] == 706213.1816144075
), "Has the DP cache been changed?"
rdp_50 = convert_constants_to_indices(np.array([50]))
assert (
ledger._cache_constant2epsilon.take(rdp_50)[0] == 100.68990516105825
), "Has the DP cache bee | Add tests for ledger and cache | test_cache | 61f4138eeb028287425f6007d692bf7faa808e75 | PySyft | data_subject_ledger_test.py | 11 | 22 | https://github.com/OpenMined/PySyft.git | 1 | 139 | 0 | 43 | 211 | Python | {
"docstring": "Ensure the most up to date RDP-to-epsilon cache is being used.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def test_cache() -> None:
ledger_store = DictLedgerStore()
user_key = b"1322"
ledger = DataSubjectLedger.get_or_create(store=ledger_store, user_key=user_key)
assert (
ledger._cache_constant2epsilon[0] == 0.05372712063485988
), "The first value in the cache is incorrect"
assert (
ledger._cache_constant2epsilon[1] == 0.07773597369831031
), "Has the DP cache been changed?"
rdp_700k = convert_constants_to_indices(np.array([700_000]))
assert (
ledger._cache_constant2epsilon.take(rdp_700k)[0] == 706213.1816144075
), "Has the DP cache been changed?"
rdp_50 = convert_constants_to_indices(np.array([50]))
assert (
ledger._cache_constant2epsilon.take(rdp_50)[0] == 100.68990516105825
), "Has the DP cache been changed?"
assert (
len(ledger._cache_constant2epsilon) >= 1_200_000
), "Has the cache been changed?"
|
|
3,380 | 20,452 | 116 | pipenv/patched/notpip/_vendor/pygments/lexers/__init__.py | 42 | 14 | def get_lexer_for_mimetype(_mime, **options):
for modname, name, _, _, mimetypes in LEXERS.values():
if _mime in mimetypes:
if name not in _lexer_cache:
_load_lexers(modname)
return _lexer_cache[name](**options)
for cls in find_plugin_lexers():
| check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4 | get_lexer_for_mimetype | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | __init__.py | 13 | 10 | https://github.com/pypa/pipenv.git | 6 | 77 | 0 | 31 | 123 | Python | {
"docstring": "Get a lexer for a mimetype.\n\n Raises ClassNotFound if not found.\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 11,
"vocab_size": 10
} | def get_lexer_for_mimetype(_mime, **options):
for modname, name, _, _, mimetypes in LEXERS.values():
if _mime in mimetypes:
if name not in _lexer_cache:
_load_lexers(modname)
return _lexer_cache[name](**options)
for cls in find_plugin_lexers():
if _mime in cls.mimetypes:
return cls(**options)
raise ClassNotFound('no lexer for mimetype %r found' % _mime)
|
|
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("fit_intercept", [True, False]) | 76,230 | 260,406 | 276 | sklearn/linear_model/_glm/tests/test_glm.py | 127 | 34 | def test_glm_regression(solver, fit_intercept, glm_dataset):
model, X, y, _, coef_with_intercept, coef_without_intercept, alpha = glm_dataset
params = dict(
alpha=alpha,
fit_intercept=fit_intercept,
# While _GeneralizedLinearRegressor exposes the solver parameter, public
# estimators currently do not, and lbfgs is the only solver anyw | TST tight tests for GLMs (#23619)
Co-authored-by: Olivier Grisel <[email protected]> | test_glm_regression | 9d863aba2b6dab9c9cbbcf2f7c3b7a99b6ad168f | scikit-learn | test_glm.py | 14 | 26 | https://github.com/scikit-learn/scikit-learn.git | 2 | 201 | 1 | 89 | 344 | Python | {
"docstring": "Test that GLM converges for all solvers to correct solution.\n\n We work with a simple constructed data set with known solution.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 21,
"vocab_size": 19
} | def test_glm_regression(solver, fit_intercept, glm_dataset):
model, X, y, _, coef_with_intercept, coef_without_intercept, alpha = glm_dataset
params = dict(
alpha=alpha,
fit_intercept=fit_intercept,
# While _GeneralizedLinearRegressor exposes the solver parameter, public
# estimators currently do not, and lbfgs is the only solver anyway.
# TODO: Expose solver as soon as we have a second solver to choose from.
# solver=solver, # only lbfgs available
tol=1e-12,
max_iter=1000,
)
model = clone(model).set_params(**params)
X = X[:, :-1] # remove intercept
if fit_intercept:
coef = coef_with_intercept
intercept = coef[-1]
coef = coef[:-1]
else:
coef = coef_without_intercept
intercept = 0
model.fit(X, y)
rtol = 5e-5
assert model.intercept_ == pytest.approx(intercept, rel=rtol)
assert_allclose(model.coef_, coef, rtol=rtol)
# Same with sample_weight.
model = (
clone(model).set_params(**params).fit(X, y, sample_weight=np.ones(X.shape[0]))
)
assert model.intercept_ == pytest.approx(intercept, rel=rtol)
assert_allclose(model.coef_, coef, rtol=rtol)
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("fit_intercept", [True, False]) |
19,908 | 100,425 | 332 | setup.py | 81 | 27 | def _cuda_check(self):
with Popen("nvcc -V", shell=True, stdout=PIPE, stderr=PIPE) as chk:
stdout, stderr = chk.communicate()
if not stderr:
version = re.search(r".*release (?P<cuda>\d+\.\d+)",
stdout.decode(locale.getpreferredencoding()))
self.cuda_version = version.groupdict().get("cuda", None)
locate = "where" if self._os == "windows" else "which"
path = os.popen(f"{locate} nvcc").read()
if path:
path = path.split(" | Update code to support Tensorflow versions up to 2.8 (#1213)
* Update maximum tf version in setup + requirements
* - bump max version of tf version in launcher
- standardise tf version check
* update keras get_custom_objects for tf>2.6
* bugfix: force black text in GUI file dialogs (linux)
* dssim loss - Move to stock tf.ssim function
* Update optimizer imports for compatibility
* fix logging for tf2.8
* Fix GUI graphing for TF2.8
* update tests
* bump requirements.txt versions
* Remove limit on nvidia-ml-py
* Graphing bugfixes
- Prevent live graph from displaying if data not yet available
* bugfix: Live graph. Collect loss labels correctly
* fix: live graph - swallow inconsistent loss errors
* Bugfix: Prevent live graph from clearing during training
* Fix graphing for AMD | _cuda_check | c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf | faceswap | setup.py | 15 | 18 | https://github.com/deepfakes/faceswap.git | 6 | 149 | 0 | 65 | 271 | Python | {
"docstring": " Obtain the location and version of Cuda and populate :attr:`cuda_version` and\n :attr:`cuda_path`\n\n Initially just calls `nvcc -V` to get the installed version of Cuda currently in use.\n If this fails, drills down to more OS specific checking methods.\n ",
"language": "en",
"n_whitespaces": 67,
"n_words": 38,
"vocab_size": 31
} | def _cuda_check(self):
with Popen("nvcc -V", shell=True, stdout=PIPE, stderr=PIPE) as chk:
stdout, stderr = chk.communicate()
if not stderr:
version = re.search(r".*release (?P<cuda>\d+\.\d+)",
stdout.decode(locale.getpreferredencoding()))
self.cuda_version = version.groupdict().get("cuda", None)
locate = "where" if self._os == "windows" else "which"
path = os.popen(f"{locate} nvcc").read()
if path:
path = path.split("\n")[0] # Split multiple entries and take first found
while True: # Get Cuda root folder
path, split = os.path.split(path)
if split == "bin":
break
self.cuda_path = path
return
# Failed to load nvcc, manual check
getattr(self, f"_cuda_check_{self._os}")()
|
|
8,477 | 45,097 | 33 | tests/models/test_taskinstance.py | 12 | 7 | def test_map_product_same(self, dag_maker, session):
outputs = | Implement mapped value unpacking (#21641) | test_map_product_same | 46a337c8cda6fcc515fffe9a4e4cc324edaefa0a | airflow | test_taskinstance.py | 12 | 20 | https://github.com/apache/airflow.git | 2 | 177 | 0 | 12 | 50 | Python | {
"docstring": "Test a mapped task can refer to the same source multiple times.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | def test_map_product_same(self, dag_maker, session):
outputs = []
with dag_maker(dag_id="product_same", session=session) as dag:
|
|
35,196 | 152,834 | 56 | modules/deepbooru.py | 24 | 13 | def get_deepbooru_tags(pil_image, threshold=0.5):
from modules import shared # prevents circular reference
create_deepbooru_process(threshold)
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_proces | refactored the deepbooru module to improve speed on running multiple interogations in a row. Added the option to generate deepbooru tags for textual inversion preproccessing. | get_deepbooru_tags | 1f92336be768d235c18a82acb2195b7135101ae7 | stable-diffusion-webui | deepbooru.py | 9 | 9 | https://github.com/AUTOMATIC1111/stable-diffusion-webui.git | 2 | 61 | 0 | 23 | 100 | Python | {
"docstring": "\n This method is for running only one image at a time for simple use. Used to the img2img interrogate.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 19,
"vocab_size": 18
} | def get_deepbooru_tags(pil_image, threshold=0.5):
from modules import shared # prevents circular reference
create_deepbooru_process(threshold)
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_process_queue.put(pil_image)
while shared.deepbooru_process_return["value"] == -1:
time.sleep(0.2)
release_process()
return ret
|
|
25,796 | 116,615 | 360 | tests/unit/test_executor.py | 101 | 25 | def test_update_from_select(self, mock_handler):
self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})
# --- use predictor ---
predictor = {
'name': 'task_model',
'predict': 'p',
'dtypes': {
'p': dtype.float,
'a': dtype.integer,
'b': dtype.categorical,
'c': dtype.datetime | support of update command
#2454 | test_update_from_select | 0dadd5cecec68f252a08637f695b0e4b573b316f | mindsdb | test_executor.py | 15 | 37 | https://github.com/mindsdb/mindsdb.git | 1 | 135 | 0 | 71 | 247 | Python | {
"docstring": "\n update \n pg.table2 \n set\n a1 = df.a,\n c1 = df.c\n from \n (\n SELECT model.a as a, model.b as b, model.p as c\n FROM pg.tasks as t\n JOIN mindsdb.task_model as model\n WHERE t.a=1 \n )\n as df\n where \n table2.a1 = df.a \n and table2.b1 = df.b \n \n # SELECT a, b FROM pg.tasks\n # UNION\n # SELECT b, a FROM pg.tasks\n # ",
"language": "en",
"n_whitespaces": 410,
"n_words": 57,
"vocab_size": 38
} | def test_update_from_select(self, mock_handler):
self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})
# --- use predictor ---
predictor = {
'name': 'task_model',
'predict': 'p',
'dtypes': {
'p': dtype.float,
'a': dtype.integer,
'b': dtype.categorical,
'c': dtype.datetime
},
'predicted_value': 'ccc'
}
self.set_predictor(predictor)
sql =
ret = self.command_executor.execute_command(
parse_sql(sql, dialect='mindsdb'))
assert ret.error_code is None
# 1 select and 2 updates
assert mock_handler().query.call_count == 3
# second is update
assert mock_handler().query.call_args_list[1][0][0].to_string() == "update table2 set a1=1, c1='ccc' where (a1 = 1) AND (b1 = 'ccc')"
# @patch('mindsdb.integrations.handlers.postgres_handler.Handler')
# def test_union_type_mismatch(self, mock_handler):
# self.set_handler(mock_handler, name='pg', tables={'tasks': self.df})
#
# sql =
# from mindsdb.api.mysql.mysql_proxy.utilities import ErSqlWrongArguments
# with pytest.raises(ErSqlWrongArguments):
# self.command_executor.execute_command(parse_sql(sql, dialect='mindsdb'))
|
|
13,942 | 65,565 | 27 | erpnext/buying/doctype/supplier_scorecard_variable/supplier_scorecard_variable.py | 38 | 12 | def get_total_shipments(scorecard):
supplier = frappe.get_doc("Supplier", scorecard.supplier)
# Loo | style: format code with black | get_total_shipments | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | supplier_scorecard_variable.py | 13 | 20 | https://github.com/frappe/erpnext.git | 2 | 68 | 0 | 33 | 114 | Python | {
"docstring": "Gets the total number of ordered shipments to arrive in the period (based on Purchase Receipts)\n\t\t\tSELECT\n\t\t\t\tCOUNT(po_item.base_amount)\n\t\t\tFROM\n\t\t\t\t`tabPurchase Order Item` po_item,\n\t\t\t\t`tabPurchase Order` po\n\t\t\tWHERE\n\t\t\t\tpo.supplier = %(supplier)s\n\t\t\t\tAND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s\n\t\t\t\tAND po_item.docstatus = 1\n\t\t\t\tAND po_item.parent = po.name",
"language": "en",
"n_whitespaces": 33,
"n_words": 44,
"vocab_size": 37
} | def get_total_shipments(scorecard):
supplier = frappe.get_doc("Supplier", scorecard.supplier)
# Look up all PO Items with delivery dates between our dates
data = frappe.db.sql(
,
{"supplier": supplier.name, "start_date": scorecard.start_date, "end_date": scorecard.end_date},
as_dict=0,
)[0][0]
if not data:
data = 0
return data
|
|
48,243 | 196,909 | 17 | sympy/utilities/source.py | 8 | 6 | def source(object):
print('In file: %s' % inspect.getsourcefile( | Update the deprecation for source() | source | 3a56f9bb1642dda441f65b3713635a8e98150247 | sympy | source.py | 10 | 3 | https://github.com/sympy/sympy.git | 1 | 26 | 0 | 8 | 48 | Python | {
"docstring": "\n Prints the source code of a given object.\n\n .. deprecated:: 1.3\n\n The ``source()`` function is deprecated. Use ``inspect.getsource()`` or\n ``??`` in IPython/Jupyter instead.\n\n ",
"language": "en",
"n_whitespaces": 45,
"n_words": 23,
"vocab_size": 23
} | def source(object):
print('In file: %s' % inspect.getsourcefile(object))
print(inspect.getsource(object))
|
|
81,856 | 277,080 | 272 | keras/utils/tf_utils.py | 98 | 16 | def validate_axis(axis, input_shape):
input_shape = tf.TensorShape(input_shape)
rank = input_shape.rank
if not rank:
raise ValueError(
f"Input has undefined rank. Received: input_shape={input_shape}"
)
# Convert axis to list and resolve negatives
if isinstance(axis, int):
axis = [axis]
else:
axis = list(axis)
for idx, x in enumerate(axis):
if x < 0:
axis[idx] = rank + x
# Va | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | validate_axis | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | tf_utils.py | 16 | 24 | https://github.com/keras-team/keras.git | 9 | 119 | 0 | 65 | 224 | Python | {
"docstring": "Validate an axis value and returns its standardized form.\n\n Args:\n axis: Value to validate. Can be an integer or a list/tuple of integers.\n Integers may be negative.\n input_shape: Reference input shape that the axis/axes refer to.\n\n Returns:\n Normalized form of `axis`, i.e. a list with all-positive values.\n ",
"language": "en",
"n_whitespaces": 78,
"n_words": 47,
"vocab_size": 43
} | def validate_axis(axis, input_shape):
input_shape = tf.TensorShape(input_shape)
rank = input_shape.rank
if not rank:
raise ValueError(
f"Input has undefined rank. Received: input_shape={input_shape}"
)
# Convert axis to list and resolve negatives
if isinstance(axis, int):
axis = [axis]
else:
axis = list(axis)
for idx, x in enumerate(axis):
if x < 0:
axis[idx] = rank + x
# Validate axes
for x in axis:
if x < 0 or x >= rank:
raise ValueError(
"Invalid value for `axis` argument. "
"Expected 0 <= axis < inputs.rank (with "
f"inputs.rank={rank}). Received: axis={tuple(axis)}"
)
if len(axis) != len(set(axis)):
raise ValueError(f"Duplicate axis: {tuple(axis)}")
return axis
|
|
3,351 | 20,375 | 232 | pipenv/patched/notpip/_vendor/pygments/formatters/latex.py | 47 | 9 | def _filter_to(self, it, pred):
| check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4 | _filter_to | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | latex.py | 13 | 15 | https://github.com/pypa/pipenv.git | 6 | 70 | 0 | 25 | 116 | Python | {
"docstring": " Keep only the tokens that match `pred`, merge the others together ",
"language": "en",
"n_whitespaces": 12,
"n_words": 11,
"vocab_size": 10
} | def _filter_to(self, it, pred):
buf = ''
idx = 0
for i, t, v in it:
if pred(t):
if buf:
yield idx, None, buf
buf = ''
yield i, t, v
else:
if not buf:
idx = i
buf += v
if buf:
yield idx, None, buf
|
|
90,371 | 291,261 | 20 | homeassistant/components/mqtt/mixins.py | 6 | 5 | def entity_registry_enabled_default(self) -> bool:
return bool(self._config[CONF_ENABLED_BY_DEFAULT])
| Strict type hints for MQTT integration (#82317)
* Strict type hints for MQTT integration
* Fix errors
* Additional corrections
* Use cv.template to avoid untyped calls
* Enable strict typing policy for MQTT integration
* Use ignore[no-untyped-call]
* Use # type: ignore[unreachable]
* Correct cast
* Refactor getting discovery_payload
* Remove unused type ignore comments | entity_registry_enabled_default | 8a8732f0bc2a7cd891a3ddaff3edbe9c246d6ebf | core | mixins.py | 9 | 3 | https://github.com/home-assistant/core.git | 1 | 18 | 0 | 6 | 31 | Python | {
"docstring": "Return if the entity should be enabled when first added to the entity registry.",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 12
} | def entity_registry_enabled_default(self) -> bool:
return bool(self._config[CONF_ENABLED_BY_DEFAULT])
|
|
47,800 | 196,300 | 139 | sympy/geometry/polygon.py | 62 | 15 | def bisectors(self):
# use lines containing sides so containment check during
# intersection calculation can be avoided, thus reducing
# the processing time for calculating the bisectors
s = [Line(l) for l in self.sides]
v = self.vertices
c = self.incenter
l1 = Segment(v[0], Line(v[0], c).intersection(s[1])[0])
l2 = Segme | Updated import locations | bisectors | 498015021131af4dbb07eb110e5badaba8250c7b | sympy | polygon.py | 14 | 8 | https://github.com/sympy/sympy.git | 2 | 143 | 0 | 53 | 213 | Python | {
"docstring": "The angle bisectors of the triangle.\n\n An angle bisector of a triangle is a straight line through a vertex\n which cuts the corresponding angle in half.\n\n Returns\n =======\n\n bisectors : dict\n Each key is a vertex (Point) and each value is the corresponding\n bisector (Segment).\n\n See Also\n ========\n\n sympy.geometry.point.Point, sympy.geometry.line.Segment\n\n Examples\n ========\n\n >>> from sympy import Point, Triangle, Segment\n >>> p1, p2, p3 = Point(0, 0), Point(1, 0), Point(0, 1)\n >>> t = Triangle(p1, p2, p3)\n >>> from sympy import sqrt\n >>> t.bisectors()[p2] == Segment(Point(1, 0), Point(0, sqrt(2) - 1))\n True\n\n ",
"language": "en",
"n_whitespaces": 232,
"n_words": 91,
"vocab_size": 63
} | def bisectors(self):
# use lines containing sides so containment check during
# intersection calculation can be avoided, thus reducing
# the processing time for calculating the bisectors
s = [Line(l) for l in self.sides]
v = self.vertices
c = self.incenter
l1 = Segment(v[0], Line(v[0], c).intersection(s[1])[0])
l2 = Segment(v[1], Line(v[1], c).intersection(s[2])[0])
l3 = Segment(v[2], Line(v[2], c).intersection(s[0])[0])
return {v[0]: l1, v[1]: l2, v[2]: l3}
|
|
74,890 | 256,545 | 523 | ui/utils.py | 124 | 30 | def query(query, filters={}, top_k_reader=5, top_k_retriever=5) -> Tuple[List[Dict[str, Any]], Dict[str, str]]:
url = f"{API_ENDPOINT}/{DOC_REQUEST}"
params = {"filters": filters, "Retriever": {"top_k": top_k_retriever}, "Reader": {"top_k": top_k_reader}}
req = {"query": query, "params": params}
response_raw = requests.post(url, json=req)
if response_raw.status_code >= 400 and response_raw.status_code != 503:
raise Exception(f"{vars(response_raw)}")
response = | Apply black formatting (#2115)
* Testing black on ui/
* Applying black on docstores
* Add latest docstring and tutorial changes
* Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too
* Remove comments
* Relax constraints on pydoc-markdown
* Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade
* Fix a couple of bugs
* Add a type: ignore that was missing somehow
* Give path to black
* Apply Black
* Apply Black
* Relocate a couple of type: ignore
* Update documentation
* Make Linux CI run after applying Black
* Triggering Black
* Apply Black
* Remove dependency, does not work well
* Remove manually double trailing commas
* Update documentation
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | query | a59bca366174d9c692fa19750c24d65f47660ef7 | haystack | utils.py | 19 | 40 | https://github.com/deepset-ai/haystack.git | 8 | 297 | 0 | 89 | 521 | Python | {
"docstring": "\n Send a query to the REST API and parse the answer.\n Returns both a ready-to-use representation of the results and the raw JSON.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 23,
"vocab_size": 18
} | def query(query, filters={}, top_k_reader=5, top_k_retriever=5) -> Tuple[List[Dict[str, Any]], Dict[str, str]]:
url = f"{API_ENDPOINT}/{DOC_REQUEST}"
params = {"filters": filters, "Retriever": {"top_k": top_k_retriever}, "Reader": {"top_k": top_k_reader}}
req = {"query": query, "params": params}
response_raw = requests.post(url, json=req)
if response_raw.status_code >= 400 and response_raw.status_code != 503:
raise Exception(f"{vars(response_raw)}")
response = response_raw.json()
if "errors" in response:
raise Exception(", ".join(response["errors"]))
# Format response
results = []
answers = response["answers"]
for answer in answers:
if answer.get("answer", None):
results.append(
{
"context": "..." + answer["context"] + "...",
"answer": answer.get("answer", None),
"source": answer["meta"]["name"],
"relevance": round(answer["score"] * 100, 2),
"document": [doc for doc in response["documents"] if doc["id"] == answer["document_id"]][0],
"offset_start_in_doc": answer["offsets_in_document"][0]["start"],
"_raw": answer,
}
)
else:
results.append(
{
"context": None,
"answer": None,
"document": None,
"relevance": round(answer["score"] * 100, 2),
"_raw": answer,
}
)
return results, response
|
|
97,491 | 298,548 | 15 | homeassistant/components/daikin/climate.py | 9 | 6 | def format_target_temperature(target_temperature):
return str(round(float(target_temperature) * 2, 0) / 2).r | Daikin AC : Round to nearest half degree (#70446) (#70452) | format_target_temperature | e2bbdb26be42d9b82538f5964819489e6f7aa656 | core | climate.py | 17 | 2 | https://github.com/home-assistant/core.git | 1 | 33 | 0 | 9 | 59 | Python | {
"docstring": "Format target temperature to be sent to the Daikin unit, rounding to nearest half degree.",
"language": "en",
"n_whitespaces": 14,
"n_words": 15,
"vocab_size": 13
} | def format_target_temperature(target_temperature):
return str(round(float(target_temperature) * 2, 0) / 2).rstrip("0").rstrip(".")
|
|
14,468 | 67,274 | 11 | erpnext/regional/report/uae_vat_201/uae_vat_201.py | 16 | 7 | def get_data(filters=None):
data = []
emirates, amounts_by_emirate = append_vat_on_sales(data, filters)
append_vat_on_expenses(data, filters)
| style: format code with black | get_data | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | uae_vat_201.py | 8 | 5 | https://github.com/frappe/erpnext.git | 1 | 34 | 0 | 12 | 55 | Python | {
"docstring": "Returns the list of dictionaries. Each dictionary is a row in the datatable and chart data.",
"language": "en",
"n_whitespaces": 15,
"n_words": 16,
"vocab_size": 15
} | def get_data(filters=None):
data = []
emirates, amounts_by_emirate = append_vat_on_sales(data, filters)
append_vat_on_expenses(data, filters)
return data, emirates, amounts_by_emirate
|
|
11,620 | 57,112 | 30 | src/prefect/utilities/callables.py | 9 | 6 | def dict(self, *args, **kwargs):
kwargs.setdefault("exclude_none", True)
r | Move parameter schema utilities to prefect.utilites.callables | dict | b13e269bdebd6248023455e7f1ccb24669cbfe3e | prefect | callables.py | 9 | 3 | https://github.com/PrefectHQ/prefect.git | 1 | 33 | 0 | 9 | 56 | Python | {
"docstring": "Exclude `None` fields by default to comply with\n the OpenAPI spec.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 11,
"vocab_size": 11
} | def dict(self, *args, **kwargs):
kwargs.setdefault("exclude_none", True)
return super().dict(*args, **kwargs)
|
|
443 | 3,326 | 353 | airbyte-integrations/bases/base-normalization/normalization/transform_catalog/stream_processor.py | 83 | 24 | def extract_column_names(self) -> Dict[str, Tuple[str, str]]:
fields = []
for field in self.properties.keys():
if not is_airbyte_column(field):
fields.append(field)
result = {}
field_names = set()
for field in fields:
field_name = self.name_transformer.normalize_column_name(field, in_jinja=False)
field_name_lookup = self.name_transformer.normalize_column_identifier_case_for_lookup(field_name)
jinja_name = self.name_transformer.normalize_column_name(field, in_jinja=True)
if field_name_lookup in field_names:
# TODO handle column name duplicates or collisions deterministically in this stream
for i in range(1, 1000):
field_name = self.name_transformer.normalize_column_name(f"{field}_{i}", in_jinja=False)
field_name_lookup = self.name_transformer.normalize_column_identifier_case_for_lookup(field_name)
jinja_name = self.name_transformer.normalize_column_name(f"{field}_{i}", in_jinja=True)
if field_name | 🐛 Fix normalization issue with quoted & case sensitive columns (#9317) | extract_column_names | c5d4a973631ccae7918b9d7881f875a265f30619 | airbyte | stream_processor.py | 16 | 28 | https://github.com/airbytehq/airbyte.git | 7 | 178 | 0 | 51 | 294 | Python | {
"docstring": "\n Generate a mapping of JSON properties to normalized SQL Column names, handling collisions and avoid duplicate names\n\n The mapped value to a field property is a tuple where:\n - the first value is the normalized \"raw\" column name\n - the second value is the normalized quoted column name to be used in jinja context\n ",
"language": "en",
"n_whitespaces": 92,
"n_words": 54,
"vocab_size": 38
} | def extract_column_names(self) -> Dict[str, Tuple[str, str]]:
fields = []
for field in self.properties.keys():
if not is_airbyte_column(field):
fields.append(field)
result = {}
field_names = set()
for field in fields:
field_name = self.name_transformer.normalize_column_name(field, in_jinja=False)
field_name_lookup = self.name_transformer.normalize_column_identifier_case_for_lookup(field_name)
jinja_name = self.name_transformer.normalize_column_name(field, in_jinja=True)
if field_name_lookup in field_names:
# TODO handle column name duplicates or collisions deterministically in this stream
for i in range(1, 1000):
field_name = self.name_transformer.normalize_column_name(f"{field}_{i}", in_jinja=False)
field_name_lookup = self.name_transformer.normalize_column_identifier_case_for_lookup(field_name)
jinja_name = self.name_transformer.normalize_column_name(f"{field}_{i}", in_jinja=True)
if field_name_lookup not in field_names:
break
field_names.add(field_name_lookup)
result[field] = (field_name, jinja_name)
return result
|
|
41,976 | 176,574 | 311 | networkx/algorithms/shortest_paths/generic.py | 88 | 19 | def _build_paths_from_predecessors(sources, target, pred):
if | Add a space in an error (#5601)
* Add a space in an error
* Fix style errors | _build_paths_from_predecessors | db20f63bd3f16dedb6c660dbc6fbc89e89892c82 | networkx | generic.py | 17 | 25 | https://github.com/networkx/networkx.git | 8 | 170 | 0 | 62 | 271 | Python | {
"docstring": "Compute all simple paths to target, given the predecessors found in\n pred, terminating when any source in sources is found.\n\n Parameters\n ----------\n sources : set\n Starting nodes for path.\n\n target : node\n Ending node for path.\n\n pred : dict\n A dictionary of predecessor lists, keyed by node\n\n Returns\n -------\n paths : generator of lists\n A generator of all paths between source and target.\n\n Raises\n ------\n NetworkXNoPath\n If `target` cannot be reached from `source`.\n\n Notes\n -----\n There may be many paths between the sources and target. If there are\n cycles among the predecessors, this function will not produce all\n possible paths because doing so would produce infinitely many paths\n of unbounded length -- instead, we only produce simple paths.\n\n See Also\n --------\n shortest_path\n single_source_shortest_path\n all_pairs_shortest_path\n all_shortest_paths\n bellman_ford_path\n ",
"language": "en",
"n_whitespaces": 237,
"n_words": 126,
"vocab_size": 92
} | def _build_paths_from_predecessors(sources, target, pred):
if target not in pred:
raise nx.NetworkXNoPath(f"Target {target} cannot be reached from given sources")
seen = {target}
stack = [[target, 0]]
top = 0
while top >= 0:
node, i = stack[top]
if node in sources:
yield [p for p, n in reversed(stack[: top + 1])]
if len(pred[node]) > i:
stack[top][1] = i + 1
next = pred[node][i]
if next in seen:
continue
else:
seen.add(next)
top += 1
if top == len(stack):
stack.append([next, 0])
else:
stack[top][:] = [next, 0]
else:
seen.discard(node)
top -= 1
|
|
86,935 | 287,747 | 25 | homeassistant/components/bluetooth/models.py | 11 | 4 | def is_connected(self) -> bool:
return self._backend is not None and self._backend.is_connected
| Update to bleak 0.18.0 (#79008) | is_connected | 1b144c0e4dd683e3b47668a89da5eb6da4ae5e08 | core | models.py | 8 | 3 | https://github.com/home-assistant/core.git | 2 | 21 | 0 | 11 | 35 | Python | {
"docstring": "Return True if the client is connected to a device.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def is_connected(self) -> bool:
return self._backend is not None and self._backend.is_connected
|
|
3,823 | 21,422 | 120 | pipenv/patched/notpip/_vendor/distlib/_backport/tarfile.py | 42 | 14 | def _create_gnu_long_header(cls, name, type, encoding, errors):
name = name.encode(encoding, errors) + NUL
info = {}
info["name"] = "././@LongLink"
info["type"] = type
info["size"] = len(name)
info["magic"] = GNU_MAGIC
# create extended header + name blocks.
return cls._create_he | Vendor in pip 22.1.2 | _create_gnu_long_header | c69d55f7c82d5ae2cce542bcfb98d043ca4836a0 | pipenv | tarfile.py | 9 | 9 | https://github.com/pypa/pipenv.git | 1 | 78 | 0 | 32 | 126 | Python | {
"docstring": "Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence\n for name.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 8,
"vocab_size": 8
} | def _create_gnu_long_header(cls, name, type, encoding, errors):
name = name.encode(encoding, errors) + NUL
info = {}
info["name"] = "././@LongLink"
info["type"] = type
info["size"] = len(name)
info["magic"] = GNU_MAGIC
# create extended header + name blocks.
return cls._create_header(info, USTAR_FORMAT, encoding, errors) + \
cls._create_payload(name)
|
|
49,318 | 199,652 | 82 | sympy/polys/appellseqs.py | 43 | 15 | def genocchi_poly(n, x=None, polys=False):
if n < 0:
raise ValueError("Cannot generate Genocchi polynomial of degree %s" % (n-1))
poly = DMP(dup_genocchi(int(n), ZZ), | Custom Appell sequence functions and a doctest | genocchi_poly | 93e4d381d35cd4c21a3a8d713c157f8fb21f725b | sympy | appellseqs.py | 14 | 9 | https://github.com/sympy/sympy.git | 4 | 87 | 0 | 36 | 139 | Python | {
"docstring": "Generates the Genocchi polynomial `\\operatorname{G}_n(x)`.\n\n `\\operatorname{G}_n(x)` is twice the difference between the plain and\n central Bernoulli polynomials, so has degree `n-1`:\n\n .. math :: \\operatorname{G}_n(x) = 2 (\\operatorname{B}_n(x) -\n \\operatorname{B}_n^c(x))\n\n The factor of 2 in the definition endows `\\operatorname{G}_n(x)` with\n integer coefficients.\n\n Parameters\n ==========\n\n n : int\n Degree of the polynomial plus one.\n x : optional\n polys : bool, optional\n If True, return a Poly, otherwise (default) return an expression.\n ",
"language": "en",
"n_whitespaces": 128,
"n_words": 70,
"vocab_size": 58
} | def genocchi_poly(n, x=None, polys=False):
if n < 0:
raise ValueError("Cannot generate Genocchi polynomial of degree %s" % (n-1))
poly = DMP(dup_genocchi(int(n), ZZ), ZZ)
if x is not None:
poly = Poly.new(poly, x)
else:
poly = PurePoly.new(poly, Dummy('x'))
return poly if polys else poly.as_expr()
|
|
26,585 | 119,314 | 237 | jax/_src/third_party/scipy/signal_helper.py | 118 | 18 | def _triage_segments(window, nperseg, input_length):
# parse window; if array like, then set nperseg = win.shape
if isinstance(window, (str, tuple)):
# if nperseg not specified
if nperseg is None:
nperseg = 256 # then change to default
if nperseg > input_length:
warnings.warn(f'nperseg = {nperseg} is greater than input length '
f' = {input_length}, using nperseg = {nperseg}')
nperseg = input_length
win = jnp.array(osp_signal.get_window(window, nperseg))
else:
win = jnp.asarray(window)
if len(win.shape) != 1:
raise Va | Add some functions for spectral analysis.
This commit adds "stft", "csd", and "welch" functions in scipy.signal. | _triage_segments | e085370ec4137cf0f73c5163cb664bc4e1c46082 | jax | signal_helper.py | 17 | 22 | https://github.com/google/jax.git | 9 | 142 | 0 | 69 | 248 | Python | {
"docstring": "\n Parses window and nperseg arguments for spectrogram and _spectral_helper.\n This is a helper function, not meant to be called externally.\n Parameters\n ----------\n window : string, tuple, or ndarray\n If window is specified by a string or tuple and nperseg is not\n specified, nperseg is set to the default of 256 and returns a window of\n that length.\n If instead the window is array_like and nperseg is not specified, then\n nperseg is set to the length of the window. A ValueError is raised if\n the user supplies both an array_like window and a value for nperseg but\n nperseg does not equal the length of the window.\n nperseg : int\n Length of each segment\n input_length: int\n Length of input signal, i.e. x.shape[-1]. Used to test for errors.\n Returns\n -------\n win : ndarray\n window. If function was called with string or tuple than this will hold\n the actual array used as a window.\n nperseg : int\n Length of each segment. If window is str or tuple, nperseg is set to\n 256. If window is array_like, nperseg is set to the length of the\n 6\n window.\n ",
"language": "en",
"n_whitespaces": 270,
"n_words": 182,
"vocab_size": 88
} | def _triage_segments(window, nperseg, input_length):
# parse window; if array like, then set nperseg = win.shape
if isinstance(window, (str, tuple)):
# if nperseg not specified
if nperseg is None:
nperseg = 256 # then change to default
if nperseg > input_length:
warnings.warn(f'nperseg = {nperseg} is greater than input length '
f' = {input_length}, using nperseg = {nperseg}')
nperseg = input_length
win = jnp.array(osp_signal.get_window(window, nperseg))
else:
win = jnp.asarray(window)
if len(win.shape) != 1:
raise ValueError('window must be 1-D')
if input_length < win.shape[-1]:
raise ValueError('window is longer than input signal')
if nperseg is None:
nperseg = win.shape[0]
elif nperseg is not None:
if nperseg != win.shape[0]:
raise ValueError("value specified for nperseg is different"
" from length of window")
return win, nperseg
|
|
6,345 | 34,811 | 113 | src/transformers/modeling_utils.py | 57 | 21 | def register_for_auto_class(cls, auto_class="AutoModel"):
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if not hasattr(auto_module, auto_class):
raise ValueError(f"{auto_class} is not a valid auto class.")
cls._auto_class = auto_class
# To update the docstring, we need to copy the method, otherwise we change the original docstring.
PreTrainedModel.push_to_hub = copy_func(PreTrainedModel.push_to_hub)
PreTrainedModel.push_to_hub.__doc__ = PreTra | Save code of registered custom models (#15379)
* Allow dynamic modules to use relative imports
* Work for configs
* Fix last merge conflict
* Save code of registered custom objects
* Map strings to strings
* Fix test
* Add tokenizer
* Rework tests
* Tests
* Ignore fixtures py files for tests
* Tokenizer test + fix collection
* With full path
* Rework integration
* Fix typo
* Remove changes in conftest
* Test for tokenizers
* Add documentation
* Update docs/source/custom_models.mdx
Co-authored-by: Lysandre Debut <[email protected]>
* Add file structure and file content
* Add more doc
* Style
* Update docs/source/custom_models.mdx
Co-authored-by: Suraj Patil <[email protected]>
* Address review comments
Co-authored-by: Lysandre Debut <[email protected]>
Co-authored-by: Suraj Patil <[email protected]> | register_for_auto_class | 44b21f117bcf71e3d88a11c3523c94b27949fdbf | transformers | modeling_utils.py | 11 | 7 | https://github.com/huggingface/transformers.git | 3 | 52 | 0 | 47 | 150 | Python | {
"docstring": "\n Register this class with a given auto class. This should only be used for custom models as the ones in the\n library are already mapped with an auto class.\n\n Args:\n auto_class (`str` or `type`, *optional*, defaults to `\"AutoModel\"`):\n The auto class to register this new model with.\n ",
"language": "en",
"n_whitespaces": 102,
"n_words": 47,
"vocab_size": 39
} | def register_for_auto_class(cls, auto_class="AutoModel"):
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if not hasattr(auto_module, auto_class):
raise ValueError(f"{auto_class} is not a valid auto class.")
cls._auto_class = auto_class
# To update the docstring, we need to copy the method, otherwise we change the original docstring.
PreTrainedModel.push_to_hub = copy_func(PreTrainedModel.push_to_hub)
PreTrainedModel.push_to_hub.__doc__ = PreTrainedModel.push_to_hub.__doc__.format(
object="model", object_class="AutoModel", object_files="model checkpoint"
)
|
|
120,941 | 336,999 | 120 | src/diffusers/utils/import_utils.py | 66 | 20 | def is_accelerate_available():
return _accelerate_available
# docstyle-ignore
FLAX_IMPORT_ERROR =
# docstyle-ignore
INFLECT_IMPORT_ERROR =
# docstyle-ignore
PYTORCH_IMPORT_ERROR =
# docstyle-ignore
ONNX_IMPORT_ERROR =
# docstyle-ignore
SCIPY_IMPORT_ERROR =
# docstyle-ignore
TENSORFLOW_IMPORT_ERROR =
# docstyle-ignore
TRANSFORMERS_IMPORT_ERROR =
# docstyle-ignore
UNIDECODE_IMP | add accelerate to load models with smaller memory footprint (#361)
* add accelerate to load models with smaller memory footprint
* remove low_cpu_mem_usage as it is reduntant
* move accelerate init weights context to modelling utils
* add test to ensure results are the same when loading with accelerate
* add tests to ensure ram usage gets lower when using accelerate
* move accelerate logic to single snippet under modelling utils and remove it from configuration utils
* format code using to pass quality check
* fix imports with isor
* add accelerate to test extra deps
* only import accelerate if device_map is set to auto
* move accelerate availability check to diffusers import utils
* format code
Co-authored-by: Patrick von Platen <[email protected]> | is_accelerate_available | 4d1cce2fd01056515f0f353322a231164a4a5c5d | diffusers | import_utils.py | 9 | 2 | https://github.com/huggingface/diffusers.git | 1 | 6 | 0 | 44 | 199 | Python | {
"docstring": "\n{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the\ninstallation page: https://github.com/google/flax and follow the ones that match your environment.\n\n{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install\ninflect`\n\n{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the\ninstallation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.\n\n{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip\ninstall onnxruntime`\n\n{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install\nscipy`\n\n{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the\ninstallation page: https://www.tensorflow.org/install and follow the ones that match your environment.\n\n{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip\ninstall transformers`\n\n{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install\nUnidecode`\n",
"language": "en",
"n_whitespaces": 181,
"n_words": 197,
"vocab_size": 44
} | def is_accelerate_available():
return _accelerate_available
# docstyle-ignore
FLAX_IMPORT_ERROR =
# docstyle-ignore
INFLECT_IMPORT_ERROR =
# docstyle-ignore
PYTORCH_IMPORT_ERROR =
# docstyle-ignore
ONNX_IMPORT_ERROR =
# docstyle-ignore
SCIPY_IMPORT_ERROR =
# docstyle-ignore
TENSORFLOW_IMPORT_ERROR =
# docstyle-ignore
TRANSFORMERS_IMPORT_ERROR =
# docstyle-ignore
UNIDECODE_IMPORT_ERROR =
BACKENDS_MAPPING = OrderedDict(
[
("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)),
("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
]
)
|
|
23,143 | 108,330 | 41 | lib/matplotlib/text.py | 13 | 7 | def set_horizontalalignment(self, align):
_api.check_i | Document text alignment
Closes #21571. | set_horizontalalignment | c0cb163c627fe52e38311954226e3349f34f6914 | matplotlib | text.py | 9 | 4 | https://github.com/matplotlib/matplotlib.git | 1 | 34 | 0 | 12 | 59 | Python | {
"docstring": "\n Set the horizontal alignment relative to the anchor point.\n\n See also :doc:`/gallery/text_labels_and_annotations/text_alignment`.\n\n Parameters\n ----------\n align : {'left', 'center', 'right'}\n ",
"language": "en",
"n_whitespaces": 62,
"n_words": 19,
"vocab_size": 18
} | def set_horizontalalignment(self, align):
_api.check_in_list(['center', 'right', 'left'], align=align)
self._horizontalalignment = align
self.stale = True
|
|
56,128 | 220,817 | 14 | python3.10.4/Lib/asyncio/tasks.py | 9 | 4 | def _wrap_awaitable(awaitable):
return (yield from awaitable.__await__())
_wrap_awaitable._is_coroutine = _is_coroutine
| add python 3.10.4 for windows | _wrap_awaitable | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | tasks.py | 9 | 2 | https://github.com/XX-net/XX-Net.git | 1 | 16 | 0 | 9 | 37 | Python | {
"docstring": "Helper for asyncio.ensure_future().\n\n Wraps awaitable (an object with __await__) into a coroutine\n that will later be wrapped in a Task by ensure_future().\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 22,
"vocab_size": 21
} | def _wrap_awaitable(awaitable):
return (yield from awaitable.__await__())
_wrap_awaitable._is_coroutine = _is_coroutine
|
|
34,216 | 148,280 | 31 | python/ray/_private/thirdparty/pathspec/util.py | 43 | 10 | def normalize_file(file, separators=None):
# Normalize path separators.
| [Bugfix] fix invalid excluding of Black (#24042)
- We should use `--force-exclude` when we pass code path explicitly https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html?highlight=--force-exclude#command-line-options
- Recover the files in `python/ray/_private/thirdparty` which has been formatted in the PR https://github.com/ray-project/ray/pull/21975 by mistake. | normalize_file | 0e6c042e29cbbe429d81c9c1af3c75c261f00980 | ray | util.py | 11 | 9 | https://github.com/ray-project/ray.git | 4 | 58 | 0 | 32 | 98 | Python | {
"docstring": "\n\tNormalizes the file path to use the POSIX path separator (i.e., ``'/'``).\n\n\t*file* (:class:`str` or :class:`pathlib.PurePath`) is the file path.\n\n\t*separators* (:class:`~collections.abc.Collection` of :class:`str`; or\n\t:data:`None`) optionally contains the path separators to normalize.\n\tThis does not need to include the POSIX path separator (``'/'``), but\n\tincluding it will not affect the results. Default is :data:`None` for\n\t:data:`NORMALIZE_PATH_SEPS`. To prevent normalization, pass an empty\n\tcontainer (e.g., an empty tuple ``()``).\n\n\tReturns the normalized file path (:class:`str`).\n\t",
"language": "en",
"n_whitespaces": 66,
"n_words": 75,
"vocab_size": 54
} | def normalize_file(file, separators=None):
# Normalize path separators.
if separators is None:
separators = NORMALIZE_PATH_SEPS
# Convert path object to string.
norm_file = str(file)
for sep in separators:
norm_file = norm_file.replace(sep, posixpath.sep)
# Remove current directory prefix.
if norm_file.startswith('./'):
norm_file = norm_file[2:]
return norm_file
|
|
73,332 | 250,235 | 29 | synapse/types/state.py | 15 | 8 | def wildcard_types(self) -> List[str]:
return [t for t, state_keys in self.types.items() if state_keys is No | Allow selecting "prejoin" events by state keys (#14642)
* Declare new config
* Parse new config
* Read new config
* Don't use trial/our TestCase where it's not needed
Before:
```
$ time trial tests/events/test_utils.py > /dev/null
real 0m2.277s
user 0m2.186s
sys 0m0.083s
```
After:
```
$ time trial tests/events/test_utils.py > /dev/null
real 0m0.566s
user 0m0.508s
sys 0m0.056s
```
* Helper to upsert to event fields
without exceeding size limits.
* Use helper when adding invite/knock state
Now that we allow admins to include events in prejoin room state with
arbitrary state keys, be a good Matrix citizen and ensure they don't
accidentally create an oversized event.
* Changelog
* Move StateFilter tests
should have done this in #14668
* Add extra methods to StateFilter
* Use StateFilter
* Ensure test file enforces typed defs; alphabetise
* Workaround surprising get_current_state_ids
* Whoops, fix mypy | wildcard_types | e2a1adbf5d11288f2134ced1f84c6ffdd91a9357 | synapse | state.py | 10 | 8 | https://github.com/matrix-org/synapse.git | 3 | 31 | 0 | 14 | 50 | Python | {
"docstring": "Returns a list of event types which require us to fetch all state keys.\n This will be empty unless `has_wildcards` returns True.\n\n Returns:\n A list of event types.\n ",
"language": "en",
"n_whitespaces": 60,
"n_words": 28,
"vocab_size": 25
} | def wildcard_types(self) -> List[str]:
return [t for t, state_keys in self.types.items() if state_keys is None]
|
|
25,438 | 115,389 | 45 | mindsdb/integrations/handlers/snowflake_handler/snowflake_handler.py | 17 | 7 | def get_columns(self, table_name) -> Response:
| Add snowflake connector | get_columns | 0e22eac78f7dd836a0e16b343d1bd02d039a3b6b | mindsdb | snowflake_handler.py | 8 | 7 | https://github.com/mindsdb/mindsdb.git | 1 | 24 | 0 | 15 | 45 | Python | {
"docstring": "\n List the columns in the tabels for which the user have access\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 12,
"vocab_size": 10
} | def get_columns(self, table_name) -> Response:
q = f"SHOW COLUMNS IN TABLE {table_name};"
result = self.native_query(q)
return result
|
|
13,939 | 65,562 | 27 | erpnext/buying/doctype/supplier_scorecard_variable/supplier_scorecard_variable.py | 38 | 12 | def get_on_time_shipments(scorecard):
supplier = frappe.get_d | style: format code with black | get_on_time_shipments | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | supplier_scorecard_variable.py | 13 | 26 | https://github.com/frappe/erpnext.git | 2 | 68 | 0 | 33 | 114 | Python | {
"docstring": "Gets the number of late shipments (counting each item) in the period (based on Purchase Receipts vs POs)\n\t\t\tSELECT\n\t\t\t\tCOUNT(pr_item.qty)\n\t\t\tFROM\n\t\t\t\t`tabPurchase Order Item` po_item,\n\t\t\t\t`tabPurchase Receipt Item` pr_item,\n\t\t\t\t`tabPurchase Order` po,\n\t\t\t\t`tabPurchase Receipt` pr\n\t\t\tWHERE\n\t\t\t\tpo.supplier = %(supplier)s\n\t\t\t\tAND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s\n\t\t\t\tAND po_item.schedule_date <= pr.posting_date\n\t\t\t\tAND po_item.qty = pr_item.qty\n\t\t\t\tAND pr_item.docstatus = 1\n\t\t\t\tAND pr_item.purchase_order_item = po_item.name\n\t\t\t\tAND po_item.parent = po.name\n\t\t\t\tAND pr_item.parent = pr.name",
"language": "en",
"n_whitespaces": 52,
"n_words": 69,
"vocab_size": 51
} | def get_on_time_shipments(scorecard):
supplier = frappe.get_doc("Supplier", scorecard.supplier)
# Look up all PO Items with delivery dates between our dates
total_items_delivered_on_time = frappe.db.sql(
,
{"supplier": supplier.name, "start_date": scorecard.start_date, "end_date": scorecard.end_date},
as_dict=0,
)[0][0]
if not total_items_delivered_on_time:
total_items_delivered_on_time = 0
return total_items_delivered_on_time
|
|
17,931 | 85,099 | 23 | zerver/webhooks/bitbucket3/tests.py | 9 | 5 | def test_commit_comment_deleted(self) -> None:
expected_message =
self.check_webhook("commit_comment_deleted", TOPIC, expected_message)
| webhooks: Pick a more reasonable length for short sha.
7 characters are not enough for large projects, so we change
it to reasonably longer. As an example, The Linux kernel needs
at least 11 characters of sha in its shortened form to identify
a revision. We pick 11 so it should work for most of the projects.
Signed-off-by: Zixuan James Li <[email protected]> | test_commit_comment_deleted | 4e4689949438735622bdf669f05d218c671e7e01 | zulip | tests.py | 8 | 3 | https://github.com/zulip/zulip.git | 1 | 20 | 0 | 9 | 38 | Python | {
"docstring": "[hypro999](http://139.59.64.214:7990/users/hypro999) deleted their comment on [508d1b67f1f](http://139.59.64.214:7990/projects/SBOX/repos/sandbox/commits/508d1b67f1f8f3a25f543a030a7a178894aa9907):\\n~~~ quote\\n~~Just an arbitrary comment on a commit. Nothing to see here...~~\\n~~~",
"language": "en",
"n_whitespaces": 16,
"n_words": 17,
"vocab_size": 15
} | def test_commit_comment_deleted(self) -> None:
expected_message =
self.check_webhook("commit_comment_deleted", TOPIC, expected_message)
|
|
11,760 | 58,372 | 235 | src/prefect/agent.py | 42 | 16 | async def get_work_queues(self) -> Optional[UUID]:
work_queues = []
for name in self.work_queues:
try:
# support IDs and names
if isinstance(name, UUID):
work_queue = await self.client.read_work_queue(id=name)
else:
work_queue = await self.client.read_work_queue_by_name(name)
except ObjectNotFound:
work_queue = await self.client.create_work_queue(
name=name, return_id=False
| Agents support multiple queues | get_work_queues | 8a4560e237b90a7b64c6bb77b6cb3ee9a6648e33 | prefect | agent.py | 17 | 17 | https://github.com/PrefectHQ/prefect.git | 4 | 86 | 0 | 34 | 142 | Python | {
"docstring": "\n Loads the work queue objects corresponding to the agent's target work queues. If any of them don't exist, they are created.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 21,
"vocab_size": 19
} | async def get_work_queues(self) -> Optional[UUID]:
work_queues = []
for name in self.work_queues:
try:
# support IDs and names
if isinstance(name, UUID):
work_queue = await self.client.read_work_queue(id=name)
else:
work_queue = await self.client.read_work_queue_by_name(name)
except ObjectNotFound:
work_queue = await self.client.create_work_queue(
name=name, return_id=False
)
work_queues.append(work_queue)
return work_queues
|
|
458 | 3,356 | 53 | airbyte-cdk/python/unit_tests/sources/test_abstract_source.py | 26 | 24 | def test_read_nonexistent_stream_raises_exception(mocker):
s1 = MockStream(name="s1")
s2 = MockStream(name="this_stream_doesnt_exist_in_the_source")
mocker.patch.object(MockStream, "get_json_schema", return_value={})
| CDK: Fix typing errors (#9037)
* fix typing, drop AirbyteLogger
* format
* bump the version
* use logger instead of fixture logger
Co-authored-by: Eugene Kulak <[email protected]>
Co-authored-by: auganbay <[email protected]> | test_read_nonexistent_stream_raises_exception | f83eca58eaf2129d21b5796a301732ab22675130 | airbyte | test_abstract_source.py | 13 | 8 | https://github.com/airbytehq/airbyte.git | 1 | 86 | 0 | 22 | 150 | Python | {
"docstring": "Tests that attempting to sync a stream which the source does not return from the `streams` method raises an exception",
"language": "en",
"n_whitespaces": 19,
"n_words": 20,
"vocab_size": 19
} | def test_read_nonexistent_stream_raises_exception(mocker):
s1 = MockStream(name="s1")
s2 = MockStream(name="this_stream_doesnt_exist_in_the_source")
mocker.patch.object(MockStream, "get_json_schema", return_value={})
src = MockSource(streams=[s1])
catalog = ConfiguredAirbyteCatalog(streams=[_configured_stream(s2, SyncMode.full_refresh)])
with pytest.raises(KeyError):
list(src.read(logger, {}, catalog))
GLOBAL_EMITTED_AT = 1
|
|
12,831 | 62,021 | 339 | .venv/lib/python3.8/site-packages/pip/_vendor/distlib/locators.py | 89 | 24 | def _should_queue(self, link, referrer, rel):
scheme, netloc, path, _, _, _ = urlparse(link)
if path.endswith(self.source_extensions + self.binary_extensions +
| upd; format | _should_queue | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | locators.py | 13 | 24 | https://github.com/jindongwang/transferlearning.git | 9 | 168 | 0 | 51 | 272 | Python | {
"docstring": "\n Determine whether a link URL from a referring page and with a\n particular \"rel\" attribute should be queued for scraping.\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 20,
"vocab_size": 18
} | def _should_queue(self, link, referrer, rel):
scheme, netloc, path, _, _, _ = urlparse(link)
if path.endswith(self.source_extensions + self.binary_extensions +
self.excluded_extensions):
result = False
elif self.skip_externals and not link.startswith(self.base_url):
result = False
elif not referrer.startswith(self.base_url):
result = False
elif rel not in ('homepage', 'download'):
result = False
elif scheme not in ('http', 'https', 'ftp'):
result = False
elif self._is_platform_dependent(link):
result = False
else:
host = netloc.split(':', 1)[0]
if host.lower() == 'localhost':
result = False
else:
result = True
logger.debug('should_queue: %s (%s) from %s -> %s', link, rel,
referrer, result)
return result
|
|
24,762 | 112,790 | 99 | nni/runtime/tuner_command_channel/legacy.py | 48 | 13 | def receive():
header = _in_file.read(16)
_logger.debug('Received command, header: [%s]', header)
if header is None or len(header) < 16:
# Pipe EOF encountered
_logger. | WebSocket (step 1) - Python client (#4806) | receive | f60d3d5e294510d99c65ba3292822cbb922adbf8 | nni | legacy.py | 10 | 12 | https://github.com/microsoft/nni.git | 3 | 91 | 0 | 34 | 157 | Python | {
"docstring": "Receive a command from Training Service.\n Returns a tuple of command (CommandType) and payload (str)\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 15,
"vocab_size": 13
} | def receive():
header = _in_file.read(16)
_logger.debug('Received command, header: [%s]', header)
if header is None or len(header) < 16:
# Pipe EOF encountered
_logger.debug('Pipe EOF encountered')
return None, None
length = int(header[2:])
data = _in_file.read(length)
command = CommandType(header[:2])
data = data.decode('utf8')
_logger.debug('Received command, data: [%s]', data)
return command, data
|
|
@register.filter() | 77,732 | 264,446 | 17 | netbox/utilities/templatetags/builtins/filters.py | 12 | 8 | def bettertitle(value):
return ' '.join([w[0].upper() + w[1:] for w in value.s | Closes #8600: Document built-in template tags & filters | bettertitle | 7c105019d8ae9205051c302e7499b33a455f9176 | netbox | filters.py | 12 | 2 | https://github.com/netbox-community/netbox.git | 2 | 36 | 1 | 12 | 72 | Python | {
"docstring": "\n Alternative to the builtin title(). Ensures that the first letter of each word is uppercase but retains the\n original case of all others.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 23,
"vocab_size": 20
} | def bettertitle(value):
return ' '.join([w[0].upper() + w[1:] for w in value.split()])
@register.filter() |
12,009 | 60,201 | 161 | code/deep/BJMMD/caffe/examples/pycaffe/layers/pascal_multilabel_datalayers.py | 45 | 11 | def load_pascal_annotation(index, pascal_root):
classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'trai | Balanced joint maximum mean discrepancy for deep transfer learning | load_pascal_annotation | cc4d0564756ca067516f71718a3d135996525909 | transferlearning | pascal_multilabel_datalayers.py | 12 | 33 | https://github.com/jindongwang/transferlearning.git | 2 | 317 | 0 | 41 | 153 | Python | {
"docstring": "\n This code is borrowed from Ross Girshick's FAST-RCNN code\n (https://github.com/rbgirshick/fast-rcnn).\n It parses the PASCAL .xml metadata files.\n See publication for further details: (http://arxiv.org/abs/1504.08083).\n\n Thanks Ross!\n\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 25,
"vocab_size": 24
} | def load_pascal_annotation(index, pascal_root):
classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
class_to_ind = dict(zip(classes, xrange(21)))
filename = osp.join(pascal_root, 'Annotations', index + '.xml')
# print 'Loading: {}'.format(filename)
|
|
36,280 | 155,189 | 75 | modin/core/execution/unidist/implementations/pandas_on_unidist/partitioning/partition.py | 15 | 13 | def get(self):
logger = get_logger()
logger.debug(f"ENTER::Partition.get::{self._identity}")
if len(self.call_queue):
| FEAT-#5053: Add pandas on unidist execution with MPI backend (#5059)
Signed-off-by: Igoshev, Iaroslav <[email protected]> | get | 193505fdf0c984743397ba3df56262f30aee13a8 | modin | partition.py | 10 | 8 | https://github.com/modin-project/modin.git | 2 | 50 | 0 | 13 | 101 | Python | {
"docstring": "\n Get the object wrapped by this partition out of the object store.\n\n Returns\n -------\n pandas.DataFrame\n The object from the object store.\n ",
"language": "en",
"n_whitespaces": 68,
"n_words": 21,
"vocab_size": 15
} | def get(self):
logger = get_logger()
logger.debug(f"ENTER::Partition.get::{self._identity}")
if len(self.call_queue):
self.drain_call_queue()
result = UnidistWrapper.materialize(self._data)
logger.debug(f"EXIT::Partition.get::{self._identity}")
return result
|
|
51,981 | 207,491 | 120 | tests/admin_views/test_actions.py | 27 | 13 | def test_custom_function_action_no_perm_response(self):
action_data = {
ACTION_CHECKBOX_NAME: [self.s1.pk],
"action": "no_perm",
"index": 0,
}
response = self.client.post(
reverse("admin:admin_views_externalsubscriber_changelist | Refs #33476 -- Reformatted code with Black. | test_custom_function_action_no_perm_response | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | test_actions.py | 11 | 11 | https://github.com/django/django.git | 1 | 64 | 0 | 25 | 107 | Python | {
"docstring": "A custom action may returns an HttpResponse with a 403 code.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def test_custom_function_action_no_perm_response(self):
action_data = {
ACTION_CHECKBOX_NAME: [self.s1.pk],
"action": "no_perm",
"index": 0,
}
response = self.client.post(
reverse("admin:admin_views_externalsubscriber_changelist"), action_data
)
self.assertEqual(response.status_code, 403)
self.assertEqual(response.content, b"No permission to perform this action")
|
|
47,891 | 196,391 | 22 | sympy/matrices/matrices.py | 9 | 5 | def limit(self, *args):
| Moved imports to higher level | limit | 59d22b6bb7287613d598611027f640d068ca5748 | sympy | matrices.py | 11 | 2 | https://github.com/sympy/sympy.git | 1 | 25 | 0 | 9 | 44 | Python | {
"docstring": "Calculate the limit of each element in the matrix.\n ``args`` will be passed to the ``limit`` function.\n\n Examples\n ========\n\n >>> from sympy import Matrix\n >>> from sympy.abc import x, y\n >>> M = Matrix([[x, y], [1, 0]])\n >>> M.limit(x, 2)\n Matrix([\n [2, y],\n [1, 0]])\n\n See Also\n ========\n\n integrate\n diff\n ",
"language": "en",
"n_whitespaces": 155,
"n_words": 50,
"vocab_size": 39
} | def limit(self, *args):
return self.applyfunc(lambda x: x.limit(*args))
# https://github.com/sympy/sympy/pull/12854 |
|
89,988 | 290,875 | 91 | tests/components/number/test_init.py | 26 | 12 | def test_device_classes_aligned():
non_numeric_device_classes = {
SensorDeviceClass.DATE,
SensorDeviceClass.DURATION,
SensorDeviceClass.TIMESTAMP,
}
for | Align number and sensor device classes (#81909)
* Align number and sensor device classes
* Add tests
* Tweak tests | test_device_classes_aligned | b6586d5c34bf7ea5c30fbb1b62c438078ea14f39 | core | test_init.py | 12 | 11 | https://github.com/home-assistant/core.git | 3 | 56 | 0 | 23 | 86 | Python | {
"docstring": "Make sure all sensor device classes are also available in NumberDeviceClass.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 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
|
|
5,628 | 30,539 | 49 | tests/test_main.py | 15 | 6 | def valid_tess_config(outdir):
cfg_file = outdir / 'test.cfg'
| tests: Extract some test fixtures for better clarity | valid_tess_config | 5d0cc0a092f93640e1d83baaf1c738768481d208 | OCRmyPDF | test_main.py | 11 | 11 | https://github.com/ocrmypdf/OCRmyPDF.git | 1 | 28 | 0 | 14 | 57 | Python | {
"docstring": "\\\nload_system_dawg 0\nlanguage_model_penalty_non_dict_word 0\nlanguage_model_penalty_non_freq_dict_word 0\n",
"language": "en",
"n_whitespaces": 3,
"n_words": 7,
"vocab_size": 5
} | def valid_tess_config(outdir):
cfg_file = outdir / 'test.cfg'
with cfg_file.open('w') as f:
f.write(
)
yield cfg_file
|
|
50,955 | 204,883 | 211 | django/db/backends/base/operations.py | 59 | 17 | def year_lookup_bounds_for_datetime_field(self, value, iso_year=False):
if iso_year:
first = datetime.datetime.fromisocalendar(value, 1, 1)
second = datetime.datetime.fromisocalendar(
| Refs #33476 -- Reformatted code with Black. | year_lookup_bounds_for_datetime_field | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | operations.py | 12 | 16 | https://github.com/django/django.git | 3 | 142 | 0 | 37 | 212 | Python | {
"docstring": "\n Return a two-elements list with the lower and upper bound to be used\n with a BETWEEN operator to query a DateTimeField value using a year\n lookup.\n\n `value` is an int, containing the looked-up year.\n If `iso_year` is True, return bounds for ISO-8601 week-numbering years.\n ",
"language": "en",
"n_whitespaces": 87,
"n_words": 44,
"vocab_size": 37
} | def year_lookup_bounds_for_datetime_field(self, value, iso_year=False):
if iso_year:
first = datetime.datetime.fromisocalendar(value, 1, 1)
second = datetime.datetime.fromisocalendar(
value + 1, 1, 1
) - datetime.timedelta(microseconds=1)
else:
first = datetime.datetime(value, 1, 1)
second = datetime.datetime(value, 12, 31, 23, 59, 59, 999999)
if settings.USE_TZ:
tz = timezone.get_current_timezone()
first = timezone.make_aware(first, tz)
second = timezone.make_aware(second, tz)
first = self.adapt_datetimefield_value(first)
second = self.adapt_datetimefield_value(second)
return [first, second]
|
|
49,350 | 199,694 | 20 | sympy/polys/orthopolys.py | 15 | 7 | def gegenbauer_poly(n, a, x=None, polys=False):
r
return named_poly(n, dup_ge | Run orthopolys and appellseqs through a common interface
Including unifying the two Chebyshev generators into one function.
There are also two kinds of Hermite polynomials, and they too share the
same recurrence, but the second type He_n(x) (aka the probabilist,
reduced or small polynomials) will not be added here. | gegenbauer_poly | d1d46df73ebaad94089847558d00a8b7269f554d | sympy | orthopolys.py | 8 | 15 | https://github.com/sympy/sympy.git | 1 | 36 | 0 | 15 | 50 | Python | {
"docstring": "Generates the Gegenbauer polynomial `C_n^{(a)}(x)`.\n\n Parameters\n ==========\n\n n : int\n Degree of the polynomial.\n x : optional\n a\n Decides minimal domain for the list of coefficients.\n polys : bool, optional\n If True, return a Poly, otherwise (default) return an expression.\n ",
"language": "en",
"n_whitespaces": 82,
"n_words": 40,
"vocab_size": 32
} | def gegenbauer_poly(n, a, x=None, polys=False):
r
return named_poly(n, dup_gegenbauer, None, "Gegenbauer polynomial", (x, a), polys)
|
|
96,818 | 297,864 | 441 | homeassistant/components/homekit_controller/connection.py | 68 | 13 | async def async_update(self, now=None):
if not self.pollable_characteristics:
self.async_update_available_state()
_LOGGER.debug(
"HomeKit connection not polling any characteristics: %s", self.unique_id
)
return
if self._polling_lock.locked():
if not self._polling_lock_warned:
_LOGGER.warning(
(
"HomeKit controller update skipped as previous poll still in"
" flight: %s"
),
self.unique_id,
)
self._polling_lock_warned = True
return
if self._polling_lock_warned:
_LOGGER.info(
(
"HomeKit controller no longer detecting back pressure - not"
" | String formatting and max line length - Part 2 (#84393) | async_update | cb13418babd21a1e9584978b0c523f1b1e4e1cb0 | core | connection.py | 14 | 44 | https://github.com/home-assistant/core.git | 8 | 177 | 0 | 49 | 158 | Python | {
"docstring": "Poll state of all entities attached to this bridge/accessory.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | async def async_update(self, now=None):
if not self.pollable_characteristics:
self.async_update_available_state()
_LOGGER.debug(
"HomeKit connection not polling any characteristics: %s", self.unique_id
)
return
if self._polling_lock.locked():
if not self._polling_lock_warned:
_LOGGER.warning(
(
"HomeKit controller update skipped as previous poll still in"
" flight: %s"
),
self.unique_id,
)
self._polling_lock_warned = True
return
if self._polling_lock_warned:
_LOGGER.info(
(
"HomeKit controller no longer detecting back pressure - not"
" skipping poll: %s"
),
self.unique_id,
)
self._polling_lock_warned = False
|
|
12,324 | 60,892 | 162 | .venv/lib/python3.8/site-packages/pip/_internal/network/lazy_wheel.py | 58 | 16 | def _merge(self, start, end, left, right):
# type: (int, int, int, int) -> Iterator[Tuple[int, int]]
lslice, rslice = self._left[left:right], self._right[left:right]
i = start = min([start]+lslice[:1])
end = max([end]+rslice[-1:])
for j, k in zip(lslice, rslice):
if j > i:
yield i, j-1
| upd; format | _merge | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | lazy_wheel.py | 12 | 11 | https://github.com/jindongwang/transferlearning.git | 4 | 128 | 0 | 43 | 197 | Python | {
"docstring": "Return an iterator of intervals to be fetched.\n\n Args:\n start (int): Start of needed interval\n end (int): End of needed interval\n left (int): Index of first overlapping downloaded data\n right (int): Index after last overlapping downloaded data\n ",
"language": "en",
"n_whitespaces": 95,
"n_words": 37,
"vocab_size": 25
} | def _merge(self, start, end, left, right):
# type: (int, int, int, int) -> Iterator[Tuple[int, int]]
lslice, rslice = self._left[left:right], self._right[left:right]
i = start = min([start]+lslice[:1])
end = max([end]+rslice[-1:])
for j, k in zip(lslice, rslice):
if j > i:
yield i, j-1
i = k + 1
if i <= end:
yield i, end
self._left[left:right], self._right[left:right] = [start], [end]
|
|
78,643 | 266,891 | 38 | lib/ansible/utils/collection_loader/_collection_finder.py | 17 | 7 | def is_python_identifier(self): # type: (str) -> bool
# Ref: https://stackoverflow.com/a/55802320/5 | Code cleanup for type hinting issues. | is_python_identifier | 4867ac217ba0164b433d0927488d153e116d175d | ansible | _collection_finder.py | 9 | 2 | https://github.com/ansible/ansible.git | 1 | 18 | 0 | 16 | 47 | Python | {
"docstring": "Determine whether the given string is a Python identifier.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | def is_python_identifier(self): # type: (str) -> bool
# Ref: https://stackoverflow.com/a/55802320/595220
return bool(re.match(_VALID_IDENTIFIER_STRING_REGEX, self))
PB_EXTENSIONS = ('.yml', '.yaml')
|
|
12,333 | 60,901 | 84 | .venv/lib/python3.8/site-packages/pip/_internal/network/lazy_wheel.py | 35 | 12 | def _stream_response(self, start, end, base_headers=HEADERS):
# type: (int, int, Dict[str, str]) -> Response
headers = base_headers.copy()
headers['Range'] = f'bytes={start}-{end}'
| upd; format | _stream_response | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | lazy_wheel.py | 8 | 5 | https://github.com/jindongwang/transferlearning.git | 1 | 53 | 0 | 32 | 97 | Python | {
"docstring": "Return HTTP response to a range request from start to end.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | def _stream_response(self, start, end, base_headers=HEADERS):
# type: (int, int, Dict[str, str]) -> Response
headers = base_headers.copy()
headers['Range'] = f'bytes={start}-{end}'
# TODO: Get range requests to be correctly cached
headers['Cache-Control'] = 'no-cache'
return self._session.get(self._url, headers=headers, stream=True)
|
|
117,562 | 321,142 | 856 | qutebrowser/browser/webengine/webenginetab.py | 203 | 44 | def _inject_greasemonkey_scripts(self, scripts):
if sip.isdeleted(self._widget):
return
# Since we are inserting scripts into a per-tab collection,
# rather than just injecting scripts on page load, we need to
# make sure we replace existing scripts, not just add new ones.
# While, taking care not to remove any other scripts that might
# have been added elsewhere, like the one for stylesheets.
page_scripts = self._widget.page().scripts()
self._remove_all_greasemonkey_scripts()
seen_names = set()
for script in scripts:
while script.full_name() in seen_names:
script.dedup_suffix += 1
seen_names.add(script.full_name())
new_script = QWebEngineScript()
try:
world = int(script.jsworld)
if not 0 <= world <= qtutils.MAX_WORLD_ID:
log.greasemonkey.error(
f"script {script.name} has invalid value for '@qute-js-world'"
f": {script.jsworld}, should be between 0 and "
f"{qtutils.MAX_WORLD_ID}")
continue
except ValueError:
try:
world = _JS_WORLD_MAP[usertypes.JsWorld[script.jsworld.lower()]]
except KeyError:
log.greasemonkey.error(
f"script {script.name} has invalid value for '@qute-js-world'"
f": {script.jsworld}")
continue
new_script.setWorldId(world)
# Corresponds to "@run-at document-end" which is the default according to
# https://wiki.greasespot.net/Metadata_Block#.40run-at - however,
# QtWebEngine uses QWebEngineScript.InjectionPoint.Deferred (@run-at document-idle) as
# default.
#
# NOTE that this needs to be done before setSourceCode, so that
# QtWebEngine's pars | Run scripts/dev/rewrite_enums.py | _inject_greasemonkey_scripts | 0877fb0d78635692e481c8bde224fac5ad0dd430 | qutebrowser | webenginetab.py | 19 | 38 | https://github.com/qutebrowser/qutebrowser.git | 8 | 232 | 0 | 145 | 453 | Python | {
"docstring": "Register user JavaScript files with the current tab.\n\n Args:\n scripts: A list of GreasemonkeyScripts.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 14,
"vocab_size": 14
} | def _inject_greasemonkey_scripts(self, scripts):
if sip.isdeleted(self._widget):
return
# Since we are inserting scripts into a per-tab collection,
# rather than just injecting scripts on page load, we need to
# make sure we replace existing scripts, not just add new ones.
# While, taking care not to remove any other scripts that might
# have been added elsewhere, like the one for stylesheets.
page_scripts = self._widget.page().scripts()
self._remove_all_greasemonkey_scripts()
seen_names = set()
for script in scripts:
while script.full_name() in seen_names:
script.dedup_suffix += 1
seen_names.add(script.full_name())
new_script = QWebEngineScript()
try:
world = int(script.jsworld)
if not 0 <= world <= qtutils.MAX_WORLD_ID:
log.greasemonkey.error(
f"script {script.name} has invalid value for '@qute-js-world'"
f": {script.jsworld}, should be between 0 and "
f"{qtutils.MAX_WORLD_ID}")
continue
except ValueError:
try:
world = _JS_WORLD_MAP[usertypes.JsWorld[script.jsworld.lower()]]
except KeyError:
log.greasemonkey.error(
f"script {script.name} has invalid value for '@qute-js-world'"
f": {script.jsworld}")
continue
new_script.setWorldId(world)
# Corresponds to "@run-at document-end" which is the default according to
# https://wiki.greasespot.net/Metadata_Block#.40run-at - however,
# QtWebEngine uses QWebEngineScript.InjectionPoint.Deferred (@run-at document-idle) as
# default.
#
# NOTE that this needs to be done before setSourceCode, so that
# QtWebEngine's parsing of GreaseMonkey tags will override it if there is a
# @run-at comment.
new_script.setInjectionPoint(QWebEngineScript.InjectionPoint.DocumentReady)
new_script.setSourceCode(script.code())
new_script.setName(script.full_name())
new_script.setRunsOnSubFrames(script.runs_on_sub_frames)
if script.needs_document_end_workaround():
log.greasemonkey.debug(
f"Forcing @run-at document-end for {script.name}")
new_script.setInjectionPoint(QWebEngineScript.InjectionPoint.DocumentReady)
log.greasemonkey.debug(f'adding script: {new_script.name()}')
page_scripts.insert(new_script)
|
|
20,264 | 100,813 | 88 | plugins/train/model/_base/model.py | 26 | 12 | def config(self) -> dict:
global _CONFIG # pylint: disable=global-statement
if not _CONFIG:
model_name = self._config_secti | Refactoring and TravisCI to Github Actions (#1239)
* refactor training
* travis to actions | config | ff6b0209dd5ad57b81b0aca570df7f39a7119bfb | faceswap | model.py | 13 | 9 | https://github.com/deepfakes/faceswap.git | 2 | 43 | 0 | 23 | 73 | Python | {
"docstring": " dict: The configuration dictionary for current plugin, as set by the user's\n configuration settings. ",
"language": "en",
"n_whitespaces": 22,
"n_words": 14,
"vocab_size": 13
} | def config(self) -> dict:
global _CONFIG # pylint: disable=global-statement
if not _CONFIG:
model_name = self._config_section
logger.debug("Loading config for: %s", model_name)
_CONFIG = Config(model_name, configfile=self._configfile).config_dict
return _CONFIG
|
|
14,342 | 66,806 | 29 | erpnext/patches/v13_0/stock_entry_enhancements.py | 44 | 12 | def execute():
frappe.relo | style: format code with black | execute | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | stock_entry_enhancements.py | 13 | 26 | https://github.com/frappe/erpnext.git | 3 | 109 | 0 | 31 | 210 | Python | {
"docstring": "\n UPDATE `tabStock Entry` SET\n stock_entry_type = 'Material Transfer',\n purpose = 'Material Transfer',\n add_to_transit = 1 WHERE stock_entry_type = 'Send to Warehouse'\n UPDATE `tabStock Entry` SET\n stock_entry_type = 'Material Transfer',\n purpose = 'Material Transfer'\n WHERE stock_entry_type = 'Receive at Warehouse'\n ",
"language": "en",
"n_whitespaces": 139,
"n_words": 39,
"vocab_size": 18
} | def execute():
frappe.reload_doc("stock", "doctype", "stock_entry")
if frappe.db.has_column("Stock Entry", "add_to_transit"):
frappe.db.sql(
)
frappe.db.sql(
)
frappe.reload_doc("stock", "doctype", "warehouse_type")
if not frappe.db.exists("Warehouse Type", "Transit"):
doc = frappe.new_doc("Warehouse Type")
doc.name = "Transit"
doc.insert()
frappe.reload_doc("stock", "doctype", "stock_entry_type")
frappe.delete_doc_if_exists("Stock Entry Type", "Send to Warehouse")
frappe.delete_doc_if_exists("Stock Entry Type", "Receive at Warehouse")
|
|
14,415 | 67,038 | 43 | erpnext/projects/utils.py | 53 | 16 | def query_task(doctype, txt, searchfield, start, page_len, filters):
from frappe.desk.reportview import build_match_conditions
search_string = "%%%s%%" % txt
order_by_string = "%s%%" % txt
match_conditions = build_match_conditions("Task")
match_conditions = ("and" + match_conditions) if match_conditions else ""
return frappe.db.sql(
% (searchfield, "%s", "%s", match_condi | style: format code with black | query_task | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | utils.py | 10 | 18 | https://github.com/frappe/erpnext.git | 2 | 96 | 0 | 37 | 150 | Python | {
"docstring": "select name, subject from `tabTask`\n\t\twhere (`%s` like %s or `subject` like %s) %s\n\t\torder by\n\t\t\tcase when `subject` like %s then 0 else 1 end,\n\t\t\tcase when `%s` like %s then 0 else 1 end,\n\t\t\t`%s`,\n\t\t\tsubject\n\t\tlimit %s, %s",
"language": "en",
"n_whitespaces": 33,
"n_words": 41,
"vocab_size": 25
} | def query_task(doctype, txt, searchfield, start, page_len, filters):
from frappe.desk.reportview import build_match_conditions
search_string = "%%%s%%" % txt
order_by_string = "%s%%" % txt
match_conditions = build_match_conditions("Task")
match_conditions = ("and" + match_conditions) if match_conditions else ""
return frappe.db.sql(
% (searchfield, "%s", "%s", match_conditions, "%s", searchfield, "%s", searchfield, "%s", "%s"),
(search_string, search_string, order_by_string, order_by_string, start, page_len),
)
|
|
81,318 | 275,145 | 735 | keras/mixed_precision/policy.py | 242 | 11 | def _parse_name(self, name):
if name.endswith("_float32_vars"):
error_msg = (
"Policies ending in '_float32_vars' have been removed "
"from TensorFlow."
)
if name in ("infer_float32_vars", "infer_with_float32_vars"):
error_msg += (
" Please use the 'mixed_float16' or 'mixed_bfloat16' "
"policy instead."
| Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | _parse_name | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | policy.py | 13 | 33 | https://github.com/keras-team/keras.git | 9 | 126 | 0 | 132 | 256 | Python | {
"docstring": "Parses a Policy name into a compute and variable dtype.\n\n Args:\n name: The name of the policy:\n\n Returns:\n The (compute_dtype, variable_dtype) pair.\n ",
"language": "en",
"n_whitespaces": 61,
"n_words": 22,
"vocab_size": 19
} | def _parse_name(self, name):
if name.endswith("_float32_vars"):
error_msg = (
"Policies ending in '_float32_vars' have been removed "
"from TensorFlow."
)
if name in ("infer_float32_vars", "infer_with_float32_vars"):
error_msg += (
" Please use the 'mixed_float16' or 'mixed_bfloat16' "
"policy instead."
)
elif name == "float16_with_float32_vars":
error_msg += " Please use the 'mixed_float16' policy instead."
elif name == "bfloat16_with_float32_vars":
error_msg += " Please use the 'mixed_bfloat16' policy instead."
error_msg += " Got policy name: '%s'" % name
raise ValueError(error_msg)
if name == "mixed_float16":
return "float16", "float32"
elif name == "mixed_bfloat16":
return "bfloat16", "float32"
elif name == "_infer":
# The "_infer" policy exists only for compatibility with TF 1, where
# "_infer" is the default. The behavior matches the behavior of TF 1's
# behavior before policies were introduced. With "_infer", the computation
# and variable dtype are inferred from the first input the first time the
# layer is called. Once the layer is called for the first time, the
# layer's policy will change to the dtype of the first input, and it will
# no longer have the "_infer" policy.
#
# The infer policy should be considered an implementation detail and may
# be removed in the future.
return None, None
try:
dtype = tf.as_dtype(name).name
except TypeError:
error = (
"Cannot convert value %s to a mixed precision Policy. "
"Valid policies include 'mixed_float16', 'mixed_bfloat16', "
"and the name of any dtype such as 'float32'." % (name,)
)
raise ValueError(error)
return dtype, dtype
|
|
11,047 | 54,365 | 47 | src/prefect/engine.py | 17 | 8 | def reraise_exceptions_as_crashes():
try:
yield
except | Move state utilities to `prefect.states` | reraise_exceptions_as_crashes | 1a3defa3a4ee74fcea9ae5fa4edf6e5eed134930 | prefect | engine.py | 12 | 6 | https://github.com/PrefectHQ/prefect.git | 2 | 38 | 0 | 17 | 64 | Python | {
"docstring": "\n Detect crashes during this context, wrapping unexpected exceptions into `Crash`\n signals.\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 11,
"vocab_size": 11
} | def reraise_exceptions_as_crashes():
try:
yield
except BaseException as exc:
state = exception_to_crashed_state(exc)
raise Crash(message=state.message, cause=exc, state=state) from exc
|
|
8,135 | 44,069 | 132 | scripts/in_container/run_resource_check.py | 60 | 27 | def resoure_check():
MINIMUM_ALLOWED_MEMORY = 4
MINIMUM_ALLOWED_CPUS = 2
MINIMUM_ALLOWED_DISK = 20
print("\nChecking r | Verify enough resources for breeze (#20763)
Verify resources, memory, cpus and disk for Docker in Python. | resoure_check | 75755d7f65fb06c6e2e74f805b877774bfa7fcda | airflow | run_resource_check.py | 11 | 17 | https://github.com/apache/airflow.git | 1 | 123 | 0 | 45 | 206 | Python | {
"docstring": "\n Use gsutil to get resources in bytes for memory and disk\n ",
"language": "en",
"n_whitespaces": 18,
"n_words": 11,
"vocab_size": 11
} | def resoure_check():
MINIMUM_ALLOWED_MEMORY = 4
MINIMUM_ALLOWED_CPUS = 2
MINIMUM_ALLOWED_DISK = 20
print("\nChecking resources.\n")
# Memory current available
svmem = psutil.virtual_memory()
mem_available = get_size(svmem.available)
# Cpus current available
cpus_available = psutil.cpu_count(logical=True)
# Disk current available
partitions = psutil.disk_partitions()
partition_usage = psutil.disk_usage(partitions[0].mountpoint)
disk_available = get_size(partition_usage.free)
resources: Dict[str, Resource] = {
'Memory': Resource(current=mem_available, minimumAllowed=MINIMUM_ALLOWED_MEMORY),
'Cpus': Resource(current=cpus_available, minimumAllowed=MINIMUM_ALLOWED_CPUS),
'Disk': Resource(current=disk_available, minimumAllowed=MINIMUM_ALLOWED_DISK),
}
return resources
|
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