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stringlengths 101
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1,800 | 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
|
Tries to execute entry orders for new trades (positions)
| 9 | 170 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Tries to execute entry orders for new trades (positions)
|
1,801 | 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
)
|
Error loading dependency. "{package_name}" is not a registered library.
Registered libraries are:
{list(registered_paths.keys())}
"{package_name}" is registered but the path requested is not valid.
The path requested: "{path_in_package_dist}"
List of registered paths: {registered_paths}
| 32 | 22 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
)
```
###Assistant :
Error loading dependency. "{package_name}" is not a registered library.
Registered libraries are:
{list(registered_paths.keys())}
"{package_name}" is registered but the path requested is not valid.
The path requested: "{path_in_package_dist}"
List of registered paths: {registered_paths}
|
1,802 | 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
| Get IDs for all live nodes in the cluster.
Returns a list of (node_id: str, ip_address: str). The node_id can be
passed into the Ray SchedulingPolicy API.
| 27 | 36 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Get IDs for all live nodes in the cluster.
Returns a list of (node_id: str, ip_address: str). The node_id can be
passed into the Ray SchedulingPolicy API.
|
1,803 | 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
|
Deletes comments from block. If comment does not end in this
block, raises a flag.
| 15 | 70 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Deletes comments from block. If comment does not end in this
block, raises a flag.
|
1,804 | def test_torch_auto_gpu_to_cpu(ray_start_4_cpus_2_gpus):
num_workers = 2
assert os.environ["CUDA_VISIBLE_DEVICES"] == ""
| Tests if GPU tensors are auto converted to CPU on driver. | 11 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_torch_auto_gpu_to_cpu(ray_start_4_cpus_2_gpus):
num_workers = 2
assert os.environ["CUDA_VISIBLE_DEVICES"] == ""
```
###Assistant : Tests if GPU tensors are auto converted to CPU on driver.
|
1,805 | 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)
|
Return a formatted year as a table of tables.
| 9 | 60 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant :
Return a formatted year as a table of tables.
|
1,806 | 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
|
Model prediction for candidate (negative) items
| 6 | 198 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Model prediction for candidate (negative) items
|
1,807 | 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"};",
]
)
|
Returns the image converted to an X11 bitmap.
.. note:: This method only works for mode "1" images.
:param name: The name prefix to use for the bitmap variables.
:returns: A string containing an X11 bitmap.
:raises ValueError: If the mode is not "1"
| 44 | 36 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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"};",
]
)
```
###Assistant :
Returns the image converted to an X11 bitmap.
.. note:: This method only works for mode "1" images.
:param name: The name prefix to use for the bitmap variables.
:returns: A string containing an X11 bitmap.
:raises ValueError: If the mode is not "1"
|
1,808 | 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
| Add all the edges in ebunch_to_add.
Parameters
----------
ebunch_to_add : container of edges
Each edge given in the container will be added to the
graph. The edges must be given as 2-tuples (u, v) or
3-tuples (u, v, d) where d is a dictionary containing edge data.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
See Also
--------
add_edge : add a single edge
add_weighted_edges_from : convenient way to add weighted edges
Notes
-----
Adding the same edge twice has no effect but any edge data
will be updated when each duplicate edge is added.
Edge attributes specified in an ebunch take precedence over
attributes specified via keyword arguments.
When adding edges from an iterator over the graph you are changing,
a `RuntimeError` can be raised with message:
`RuntimeError: dictionary changed size during iteration`. This
happens when the graph's underlying dictionary is modified during
iteration. To avoid this error, evaluate the iterator into a separate
object, e.g. by using `list(iterator_of_edges)`, and pass this
object to `G.add_edges_from`.
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples
>>> e = zip(range(0, 3), range(1, 4))
>>> G.add_edges_from(e) # Add the path graph 0-1-2-3
Associate data to edges
>>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
>>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
Evaluate an iterator over a graph if using it to modify the same graph
>>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
>>> # Grow graph by one new node, adding edges to all existing nodes.
>>> # wrong way - will raise RuntimeError
>>> # G.add_edges_from(((5, n) for n in G.nodes))
>>> # right way - note that there will be no self-edge for node 5
>>> G.add_edges_from(list((5, n) for n in G.nodes))
| 305 | 102 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Add all the edges in ebunch_to_add.
Parameters
----------
ebunch_to_add : container of edges
Each edge given in the container will be added to the
graph. The edges must be given as 2-tuples (u, v) or
3-tuples (u, v, d) where d is a dictionary containing edge data.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
See Also
--------
add_edge : add a single edge
add_weighted_edges_from : convenient way to add weighted edges
Notes
-----
Adding the same edge twice has no effect but any edge data
will be updated when each duplicate edge is added.
Edge attributes specified in an ebunch take precedence over
attributes specified via keyword arguments.
When adding edges from an iterator over the graph you are changing,
a `RuntimeError` can be raised with message:
`RuntimeError: dictionary changed size during iteration`. This
happens when the graph's underlying dictionary is modified during
iteration. To avoid this error, evaluate the iterator into a separate
object, e.g. by using `list(iterator_of_edges)`, and pass this
object to `G.add_edges_from`.
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples
>>> e = zip(range(0, 3), range(1, 4))
>>> G.add_edges_from(e) # Add the path graph 0-1-2-3
Associate data to edges
>>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
>>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
Evaluate an iterator over a graph if using it to modify the same graph
>>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
>>> # Grow graph by one new node, adding edges to all existing nodes.
>>> # wrong way - will raise RuntimeError
>>> # G.add_edges_from(((5, n) for n in G.nodes))
>>> # right way - note that there will be no self-edge for node 5
>>> G.add_edges_from(list((5, n) for n in G.nodes))
|
1,809 | 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()
|
If the plaidml user configuration settings exist, then set the default GPU from the
settings file, Otherwise set the GPU to be the one with most VRAM. | 27 | 16 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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()
```
###Assistant :
If the plaidml user configuration settings exist, then set the default GPU from the
settings file, Otherwise set the GPU to be the one with most VRAM.
|
1,810 | 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()
|
Test readlink where there are nested symlinks and canonicalize=False
Should resolve to the first symlink
| 15 | 26 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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()
```
###Assistant :
Test readlink where there are nested symlinks and canonicalize=False
Should resolve to the first symlink
|
1,811 | 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
| Select a proxy for the url, if applicable.
:param url: The url being for the request
:param proxies: A dictionary of schemes or schemes and hosts to proxy URLs
| 29 | 49 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Select a proxy for the url, if applicable.
:param url: The url being for the request
:param proxies: A dictionary of schemes or schemes and hosts to proxy URLs
|
1,812 | 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()
| Called when the other end of the low-level stream
is half-closed.
If this returns a false value (including None), the transport
will close itself. If it returns a true value, closing the
transport is up to the protocol.
| 38 | 31 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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()
```
###Assistant : Called when the other end of the low-level stream
is half-closed.
If this returns a false value (including None), the transport
will close itself. If it returns a true value, closing the
transport is up to the protocol.
|
1,813 | 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,
}
| Returns a URL that points to a page that should be processed | 12 | 49 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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,
}
```
###Assistant : Returns a URL that points to a page that should be processed
|
1,814 | 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
|
Shade *color* using normal vectors given by *normals*,
assuming a *lightsource* (using default position if not given).
*color* can also be an array of the same length as *normals*.
| 29 | 48 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Shade *color* using normal vectors given by *normals*,
assuming a *lightsource* (using default position if not given).
*color* can also be an array of the same length as *normals*.
|
1,815 | def score(self, X, y, **fit_params):
check_is_fitted(self)
return self.estimator_.score(self.transform(X), y, **fit_params)
| Reduce X to the selected features and return the score of the estimator.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
y : array of shape [n_samples]
The target values.
**fit_params : dict
Parameters to pass to the `score` method of the underlying
estimator.
.. versionadded:: 1.0
Returns
-------
score : float
Score of the underlying base estimator computed with the selected
features returned by `rfe.transform(X)` and `y`.
| 72 | 10 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def score(self, X, y, **fit_params):
check_is_fitted(self)
return self.estimator_.score(self.transform(X), y, **fit_params)
```
###Assistant : Reduce X to the selected features and return the score of the estimator.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
y : array of shape [n_samples]
The target values.
**fit_params : dict
Parameters to pass to the `score` method of the underlying
estimator.
.. versionadded:: 1.0
Returns
-------
score : float
Score of the underlying base estimator computed with the selected
features returned by `rfe.transform(X)` and `y`.
|
1,816 | def reset(self, pos):
self.value = pos
self.velocity = 0
if self.history:
val = self.history[-1][1]
self.history = [(time(), val)]
| (internal) Reset the value and the velocity to the `pos`.
Mostly used when the bounds are checked.
| 17 | 18 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def reset(self, pos):
self.value = pos
self.velocity = 0
if self.history:
val = self.history[-1][1]
self.history = [(time(), val)]
```
###Assistant : (internal) Reset the value and the velocity to the `pos`.
Mostly used when the bounds are checked.
|
1,817 | 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),
],
) | Test that we can convert fan speed to percentage value. | 10 | 19 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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),
],
)
```
###Assistant : Test that we can convert fan speed to percentage value.
|
1,818 | def task_runner():
from sentry.testutils.helpers.task_runner import TaskRunner
return TaskRunner
@pytest.fixture | Context manager that ensures Celery tasks run directly inline where invoked.
While this context manager is active any Celery tasks created will run immediately at
the callsite rather than being sent to RabbitMQ and handled by a worker.
| 38 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def task_runner():
from sentry.testutils.helpers.task_runner import TaskRunner
return TaskRunner
@pytest.fixture
```
###Assistant : Context manager that ensures Celery tasks run directly inline where invoked.
While this context manager is active any Celery tasks created will run immediately at
the callsite rather than being sent to RabbitMQ and handled by a worker.
|
1,819 | def get_random_string(length, allowed_chars=RANDOM_STRING_CHARS):
return "".join(secrets.choice(allowed_chars) for i in range(length))
|
Return a securely generated random string.
The bit length of the returned value can be calculated with the formula:
log_2(len(allowed_chars)^length)
For example, with default `allowed_chars` (26+26+10), this gives:
* length: 12, bit length =~ 71 bits
* length: 22, bit length =~ 131 bits
| 44 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_random_string(length, allowed_chars=RANDOM_STRING_CHARS):
return "".join(secrets.choice(allowed_chars) for i in range(length))
```
###Assistant :
Return a securely generated random string.
The bit length of the returned value can be calculated with the formula:
log_2(len(allowed_chars)^length)
For example, with default `allowed_chars` (26+26+10), this gives:
* length: 12, bit length =~ 71 bits
* length: 22, bit length =~ 131 bits
|
1,820 | def test_command_reply(tplaybook):
tplaybook >> TEvent()
tplaybook << TCommand()
tplaybook >> tutils.reply()
assert tplaybook
assert tplaybook.actual[1] == tplaybook.actual[2].command
| CommandReplies can use relative offsets to point to the matching command. | 11 | 17 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_command_reply(tplaybook):
tplaybook >> TEvent()
tplaybook << TCommand()
tplaybook >> tutils.reply()
assert tplaybook
assert tplaybook.actual[1] == tplaybook.actual[2].command
```
###Assistant : CommandReplies can use relative offsets to point to the matching command.
|
1,821 | 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)
| Return a CrossDeviceOps based on all_reduce_alg and num_packs.
Args:
all_reduce_alg: a string specifying which cross device op to pick, or None.
num_packs: an integer specifying number of packs for the cross device op.
Returns:
tf.distribute.CrossDeviceOps object or None.
Raises:
ValueError: if `all_reduce_alg` not in [None, "nccl", "hierarchical_copy"].
| 47 | 47 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant : Return a CrossDeviceOps based on all_reduce_alg and num_packs.
Args:
all_reduce_alg: a string specifying which cross device op to pick, or None.
num_packs: an integer specifying number of packs for the cross device op.
Returns:
tf.distribute.CrossDeviceOps object or None.
Raises:
ValueError: if `all_reduce_alg` not in [None, "nccl", "hierarchical_copy"].
|
1,822 | 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)
|
Test Delete queue functionality by passing queue name, assert the function with values,
mock the azure service bus function `delete_queue`
| 20 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant :
Test Delete queue functionality by passing queue name, assert the function with values,
mock the azure service bus function `delete_queue`
|
1,823 | 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
| Return the message's content-disposition if it exists, or None.
The return values can be either 'inline', 'attachment' or None
according to the rfc2183.
| 23 | 25 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Return the message's content-disposition if it exists, or None.
The return values can be either 'inline', 'attachment' or None
according to the rfc2183.
|
1,824 | 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
|
Get the Aesara variable for a SymPy symbol from the cache, or create it
if it does not exist.
| 19 | 51 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Get the Aesara variable for a SymPy symbol from the cache, or create it
if it does not exist.
|
1,825 | 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
|
Defer the loading of data for certain fields until they are accessed.
Add the set of deferred fields to any existing set of deferred fields.
The only exception to this is if None is passed in as the only
parameter, in which case removal all deferrals.
| 46 | 29 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Defer the loading of data for certain fields until they are accessed.
Add the set of deferred fields to any existing set of deferred fields.
The only exception to this is if None is passed in as the only
parameter, in which case removal all deferrals.
|
1,826 | def match_seq(self, nodes, results=None):
if len(nodes) != 1:
return False
return self.match(nodes[0], results)
|
Does this pattern exactly match a sequence of nodes?
Default implementation for non-wildcard patterns.
| 14 | 13 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def match_seq(self, nodes, results=None):
if len(nodes) != 1:
return False
return self.match(nodes[0], results)
```
###Assistant :
Does this pattern exactly match a sequence of nodes?
Default implementation for non-wildcard patterns.
|
1,827 | def team_ids_with_membership(self) -> FrozenSet[int]:
return frozenset(team.id for team in self._team_memberships.keys())
| Return the IDs of teams in which the user has actual membership.
This represents the set of all teams for which `has_team_membership` returns
true. Use that method where possible and use this property only when you need
to iterate or query for all such teams.
Compare to accessible_team_ids, which is equal to this property in the
typical case but represents a superset of IDs in case of superuser access.
| 69 | 10 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def team_ids_with_membership(self) -> FrozenSet[int]:
return frozenset(team.id for team in self._team_memberships.keys())
```
###Assistant : Return the IDs of teams in which the user has actual membership.
This represents the set of all teams for which `has_team_membership` returns
true. Use that method where possible and use this property only when you need
to iterate or query for all such teams.
Compare to accessible_team_ids, which is equal to this property in the
typical case but represents a superset of IDs in case of superuser access.
|
1,828 | 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)
| Tests the GumbelSoftmax ActionDistribution (tf + eager only). | 8 | 99 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant : Tests the GumbelSoftmax ActionDistribution (tf + eager only).
|
1,829 | 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
|
Calcute the confusion matrix by given label and pred
:param gt_label: the ground truth label
:param pred_label: the pred label
:param num_classes: the nunber of class
:return: the confusion matrix
| 30 | 43 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Calcute the confusion matrix by given label and pred
:param gt_label: the ground truth label
:param pred_label: the pred label
:param num_classes: the nunber of class
:return: the confusion matrix
|
1,830 | 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
| Make mask tensor containing indices of padded part.
Args:
lengths (LongTensor or List): Batch of lengths (B,).
xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor.
length_dim (int, optional): Dimension indicator of the above tensor. See the example.
Returns:
Tensor: Mask tensor containing indices of padded part.
dtype=torch.uint8 in PyTorch 1.2-
dtype=torch.bool in PyTorch 1.2+ (including 1.2)
Examples:
With only lengths.
>>> lengths = [5, 3, 2]
>>> make_non_pad_mask(lengths)
masks = [[0, 0, 0, 0 ,0],
[0, 0, 0, 1, 1],
[0, 0, 1, 1, 1]]
With the reference tensor.
>>> xs = torch.zeros((3, 2, 4))
>>> make_pad_mask(lengths, xs)
tensor([[[0, 0, 0, 0],
[0, 0, 0, 0]],
[[0, 0, 0, 1],
[0, 0, 0, 1]],
[[0, 0, 1, 1],
[0, 0, 1, 1]]], dtype=torch.uint8)
>>> xs = torch.zeros((3, 2, 6))
>>> make_pad_mask(lengths, xs)
tensor([[[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]],
[[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
With the reference tensor and dimension indicator.
>>> xs = torch.zeros((3, 6, 6))
>>> make_pad_mask(lengths, xs, 1)
tensor([[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]]], dtype=torch.uint8)
>>> make_pad_mask(lengths, xs, 2)
tensor([[[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]],
[[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
| 417 | 103 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Make mask tensor containing indices of padded part.
Args:
lengths (LongTensor or List): Batch of lengths (B,).
xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor.
length_dim (int, optional): Dimension indicator of the above tensor. See the example.
Returns:
Tensor: Mask tensor containing indices of padded part.
dtype=torch.uint8 in PyTorch 1.2-
dtype=torch.bool in PyTorch 1.2+ (including 1.2)
Examples:
With only lengths.
>>> lengths = [5, 3, 2]
>>> make_non_pad_mask(lengths)
masks = [[0, 0, 0, 0 ,0],
[0, 0, 0, 1, 1],
[0, 0, 1, 1, 1]]
With the reference tensor.
>>> xs = torch.zeros((3, 2, 4))
>>> make_pad_mask(lengths, xs)
tensor([[[0, 0, 0, 0],
[0, 0, 0, 0]],
[[0, 0, 0, 1],
[0, 0, 0, 1]],
[[0, 0, 1, 1],
[0, 0, 1, 1]]], dtype=torch.uint8)
>>> xs = torch.zeros((3, 2, 6))
>>> make_pad_mask(lengths, xs)
tensor([[[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]],
[[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
With the reference tensor and dimension indicator.
>>> xs = torch.zeros((3, 6, 6))
>>> make_pad_mask(lengths, xs, 1)
tensor([[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]]], dtype=torch.uint8)
>>> make_pad_mask(lengths, xs, 2)
tensor([[[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]],
[[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
|
1,831 | 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. |
Test coverage for files without content-type in the metadata;
in which case we try to guess the content-type from the filename.
| 21 | 58 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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.
```
###Assistant :
Test coverage for files without content-type in the metadata;
in which case we try to guess the content-type from the filename.
|
1,832 | 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)
|
Return an archive member matching the request in name.
Name is the library name without any prefix like lib, suffix like .so,
or version number.
Given a list of members find and return the most appropriate result
Priority is given to generic libXXX.so, then a versioned libXXX.so.a.b.c
and finally, legacy AIX naming scheme.
| 53 | 85 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant :
Return an archive member matching the request in name.
Name is the library name without any prefix like lib, suffix like .so,
or version number.
Given a list of members find and return the most appropriate result
Priority is given to generic libXXX.so, then a versioned libXXX.so.a.b.c
and finally, legacy AIX naming scheme.
|
1,833 | def check_original_docker_image():
if not os.path.isfile('/.dockerenv') or os.environ.get('PYTHON_BASE_IMAGE') is None:
raise pytest.skip(
)
| Adding/removing a user as part of a test is very bad for host os
(especially if the user already existed to begin with on the OS), therefore we check if we run inside a
the official docker container and only allow to run the test there. This is done by checking /.dockerenv
file (always present inside container) and checking for PYTHON_BASE_IMAGE variable.
| 62 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def check_original_docker_image():
if not os.path.isfile('/.dockerenv') or os.environ.get('PYTHON_BASE_IMAGE') is None:
raise pytest.skip(
)
```
###Assistant : Adding/removing a user as part of a test is very bad for host os
(especially if the user already existed to begin with on the OS), therefore we check if we run inside a
the official docker container and only allow to run the test there. This is done by checking /.dockerenv
file (always present inside container) and checking for PYTHON_BASE_IMAGE variable.
|
1,834 | 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
|
Presents simple wizard interface for beginner users
| 7 | 253 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Presents simple wizard interface for beginner users
|
1,835 | 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]
|
Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
| 21 | 63 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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]
```
###Assistant :
Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
|
1,836 | 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 | select mode_of_payment from `tabPayment Order Reference`
where parent = %(parent)s and mode_of_payment like %(txt)s
limit %(start)s, %(page_len)s | 17 | 23 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : select mode_of_payment from `tabPayment Order Reference`
where parent = %(parent)s and mode_of_payment like %(txt)s
limit %(start)s, %(page_len)s
|
1,837 | 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}")
| Test that pyplot_summary lists all the plot functions. | 8 | 88 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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}")
```
###Assistant : Test that pyplot_summary lists all the plot functions.
|
1,838 | 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
)
)
| Check if partially transacted item (row) is being deleted. | 9 | 107 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
)
)
```
###Assistant : Check if partially transacted item (row) is being deleted.
|
1,839 | 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
| Check the passed in command line arguments for requested actions,
For any requested actions, add the item to the actions list along with
any relevant arguments and keyword arguments.
Returns
-------
dict
The name of the action to be performed as the key. Any action specific
arguments and keyword arguments as the value.
| 53 | 108 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Check the passed in command line arguments for requested actions,
For any requested actions, add the item to the actions list along with
any relevant arguments and keyword arguments.
Returns
-------
dict
The name of the action to be performed as the key. Any action specific
arguments and keyword arguments as the value.
|
1,840 | 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
| Loops through the addresses, collapsing concurrent netblocks.
Example:
ip1 = IPv4Network('192.0.2.0/26')
ip2 = IPv4Network('192.0.2.64/26')
ip3 = IPv4Network('192.0.2.128/26')
ip4 = IPv4Network('192.0.2.192/26')
_collapse_addresses_internal([ip1, ip2, ip3, ip4]) ->
[IPv4Network('192.0.2.0/24')]
This shouldn't be called directly; it is called via
collapse_addresses([]).
Args:
addresses: A list of IPv4Network's or IPv6Network's
Returns:
A list of IPv4Network's or IPv6Network's depending on what we were
passed.
| 57 | 83 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Loops through the addresses, collapsing concurrent netblocks.
Example:
ip1 = IPv4Network('192.0.2.0/26')
ip2 = IPv4Network('192.0.2.64/26')
ip3 = IPv4Network('192.0.2.128/26')
ip4 = IPv4Network('192.0.2.192/26')
_collapse_addresses_internal([ip1, ip2, ip3, ip4]) ->
[IPv4Network('192.0.2.0/24')]
This shouldn't be called directly; it is called via
collapse_addresses([]).
Args:
addresses: A list of IPv4Network's or IPv6Network's
Returns:
A list of IPv4Network's or IPv6Network's depending on what we were
passed.
|
1,841 | 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
|
Return the topmost visible `~.axes.Axes` containing the point *xy*.
Parameters
----------
xy : (float, float)
(x, y) pixel positions from left/bottom of the canvas.
Returns
-------
`~matplotlib.axes.Axes` or None
The topmost visible Axes containing the point, or None if there
is no Axes at the point.
| 46 | 25 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Return the topmost visible `~.axes.Axes` containing the point *xy*.
Parameters
----------
xy : (float, float)
(x, y) pixel positions from left/bottom of the canvas.
Returns
-------
`~matplotlib.axes.Axes` or None
The topmost visible Axes containing the point, or None if there
is no Axes at the point.
|
1,842 | 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")
| Apply Add ``scheduling_decision`` to ``DagRun`` and ``DAG``
UPDATE dag SET
concurrency={concurrency},
has_task_concurrency_limits={1 if is_sqlite or is_mssql else sa.true()}
where concurrency IS NULL
| 22 | 135 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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")
```
###Assistant : Apply Add ``scheduling_decision`` to ``DagRun`` and ``DAG``
UPDATE dag SET
concurrency={concurrency},
has_task_concurrency_limits={1 if is_sqlite or is_mssql else sa.true()}
where concurrency IS NULL
|
1,843 | 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
|
Enriches a list of ledger entries with event metadata (applies only to decrements that
have an event_id property set, i.e. automated decrements to the ledger applied by Orb).
| 28 | 50 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Enriches a list of ledger entries with event metadata (applies only to decrements that
have an event_id property set, i.e. automated decrements to the ledger applied by Orb).
|
1,844 | 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?"
| Ensure the most up to date RDP-to-epsilon cache is being used. | 11 | 81 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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?"
```
###Assistant : Ensure the most up to date RDP-to-epsilon cache is being used.
|
1,845 | 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)
| Get a lexer for a mimetype.
Raises ClassNotFound if not found.
| 11 | 42 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant : Get a lexer for a mimetype.
Raises ClassNotFound if not found.
|
1,846 | 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]) | Test that GLM converges for all solvers to correct solution.
We work with a simple constructed data set with known solution.
| 21 | 127 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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])
```
###Assistant : Test that GLM converges for all solvers to correct solution.
We work with a simple constructed data set with known solution.
|
1,847 | 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}")()
| Obtain the location and version of Cuda and populate :attr:`cuda_version` and
:attr:`cuda_path`
Initially just calls `nvcc -V` to get the installed version of Cuda currently in use.
If this fails, drills down to more OS specific checking methods.
| 38 | 81 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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}")()
```
###Assistant : Obtain the location and version of Cuda and populate :attr:`cuda_version` and
:attr:`cuda_path`
Initially just calls `nvcc -V` to get the installed version of Cuda currently in use.
If this fails, drills down to more OS specific checking methods.
|
1,848 | def test_map_product_same(self, dag_maker, session):
outputs = []
with dag_maker(dag_id="product_same", session=session) as dag:
| Test a mapped task can refer to the same source multiple times. | 12 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_map_product_same(self, dag_maker, session):
outputs = []
with dag_maker(dag_id="product_same", session=session) as dag:
```
###Assistant : Test a mapped task can refer to the same source multiple times.
|
1,849 | 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
|
This method is for running only one image at a time for simple use. Used to the img2img interrogate.
| 19 | 24 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
This method is for running only one image at a time for simple use. Used to the img2img interrogate.
|
1,850 | 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'))
|
update
pg.table2
set
a1 = df.a,
c1 = df.c
from
(
SELECT model.a as a, model.b as b, model.p as c
FROM pg.tasks as t
JOIN mindsdb.task_model as model
WHERE t.a=1
)
as df
where
table2.a1 = df.a
and table2.b1 = df.b
# SELECT a, b FROM pg.tasks
# UNION
# SELECT b, a FROM pg.tasks
# | 57 | 101 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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'))
```
###Assistant :
update
pg.table2
set
a1 = df.a,
c1 = df.c
from
(
SELECT model.a as a, model.b as b, model.p as c
FROM pg.tasks as t
JOIN mindsdb.task_model as model
WHERE t.a=1
)
as df
where
table2.a1 = df.a
and table2.b1 = df.b
# SELECT a, b FROM pg.tasks
# UNION
# SELECT b, a FROM pg.tasks
#
|
1,851 | 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
| Gets the total number of ordered shipments to arrive in the period (based on Purchase Receipts)
SELECT
COUNT(po_item.base_amount)
FROM
`tabPurchase Order Item` po_item,
`tabPurchase Order` po
WHERE
po.supplier = %(supplier)s
AND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s
AND po_item.docstatus = 1
AND po_item.parent = po.name | 44 | 38 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Gets the total number of ordered shipments to arrive in the period (based on Purchase Receipts)
SELECT
COUNT(po_item.base_amount)
FROM
`tabPurchase Order Item` po_item,
`tabPurchase Order` po
WHERE
po.supplier = %(supplier)s
AND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s
AND po_item.docstatus = 1
AND po_item.parent = po.name
|
1,852 | def source(object):
print('In file: %s' % inspect.getsourcefile(object))
print(inspect.getsource(object))
|
Prints the source code of a given object.
.. deprecated:: 1.3
The ``source()`` function is deprecated. Use ``inspect.getsource()`` or
``??`` in IPython/Jupyter instead.
| 23 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def source(object):
print('In file: %s' % inspect.getsourcefile(object))
print(inspect.getsource(object))
```
###Assistant :
Prints the source code of a given object.
.. deprecated:: 1.3
The ``source()`` function is deprecated. Use ``inspect.getsource()`` or
``??`` in IPython/Jupyter instead.
|
1,853 | 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
| Validate an axis value and returns its standardized form.
Args:
axis: Value to validate. Can be an integer or a list/tuple of integers.
Integers may be negative.
input_shape: Reference input shape that the axis/axes refer to.
Returns:
Normalized form of `axis`, i.e. a list with all-positive values.
| 47 | 98 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Validate an axis value and returns its standardized form.
Args:
axis: Value to validate. Can be an integer or a list/tuple of integers.
Integers may be negative.
input_shape: Reference input shape that the axis/axes refer to.
Returns:
Normalized form of `axis`, i.e. a list with all-positive values.
|
1,854 | 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
| Keep only the tokens that match `pred`, merge the others together | 11 | 47 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Keep only the tokens that match `pred`, merge the others together
|
1,855 | def entity_registry_enabled_default(self) -> bool:
return bool(self._config[CONF_ENABLED_BY_DEFAULT])
| Return if the entity should be enabled when first added to the entity registry. | 14 | 6 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def entity_registry_enabled_default(self) -> bool:
return bool(self._config[CONF_ENABLED_BY_DEFAULT])
```
###Assistant : Return if the entity should be enabled when first added to the entity registry.
|
1,856 | 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}
| The angle bisectors of the triangle.
An angle bisector of a triangle is a straight line through a vertex
which cuts the corresponding angle in half.
Returns
=======
bisectors : dict
Each key is a vertex (Point) and each value is the corresponding
bisector (Segment).
See Also
========
sympy.geometry.point.Point, sympy.geometry.line.Segment
Examples
========
>>> from sympy import Point, Triangle, Segment
>>> p1, p2, p3 = Point(0, 0), Point(1, 0), Point(0, 1)
>>> t = Triangle(p1, p2, p3)
>>> from sympy import sqrt
>>> t.bisectors()[p2] == Segment(Point(1, 0), Point(0, sqrt(2) - 1))
True
| 91 | 62 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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}
```
###Assistant : The angle bisectors of the triangle.
An angle bisector of a triangle is a straight line through a vertex
which cuts the corresponding angle in half.
Returns
=======
bisectors : dict
Each key is a vertex (Point) and each value is the corresponding
bisector (Segment).
See Also
========
sympy.geometry.point.Point, sympy.geometry.line.Segment
Examples
========
>>> from sympy import Point, Triangle, Segment
>>> p1, p2, p3 = Point(0, 0), Point(1, 0), Point(0, 1)
>>> t = Triangle(p1, p2, p3)
>>> from sympy import sqrt
>>> t.bisectors()[p2] == Segment(Point(1, 0), Point(0, sqrt(2) - 1))
True
|
1,857 | 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
|
Send a query to the REST API and parse the answer.
Returns both a ready-to-use representation of the results and the raw JSON.
| 23 | 124 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Send a query to the REST API and parse the answer.
Returns both a ready-to-use representation of the results and the raw JSON.
|
1,858 | def format_target_temperature(target_temperature):
return str(round(float(target_temperature) * 2, 0) / 2).rstrip("0").rstrip(".")
| Format target temperature to be sent to the Daikin unit, rounding to nearest half degree. | 15 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def format_target_temperature(target_temperature):
return str(round(float(target_temperature) * 2, 0) / 2).rstrip("0").rstrip(".")
```
###Assistant : Format target temperature to be sent to the Daikin unit, rounding to nearest half degree.
|
1,859 | 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
| Returns the list of dictionaries. Each dictionary is a row in the datatable and chart data. | 16 | 16 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Returns the list of dictionaries. Each dictionary is a row in the datatable and chart data.
|
1,860 | def dict(self, *args, **kwargs):
kwargs.setdefault("exclude_none", True)
return super().dict(*args, **kwargs)
| Exclude `None` fields by default to comply with
the OpenAPI spec.
| 11 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def dict(self, *args, **kwargs):
kwargs.setdefault("exclude_none", True)
return super().dict(*args, **kwargs)
```
###Assistant : Exclude `None` fields by default to comply with
the OpenAPI spec.
|
1,861 | 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
|
Generate a mapping of JSON properties to normalized SQL Column names, handling collisions and avoid duplicate names
The mapped value to a field property is a tuple where:
- the first value is the normalized "raw" column name
- the second value is the normalized quoted column name to be used in jinja context
| 54 | 83 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Generate a mapping of JSON properties to normalized SQL Column names, handling collisions and avoid duplicate names
The mapped value to a field property is a tuple where:
- the first value is the normalized "raw" column name
- the second value is the normalized quoted column name to be used in jinja context
|
1,862 | 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
| Compute all simple paths to target, given the predecessors found in
pred, terminating when any source in sources is found.
Parameters
----------
sources : set
Starting nodes for path.
target : node
Ending node for path.
pred : dict
A dictionary of predecessor lists, keyed by node
Returns
-------
paths : generator of lists
A generator of all paths between source and target.
Raises
------
NetworkXNoPath
If `target` cannot be reached from `source`.
Notes
-----
There may be many paths between the sources and target. If there are
cycles among the predecessors, this function will not produce all
possible paths because doing so would produce infinitely many paths
of unbounded length -- instead, we only produce simple paths.
See Also
--------
shortest_path
single_source_shortest_path
all_pairs_shortest_path
all_shortest_paths
bellman_ford_path
| 126 | 88 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Compute all simple paths to target, given the predecessors found in
pred, terminating when any source in sources is found.
Parameters
----------
sources : set
Starting nodes for path.
target : node
Ending node for path.
pred : dict
A dictionary of predecessor lists, keyed by node
Returns
-------
paths : generator of lists
A generator of all paths between source and target.
Raises
------
NetworkXNoPath
If `target` cannot be reached from `source`.
Notes
-----
There may be many paths between the sources and target. If there are
cycles among the predecessors, this function will not produce all
possible paths because doing so would produce infinitely many paths
of unbounded length -- instead, we only produce simple paths.
See Also
--------
shortest_path
single_source_shortest_path
all_pairs_shortest_path
all_shortest_paths
bellman_ford_path
|
1,863 | def is_connected(self) -> bool:
return self._backend is not None and self._backend.is_connected
| Return True if the client is connected to a device. | 10 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def is_connected(self) -> bool:
return self._backend is not None and self._backend.is_connected
```
###Assistant : Return True if the client is connected to a device.
|
1,864 | 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)
| Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence
for name.
| 8 | 42 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant : Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence
for name.
|
1,865 | 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()
| Generates the Genocchi polynomial `\operatorname{G}_n(x)`.
`\operatorname{G}_n(x)` is twice the difference between the plain and
central Bernoulli polynomials, so has degree `n-1`:
.. math :: \operatorname{G}_n(x) = 2 (\operatorname{B}_n(x) -
\operatorname{B}_n^c(x))
The factor of 2 in the definition endows `\operatorname{G}_n(x)` with
integer coefficients.
Parameters
==========
n : int
Degree of the polynomial plus one.
x : optional
polys : bool, optional
If True, return a Poly, otherwise (default) return an expression.
| 70 | 43 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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()
```
###Assistant : Generates the Genocchi polynomial `\operatorname{G}_n(x)`.
`\operatorname{G}_n(x)` is twice the difference between the plain and
central Bernoulli polynomials, so has degree `n-1`:
.. math :: \operatorname{G}_n(x) = 2 (\operatorname{B}_n(x) -
\operatorname{B}_n^c(x))
The factor of 2 in the definition endows `\operatorname{G}_n(x)` with
integer coefficients.
Parameters
==========
n : int
Degree of the polynomial plus one.
x : optional
polys : bool, optional
If True, return a Poly, otherwise (default) return an expression.
|
1,866 | 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
|
Parses window and nperseg arguments for spectrogram and _spectral_helper.
This is a helper function, not meant to be called externally.
Parameters
----------
window : string, tuple, or ndarray
If window is specified by a string or tuple and nperseg is not
specified, nperseg is set to the default of 256 and returns a window of
that length.
If instead the window is array_like and nperseg is not specified, then
nperseg is set to the length of the window. A ValueError is raised if
the user supplies both an array_like window and a value for nperseg but
nperseg does not equal the length of the window.
nperseg : int
Length of each segment
input_length: int
Length of input signal, i.e. x.shape[-1]. Used to test for errors.
Returns
-------
win : ndarray
window. If function was called with string or tuple than this will hold
the actual array used as a window.
nperseg : int
Length of each segment. If window is str or tuple, nperseg is set to
256. If window is array_like, nperseg is set to the length of the
6
window.
| 182 | 118 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Parses window and nperseg arguments for spectrogram and _spectral_helper.
This is a helper function, not meant to be called externally.
Parameters
----------
window : string, tuple, or ndarray
If window is specified by a string or tuple and nperseg is not
specified, nperseg is set to the default of 256 and returns a window of
that length.
If instead the window is array_like and nperseg is not specified, then
nperseg is set to the length of the window. A ValueError is raised if
the user supplies both an array_like window and a value for nperseg but
nperseg does not equal the length of the window.
nperseg : int
Length of each segment
input_length: int
Length of input signal, i.e. x.shape[-1]. Used to test for errors.
Returns
-------
win : ndarray
window. If function was called with string or tuple than this will hold
the actual array used as a window.
nperseg : int
Length of each segment. If window is str or tuple, nperseg is set to
256. If window is array_like, nperseg is set to the length of the
6
window.
|
1,867 | 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"
)
|
Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.
Args:
auto_class (`str` or `type`, *optional*, defaults to `"AutoModel"`):
The auto class to register this new model with.
| 47 | 57 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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"
)
```
###Assistant :
Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.
Args:
auto_class (`str` or `type`, *optional*, defaults to `"AutoModel"`):
The auto class to register this new model with.
|
1,868 | 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)),
]
)
|
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install
inflect`
{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip
install onnxruntime`
{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install
scipy`
{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
install transformers`
{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install
Unidecode`
| 197 | 66 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)),
]
)
```
###Assistant :
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install
inflect`
{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip
install onnxruntime`
{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install
scipy`
{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
install transformers`
{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install
Unidecode`
|
1,869 | def set_horizontalalignment(self, align):
_api.check_in_list(['center', 'right', 'left'], align=align)
self._horizontalalignment = align
self.stale = True
|
Set the horizontal alignment relative to the anchor point.
See also :doc:`/gallery/text_labels_and_annotations/text_alignment`.
Parameters
----------
align : {'left', 'center', 'right'}
| 19 | 13 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def set_horizontalalignment(self, align):
_api.check_in_list(['center', 'right', 'left'], align=align)
self._horizontalalignment = align
self.stale = True
```
###Assistant :
Set the horizontal alignment relative to the anchor point.
See also :doc:`/gallery/text_labels_and_annotations/text_alignment`.
Parameters
----------
align : {'left', 'center', 'right'}
|
1,870 | def _wrap_awaitable(awaitable):
return (yield from awaitable.__await__())
_wrap_awaitable._is_coroutine = _is_coroutine
| Helper for asyncio.ensure_future().
Wraps awaitable (an object with __await__) into a coroutine
that will later be wrapped in a Task by ensure_future().
| 22 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _wrap_awaitable(awaitable):
return (yield from awaitable.__await__())
_wrap_awaitable._is_coroutine = _is_coroutine
```
###Assistant : Helper for asyncio.ensure_future().
Wraps awaitable (an object with __await__) into a coroutine
that will later be wrapped in a Task by ensure_future().
|
1,871 | 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
|
Normalizes the file path to use the POSIX path separator (i.e., ``'/'``).
*file* (:class:`str` or :class:`pathlib.PurePath`) is the file path.
*separators* (:class:`~collections.abc.Collection` of :class:`str`; or
:data:`None`) optionally contains the path separators to normalize.
This does not need to include the POSIX path separator (``'/'``), but
including it will not affect the results. Default is :data:`None` for
:data:`NORMALIZE_PATH_SEPS`. To prevent normalization, pass an empty
container (e.g., an empty tuple ``()``).
Returns the normalized file path (:class:`str`).
| 75 | 43 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Normalizes the file path to use the POSIX path separator (i.e., ``'/'``).
*file* (:class:`str` or :class:`pathlib.PurePath`) is the file path.
*separators* (:class:`~collections.abc.Collection` of :class:`str`; or
:data:`None`) optionally contains the path separators to normalize.
This does not need to include the POSIX path separator (``'/'``), but
including it will not affect the results. Default is :data:`None` for
:data:`NORMALIZE_PATH_SEPS`. To prevent normalization, pass an empty
container (e.g., an empty tuple ``()``).
Returns the normalized file path (:class:`str`).
|
1,872 | def wildcard_types(self) -> List[str]:
return [t for t, state_keys in self.types.items() if state_keys is None]
| Returns a list of event types which require us to fetch all state keys.
This will be empty unless `has_wildcards` returns True.
Returns:
A list of event types.
| 28 | 15 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def wildcard_types(self) -> List[str]:
return [t for t, state_keys in self.types.items() if state_keys is None]
```
###Assistant : Returns a list of event types which require us to fetch all state keys.
This will be empty unless `has_wildcards` returns True.
Returns:
A list of event types.
|
1,873 | def get_columns(self, table_name) -> Response:
q = f"SHOW COLUMNS IN TABLE {table_name};"
result = self.native_query(q)
return result
|
List the columns in the tabels for which the user have access
| 12 | 17 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_columns(self, table_name) -> Response:
q = f"SHOW COLUMNS IN TABLE {table_name};"
result = self.native_query(q)
return result
```
###Assistant :
List the columns in the tabels for which the user have access
|
1,874 | 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
| Gets the number of late shipments (counting each item) in the period (based on Purchase Receipts vs POs)
SELECT
COUNT(pr_item.qty)
FROM
`tabPurchase Order Item` po_item,
`tabPurchase Receipt Item` pr_item,
`tabPurchase Order` po,
`tabPurchase Receipt` pr
WHERE
po.supplier = %(supplier)s
AND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s
AND po_item.schedule_date <= pr.posting_date
AND po_item.qty = pr_item.qty
AND pr_item.docstatus = 1
AND pr_item.purchase_order_item = po_item.name
AND po_item.parent = po.name
AND pr_item.parent = pr.name | 69 | 38 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Gets the number of late shipments (counting each item) in the period (based on Purchase Receipts vs POs)
SELECT
COUNT(pr_item.qty)
FROM
`tabPurchase Order Item` po_item,
`tabPurchase Receipt Item` pr_item,
`tabPurchase Order` po,
`tabPurchase Receipt` pr
WHERE
po.supplier = %(supplier)s
AND po_item.schedule_date BETWEEN %(start_date)s AND %(end_date)s
AND po_item.schedule_date <= pr.posting_date
AND po_item.qty = pr_item.qty
AND pr_item.docstatus = 1
AND pr_item.purchase_order_item = po_item.name
AND po_item.parent = po.name
AND pr_item.parent = pr.name
|
1,875 | def test_commit_comment_deleted(self) -> None:
expected_message =
self.check_webhook("commit_comment_deleted", TOPIC, expected_message)
| [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~~~ | 17 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_commit_comment_deleted(self) -> None:
expected_message =
self.check_webhook("commit_comment_deleted", TOPIC, expected_message)
```
###Assistant : [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~~~
|
1,876 | 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
|
Loads the work queue objects corresponding to the agent's target work queues. If any of them don't exist, they are created.
| 21 | 42 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Loads the work queue objects corresponding to the agent's target work queues. If any of them don't exist, they are created.
|
1,877 | 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
| Tests that attempting to sync a stream which the source does not return from the `streams` method raises an exception | 20 | 26 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Tests that attempting to sync a stream which the source does not return from the `streams` method raises an exception
|
1,878 | 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
|
Determine whether a link URL from a referring page and with a
particular "rel" attribute should be queued for scraping.
| 20 | 89 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Determine whether a link URL from a referring page and with a
particular "rel" attribute should be queued for scraping.
|
1,879 | 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
| Receive a command from Training Service.
Returns a tuple of command (CommandType) and payload (str)
| 15 | 48 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Receive a command from Training Service.
Returns a tuple of command (CommandType) and payload (str)
|
1,880 | def bettertitle(value):
return ' '.join([w[0].upper() + w[1:] for w in value.split()])
@register.filter() |
Alternative to the builtin title(). Ensures that the first letter of each word is uppercase but retains the
original case of all others.
| 23 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def bettertitle(value):
return ' '.join([w[0].upper() + w[1:] for w in value.split()])
@register.filter()
```
###Assistant :
Alternative to the builtin title(). Ensures that the first letter of each word is uppercase but retains the
original case of all others.
|
1,881 | 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)
|
This code is borrowed from Ross Girshick's FAST-RCNN code
(https://github.com/rbgirshick/fast-rcnn).
It parses the PASCAL .xml metadata files.
See publication for further details: (http://arxiv.org/abs/1504.08083).
Thanks Ross!
| 25 | 45 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant :
This code is borrowed from Ross Girshick's FAST-RCNN code
(https://github.com/rbgirshick/fast-rcnn).
It parses the PASCAL .xml metadata files.
See publication for further details: (http://arxiv.org/abs/1504.08083).
Thanks Ross!
|
1,882 | 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
|
Get the object wrapped by this partition out of the object store.
Returns
-------
pandas.DataFrame
The object from the object store.
| 21 | 15 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get(self):
logger = get_logger()
logger.debug(f"ENTER::Partition.get::{self._identity}")
if len(self.call_queue):
self.drain_call_queue()
result = UnidistWrapper.materialize(self._data)
logger.debug(f"EXIT::Partition.get::{self._identity}")
return result
```
###Assistant :
Get the object wrapped by this partition out of the object store.
Returns
-------
pandas.DataFrame
The object from the object store.
|
1,883 | 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")
| A custom action may returns an HttpResponse with a 403 code. | 11 | 27 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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")
```
###Assistant : A custom action may returns an HttpResponse with a 403 code.
|
1,884 | def limit(self, *args):
return self.applyfunc(lambda x: x.limit(*args))
# https://github.com/sympy/sympy/pull/12854 | Calculate the limit of each element in the matrix.
``args`` will be passed to the ``limit`` function.
Examples
========
>>> from sympy import Matrix
>>> from sympy.abc import x, y
>>> M = Matrix([[x, y], [1, 0]])
>>> M.limit(x, 2)
Matrix([
[2, y],
[1, 0]])
See Also
========
integrate
diff
| 50 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def limit(self, *args):
return self.applyfunc(lambda x: x.limit(*args))
# https://github.com/sympy/sympy/pull/12854
```
###Assistant : Calculate the limit of each element in the matrix.
``args`` will be passed to the ``limit`` function.
Examples
========
>>> from sympy import Matrix
>>> from sympy.abc import x, y
>>> M = Matrix([[x, y], [1, 0]])
>>> M.limit(x, 2)
Matrix([
[2, y],
[1, 0]])
See Also
========
integrate
diff
|
1,885 | 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
| Make sure all sensor device classes are also available in NumberDeviceClass. | 11 | 26 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Make sure all sensor device classes are also available in NumberDeviceClass.
|
1,886 | def valid_tess_config(outdir):
cfg_file = outdir / 'test.cfg'
with cfg_file.open('w') as f:
f.write(
)
yield cfg_file
| \
load_system_dawg 0
language_model_penalty_non_dict_word 0
language_model_penalty_non_freq_dict_word 0
| 7 | 15 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def valid_tess_config(outdir):
cfg_file = outdir / 'test.cfg'
with cfg_file.open('w') as f:
f.write(
)
yield cfg_file
```
###Assistant : \
load_system_dawg 0
language_model_penalty_non_dict_word 0
language_model_penalty_non_freq_dict_word 0
|
1,887 | 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]
|
Return a two-elements list with the lower and upper bound to be used
with a BETWEEN operator to query a DateTimeField value using a year
lookup.
`value` is an int, containing the looked-up year.
If `iso_year` is True, return bounds for ISO-8601 week-numbering years.
| 44 | 59 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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]
```
###Assistant :
Return a two-elements list with the lower and upper bound to be used
with a BETWEEN operator to query a DateTimeField value using a year
lookup.
`value` is an int, containing the looked-up year.
If `iso_year` is True, return bounds for ISO-8601 week-numbering years.
|
1,888 | def gegenbauer_poly(n, a, x=None, polys=False):
r
return named_poly(n, dup_gegenbauer, None, "Gegenbauer polynomial", (x, a), polys)
| Generates the Gegenbauer polynomial `C_n^{(a)}(x)`.
Parameters
==========
n : int
Degree of the polynomial.
x : optional
a
Decides minimal domain for the list of coefficients.
polys : bool, optional
If True, return a Poly, otherwise (default) return an expression.
| 40 | 15 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def gegenbauer_poly(n, a, x=None, polys=False):
r
return named_poly(n, dup_gegenbauer, None, "Gegenbauer polynomial", (x, a), polys)
```
###Assistant : Generates the Gegenbauer polynomial `C_n^{(a)}(x)`.
Parameters
==========
n : int
Degree of the polynomial.
x : optional
a
Decides minimal domain for the list of coefficients.
polys : bool, optional
If True, return a Poly, otherwise (default) return an expression.
|
1,889 | 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
| Poll state of all entities attached to this bridge/accessory. | 9 | 68 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Poll state of all entities attached to this bridge/accessory.
|
1,890 | 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]
| Return an iterator of intervals to be fetched.
Args:
start (int): Start of needed interval
end (int): End of needed interval
left (int): Index of first overlapping downloaded data
right (int): Index after last overlapping downloaded data
| 37 | 58 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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]
```
###Assistant : Return an iterator of intervals to be fetched.
Args:
start (int): Start of needed interval
end (int): End of needed interval
left (int): Index of first overlapping downloaded data
right (int): Index after last overlapping downloaded data
|
1,891 | 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')
| Determine whether the given string is a Python identifier. | 9 | 17 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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')
```
###Assistant : Determine whether the given string is a Python identifier.
|
1,892 | 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)
| Return HTTP response to a range request from start to end. | 11 | 35 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant : Return HTTP response to a range request from start to end.
|
1,893 | 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)
| Register user JavaScript files with the current tab.
Args:
scripts: A list of GreasemonkeyScripts.
| 14 | 203 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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)
```
###Assistant : Register user JavaScript files with the current tab.
Args:
scripts: A list of GreasemonkeyScripts.
|
1,894 | 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
| dict: The configuration dictionary for current plugin, as set by the user's
configuration settings. | 14 | 26 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : dict: The configuration dictionary for current plugin, as set by the user's
configuration settings.
|
1,895 | 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")
|
UPDATE `tabStock Entry` SET
stock_entry_type = 'Material Transfer',
purpose = 'Material Transfer',
add_to_transit = 1 WHERE stock_entry_type = 'Send to Warehouse'
UPDATE `tabStock Entry` SET
stock_entry_type = 'Material Transfer',
purpose = 'Material Transfer'
WHERE stock_entry_type = 'Receive at Warehouse'
| 39 | 44 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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")
```
###Assistant :
UPDATE `tabStock Entry` SET
stock_entry_type = 'Material Transfer',
purpose = 'Material Transfer',
add_to_transit = 1 WHERE stock_entry_type = 'Send to Warehouse'
UPDATE `tabStock Entry` SET
stock_entry_type = 'Material Transfer',
purpose = 'Material Transfer'
WHERE stock_entry_type = 'Receive at Warehouse'
|
1,896 | 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),
)
| select name, subject from `tabTask`
where (`%s` like %s or `subject` like %s) %s
order by
case when `subject` like %s then 0 else 1 end,
case when `%s` like %s then 0 else 1 end,
`%s`,
subject
limit %s, %s | 41 | 53 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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),
)
```
###Assistant : select name, subject from `tabTask`
where (`%s` like %s or `subject` like %s) %s
order by
case when `subject` like %s then 0 else 1 end,
case when `%s` like %s then 0 else 1 end,
`%s`,
subject
limit %s, %s
|
1,897 | 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
| Parses a Policy name into a compute and variable dtype.
Args:
name: The name of the policy:
Returns:
The (compute_dtype, variable_dtype) pair.
| 22 | 242 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant : Parses a Policy name into a compute and variable dtype.
Args:
name: The name of the policy:
Returns:
The (compute_dtype, variable_dtype) pair.
|
1,898 | 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
|
Detect crashes during this context, wrapping unexpected exceptions into `Crash`
signals.
| 11 | 17 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Detect crashes during this context, wrapping unexpected exceptions into `Crash`
signals.
|
1,899 | 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
|
Use gsutil to get resources in bytes for memory and disk
| 11 | 60 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
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
```
###Assistant :
Use gsutil to get resources in bytes for memory and disk
|
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