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2,600 | def delete_project_summary_annotations_before_updating_annotation(sender, instance, **kwargs):
try:
old_annotation = sender.objects.get(id=instance.id)
except Annotation.DoesNotExist:
# annotation just created - do nothing
return
old_annotation.decrease_project_summary_counters()
# update task counters if annotation changes it's was_cancelled status
task = instance.task
if old_annotation.was_cancelled != instance.was_cancelled:
if instance.was_cancelled:
task.cancelled_annotations = task.cancelled_annotations + 1
task.total_annotations = task.total_annotations - 1
else:
task.cancelled_annotations = task.cancelled_annotations - 1
task.total_annotations = task.total_annotations + 1
task.update_is_labeled()
Task.objects.filter(id=instance.task.id).update(
is_labeled=task.is_labeled,
total_annotations=task.total_annotations,
cancelled_annotations=task.cancelled_annotations
)
@receiver(post_save, sender=Annotation) | Before updating annotation fields - ensure previous info removed from project.summary | 11 | 67 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def delete_project_summary_annotations_before_updating_annotation(sender, instance, **kwargs):
try:
old_annotation = sender.objects.get(id=instance.id)
except Annotation.DoesNotExist:
# annotation just created - do nothing
return
old_annotation.decrease_project_summary_counters()
# update task counters if annotation changes it's was_cancelled status
task = instance.task
if old_annotation.was_cancelled != instance.was_cancelled:
if instance.was_cancelled:
task.cancelled_annotations = task.cancelled_annotations + 1
task.total_annotations = task.total_annotations - 1
else:
task.cancelled_annotations = task.cancelled_annotations - 1
task.total_annotations = task.total_annotations + 1
task.update_is_labeled()
Task.objects.filter(id=instance.task.id).update(
is_labeled=task.is_labeled,
total_annotations=task.total_annotations,
cancelled_annotations=task.cancelled_annotations
)
@receiver(post_save, sender=Annotation)
```
###Assistant : Before updating annotation fields - ensure previous info removed from project.summary
|
2,601 | def load_config(self, modelpath, use_gpu, gpu_id, use_mkldnn, cpu_threads):
# 对运行位置进行配置
if use_gpu:
try:
int(os.environ.get('CUDA_VISIBLE_DEVICES'))
except Exception:
print(
)
use_gpu = False
if os.path.isdir(modelpath):
if os.path.exists(os.path.join(modelpath, "__params__")):
# __model__ + __params__
model = os.path.join(modelpath, "__model__")
params = os.path.join(modelpath, "__params__")
config = Config(model, params)
elif os.path.exists(os.path.join(modelpath, "params")):
# model + params
model = os.path.join(modelpath, "model")
params = os.path.join(modelpath, "params")
config = Config(model, params)
elif os.path.exists(os.path.join(modelpath, "__model__")):
# __model__ + others
config = Config(modelpath)
else:
raise Exception(
"Error! Can\'t find the model in: %s. Please check your model path." % os.path.abspath(modelpath))
elif os.path.exists(modelpath + ".pdmodel"):
# *.pdmodel + *.pdiparams
model = modelpath + ".pdmodel"
params = modelpath + ".pdiparams"
config = Config(model, params)
elif isinstance(modelpath, Config):
config = modelpath
else:
raise Exception(
"Error! Can\'t find the model in: %s. Please check your model path." % os.path.abspath(modelpath))
# 设置参数
if use_gpu:
config.enable_use_gpu(100, gpu_id)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(cpu_threads)
if use_mkldnn:
config.enable_mkldnn()
config.disable_glog_info()
# 返回配置
return config
# 预测器创建函数
|
load the model config
modelpath: inference model path
use_gpu: use gpu or not
use_mkldnn: use mkldnn or not
Error! Unable to use GPU. Please set the environment variables "CUDA_VISIBLE_DEVICES=GPU_id" to use GPU. Now switch to CPU to continue... | 38 | 151 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def load_config(self, modelpath, use_gpu, gpu_id, use_mkldnn, cpu_threads):
# 对运行位置进行配置
if use_gpu:
try:
int(os.environ.get('CUDA_VISIBLE_DEVICES'))
except Exception:
print(
)
use_gpu = False
if os.path.isdir(modelpath):
if os.path.exists(os.path.join(modelpath, "__params__")):
# __model__ + __params__
model = os.path.join(modelpath, "__model__")
params = os.path.join(modelpath, "__params__")
config = Config(model, params)
elif os.path.exists(os.path.join(modelpath, "params")):
# model + params
model = os.path.join(modelpath, "model")
params = os.path.join(modelpath, "params")
config = Config(model, params)
elif os.path.exists(os.path.join(modelpath, "__model__")):
# __model__ + others
config = Config(modelpath)
else:
raise Exception(
"Error! Can\'t find the model in: %s. Please check your model path." % os.path.abspath(modelpath))
elif os.path.exists(modelpath + ".pdmodel"):
# *.pdmodel + *.pdiparams
model = modelpath + ".pdmodel"
params = modelpath + ".pdiparams"
config = Config(model, params)
elif isinstance(modelpath, Config):
config = modelpath
else:
raise Exception(
"Error! Can\'t find the model in: %s. Please check your model path." % os.path.abspath(modelpath))
# 设置参数
if use_gpu:
config.enable_use_gpu(100, gpu_id)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(cpu_threads)
if use_mkldnn:
config.enable_mkldnn()
config.disable_glog_info()
# 返回配置
return config
# 预测器创建函数
```
###Assistant :
load the model config
modelpath: inference model path
use_gpu: use gpu or not
use_mkldnn: use mkldnn or not
Error! Unable to use GPU. Please set the environment variables "CUDA_VISIBLE_DEVICES=GPU_id" to use GPU. Now switch to CPU to continue...
|
2,602 | def trim_line(line, column=0):
line = line.strip("\n")
ll = len(line)
if ll <= 150:
return line
if column > ll:
column = ll
start = max(column - 60, 0)
# Round down if it brings us close to the edge
if start < 5:
start = 0
end = min(start + 140, ll)
# Round up to the end if it's close
if end > ll - 5:
end = ll
# If we are bumped all the way to the end,
# make sure we still get a full 140 characters in the line
if end == ll:
start = max(end - 140, 0)
line = line[start:end]
if end < ll:
# we've snipped from the end
line += " {snip}"
if start > 0:
# we've snipped from the beginning
line = "{snip} " + line
return line
|
Trims a line down to a goal of 140 characters, with a little
wiggle room to be sensible and tries to trim around the given
`column`. So it tries to extract 60 characters before and after
the provided `column` and yield a better context.
| 44 | 139 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def trim_line(line, column=0):
line = line.strip("\n")
ll = len(line)
if ll <= 150:
return line
if column > ll:
column = ll
start = max(column - 60, 0)
# Round down if it brings us close to the edge
if start < 5:
start = 0
end = min(start + 140, ll)
# Round up to the end if it's close
if end > ll - 5:
end = ll
# If we are bumped all the way to the end,
# make sure we still get a full 140 characters in the line
if end == ll:
start = max(end - 140, 0)
line = line[start:end]
if end < ll:
# we've snipped from the end
line += " {snip}"
if start > 0:
# we've snipped from the beginning
line = "{snip} " + line
return line
```
###Assistant :
Trims a line down to a goal of 140 characters, with a little
wiggle room to be sensible and tries to trim around the given
`column`. So it tries to extract 60 characters before and after
the provided `column` and yield a better context.
|
2,603 | def test_background_update_default_batch_set_by_config(self):
self.get_success(
self.store.db_pool.simple_insert(
"background_updates",
values={"update_name": "test_update", "progress_json": '{"my_key": 1}'},
)
)
self.update_handler.side_effect = self.update
self.update_handler.reset_mock()
res = self.get_success(
self.updates.do_next_background_update(False),
by=0.01,
)
self.assertFalse(res)
# on the first call, we should get run with the default background update size specified in the config
self.update_handler.assert_called_once_with({"my_key": 1}, 20)
|
Test that the background update is run with the default_batch_size set by the config
| 14 | 45 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_background_update_default_batch_set_by_config(self):
self.get_success(
self.store.db_pool.simple_insert(
"background_updates",
values={"update_name": "test_update", "progress_json": '{"my_key": 1}'},
)
)
self.update_handler.side_effect = self.update
self.update_handler.reset_mock()
res = self.get_success(
self.updates.do_next_background_update(False),
by=0.01,
)
self.assertFalse(res)
# on the first call, we should get run with the default background update size specified in the config
self.update_handler.assert_called_once_with({"my_key": 1}, 20)
```
###Assistant :
Test that the background update is run with the default_batch_size set by the config
|
2,604 | def _expiry_date(self, session_data):
return session_data.get("_session_expiry") or (
self._last_modification()
+ datetime.timedelta(seconds=self.get_session_cookie_age())
)
|
Return the expiry time of the file storing the session's content.
| 11 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _expiry_date(self, session_data):
return session_data.get("_session_expiry") or (
self._last_modification()
+ datetime.timedelta(seconds=self.get_session_cookie_age())
)
```
###Assistant :
Return the expiry time of the file storing the session's content.
|
2,605 | def __call__(self, *args, **kwargs):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*args, **kwargs)
images = kwargs.pop("images", None)
text = kwargs.pop("text", None)
if len(args) > 0:
images = args[0]
args = args[1:]
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process.")
if images is not None:
inputs = self.feature_extractor(images, *args, **kwargs)
if text is not None:
encodings = self.tokenizer(text, **kwargs)
if text is None:
return inputs
elif images is None:
return encodings
else:
inputs["labels"] = encodings["input_ids"]
return inputs
|
When used in normal mode, this method forwards all its arguments to AutoFeatureExtractor's
[`~AutoFeatureExtractor.__call__`] and returns its output. If used in the context
[`~TrOCRProcessor.as_target_processor`] this method forwards all its arguments to TrOCRTokenizer's
[`~TrOCRTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information.
| 46 | 89 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def __call__(self, *args, **kwargs):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*args, **kwargs)
images = kwargs.pop("images", None)
text = kwargs.pop("text", None)
if len(args) > 0:
images = args[0]
args = args[1:]
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process.")
if images is not None:
inputs = self.feature_extractor(images, *args, **kwargs)
if text is not None:
encodings = self.tokenizer(text, **kwargs)
if text is None:
return inputs
elif images is None:
return encodings
else:
inputs["labels"] = encodings["input_ids"]
return inputs
```
###Assistant :
When used in normal mode, this method forwards all its arguments to AutoFeatureExtractor's
[`~AutoFeatureExtractor.__call__`] and returns its output. If used in the context
[`~TrOCRProcessor.as_target_processor`] this method forwards all its arguments to TrOCRTokenizer's
[`~TrOCRTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information.
|
2,606 | def get_report(module_name, report_name):
reports = get_reports()
module = reports.get(module_name)
if module is None:
return None
report = module.get(report_name)
if report is None:
return None
return report
|
Return a specific report from within a module.
| 8 | 26 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_report(module_name, report_name):
reports = get_reports()
module = reports.get(module_name)
if module is None:
return None
report = module.get(report_name)
if report is None:
return None
return report
```
###Assistant :
Return a specific report from within a module.
|
2,607 | def test_worker_duty_configs(self) -> None:
worker1_config = self._make_worker_config(
worker_app="synapse.app.generic_worker",
worker_name="worker1",
extras={
"notify_appservices_from_worker": "worker2",
"update_user_directory_from_worker": "worker1",
},
)
self.assertFalse(worker1_config.should_notify_appservices)
self.assertTrue(worker1_config.should_update_user_directory)
worker2_config = self._make_worker_config(
worker_app="synapse.app.generic_worker",
worker_name="worker2",
extras={
"notify_appservices_from_worker": "worker2",
"update_user_directory_from_worker": "worker1",
},
)
self.assertTrue(worker2_config.should_notify_appservices)
self.assertFalse(worker2_config.should_update_user_directory)
|
Additional tests for the worker duties
| 6 | 32 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_worker_duty_configs(self) -> None:
worker1_config = self._make_worker_config(
worker_app="synapse.app.generic_worker",
worker_name="worker1",
extras={
"notify_appservices_from_worker": "worker2",
"update_user_directory_from_worker": "worker1",
},
)
self.assertFalse(worker1_config.should_notify_appservices)
self.assertTrue(worker1_config.should_update_user_directory)
worker2_config = self._make_worker_config(
worker_app="synapse.app.generic_worker",
worker_name="worker2",
extras={
"notify_appservices_from_worker": "worker2",
"update_user_directory_from_worker": "worker1",
},
)
self.assertTrue(worker2_config.should_notify_appservices)
self.assertFalse(worker2_config.should_update_user_directory)
```
###Assistant :
Additional tests for the worker duties
|
2,608 | def actor_id(self):
# only worker mode has actor_id
assert (
self.worker.mode == ray.worker.WORKER_MODE
), f"This method is only available when the process is a\
worker. Current mode: {self.worker.mode}"
actor_id = self.worker.actor_id
return actor_id if not actor_id.is_nil() else None
| Get the current actor ID in this worker.
ID of the actor of the current process.
This shouldn't be used in a driver process.
Returns:
The current actor id in this worker. None if there's no actor id.
| 38 | 38 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def actor_id(self):
# only worker mode has actor_id
assert (
self.worker.mode == ray.worker.WORKER_MODE
), f"This method is only available when the process is a\
worker. Current mode: {self.worker.mode}"
actor_id = self.worker.actor_id
return actor_id if not actor_id.is_nil() else None
```
###Assistant : Get the current actor ID in this worker.
ID of the actor of the current process.
This shouldn't be used in a driver process.
Returns:
The current actor id in this worker. None if there's no actor id.
|
2,609 | def test_redirect_to_current(self):
start_url = reverse("wagtailsettings:edit", args=["tests", "testsetting"])
dest_url = reverse(
"wagtailsettings:edit", args=["tests", "testsetting", self.other_site.pk]
)
response = self.client.get(
start_url, follow=True, HTTP_HOST=self.other_site.hostname
)
self.assertRedirects(
response, dest_url, status_code=302, fetch_redirect_response=False
)
|
Should redirect to the setting for the current site taken from the URL,
by default
| 15 | 28 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_redirect_to_current(self):
start_url = reverse("wagtailsettings:edit", args=["tests", "testsetting"])
dest_url = reverse(
"wagtailsettings:edit", args=["tests", "testsetting", self.other_site.pk]
)
response = self.client.get(
start_url, follow=True, HTTP_HOST=self.other_site.hostname
)
self.assertRedirects(
response, dest_url, status_code=302, fetch_redirect_response=False
)
```
###Assistant :
Should redirect to the setting for the current site taken from the URL,
by default
|
2,610 | def fit(self, X, y, sample_weight=None, fit_params=None):
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = self._validate_data(X="no_validation", y=y, multi_output=True)
if is_classifier(self):
check_classification_targets(y)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
if not fit_params:
fit_params = [None] * y.shape[1]
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params[i]
)
for i in range(y.shape[1])
)
self.classes_ = []
for estimator in self.estimators_:
self.classes_.extend(estimator.classes_)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self
| Fit the model to data, separately for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying classifier supports sample
weights.
fit_params : A list of dicts for the fit_params
Parameters passed to the ``estimator.fit`` method of each step.
Each dict may contain same or different values (e.g. different
eval_sets or init_models)
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
| 110 | 114 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def fit(self, X, y, sample_weight=None, fit_params=None):
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = self._validate_data(X="no_validation", y=y, multi_output=True)
if is_classifier(self):
check_classification_targets(y)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
if not fit_params:
fit_params = [None] * y.shape[1]
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params[i]
)
for i in range(y.shape[1])
)
self.classes_ = []
for estimator in self.estimators_:
self.classes_.extend(estimator.classes_)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self
```
###Assistant : Fit the model to data, separately for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying classifier supports sample
weights.
fit_params : A list of dicts for the fit_params
Parameters passed to the ``estimator.fit`` method of each step.
Each dict may contain same or different values (e.g. different
eval_sets or init_models)
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
|
2,611 | def inner_choices(self) -> Iterable['ValueChoice']:
for arg in self.arguments:
if isinstance(arg, ValueChoiceX):
yield from arg.inner_choices()
|
Return an iterable of all leaf value choices.
Useful for composition of value choices.
No deduplication on labels. Mutators should take care.
| 22 | 14 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def inner_choices(self) -> Iterable['ValueChoice']:
for arg in self.arguments:
if isinstance(arg, ValueChoiceX):
yield from arg.inner_choices()
```
###Assistant :
Return an iterable of all leaf value choices.
Useful for composition of value choices.
No deduplication on labels. Mutators should take care.
|
2,612 | def get_indices(expr):
# We call ourself recursively to determine indices of sub expressions.
# break recursion
if isinstance(expr, Indexed):
c = expr.indices
inds, dummies = _remove_repeated(c)
return inds, {}
elif expr is None:
return set(), {}
elif isinstance(expr, Idx):
return {expr}, {}
elif expr.is_Atom:
return set(), {}
# recurse via specialized functions
else:
if expr.is_Mul:
return _get_indices_Mul(expr)
elif expr.is_Add:
return _get_indices_Add(expr)
elif expr.is_Pow or isinstance(expr, exp):
return _get_indices_Pow(expr)
elif isinstance(expr, Piecewise):
# FIXME: No support for Piecewise yet
return set(), {}
elif isinstance(expr, Function):
# Support ufunc like behaviour by returning indices from arguments.
# Functions do not interpret repeated indices across arguments
# as summation
ind0 = set()
for arg in expr.args:
ind, sym = get_indices(arg)
ind0 |= ind
return ind0, sym
# this test is expensive, so it should be at the end
elif not expr.has(Indexed):
return set(), {}
raise NotImplementedError(
"FIXME: No specialized handling of type %s" % type(expr))
| Determine the outer indices of expression ``expr``
By *outer* we mean indices that are not summation indices. Returns a set
and a dict. The set contains outer indices and the dict contains
information about index symmetries.
Examples
========
>>> from sympy.tensor.index_methods import get_indices
>>> from sympy import symbols
>>> from sympy.tensor import IndexedBase
>>> x, y, A = map(IndexedBase, ['x', 'y', 'A'])
>>> i, j, a, z = symbols('i j a z', integer=True)
The indices of the total expression is determined, Repeated indices imply a
summation, for instance the trace of a matrix A:
>>> get_indices(A[i, i])
(set(), {})
In the case of many terms, the terms are required to have identical
outer indices. Else an IndexConformanceException is raised.
>>> get_indices(x[i] + A[i, j]*y[j])
({i}, {})
:Exceptions:
An IndexConformanceException means that the terms ar not compatible, e.g.
>>> get_indices(x[i] + y[j]) #doctest: +SKIP
(...)
IndexConformanceException: Indices are not consistent: x(i) + y(j)
.. warning::
The concept of *outer* indices applies recursively, starting on the deepest
level. This implies that dummies inside parenthesis are assumed to be
summed first, so that the following expression is handled gracefully:
>>> get_indices((x[i] + A[i, j]*y[j])*x[j])
({i, j}, {})
This is correct and may appear convenient, but you need to be careful
with this as SymPy will happily .expand() the product, if requested. The
resulting expression would mix the outer ``j`` with the dummies inside
the parenthesis, which makes it a different expression. To be on the
safe side, it is best to avoid such ambiguities by using unique indices
for all contractions that should be held separate.
| 263 | 152 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_indices(expr):
# We call ourself recursively to determine indices of sub expressions.
# break recursion
if isinstance(expr, Indexed):
c = expr.indices
inds, dummies = _remove_repeated(c)
return inds, {}
elif expr is None:
return set(), {}
elif isinstance(expr, Idx):
return {expr}, {}
elif expr.is_Atom:
return set(), {}
# recurse via specialized functions
else:
if expr.is_Mul:
return _get_indices_Mul(expr)
elif expr.is_Add:
return _get_indices_Add(expr)
elif expr.is_Pow or isinstance(expr, exp):
return _get_indices_Pow(expr)
elif isinstance(expr, Piecewise):
# FIXME: No support for Piecewise yet
return set(), {}
elif isinstance(expr, Function):
# Support ufunc like behaviour by returning indices from arguments.
# Functions do not interpret repeated indices across arguments
# as summation
ind0 = set()
for arg in expr.args:
ind, sym = get_indices(arg)
ind0 |= ind
return ind0, sym
# this test is expensive, so it should be at the end
elif not expr.has(Indexed):
return set(), {}
raise NotImplementedError(
"FIXME: No specialized handling of type %s" % type(expr))
```
###Assistant : Determine the outer indices of expression ``expr``
By *outer* we mean indices that are not summation indices. Returns a set
and a dict. The set contains outer indices and the dict contains
information about index symmetries.
Examples
========
>>> from sympy.tensor.index_methods import get_indices
>>> from sympy import symbols
>>> from sympy.tensor import IndexedBase
>>> x, y, A = map(IndexedBase, ['x', 'y', 'A'])
>>> i, j, a, z = symbols('i j a z', integer=True)
The indices of the total expression is determined, Repeated indices imply a
summation, for instance the trace of a matrix A:
>>> get_indices(A[i, i])
(set(), {})
In the case of many terms, the terms are required to have identical
outer indices. Else an IndexConformanceException is raised.
>>> get_indices(x[i] + A[i, j]*y[j])
({i}, {})
:Exceptions:
An IndexConformanceException means that the terms ar not compatible, e.g.
>>> get_indices(x[i] + y[j]) #doctest: +SKIP
(...)
IndexConformanceException: Indices are not consistent: x(i) + y(j)
.. warning::
The concept of *outer* indices applies recursively, starting on the deepest
level. This implies that dummies inside parenthesis are assumed to be
summed first, so that the following expression is handled gracefully:
>>> get_indices((x[i] + A[i, j]*y[j])*x[j])
({i, j}, {})
This is correct and may appear convenient, but you need to be careful
with this as SymPy will happily .expand() the product, if requested. The
resulting expression would mix the outer ``j`` with the dummies inside
the parenthesis, which makes it a different expression. To be on the
safe side, it is best to avoid such ambiguities by using unique indices
for all contractions that should be held separate.
|
2,613 | def dist_location(dist):
# type: (Distribution) -> str
egg_link = egg_link_path(dist)
if egg_link:
return normalize_path(egg_link)
return normalize_path(dist.location)
|
Get the site-packages location of this distribution. Generally
this is dist.location, except in the case of develop-installed
packages, where dist.location is the source code location, and we
want to know where the egg-link file is.
The returned location is normalized (in particular, with symlinks removed).
| 45 | 16 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def dist_location(dist):
# type: (Distribution) -> str
egg_link = egg_link_path(dist)
if egg_link:
return normalize_path(egg_link)
return normalize_path(dist.location)
```
###Assistant :
Get the site-packages location of this distribution. Generally
this is dist.location, except in the case of develop-installed
packages, where dist.location is the source code location, and we
want to know where the egg-link file is.
The returned location is normalized (in particular, with symlinks removed).
|
2,614 | def can_connect(url, error_classes=None):
if error_classes is None:
error_classes = _get_default_network_errors()
try:
with urlopen(url, timeout=20) as response:
# Timeout just in case rate-limiting is applied
if response.status != 200:
return False
except error_classes:
return False
else:
return True
# ------------------------------------------------------------------
# File-IO
|
Try to connect to the given url. True if succeeds, False if OSError
raised
Parameters
----------
url : basestring
The URL to try to connect to
Returns
-------
connectable : bool
Return True if no OSError (unable to connect) or URLError (bad url) was
raised
| 45 | 41 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def can_connect(url, error_classes=None):
if error_classes is None:
error_classes = _get_default_network_errors()
try:
with urlopen(url, timeout=20) as response:
# Timeout just in case rate-limiting is applied
if response.status != 200:
return False
except error_classes:
return False
else:
return True
# ------------------------------------------------------------------
# File-IO
```
###Assistant :
Try to connect to the given url. True if succeeds, False if OSError
raised
Parameters
----------
url : basestring
The URL to try to connect to
Returns
-------
connectable : bool
Return True if no OSError (unable to connect) or URLError (bad url) was
raised
|
2,615 | def test_valid_slack_channel_id(self):
integration = Integration.objects.create(
external_id="1",
provider="slack",
metadata={"access_token": "xoxp-xxxxxxxxx-xxxxxxxxxx-xxxxxxxxxxxx"},
)
integration.add_organization(self.organization, self.user)
base_params = self.valid_params.copy()
base_params.update(
{
"type": AlertRuleTriggerAction.get_registered_type(
AlertRuleTriggerAction.Type.SLACK
).slug,
"targetType": ACTION_TARGET_TYPE_TO_STRING[
AlertRuleTriggerAction.TargetType.SPECIFIC
],
"targetIdentifier": "merp",
"integration": str(integration.id),
}
)
context = self.context.copy()
context.update({"input_channel_id": "CSVK0921"})
responses.add(
method=responses.GET,
url="https://slack.com/api/conversations.info",
status=200,
content_type="application/json",
body=json.dumps({"ok": "true", "channel": {"name": "merp", "id": "CSVK0921"}}),
)
serializer = AlertRuleTriggerActionSerializer(context=context, data=base_params)
assert serializer.is_valid()
serializer.save()
# # Make sure the action was created.
alert_rule_trigger_actions = list(
AlertRuleTriggerAction.objects.filter(integration=integration)
)
assert len(alert_rule_trigger_actions) == 1
|
Test that when a valid Slack channel ID is provided, we look up the channel name and validate it against the targetIdentifier.
| 22 | 73 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_valid_slack_channel_id(self):
integration = Integration.objects.create(
external_id="1",
provider="slack",
metadata={"access_token": "xoxp-xxxxxxxxx-xxxxxxxxxx-xxxxxxxxxxxx"},
)
integration.add_organization(self.organization, self.user)
base_params = self.valid_params.copy()
base_params.update(
{
"type": AlertRuleTriggerAction.get_registered_type(
AlertRuleTriggerAction.Type.SLACK
).slug,
"targetType": ACTION_TARGET_TYPE_TO_STRING[
AlertRuleTriggerAction.TargetType.SPECIFIC
],
"targetIdentifier": "merp",
"integration": str(integration.id),
}
)
context = self.context.copy()
context.update({"input_channel_id": "CSVK0921"})
responses.add(
method=responses.GET,
url="https://slack.com/api/conversations.info",
status=200,
content_type="application/json",
body=json.dumps({"ok": "true", "channel": {"name": "merp", "id": "CSVK0921"}}),
)
serializer = AlertRuleTriggerActionSerializer(context=context, data=base_params)
assert serializer.is_valid()
serializer.save()
# # Make sure the action was created.
alert_rule_trigger_actions = list(
AlertRuleTriggerAction.objects.filter(integration=integration)
)
assert len(alert_rule_trigger_actions) == 1
```
###Assistant :
Test that when a valid Slack channel ID is provided, we look up the channel name and validate it against the targetIdentifier.
|
2,616 | def get_legacy(members):
if AIX_ABI == 64:
# AIX 64-bit member is one of shr64.o, shr_64.o, or shr4_64.o
expr = r'shr4?_?64\.o'
member = get_one_match(expr, members)
if member:
return member
else:
# 32-bit legacy names - both shr.o and shr4.o exist.
# shr.o is the preferred name so we look for shr.o first
# i.e., shr4.o is returned only when shr.o does not exist
for name in ['shr.o', 'shr4.o']:
member = get_one_match(re.escape(name), members)
if member:
return member
return None
|
This routine provides historical aka legacy naming schemes started
in AIX4 shared library support for library members names.
e.g., in /usr/lib/libc.a the member name shr.o for 32-bit binary and
shr_64.o for 64-bit binary.
| 33 | 77 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_legacy(members):
if AIX_ABI == 64:
# AIX 64-bit member is one of shr64.o, shr_64.o, or shr4_64.o
expr = r'shr4?_?64\.o'
member = get_one_match(expr, members)
if member:
return member
else:
# 32-bit legacy names - both shr.o and shr4.o exist.
# shr.o is the preferred name so we look for shr.o first
# i.e., shr4.o is returned only when shr.o does not exist
for name in ['shr.o', 'shr4.o']:
member = get_one_match(re.escape(name), members)
if member:
return member
return None
```
###Assistant :
This routine provides historical aka legacy naming schemes started
in AIX4 shared library support for library members names.
e.g., in /usr/lib/libc.a the member name shr.o for 32-bit binary and
shr_64.o for 64-bit binary.
|
2,617 | def set_policy(name, table="filter", family="ipv4", **kwargs):
ret = {"name": name, "changes": {}, "result": None, "comment": ""}
for ignore in _STATE_INTERNAL_KEYWORDS:
if ignore in kwargs:
del kwargs[ignore]
if (
__salt__["iptables.get_policy"](table, kwargs["chain"], family)
== kwargs["policy"]
):
ret["result"] = True
ret[
"comment"
] = "iptables default policy for chain {} on table {} for {} already set to {}".format(
kwargs["chain"], table, family, kwargs["policy"]
)
return ret
if __opts__["test"]:
ret["comment"] = (
"iptables default policy for chain {} on table {} for {} needs to be set"
" to {}".format(kwargs["chain"], table, family, kwargs["policy"])
)
return ret
if not __salt__["iptables.set_policy"](
table, kwargs["chain"], kwargs["policy"], family
):
ret["changes"] = {"locale": name}
ret["result"] = True
ret["comment"] = "Set default policy for {} to {} family {}".format(
kwargs["chain"], kwargs["policy"], family
)
if "save" in kwargs and kwargs["save"]:
if kwargs["save"] is not True:
filename = kwargs["save"]
else:
filename = None
__salt__["iptables.save"](filename=filename, family=family)
ret[
"comment"
] = "Set and saved default policy for {} to {} family {}".format(
kwargs["chain"], kwargs["policy"], family
)
return ret
else:
ret["result"] = False
ret["comment"] = "Failed to set iptables default policy"
return ret
|
.. versionadded:: 2014.1.0
Sets the default policy for iptables firewall tables
table
The table that owns the chain that should be modified
family
Networking family, either ipv4 or ipv6
policy
The requested table policy
save
If set to a true value, the new iptables rules for the given family
will be saved to a file. See the ``append`` state for more details.
| 62 | 176 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def set_policy(name, table="filter", family="ipv4", **kwargs):
ret = {"name": name, "changes": {}, "result": None, "comment": ""}
for ignore in _STATE_INTERNAL_KEYWORDS:
if ignore in kwargs:
del kwargs[ignore]
if (
__salt__["iptables.get_policy"](table, kwargs["chain"], family)
== kwargs["policy"]
):
ret["result"] = True
ret[
"comment"
] = "iptables default policy for chain {} on table {} for {} already set to {}".format(
kwargs["chain"], table, family, kwargs["policy"]
)
return ret
if __opts__["test"]:
ret["comment"] = (
"iptables default policy for chain {} on table {} for {} needs to be set"
" to {}".format(kwargs["chain"], table, family, kwargs["policy"])
)
return ret
if not __salt__["iptables.set_policy"](
table, kwargs["chain"], kwargs["policy"], family
):
ret["changes"] = {"locale": name}
ret["result"] = True
ret["comment"] = "Set default policy for {} to {} family {}".format(
kwargs["chain"], kwargs["policy"], family
)
if "save" in kwargs and kwargs["save"]:
if kwargs["save"] is not True:
filename = kwargs["save"]
else:
filename = None
__salt__["iptables.save"](filename=filename, family=family)
ret[
"comment"
] = "Set and saved default policy for {} to {} family {}".format(
kwargs["chain"], kwargs["policy"], family
)
return ret
else:
ret["result"] = False
ret["comment"] = "Failed to set iptables default policy"
return ret
```
###Assistant :
.. versionadded:: 2014.1.0
Sets the default policy for iptables firewall tables
table
The table that owns the chain that should be modified
family
Networking family, either ipv4 or ipv6
policy
The requested table policy
save
If set to a true value, the new iptables rules for the given family
will be saved to a file. See the ``append`` state for more details.
|
2,618 | def _after_start(self):
delay = self.request.config.getoption('--qute-delay-start')
if delay:
with self.disable_capturing():
print(f"- waiting {delay}ms for quteprocess "
f"(PID: {self.proc.processId()})...")
time.sleep(delay / 1000)
| Wait before continuing if requested, e.g. for debugger attachment. | 9 | 20 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _after_start(self):
delay = self.request.config.getoption('--qute-delay-start')
if delay:
with self.disable_capturing():
print(f"- waiting {delay}ms for quteprocess "
f"(PID: {self.proc.processId()})...")
time.sleep(delay / 1000)
```
###Assistant : Wait before continuing if requested, e.g. for debugger attachment.
|
2,619 | def list_secrets(path, default=None):
if default is None:
default = CommandExecutionError
log.debug("Listing vault secret keys for %s in %s", __grains__["id"], path)
version2 = __utils__["vault.is_v2"](path)
if version2["v2"]:
path = version2["metadata"]
try:
url = "v1/{}".format(path)
response = __utils__["vault.make_request"]("LIST", url)
if response.status_code != 200:
response.raise_for_status()
return response.json()["data"]
except Exception as err: # pylint: disable=broad-except
if default is CommandExecutionError:
raise CommandExecutionError(
"Failed to list secrets! {}: {}".format(type(err).__name__, err)
)
return default
|
.. versionchanged:: 3001
The ``default`` argument has been added. When the path or path/key
combination is not found, an exception will be raised, unless a default
is provided.
List secret keys at the path in vault. The vault policy used must allow this.
The path should end with a trailing slash.
CLI Example:
.. code-block:: bash
salt '*' vault.list_secrets "secret/my/"
| 60 | 66 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def list_secrets(path, default=None):
if default is None:
default = CommandExecutionError
log.debug("Listing vault secret keys for %s in %s", __grains__["id"], path)
version2 = __utils__["vault.is_v2"](path)
if version2["v2"]:
path = version2["metadata"]
try:
url = "v1/{}".format(path)
response = __utils__["vault.make_request"]("LIST", url)
if response.status_code != 200:
response.raise_for_status()
return response.json()["data"]
except Exception as err: # pylint: disable=broad-except
if default is CommandExecutionError:
raise CommandExecutionError(
"Failed to list secrets! {}: {}".format(type(err).__name__, err)
)
return default
```
###Assistant :
.. versionchanged:: 3001
The ``default`` argument has been added. When the path or path/key
combination is not found, an exception will be raised, unless a default
is provided.
List secret keys at the path in vault. The vault policy used must allow this.
The path should end with a trailing slash.
CLI Example:
.. code-block:: bash
salt '*' vault.list_secrets "secret/my/"
|
2,620 | def test_custom_kwargs_sharded(tmpdir, cls):
strategy = cls(reduce_fp16=True)
strategy.model = Mock(spec=LightningModule)
strategy.model.trainer = Mock()
class_name = "sharded" if isinstance(strategy, DDPShardedStrategy) else "sharded_spawn"
with mock.patch(f"pytorch_lightning.strategies.{class_name}.ShardedDataParallel", autospec=True) as mock_sharded:
strategy.configure_ddp()
args, kwargs = mock_sharded.call_args
assert "reduce_fp16" in kwargs
assert kwargs["reduce_fp16"]
@RunIf(skip_windows=True, fairscale=True)
@mock.patch("pytorch_lightning.strategies.DDPShardedStrategy._wrap_optimizers", autospec=True)
@pytest.mark.parametrize(["params", "expected_buffer_size"], [(dict(), 0), (dict(reduce_buffer_size=128), 128)])
@pytest.mark.parametrize("num_nodes", [1, 2]) | Tests to ensure that if custom kwargs are passed, they are set correctly. | 13 | 49 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_custom_kwargs_sharded(tmpdir, cls):
strategy = cls(reduce_fp16=True)
strategy.model = Mock(spec=LightningModule)
strategy.model.trainer = Mock()
class_name = "sharded" if isinstance(strategy, DDPShardedStrategy) else "sharded_spawn"
with mock.patch(f"pytorch_lightning.strategies.{class_name}.ShardedDataParallel", autospec=True) as mock_sharded:
strategy.configure_ddp()
args, kwargs = mock_sharded.call_args
assert "reduce_fp16" in kwargs
assert kwargs["reduce_fp16"]
@RunIf(skip_windows=True, fairscale=True)
@mock.patch("pytorch_lightning.strategies.DDPShardedStrategy._wrap_optimizers", autospec=True)
@pytest.mark.parametrize(["params", "expected_buffer_size"], [(dict(), 0), (dict(reduce_buffer_size=128), 128)])
@pytest.mark.parametrize("num_nodes", [1, 2])
```
###Assistant : Tests to ensure that if custom kwargs are passed, they are set correctly.
|
2,621 | def versions_from_parentdir(parentdir_prefix, root, verbose):
rootdirs = []
for _ in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None, "date": None}
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print("Tried directories %s but none started with prefix %s" %
(str(rootdirs), parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
SHORT_VERSION_PY =
%s
| Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
# This file was generated by 'versioneer.py' (0.21) from
# revision-control system data, or from the parent directory name of an
# unpacked source archive. Distribution tarballs contain a pre-generated copy
# of this file.
import json
version_json = # END VERSION_JSON
| 84 | 58 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def versions_from_parentdir(parentdir_prefix, root, verbose):
rootdirs = []
for _ in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None, "date": None}
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print("Tried directories %s but none started with prefix %s" %
(str(rootdirs), parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
SHORT_VERSION_PY =
%s
```
###Assistant : Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
# This file was generated by 'versioneer.py' (0.21) from
# revision-control system data, or from the parent directory name of an
# unpacked source archive. Distribution tarballs contain a pre-generated copy
# of this file.
import json
version_json = # END VERSION_JSON
|
2,622 | def response_add(self, request, obj, post_url_continue=None):
opts = obj._meta
preserved_filters = self.get_preserved_filters(request)
obj_url = reverse(
"admin:%s_%s_change" % (opts.app_label, opts.model_name),
args=(quote(obj.pk),),
current_app=self.admin_site.name,
)
# Add a link to the object's change form if the user can edit the obj.
if self.has_change_permission(request, obj):
obj_repr = format_html('<a href="{}">{}</a>', urlquote(obj_url), obj)
else:
obj_repr = str(obj)
msg_dict = {
"name": opts.verbose_name,
"obj": obj_repr,
}
# Here, we distinguish between different save types by checking for
# the presence of keys in request.POST.
if IS_POPUP_VAR in request.POST:
to_field = request.POST.get(TO_FIELD_VAR)
if to_field:
attr = str(to_field)
else:
attr = obj._meta.pk.attname
value = obj.serializable_value(attr)
popup_response_data = json.dumps(
{
"value": str(value),
"obj": str(obj),
}
)
return TemplateResponse(
request,
self.popup_response_template
or [
"admin/%s/%s/popup_response.html"
% (opts.app_label, opts.model_name),
"admin/%s/popup_response.html" % opts.app_label,
"admin/popup_response.html",
],
{
"popup_response_data": popup_response_data,
},
)
elif "_continue" in request.POST or (
# Redirecting after "Save as new".
"_saveasnew" in request.POST
and self.save_as_continue
and self.has_change_permission(request, obj)
):
msg = _("The {name} “{obj}” was added successfully.")
if self.has_change_permission(request, obj):
msg += " " + _("You may edit it again below.")
self.message_user(request, format_html(msg, **msg_dict), messages.SUCCESS)
if post_url_continue is None:
post_url_continue = obj_url
post_url_continue = add_preserved_filters(
{"preserved_filters": preserved_filters, "opts": opts},
post_url_continue,
)
return HttpResponseRedirect(post_url_continue)
elif "_addanother" in request.POST:
msg = format_html(
_(
"The {name} “{obj}” was added successfully. You may add another {name} below."
),
**msg_dict,
)
self.message_user(request, msg, messages.SUCCESS)
redirect_url = request.path
redirect_url = add_preserved_filters(
{"preserved_filters": preserved_filters, "opts": opts}, redirect_url
)
return HttpResponseRedirect(redirect_url)
else:
msg = format_html(
_("The {name} “{obj}” was added successfully."), **msg_dict
)
self.message_user(request, msg, messages.SUCCESS)
return self.response_post_save_add(request, obj)
|
Determine the HttpResponse for the add_view stage.
| 7 | 248 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def response_add(self, request, obj, post_url_continue=None):
opts = obj._meta
preserved_filters = self.get_preserved_filters(request)
obj_url = reverse(
"admin:%s_%s_change" % (opts.app_label, opts.model_name),
args=(quote(obj.pk),),
current_app=self.admin_site.name,
)
# Add a link to the object's change form if the user can edit the obj.
if self.has_change_permission(request, obj):
obj_repr = format_html('<a href="{}">{}</a>', urlquote(obj_url), obj)
else:
obj_repr = str(obj)
msg_dict = {
"name": opts.verbose_name,
"obj": obj_repr,
}
# Here, we distinguish between different save types by checking for
# the presence of keys in request.POST.
if IS_POPUP_VAR in request.POST:
to_field = request.POST.get(TO_FIELD_VAR)
if to_field:
attr = str(to_field)
else:
attr = obj._meta.pk.attname
value = obj.serializable_value(attr)
popup_response_data = json.dumps(
{
"value": str(value),
"obj": str(obj),
}
)
return TemplateResponse(
request,
self.popup_response_template
or [
"admin/%s/%s/popup_response.html"
% (opts.app_label, opts.model_name),
"admin/%s/popup_response.html" % opts.app_label,
"admin/popup_response.html",
],
{
"popup_response_data": popup_response_data,
},
)
elif "_continue" in request.POST or (
# Redirecting after "Save as new".
"_saveasnew" in request.POST
and self.save_as_continue
and self.has_change_permission(request, obj)
):
msg = _("The {name} “{obj}” was added successfully.")
if self.has_change_permission(request, obj):
msg += " " + _("You may edit it again below.")
self.message_user(request, format_html(msg, **msg_dict), messages.SUCCESS)
if post_url_continue is None:
post_url_continue = obj_url
post_url_continue = add_preserved_filters(
{"preserved_filters": preserved_filters, "opts": opts},
post_url_continue,
)
return HttpResponseRedirect(post_url_continue)
elif "_addanother" in request.POST:
msg = format_html(
_(
"The {name} “{obj}” was added successfully. You may add another {name} below."
),
**msg_dict,
)
self.message_user(request, msg, messages.SUCCESS)
redirect_url = request.path
redirect_url = add_preserved_filters(
{"preserved_filters": preserved_filters, "opts": opts}, redirect_url
)
return HttpResponseRedirect(redirect_url)
else:
msg = format_html(
_("The {name} “{obj}” was added successfully."), **msg_dict
)
self.message_user(request, msg, messages.SUCCESS)
return self.response_post_save_add(request, obj)
```
###Assistant :
Determine the HttpResponse for the add_view stage.
|
2,623 | def test_tika_parse_unreachable(self):
html = '<html><head><meta http-equiv="content-type" content="text/html; charset=UTF-8"></head><body><p>Some Text</p></body></html>'
# Check if exception is raised when Tika cannot be reached.
self.parser.tika_server = ""
self.assertRaises(ParseError, self.parser.tika_parse, html)
|
GIVEN:
- Fresh start
WHEN:
- tika parsing is called but tika is not available
THEN:
- a ParseError Exception is thrown
| 22 | 26 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_tika_parse_unreachable(self):
html = '<html><head><meta http-equiv="content-type" content="text/html; charset=UTF-8"></head><body><p>Some Text</p></body></html>'
# Check if exception is raised when Tika cannot be reached.
self.parser.tika_server = ""
self.assertRaises(ParseError, self.parser.tika_parse, html)
```
###Assistant :
GIVEN:
- Fresh start
WHEN:
- tika parsing is called but tika is not available
THEN:
- a ParseError Exception is thrown
|
2,624 | def test_read_config_file_2():
tpot_obj = TPOTRegressor()
assert_raises(ValueError, tpot_obj._read_config_file, "tests/test_config.py.bad")
| Assert that _read_config_file rasies ValueError with wrong dictionary format | 9 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_read_config_file_2():
tpot_obj = TPOTRegressor()
assert_raises(ValueError, tpot_obj._read_config_file, "tests/test_config.py.bad")
```
###Assistant : Assert that _read_config_file rasies ValueError with wrong dictionary format
|
2,625 | def pre_delete_handler(self, sender, instance, **kwargs):
key = self.get_key_for_instance(instance)
object_type = instance._meta.verbose_name
# Delete an existing object
logger.debug(f"[{self.branch}] Staging deletion of {object_type} {instance} (PK: {instance.pk})")
self.queue[key] = (ChangeActionChoices.ACTION_DELETE, None)
|
Hooks to the pre_delete signal when a branch is active to queue delete actions.
| 14 | 28 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def pre_delete_handler(self, sender, instance, **kwargs):
key = self.get_key_for_instance(instance)
object_type = instance._meta.verbose_name
# Delete an existing object
logger.debug(f"[{self.branch}] Staging deletion of {object_type} {instance} (PK: {instance.pk})")
self.queue[key] = (ChangeActionChoices.ACTION_DELETE, None)
```
###Assistant :
Hooks to the pre_delete signal when a branch is active to queue delete actions.
|
2,626 | def _reset_state(self):
self.cache = {}
self.resolved_nodes = 0
self.finished_last_inference = True
# maps DAGNode uuid to unique instance of a gradio block
self.node_to_block: Dict[DAGNode, Any] = {}
# maps InputAttributeNodes to unique instance of interactive gradio block
self.input_key_to_blocks: Dict[int, Any] = {}
| Resets state for each new RayServeHandle representing a new DAG. | 10 | 42 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _reset_state(self):
self.cache = {}
self.resolved_nodes = 0
self.finished_last_inference = True
# maps DAGNode uuid to unique instance of a gradio block
self.node_to_block: Dict[DAGNode, Any] = {}
# maps InputAttributeNodes to unique instance of interactive gradio block
self.input_key_to_blocks: Dict[int, Any] = {}
```
###Assistant : Resets state for each new RayServeHandle representing a new DAG.
|
2,627 | def find_element(self, selector):
return self.driver.find_element(By.CSS_SELECTOR, selector)
| find_element returns the first found element by the css `selector`
shortcut to `driver.find_element(By.CSS_SELECTOR, ...)`. | 14 | 6 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def find_element(self, selector):
return self.driver.find_element(By.CSS_SELECTOR, selector)
```
###Assistant : find_element returns the first found element by the css `selector`
shortcut to `driver.find_element(By.CSS_SELECTOR, ...)`.
|
2,628 | def test_bad_persist_value(self):
with self.assertRaises(StreamlitAPIException) as e:
| Throw an error if an invalid value is passed to 'persist'. | 11 | 6 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_bad_persist_value(self):
with self.assertRaises(StreamlitAPIException) as e:
```
###Assistant : Throw an error if an invalid value is passed to 'persist'.
|
2,629 | def _getDataFileTagsOptionHelp():
return % ", ".join(
"'%s' (%s)" % d for d in data_files_tags
)
data_file_tags_option = data_group.add_option(
"--data-file-tags",
action="append",
dest="data_file_tags",
metavar="DATA_TAGS",
default=[],
)
parser.add_option_group(data_group)
execute_group = OptionGroup(parser, "Immediate execution after compilation")
execute_group.add_option(
"--run",
action="store_true",
dest="immediate_execution",
default=is_nuitka_run,
help=
% ("on" if is_nuitka_run else "off"),
)
execute_group.add_option(
"--debugger",
"--gdb",
action="store_true",
dest="debugger",
default=False,
help=,
)
execute_group.add_option(
"--execute-with-pythonpath",
action="store_true",
dest="keep_pythonpath",
default=False,
help=,
)
parser.add_option_group(execute_group)
dump_group = OptionGroup(parser, "Dump options for internal tree")
dump_group.add_option(
"--xml",
action="store_true",
dest="dump_xml",
default=False,
help="Dump the final result of optimization as XML, then exit.",
)
parser.add_option_group(dump_group)
codegen_group = OptionGroup(parser, "Code generation choices")
codegen_group.add_option(
"--disable-bytecode-cache",
action="store_true",
dest="disable_bytecode_cache",
default=False,
help=,
)
codegen_group.add_option(
"--full-compat",
action="store_false",
dest="improved",
default=True,
help=,
)
codegen_group.add_option(
"--file-reference-choice",
action="store",
dest="file_reference_mode",
metavar="MODE",
choices=("original", "runtime", "frozen"),
default=None,
help=,
)
codegen_group.add_option(
"--module-name-choice",
action="store",
dest="module_name_mode",
metavar="MODE",
choices=("original", "runtime"),
default=None,
help=,
)
parser.add_option_group(codegen_group)
output_group = OptionGroup(parser, "Output choices")
output_group.add_option(
"-o",
action="store",
dest="output_filename",
metavar="FILENAME",
default=None,
help=
% "<program_name>"
+ (".exe" if getOS() == "Windows" else ".bin"),
)
output_group.add_option(
"--output-dir",
action="store",
dest="output_dir",
metavar="DIRECTORY",
default="",
help=,
)
output_group.add_option(
"--remove-output",
action="store_true",
dest="remove_build",
default=False,
help=,
)
output_group.add_option(
"--no-pyi-file",
action="store_false",
dest="pyi_file",
default=True,
help=,
)
parser.add_option_group(output_group)
debug_group = OptionGroup(parser, "Debug features")
debug_group.add_option(
"--debug",
action="store_true",
dest="debug",
default=False,
help=,
)
debug_group.add_option(
"--unstripped",
action="store_true",
dest="unstripped",
default=False,
help=,
)
debug_group.add_option(
"--profile",
action="store_true",
dest="profile",
default=False,
help=,
)
debug_group.add_option(
"--internal-graph",
action="store_true",
dest="graph",
default=False,
help=,
)
debug_group.add_option(
"--trace-execution",
action="store_true",
dest="trace_execution",
default=False,
help=,
)
debug_group.add_option(
"--recompile-c-only",
action="store_true",
dest="recompile_c_only",
default=False,
help=,
)
debug_group.add_option(
"--generate-c-only",
action="store_true",
dest="generate_c_only",
default=False,
help=,
)
debug_group.add_option(
"--experimental",
action="append",
dest="experimental",
metavar="FLAG",
default=[],
help=,
)
debug_group.add_option(
"--explain-imports",
action="store_true",
dest="explain_imports",
default=False,
help=SUPPRESS_HELP,
)
debug_group.add_option(
"--low-memory",
action="store_true",
dest="low_memory",
default=False,
help=,
)
if os.name == "nt":
debug_group.add_option(
"--disable-dll-dependency-cache",
action="store_true",
dest="no_dependency_cache",
default=False,
help=,
)
debug_group.add_option(
"--force-dll-dependency-cache-update",
action="store_true",
dest="update_dependency_cache",
default=False,
help=,
)
# This is for testing framework, "coverage.py" hates to loose the process. And
# we can use it to make sure it's not done unknowingly.
parser.add_option(
"--must-not-re-execute",
action="store_false",
dest="allow_reexecute",
default=True,
help=SUPPRESS_HELP,
)
parser.add_option_group(debug_group)
c_compiler_group = OptionGroup(parser, "Backend C compiler choice")
c_compiler_group.add_option(
"--clang",
action="store_true",
dest="clang",
default=False,
help=,
)
c_compiler_group.add_option(
"--mingw64",
action="store_true",
dest="mingw64",
default=False,
help=,
)
c_compiler_group.add_option(
"--msvc",
action="store",
dest="msvc_version",
default=None,
help=,
)
c_compiler_group.add_option(
"-j",
"--jobs",
action="store",
dest="jobs",
metavar="N",
default=None,
help=,
)
c_compiler_group.add_option(
"--lto",
action="store",
dest="lto",
metavar="choice",
default="auto",
choices=("yes", "no", "auto"),
help=,
)
c_compiler_group.add_option(
"--static-libpython",
action="store",
dest="static_libpython",
metavar="choice",
default="auto",
choices=("yes", "no", "auto"),
help=,
)
c_compiler_group.add_option(
"--disable-ccache",
action="store_true",
dest="disable_ccache",
default=False,
help=,
)
parser.add_option_group(c_compiler_group)
pgo_group = OptionGroup(parser, "PGO compilation choices")
pgo_group.add_option(
"--pgo",
action="store_true",
dest="is_c_pgo",
default=False,
help=,
)
pgo_group.add_option(
"--pgo-python",
action="store_true",
dest="is_python_pgo",
default=False,
help=SUPPRESS_HELP,
)
pgo_group.add_option(
"--pgo-python-input",
action="store",
dest="python_pgo_input",
default=None,
help=SUPPRESS_HELP,
)
pgo_group.add_option(
"--pgo-python-policy-unused-module",
action="store",
dest="python_pgo_policy_unused_module",
choices=("include", "exclude", "bytecode"),
default="include",
help=SUPPRESS_HELP,
)
pgo_group.add_option(
"--pgo-args",
action="store",
dest="pgo_args",
default="",
help=,
)
pgo_group.add_option(
"--pgo-executable",
action="store",
dest="pgo_executable",
default=None,
help=,
)
parser.add_option_group(pgo_group)
tracing_group = OptionGroup(parser, "Tracing features")
tracing_group.add_option(
"--quiet",
action="store_true",
dest="quiet",
default=False,
help=,
)
tracing_group.add_option(
"--show-scons",
action="store_true",
dest="show_scons",
default=False,
help=,
)
tracing_group.add_option(
"--show-progress",
action="store_true",
dest="show_progress",
default=False,
help=,
)
tracing_group.add_option(
"--no-progressbar",
action="store_false",
dest="progress_bar",
default=True,
help=,
)
tracing_group.add_option(
"--show-memory",
action="store_true",
dest="show_memory",
default=False,
help=,
)
tracing_group.add_option(
"--show-modules",
action="store_true",
dest="show_inclusion",
default=False,
help=,
)
tracing_group.add_option(
"--show-modules-output",
action="store",
dest="show_inclusion_output",
metavar="PATH",
default=None,
help=,
)
tracing_group.add_option(
"--report",
action="store",
dest="compilation_report_filename",
default=None,
help=,
)
tracing_group.add_option(
"--verbose",
action="store_true",
dest="verbose",
default=False,
help=,
)
tracing_group.add_option(
"--verbose-output",
action="store",
dest="verbose_output",
metavar="PATH",
default=None,
help=,
)
parser.add_option_group(tracing_group)
windows_group = OptionGroup(parser, "Windows specific controls")
windows_group.add_option(
"--windows-dependency-tool",
action="store",
dest="dependency_tool",
default=None,
help=SUPPRESS_HELP,
)
windows_group.add_option(
"--windows-disable-console",
action="store_true",
dest="disable_console",
default=False,
help=,
)
windows_group.add_option(
"--windows-icon-from-ico",
action="append",
dest="icon_path",
metavar="ICON_PATH",
default=[],
help=,
)
windows_group.add_option(
"--windows-icon-from-exe",
action="store",
dest="icon_exe_path",
metavar="ICON_EXE_PATH",
default=None,
help="Copy executable icons from this existing executable (Windows only).",
)
windows_group.add_option(
"--onefile-windows-splash-screen-image",
action="store",
dest="splash_screen_image",
default=None,
help=,
)
windows_group.add_option(
"--windows-uac-admin",
action="store_true",
dest="windows_uac_admin",
metavar="WINDOWS_UAC_ADMIN",
default=False,
help="Request Windows User Control, to grant admin rights on execution. (Windows only). Defaults to off.",
)
windows_group.add_option(
"--windows-uac-uiaccess",
action="store_true",
dest="windows_uac_uiaccess",
metavar="WINDOWS_UAC_UIACCESS",
default=False,
help=,
)
windows_group.add_option(
"--windows-company-name",
action="store",
dest="windows_company_name",
metavar="WINDOWS_COMPANY_NAME",
default=None,
help=,
)
windows_group.add_option(
"--windows-product-name",
action="store",
dest="windows_product_name",
metavar="WINDOWS_PRODUCT_NAME",
default=None,
help=,
)
windows_group.add_option(
"--windows-file-version",
action="store",
dest="windows_file_version",
metavar="WINDOWS_FILE_VERSION",
default=None,
help=,
)
windows_group.add_option(
"--windows-product-version",
action="store",
dest="windows_product_version",
metavar="WINDOWS_PRODUCT_VERSION",
default=None,
help=,
)
windows_group.add_option(
"--windows-file-description",
action="store",
dest="windows_file_description",
metavar="WINDOWS_FILE_DESCRIPTION",
default=None,
help=,
)
windows_group.add_option(
"--windows-onefile-tempdir",
"--onefile-tempdir",
action="store_true",
dest="is_onefile_tempdir",
metavar="ONEFILE_TEMPDIR",
default=False,
help=SUPPRESS_HELP,
)
windows_group.add_option(
"--windows-onefile-tempdir-spec",
"--onefile-tempdir-spec",
action="store",
dest="onefile_tempdir_spec",
metavar="ONEFILE_TEMPDIR_SPEC",
default=None,
help=,
)
windows_group.add_option(
"--windows-force-stdout-spec",
action="store",
dest="force_stdout_spec",
metavar="WINDOWS_FORCE_STDOUT_SPEC",
default=None,
help=,
)
windows_group.add_option(
"--windows-force-stderr-spec",
action="store",
dest="force_stderr_spec",
metavar="WINDOWS_FORCE_STDERR_SPEC",
default=None,
help=,
)
parser.add_option_group(windows_group)
macos_group = OptionGroup(parser, "macOS specific controls")
macos_group.add_option(
"--macos-target-arch",
action="store",
dest="macos_target_arch",
choices=("universal", "arm64", "x86_64"),
metavar="MACOS_TARGET_ARCH",
default=None,
help=,
)
macos_group.add_option(
"--macos-disable-console",
"--disable-console",
action="store_true",
dest="disable_console",
default=False,
help=,
)
macos_group.add_option(
"--macos-create-app-bundle",
action="store_true",
dest="macos_create_bundle",
default=False,
help=,
)
macos_group.add_option(
"--macos-onefile-icon",
action="append",
dest="icon_path",
metavar="ICON_PATH",
default=[],
help="Add executable icon for binary to use. Can be given only one time. Defaults to Python icon if available.",
)
macos_group.add_option(
"--macos-signed-app-name",
action="store",
dest="macos_signed_app_name",
metavar="MACOS_SIGNED_APP_NAME",
default=None,
help=,
)
macos_group.add_option(
"--macos-app-name",
action="store",
dest="macos_app_name",
metavar="MACOS_APP_NAME",
default=None,
help=,
)
macos_group.add_option(
"--macos-app-version",
action="store",
dest="macos_app_version",
metavar="MACOS_APP_VERSION",
default=None,
help=,
)
parser.add_option_group(macos_group)
linux_group = OptionGroup(parser, "Linux specific controls")
linux_group.add_option(
"--linux-onefile-icon",
action="append",
dest="icon_path",
metavar="ICON_PATH",
default=[],
help="Add executable icon for onefile binary to use. Can be given only one time. Defaults to Python icon if available.",
)
linux_group.add_option(
"--linux-onefile-compression",
action="store",
dest="appimage_compression",
choices=("gzip", "xz"),
metavar="COMPRESSION",
default="gzip",
help="Compression method to use for Linux onefile builds. Defaults to gzip for faster decompression",
)
parser.add_option_group(linux_group)
plugin_group = OptionGroup(parser, "Plugin control")
plugin_group.add_option(
"--enable-plugin",
"--plugin-enable",
action="append",
dest="plugins_enabled",
metavar="PLUGIN_NAME",
default=[],
help=,
)
plugin_group.add_option(
"--disable-plugin",
"--plugin-disable",
action="append",
dest="plugins_disabled",
metavar="PLUGIN_NAME",
default=[],
help=,
)
plugin_group.add_option(
"--plugin-no-detection",
action="store_false",
dest="detect_missing_plugins",
default=True,
help=,
)
plugin_group.add_option(
"--plugin-list",
action="store_true",
dest="list_plugins",
default=False,
help=,
)
parser.add_option_group(plugin_group)
plugin_group.add_option(
"--user-plugin",
action="append",
dest="user_plugins",
metavar="PATH",
default=[],
help="The file name of user plugin. Can be given multiple times. Default empty.",
)
plugin_group.add_option(
"--persist-source-changes",
action="store_true",
dest="persist_source_changes",
default=False,
help=,
)
| \
For included data files, special handlings can be chosen. With the
commercial plugins, e.g. files can be included directly in the
binary. The list is completed by some plugins. With the current
list of plugins, these are available: %s.
The default is empty.\
Execute immediately the created binary (or import the compiled module).
Defaults to %s.\
Execute inside a debugger, e.g. "gdb" or "lldb" to automatically get a stack trace.
Defaults to off.\
When immediately executing the created binary (--execute), don't reset
PYTHONPATH. When all modules are successfully included, you ought to not need
PYTHONPATH anymore.\
Do not reuse dependency analysis results for modules, esp. from standard library,
that are included as bytecode.\
Enforce absolute compatibility with CPython. Do not even allow minor
deviations from CPython behavior, e.g. not having better tracebacks
or exception messages which are not really incompatible, but only
different. This is intended for tests only and should not be used
for normal use.\
Select what value "__file__" is going to be. With "runtime" (default for
standalone binary mode and module mode), the created binaries and modules,
use the location of themselves to deduct the value of "__file__". Included
packages pretend to be in directories below that location. This allows you
to include data files in deployments. If you merely seek acceleration, it's
better for you to use the "original" value, where the source files location
will be used. With "frozen" a notation "<frozen module_name>" is used. For
compatibility reasons, the "__file__" value will always have ".py" suffix
independent of what it really is.\
Select what value "__name__" and "__package__" are going to be. With "runtime"
(default for module mode), the created module, it uses the parent package to
deduct the value of "__package__", to be fully compatible. This allows for more
optimization to happen, but normally any package can be loaded into another one,
but this will raise an import error when it detects that with "original" mode.\
Specify how the executable should be named. For extension modules there is no
choice, also not for standalone mode and using it will be an error. This may
include path information that needs to exist though. Defaults to '%s' on this
platform.
\
Specify where intermediate and final output files should be put. The DIRECTORY
will be populated with C files, object files, etc.
Defaults to current directory.
\
Removes the build directory after producing the module or exe file.
Defaults to off.\
Do not create a ".pyi" file for extension modules created by Nuitka. This is
used to detect implicit imports.
Defaults to off.\
Executing all self checks possible to find errors in Nuitka, do not use for
production. Defaults to off.\
Keep debug info in the resulting object file for better debugger interaction.
Defaults to off.\
Enable vmprof based profiling of time spent. Not working currently. Defaults to off.\
Create graph of optimization process internals, do not use for whole programs, but only
for small test cases. Defaults to off.\
Traced execution output, output the line of code before executing it.
Defaults to off.\
This is not incremental compilation, but for Nuitka development only. Takes
existing files and simply compile them as C again. Allows compiling edited
C files for quick debugging changes to the generated source, e.g. to see if
code is passed by, values output, etc, Defaults to off. Depends on compiling
Python source to determine which files it should look at.\
Generate only C source code, and do not compile it to binary or module. This
is for debugging and code coverage analysis that doesn't waste CPU. Defaults to
off. Do not think you can use this directly.\
Use features declared as 'experimental'. May have no effect if no experimental
features are present in the code. Uses secret tags (check source) per
experimented feature.\
Attempt to use less memory, by forking less C compilation jobs and using
options that use less memory. For use on embedded machines. Use this in
case of out of memory problems. Defaults to off.\
Disable the dependency walker cache. Will result in much longer times to create
the distribution folder, but might be used in case the cache is suspect to cause
errors.
\
For an update of the dependency walker cache. Will result in much longer times
to create the distribution folder, but might be used in case the cache is suspect
to cause errors or known to need an update.
\
Enforce the use of clang. On Windows this requires a working Visual
Studio version to piggy back on. Defaults to off.\
Enforce the use of MinGW64 on Windows. Defaults to off.\
Enforce the use of specific MSVC version on Windows. Allowed values
are e.g. "14.3" (MSVC 2022) and other MSVC version numbers, specify
"list" for a list of installed compilers, or use "latest".
Defaults to latest MSVC being used if installed, otherwise MinGW64
is used.\
Specify the allowed number of parallel C compiler jobs. Defaults to the
system CPU count.\
Use link time optimizations (MSVC, gcc, clang). Allowed values are
"yes", "no", and "auto" (when it's known to work). Defaults to
"auto".\
Use static link library of Python. Allowed values are "yes", "no",
and "auto" (when it's known to work). Defaults to "auto".\
Do not attempt to use ccache (gcc, clang, etc.) or clcache (MSVC, clangcl).\
Enables C level profile guided optimization (PGO), by executing a dedicated build first
for a profiling run, and then using the result to feedback into the C compilation.
Note: This is experimental and not working with standalone modes of Nuitka yet.
Defaults to off.\
Arguments to be passed in case of profile guided optimization. These are passed to the special
built executable during the PGO profiling run. Default empty.\
Command to execute when collecting profile information. Use this only, if you need to
launch it through a script that prepares it to run. Default use created program.\
Disable all information outputs, but show warnings.
Defaults to off.\
Operate Scons in non-quiet mode, showing the executed commands.
Defaults to off.Provide progress information and statistics.
Defaults to off.Disable progress bar outputs (if tqdm is installed).
Defaults to off.Provide memory information and statistics.
Defaults to off.\
Provide information for included modules and DLLs
Defaults to off.\
Where to output --show-modules, should be a filename. Default is standard output.\
Report module inclusion in an XML output file. Default is off.\
Output details of actions taken, esp. in optimizations. Can become a lot.
Defaults to off.\
Where to output --verbose, should be a filename. Default is standard output.\
When compiling for Windows, disable the console window. Defaults to off.\
Add executable icon. Can be given multiple times for different resolutions
or files with multiple icons inside. In the later case, you may also suffix
with #<n> where n is an integer index starting from 1, specifying a specific
icon to be included, and all others to be ignored.\
When compiling for Windows and onefile, show this while loading the application. Defaults to off.\
Request Windows User Control, to enforce running from a few folders only, remote
desktop access. (Windows only). Defaults to off.\
Name of the company to use in Windows Version information.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Name of the product to use in Windows Version information. Defaults to base
filename of the binary.\
File version to use in Windows Version information. Must be a sequence of
up to 4 numbers, e.g. 1.0.0.0, only this format is allowed.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Product version to use in Windows Version information. Must be a sequence of
up to 4 numbers, e.g. 1.0.0.0, only this format is allowed.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Description of the file use in Windows Version information.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to nonsense.\
Use this as a temporary folder. Defaults to '%TEMP%\\onefile_%PID%_%TIME%', i.e. system temporary directory.\
Force standard output of the program to go to this location. Useful for programs with
disabled console and programs using the Windows Services Plugin of Nuitka. Defaults
to not active, use e.g. '%PROGRAM%.out.txt', i.e. file near your program.\
Force standard error of the program to go to this location. Useful for programs with
disabled console and programs using the Windows Services Plugin of Nuitka. Defaults
to not active, use e.g. '%PROGRAM%.err.txt', i.e. file near your program.\
What architectures is this to supposed to run on. Default and limit
is what the running Python allows for. Default is "native" which is
the architecture the Python is run with.\
When compiling for macOS, disable the console window and create a GUI
application. Defaults to off.\
When compiling for macOS, create a bundle rather than a plain binary
application. Currently experimental and incomplete. Currently this
is the only way to unlock disabling of console.Defaults to off.\
Name of the application to use for macOS signing. Follow com.yourcompany.appname naming
results for best results, as these have to be globally unique, and will grant protected
API accesses.\
Name of the product to use in macOS bundle information. Defaults to base
filename of the binary.\
Product version to use in macOS bundle information. Defaults to 1.0 if
not given.\
Enabled plugins. Must be plug-in names. Use --plugin-list to query the
full list and exit. Default empty.\
Disabled plugins. Must be plug-in names. Use --plugin-list to query the
full list and exit. Default empty.\
Plugins can detect if they might be used, and the you can disable the warning
via "--disable-plugin=plugin-that-warned", or you can use this option to disable
the mechanism entirely, which also speeds up compilation slightly of course as
this detection code is run in vain once you are certain of which plugins to
use. Defaults to off.\
Show list of all available plugins and exit. Defaults to off.\
Write source changes to original Python files. Use with care. May need
permissions, best for use in a virtualenv to debug if plugin code
changes work with standard Python or to benefit from bloat removal
even with pure Python. Default False. | 1,740 | 859 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _getDataFileTagsOptionHelp():
return % ", ".join(
"'%s' (%s)" % d for d in data_files_tags
)
data_file_tags_option = data_group.add_option(
"--data-file-tags",
action="append",
dest="data_file_tags",
metavar="DATA_TAGS",
default=[],
)
parser.add_option_group(data_group)
execute_group = OptionGroup(parser, "Immediate execution after compilation")
execute_group.add_option(
"--run",
action="store_true",
dest="immediate_execution",
default=is_nuitka_run,
help=
% ("on" if is_nuitka_run else "off"),
)
execute_group.add_option(
"--debugger",
"--gdb",
action="store_true",
dest="debugger",
default=False,
help=,
)
execute_group.add_option(
"--execute-with-pythonpath",
action="store_true",
dest="keep_pythonpath",
default=False,
help=,
)
parser.add_option_group(execute_group)
dump_group = OptionGroup(parser, "Dump options for internal tree")
dump_group.add_option(
"--xml",
action="store_true",
dest="dump_xml",
default=False,
help="Dump the final result of optimization as XML, then exit.",
)
parser.add_option_group(dump_group)
codegen_group = OptionGroup(parser, "Code generation choices")
codegen_group.add_option(
"--disable-bytecode-cache",
action="store_true",
dest="disable_bytecode_cache",
default=False,
help=,
)
codegen_group.add_option(
"--full-compat",
action="store_false",
dest="improved",
default=True,
help=,
)
codegen_group.add_option(
"--file-reference-choice",
action="store",
dest="file_reference_mode",
metavar="MODE",
choices=("original", "runtime", "frozen"),
default=None,
help=,
)
codegen_group.add_option(
"--module-name-choice",
action="store",
dest="module_name_mode",
metavar="MODE",
choices=("original", "runtime"),
default=None,
help=,
)
parser.add_option_group(codegen_group)
output_group = OptionGroup(parser, "Output choices")
output_group.add_option(
"-o",
action="store",
dest="output_filename",
metavar="FILENAME",
default=None,
help=
% "<program_name>"
+ (".exe" if getOS() == "Windows" else ".bin"),
)
output_group.add_option(
"--output-dir",
action="store",
dest="output_dir",
metavar="DIRECTORY",
default="",
help=,
)
output_group.add_option(
"--remove-output",
action="store_true",
dest="remove_build",
default=False,
help=,
)
output_group.add_option(
"--no-pyi-file",
action="store_false",
dest="pyi_file",
default=True,
help=,
)
parser.add_option_group(output_group)
debug_group = OptionGroup(parser, "Debug features")
debug_group.add_option(
"--debug",
action="store_true",
dest="debug",
default=False,
help=,
)
debug_group.add_option(
"--unstripped",
action="store_true",
dest="unstripped",
default=False,
help=,
)
debug_group.add_option(
"--profile",
action="store_true",
dest="profile",
default=False,
help=,
)
debug_group.add_option(
"--internal-graph",
action="store_true",
dest="graph",
default=False,
help=,
)
debug_group.add_option(
"--trace-execution",
action="store_true",
dest="trace_execution",
default=False,
help=,
)
debug_group.add_option(
"--recompile-c-only",
action="store_true",
dest="recompile_c_only",
default=False,
help=,
)
debug_group.add_option(
"--generate-c-only",
action="store_true",
dest="generate_c_only",
default=False,
help=,
)
debug_group.add_option(
"--experimental",
action="append",
dest="experimental",
metavar="FLAG",
default=[],
help=,
)
debug_group.add_option(
"--explain-imports",
action="store_true",
dest="explain_imports",
default=False,
help=SUPPRESS_HELP,
)
debug_group.add_option(
"--low-memory",
action="store_true",
dest="low_memory",
default=False,
help=,
)
if os.name == "nt":
debug_group.add_option(
"--disable-dll-dependency-cache",
action="store_true",
dest="no_dependency_cache",
default=False,
help=,
)
debug_group.add_option(
"--force-dll-dependency-cache-update",
action="store_true",
dest="update_dependency_cache",
default=False,
help=,
)
# This is for testing framework, "coverage.py" hates to loose the process. And
# we can use it to make sure it's not done unknowingly.
parser.add_option(
"--must-not-re-execute",
action="store_false",
dest="allow_reexecute",
default=True,
help=SUPPRESS_HELP,
)
parser.add_option_group(debug_group)
c_compiler_group = OptionGroup(parser, "Backend C compiler choice")
c_compiler_group.add_option(
"--clang",
action="store_true",
dest="clang",
default=False,
help=,
)
c_compiler_group.add_option(
"--mingw64",
action="store_true",
dest="mingw64",
default=False,
help=,
)
c_compiler_group.add_option(
"--msvc",
action="store",
dest="msvc_version",
default=None,
help=,
)
c_compiler_group.add_option(
"-j",
"--jobs",
action="store",
dest="jobs",
metavar="N",
default=None,
help=,
)
c_compiler_group.add_option(
"--lto",
action="store",
dest="lto",
metavar="choice",
default="auto",
choices=("yes", "no", "auto"),
help=,
)
c_compiler_group.add_option(
"--static-libpython",
action="store",
dest="static_libpython",
metavar="choice",
default="auto",
choices=("yes", "no", "auto"),
help=,
)
c_compiler_group.add_option(
"--disable-ccache",
action="store_true",
dest="disable_ccache",
default=False,
help=,
)
parser.add_option_group(c_compiler_group)
pgo_group = OptionGroup(parser, "PGO compilation choices")
pgo_group.add_option(
"--pgo",
action="store_true",
dest="is_c_pgo",
default=False,
help=,
)
pgo_group.add_option(
"--pgo-python",
action="store_true",
dest="is_python_pgo",
default=False,
help=SUPPRESS_HELP,
)
pgo_group.add_option(
"--pgo-python-input",
action="store",
dest="python_pgo_input",
default=None,
help=SUPPRESS_HELP,
)
pgo_group.add_option(
"--pgo-python-policy-unused-module",
action="store",
dest="python_pgo_policy_unused_module",
choices=("include", "exclude", "bytecode"),
default="include",
help=SUPPRESS_HELP,
)
pgo_group.add_option(
"--pgo-args",
action="store",
dest="pgo_args",
default="",
help=,
)
pgo_group.add_option(
"--pgo-executable",
action="store",
dest="pgo_executable",
default=None,
help=,
)
parser.add_option_group(pgo_group)
tracing_group = OptionGroup(parser, "Tracing features")
tracing_group.add_option(
"--quiet",
action="store_true",
dest="quiet",
default=False,
help=,
)
tracing_group.add_option(
"--show-scons",
action="store_true",
dest="show_scons",
default=False,
help=,
)
tracing_group.add_option(
"--show-progress",
action="store_true",
dest="show_progress",
default=False,
help=,
)
tracing_group.add_option(
"--no-progressbar",
action="store_false",
dest="progress_bar",
default=True,
help=,
)
tracing_group.add_option(
"--show-memory",
action="store_true",
dest="show_memory",
default=False,
help=,
)
tracing_group.add_option(
"--show-modules",
action="store_true",
dest="show_inclusion",
default=False,
help=,
)
tracing_group.add_option(
"--show-modules-output",
action="store",
dest="show_inclusion_output",
metavar="PATH",
default=None,
help=,
)
tracing_group.add_option(
"--report",
action="store",
dest="compilation_report_filename",
default=None,
help=,
)
tracing_group.add_option(
"--verbose",
action="store_true",
dest="verbose",
default=False,
help=,
)
tracing_group.add_option(
"--verbose-output",
action="store",
dest="verbose_output",
metavar="PATH",
default=None,
help=,
)
parser.add_option_group(tracing_group)
windows_group = OptionGroup(parser, "Windows specific controls")
windows_group.add_option(
"--windows-dependency-tool",
action="store",
dest="dependency_tool",
default=None,
help=SUPPRESS_HELP,
)
windows_group.add_option(
"--windows-disable-console",
action="store_true",
dest="disable_console",
default=False,
help=,
)
windows_group.add_option(
"--windows-icon-from-ico",
action="append",
dest="icon_path",
metavar="ICON_PATH",
default=[],
help=,
)
windows_group.add_option(
"--windows-icon-from-exe",
action="store",
dest="icon_exe_path",
metavar="ICON_EXE_PATH",
default=None,
help="Copy executable icons from this existing executable (Windows only).",
)
windows_group.add_option(
"--onefile-windows-splash-screen-image",
action="store",
dest="splash_screen_image",
default=None,
help=,
)
windows_group.add_option(
"--windows-uac-admin",
action="store_true",
dest="windows_uac_admin",
metavar="WINDOWS_UAC_ADMIN",
default=False,
help="Request Windows User Control, to grant admin rights on execution. (Windows only). Defaults to off.",
)
windows_group.add_option(
"--windows-uac-uiaccess",
action="store_true",
dest="windows_uac_uiaccess",
metavar="WINDOWS_UAC_UIACCESS",
default=False,
help=,
)
windows_group.add_option(
"--windows-company-name",
action="store",
dest="windows_company_name",
metavar="WINDOWS_COMPANY_NAME",
default=None,
help=,
)
windows_group.add_option(
"--windows-product-name",
action="store",
dest="windows_product_name",
metavar="WINDOWS_PRODUCT_NAME",
default=None,
help=,
)
windows_group.add_option(
"--windows-file-version",
action="store",
dest="windows_file_version",
metavar="WINDOWS_FILE_VERSION",
default=None,
help=,
)
windows_group.add_option(
"--windows-product-version",
action="store",
dest="windows_product_version",
metavar="WINDOWS_PRODUCT_VERSION",
default=None,
help=,
)
windows_group.add_option(
"--windows-file-description",
action="store",
dest="windows_file_description",
metavar="WINDOWS_FILE_DESCRIPTION",
default=None,
help=,
)
windows_group.add_option(
"--windows-onefile-tempdir",
"--onefile-tempdir",
action="store_true",
dest="is_onefile_tempdir",
metavar="ONEFILE_TEMPDIR",
default=False,
help=SUPPRESS_HELP,
)
windows_group.add_option(
"--windows-onefile-tempdir-spec",
"--onefile-tempdir-spec",
action="store",
dest="onefile_tempdir_spec",
metavar="ONEFILE_TEMPDIR_SPEC",
default=None,
help=,
)
windows_group.add_option(
"--windows-force-stdout-spec",
action="store",
dest="force_stdout_spec",
metavar="WINDOWS_FORCE_STDOUT_SPEC",
default=None,
help=,
)
windows_group.add_option(
"--windows-force-stderr-spec",
action="store",
dest="force_stderr_spec",
metavar="WINDOWS_FORCE_STDERR_SPEC",
default=None,
help=,
)
parser.add_option_group(windows_group)
macos_group = OptionGroup(parser, "macOS specific controls")
macos_group.add_option(
"--macos-target-arch",
action="store",
dest="macos_target_arch",
choices=("universal", "arm64", "x86_64"),
metavar="MACOS_TARGET_ARCH",
default=None,
help=,
)
macos_group.add_option(
"--macos-disable-console",
"--disable-console",
action="store_true",
dest="disable_console",
default=False,
help=,
)
macos_group.add_option(
"--macos-create-app-bundle",
action="store_true",
dest="macos_create_bundle",
default=False,
help=,
)
macos_group.add_option(
"--macos-onefile-icon",
action="append",
dest="icon_path",
metavar="ICON_PATH",
default=[],
help="Add executable icon for binary to use. Can be given only one time. Defaults to Python icon if available.",
)
macos_group.add_option(
"--macos-signed-app-name",
action="store",
dest="macos_signed_app_name",
metavar="MACOS_SIGNED_APP_NAME",
default=None,
help=,
)
macos_group.add_option(
"--macos-app-name",
action="store",
dest="macos_app_name",
metavar="MACOS_APP_NAME",
default=None,
help=,
)
macos_group.add_option(
"--macos-app-version",
action="store",
dest="macos_app_version",
metavar="MACOS_APP_VERSION",
default=None,
help=,
)
parser.add_option_group(macos_group)
linux_group = OptionGroup(parser, "Linux specific controls")
linux_group.add_option(
"--linux-onefile-icon",
action="append",
dest="icon_path",
metavar="ICON_PATH",
default=[],
help="Add executable icon for onefile binary to use. Can be given only one time. Defaults to Python icon if available.",
)
linux_group.add_option(
"--linux-onefile-compression",
action="store",
dest="appimage_compression",
choices=("gzip", "xz"),
metavar="COMPRESSION",
default="gzip",
help="Compression method to use for Linux onefile builds. Defaults to gzip for faster decompression",
)
parser.add_option_group(linux_group)
plugin_group = OptionGroup(parser, "Plugin control")
plugin_group.add_option(
"--enable-plugin",
"--plugin-enable",
action="append",
dest="plugins_enabled",
metavar="PLUGIN_NAME",
default=[],
help=,
)
plugin_group.add_option(
"--disable-plugin",
"--plugin-disable",
action="append",
dest="plugins_disabled",
metavar="PLUGIN_NAME",
default=[],
help=,
)
plugin_group.add_option(
"--plugin-no-detection",
action="store_false",
dest="detect_missing_plugins",
default=True,
help=,
)
plugin_group.add_option(
"--plugin-list",
action="store_true",
dest="list_plugins",
default=False,
help=,
)
parser.add_option_group(plugin_group)
plugin_group.add_option(
"--user-plugin",
action="append",
dest="user_plugins",
metavar="PATH",
default=[],
help="The file name of user plugin. Can be given multiple times. Default empty.",
)
plugin_group.add_option(
"--persist-source-changes",
action="store_true",
dest="persist_source_changes",
default=False,
help=,
)
```
###Assistant : \
For included data files, special handlings can be chosen. With the
commercial plugins, e.g. files can be included directly in the
binary. The list is completed by some plugins. With the current
list of plugins, these are available: %s.
The default is empty.\
Execute immediately the created binary (or import the compiled module).
Defaults to %s.\
Execute inside a debugger, e.g. "gdb" or "lldb" to automatically get a stack trace.
Defaults to off.\
When immediately executing the created binary (--execute), don't reset
PYTHONPATH. When all modules are successfully included, you ought to not need
PYTHONPATH anymore.\
Do not reuse dependency analysis results for modules, esp. from standard library,
that are included as bytecode.\
Enforce absolute compatibility with CPython. Do not even allow minor
deviations from CPython behavior, e.g. not having better tracebacks
or exception messages which are not really incompatible, but only
different. This is intended for tests only and should not be used
for normal use.\
Select what value "__file__" is going to be. With "runtime" (default for
standalone binary mode and module mode), the created binaries and modules,
use the location of themselves to deduct the value of "__file__". Included
packages pretend to be in directories below that location. This allows you
to include data files in deployments. If you merely seek acceleration, it's
better for you to use the "original" value, where the source files location
will be used. With "frozen" a notation "<frozen module_name>" is used. For
compatibility reasons, the "__file__" value will always have ".py" suffix
independent of what it really is.\
Select what value "__name__" and "__package__" are going to be. With "runtime"
(default for module mode), the created module, it uses the parent package to
deduct the value of "__package__", to be fully compatible. This allows for more
optimization to happen, but normally any package can be loaded into another one,
but this will raise an import error when it detects that with "original" mode.\
Specify how the executable should be named. For extension modules there is no
choice, also not for standalone mode and using it will be an error. This may
include path information that needs to exist though. Defaults to '%s' on this
platform.
\
Specify where intermediate and final output files should be put. The DIRECTORY
will be populated with C files, object files, etc.
Defaults to current directory.
\
Removes the build directory after producing the module or exe file.
Defaults to off.\
Do not create a ".pyi" file for extension modules created by Nuitka. This is
used to detect implicit imports.
Defaults to off.\
Executing all self checks possible to find errors in Nuitka, do not use for
production. Defaults to off.\
Keep debug info in the resulting object file for better debugger interaction.
Defaults to off.\
Enable vmprof based profiling of time spent. Not working currently. Defaults to off.\
Create graph of optimization process internals, do not use for whole programs, but only
for small test cases. Defaults to off.\
Traced execution output, output the line of code before executing it.
Defaults to off.\
This is not incremental compilation, but for Nuitka development only. Takes
existing files and simply compile them as C again. Allows compiling edited
C files for quick debugging changes to the generated source, e.g. to see if
code is passed by, values output, etc, Defaults to off. Depends on compiling
Python source to determine which files it should look at.\
Generate only C source code, and do not compile it to binary or module. This
is for debugging and code coverage analysis that doesn't waste CPU. Defaults to
off. Do not think you can use this directly.\
Use features declared as 'experimental'. May have no effect if no experimental
features are present in the code. Uses secret tags (check source) per
experimented feature.\
Attempt to use less memory, by forking less C compilation jobs and using
options that use less memory. For use on embedded machines. Use this in
case of out of memory problems. Defaults to off.\
Disable the dependency walker cache. Will result in much longer times to create
the distribution folder, but might be used in case the cache is suspect to cause
errors.
\
For an update of the dependency walker cache. Will result in much longer times
to create the distribution folder, but might be used in case the cache is suspect
to cause errors or known to need an update.
\
Enforce the use of clang. On Windows this requires a working Visual
Studio version to piggy back on. Defaults to off.\
Enforce the use of MinGW64 on Windows. Defaults to off.\
Enforce the use of specific MSVC version on Windows. Allowed values
are e.g. "14.3" (MSVC 2022) and other MSVC version numbers, specify
"list" for a list of installed compilers, or use "latest".
Defaults to latest MSVC being used if installed, otherwise MinGW64
is used.\
Specify the allowed number of parallel C compiler jobs. Defaults to the
system CPU count.\
Use link time optimizations (MSVC, gcc, clang). Allowed values are
"yes", "no", and "auto" (when it's known to work). Defaults to
"auto".\
Use static link library of Python. Allowed values are "yes", "no",
and "auto" (when it's known to work). Defaults to "auto".\
Do not attempt to use ccache (gcc, clang, etc.) or clcache (MSVC, clangcl).\
Enables C level profile guided optimization (PGO), by executing a dedicated build first
for a profiling run, and then using the result to feedback into the C compilation.
Note: This is experimental and not working with standalone modes of Nuitka yet.
Defaults to off.\
Arguments to be passed in case of profile guided optimization. These are passed to the special
built executable during the PGO profiling run. Default empty.\
Command to execute when collecting profile information. Use this only, if you need to
launch it through a script that prepares it to run. Default use created program.\
Disable all information outputs, but show warnings.
Defaults to off.\
Operate Scons in non-quiet mode, showing the executed commands.
Defaults to off.Provide progress information and statistics.
Defaults to off.Disable progress bar outputs (if tqdm is installed).
Defaults to off.Provide memory information and statistics.
Defaults to off.\
Provide information for included modules and DLLs
Defaults to off.\
Where to output --show-modules, should be a filename. Default is standard output.\
Report module inclusion in an XML output file. Default is off.\
Output details of actions taken, esp. in optimizations. Can become a lot.
Defaults to off.\
Where to output --verbose, should be a filename. Default is standard output.\
When compiling for Windows, disable the console window. Defaults to off.\
Add executable icon. Can be given multiple times for different resolutions
or files with multiple icons inside. In the later case, you may also suffix
with #<n> where n is an integer index starting from 1, specifying a specific
icon to be included, and all others to be ignored.\
When compiling for Windows and onefile, show this while loading the application. Defaults to off.\
Request Windows User Control, to enforce running from a few folders only, remote
desktop access. (Windows only). Defaults to off.\
Name of the company to use in Windows Version information.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Name of the product to use in Windows Version information. Defaults to base
filename of the binary.\
File version to use in Windows Version information. Must be a sequence of
up to 4 numbers, e.g. 1.0.0.0, only this format is allowed.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Product version to use in Windows Version information. Must be a sequence of
up to 4 numbers, e.g. 1.0.0.0, only this format is allowed.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to unused.\
Description of the file use in Windows Version information.
One of file or product version is required, when a version resource needs to be
added, e.g. to specify product name, or company name. Defaults to nonsense.\
Use this as a temporary folder. Defaults to '%TEMP%\\onefile_%PID%_%TIME%', i.e. system temporary directory.\
Force standard output of the program to go to this location. Useful for programs with
disabled console and programs using the Windows Services Plugin of Nuitka. Defaults
to not active, use e.g. '%PROGRAM%.out.txt', i.e. file near your program.\
Force standard error of the program to go to this location. Useful for programs with
disabled console and programs using the Windows Services Plugin of Nuitka. Defaults
to not active, use e.g. '%PROGRAM%.err.txt', i.e. file near your program.\
What architectures is this to supposed to run on. Default and limit
is what the running Python allows for. Default is "native" which is
the architecture the Python is run with.\
When compiling for macOS, disable the console window and create a GUI
application. Defaults to off.\
When compiling for macOS, create a bundle rather than a plain binary
application. Currently experimental and incomplete. Currently this
is the only way to unlock disabling of console.Defaults to off.\
Name of the application to use for macOS signing. Follow com.yourcompany.appname naming
results for best results, as these have to be globally unique, and will grant protected
API accesses.\
Name of the product to use in macOS bundle information. Defaults to base
filename of the binary.\
Product version to use in macOS bundle information. Defaults to 1.0 if
not given.\
Enabled plugins. Must be plug-in names. Use --plugin-list to query the
full list and exit. Default empty.\
Disabled plugins. Must be plug-in names. Use --plugin-list to query the
full list and exit. Default empty.\
Plugins can detect if they might be used, and the you can disable the warning
via "--disable-plugin=plugin-that-warned", or you can use this option to disable
the mechanism entirely, which also speeds up compilation slightly of course as
this detection code is run in vain once you are certain of which plugins to
use. Defaults to off.\
Show list of all available plugins and exit. Defaults to off.\
Write source changes to original Python files. Use with care. May need
permissions, best for use in a virtualenv to debug if plugin code
changes work with standard Python or to benefit from bloat removal
even with pure Python. Default False.
|
2,630 | def test_collect_workflow_action_data_post(self):
response = self.client.post(
reverse(
"wagtailadmin_pages:collect_workflow_action_data",
args=(
self.page.id,
"approve",
self.page.current_workflow_task_state.id,
),
),
{"comment": "This is my comment"},
)
self.assertEqual(response.status_code, 200)
response_json = json.loads(response.content)
self.assertEqual(response_json["step"], "success")
self.assertEqual(
response_json["cleaned_data"], {"comment": "This is my comment"}
)
|
This tests that a POST request to the collect_workflow_action_data view (for the approve action) returns a modal response with the validated data
| 22 | 34 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_collect_workflow_action_data_post(self):
response = self.client.post(
reverse(
"wagtailadmin_pages:collect_workflow_action_data",
args=(
self.page.id,
"approve",
self.page.current_workflow_task_state.id,
),
),
{"comment": "This is my comment"},
)
self.assertEqual(response.status_code, 200)
response_json = json.loads(response.content)
self.assertEqual(response_json["step"], "success")
self.assertEqual(
response_json["cleaned_data"], {"comment": "This is my comment"}
)
```
###Assistant :
This tests that a POST request to the collect_workflow_action_data view (for the approve action) returns a modal response with the validated data
|
2,631 | def test_02_train_predictor(self):
query = f
response = self.handler.native_query(query)
self.assertTrue(response.type == RESPONSE_TYPE.OK)
|
CREATE PREDICTOR {self.test_model_1}
FROM {PG_HANDLER_NAME} (SELECT * FROM {self.data_table_1} limit 50)
PREDICT rental_price
| 13 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_02_train_predictor(self):
query = f
response = self.handler.native_query(query)
self.assertTrue(response.type == RESPONSE_TYPE.OK)
```
###Assistant :
CREATE PREDICTOR {self.test_model_1}
FROM {PG_HANDLER_NAME} (SELECT * FROM {self.data_table_1} limit 50)
PREDICT rental_price
|
2,632 | def __call__(self, results):
img = results['img']
if self.to_float32:
img = img.astype(np.float32)
results['img_path'] = None
results['img'] = img
height, width = img.shape[:2]
results['height'] = height
results['width'] = width
results['ori_height'] = height
results['ori_width'] = width
return results
@TRANSFORMS.register_module() | Call functions to add image meta information.
Args:
results (dict): Result dict with Webcam read image in
``results['img']``.
Returns:
dict: The dict contains loaded image and meta information.
| 28 | 36 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def __call__(self, results):
img = results['img']
if self.to_float32:
img = img.astype(np.float32)
results['img_path'] = None
results['img'] = img
height, width = img.shape[:2]
results['height'] = height
results['width'] = width
results['ori_height'] = height
results['ori_width'] = width
return results
@TRANSFORMS.register_module()
```
###Assistant : Call functions to add image meta information.
Args:
results (dict): Result dict with Webcam read image in
``results['img']``.
Returns:
dict: The dict contains loaded image and meta information.
|
2,633 | def test_jemalloc_env_var_propagate():
gcs_ptype = ray.ray_constants.PROCESS_TYPE_GCS_SERVER
expected = {}
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path="", jemalloc_conf="", jemalloc_comps=[], process_type=gcs_ptype
)
assert actual == expected
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=None,
jemalloc_conf="a,b,c",
jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
process_type=gcs_ptype,
)
assert actual == expected
library_path = "/abc"
expected = {"LD_PRELOAD": library_path}
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=library_path,
jemalloc_conf="",
jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
process_type=gcs_ptype,
)
assert actual == expected
# comps should be a list type.
with pytest.raises(AssertionError):
ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=library_path,
jemalloc_conf="",
jemalloc_comps="ray.ray_constants.PROCESS_TYPE_GCS_SERVER,",
process_type=gcs_ptype,
)
# When comps don't match the process_type, it should return an empty dict.
expected = {}
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=library_path,
jemalloc_conf="",
jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_RAYLET],
process_type=gcs_ptype,
)
library_path = "/abc"
malloc_conf = "a,b,c"
expected = {"LD_PRELOAD": library_path, "MALLOC_CONF": malloc_conf}
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=library_path,
jemalloc_conf=malloc_conf,
jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
process_type=gcs_ptype,
)
assert actual == expected
| Test `propagate_jemalloc_env_var`
If the shared library path is not specified,
it should return an empty dict.
When the shared library is specified
When the malloc config is specified
| 28 | 114 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_jemalloc_env_var_propagate():
gcs_ptype = ray.ray_constants.PROCESS_TYPE_GCS_SERVER
expected = {}
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path="", jemalloc_conf="", jemalloc_comps=[], process_type=gcs_ptype
)
assert actual == expected
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=None,
jemalloc_conf="a,b,c",
jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
process_type=gcs_ptype,
)
assert actual == expected
library_path = "/abc"
expected = {"LD_PRELOAD": library_path}
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=library_path,
jemalloc_conf="",
jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
process_type=gcs_ptype,
)
assert actual == expected
# comps should be a list type.
with pytest.raises(AssertionError):
ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=library_path,
jemalloc_conf="",
jemalloc_comps="ray.ray_constants.PROCESS_TYPE_GCS_SERVER,",
process_type=gcs_ptype,
)
# When comps don't match the process_type, it should return an empty dict.
expected = {}
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=library_path,
jemalloc_conf="",
jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_RAYLET],
process_type=gcs_ptype,
)
library_path = "/abc"
malloc_conf = "a,b,c"
expected = {"LD_PRELOAD": library_path, "MALLOC_CONF": malloc_conf}
actual = ray._private.services.propagate_jemalloc_env_var(
jemalloc_path=library_path,
jemalloc_conf=malloc_conf,
jemalloc_comps=[ray.ray_constants.PROCESS_TYPE_GCS_SERVER],
process_type=gcs_ptype,
)
assert actual == expected
```
###Assistant : Test `propagate_jemalloc_env_var`
If the shared library path is not specified,
it should return an empty dict.
When the shared library is specified
When the malloc config is specified
|
2,634 | def aug_test_bboxes(self, feats, img_metas, rescale=False):
# check with_nms argument
gb_sig = signature(self.get_results)
gb_args = [p.name for p in gb_sig.parameters.values()]
gbs_sig = signature(self._get_results_single)
gbs_args = [p.name for p in gbs_sig.parameters.values()]
assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
f'{self.__class__.__name__}' \
' does not support test-time augmentation'
aug_bboxes = []
aug_scores = []
aug_labels = []
for x, img_meta in zip(feats, img_metas):
# only one image in the batch
outs = self.forward(x)
bbox_outputs = self.get_results(
*outs,
img_metas=img_meta,
cfg=self.test_cfg,
rescale=False,
with_nms=False)[0]
aug_bboxes.append(bbox_outputs.bboxes)
aug_scores.append(bbox_outputs.scores)
if len(bbox_outputs) >= 3:
aug_labels.append(bbox_outputs.labels)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = self.merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas)
merged_labels = torch.cat(aug_labels, dim=0) if aug_labels else None
if merged_bboxes.numel() == 0:
det_bboxes = torch.cat([merged_bboxes, merged_scores[:, None]], -1)
return [
(det_bboxes, merged_labels),
]
det_bboxes, keep_idxs = batched_nms(merged_bboxes, merged_scores,
merged_labels, self.test_cfg.nms)
det_bboxes = det_bboxes[:self.test_cfg.max_per_img]
det_labels = merged_labels[keep_idxs][:self.test_cfg.max_per_img]
if rescale:
_det_bboxes = det_bboxes
else:
_det_bboxes = det_bboxes.clone()
_det_bboxes[:, :4] *= det_bboxes.new_tensor(
img_metas[0][0]['scale_factor'])
results = InstanceData()
results.bboxes = _det_bboxes[:, :4]
results.scores = _det_bboxes[:, 4]
results.labels = det_labels
return [results]
| Test det bboxes with test time augmentation, can be applied in
DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
etc.
Args:
feats (list[Tensor]): the outer list indicates test-time
augmentations and inner Tensor should have a shape NxCxHxW,
which contains features for all images in the batch.
img_metas (list[list[dict]]): the outer list indicates test-time
augs (multiscale, flip, etc.) and the inner list indicates
images in a batch. each dict has image information.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is ``bboxes`` with shape (n, 5),
where 5 represent (tl_x, tl_y, br_x, br_y, score).
The shape of the second tensor in the tuple is ``labels``
with shape (n,). The length of list should always be 1.
| 131 | 171 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def aug_test_bboxes(self, feats, img_metas, rescale=False):
# check with_nms argument
gb_sig = signature(self.get_results)
gb_args = [p.name for p in gb_sig.parameters.values()]
gbs_sig = signature(self._get_results_single)
gbs_args = [p.name for p in gbs_sig.parameters.values()]
assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
f'{self.__class__.__name__}' \
' does not support test-time augmentation'
aug_bboxes = []
aug_scores = []
aug_labels = []
for x, img_meta in zip(feats, img_metas):
# only one image in the batch
outs = self.forward(x)
bbox_outputs = self.get_results(
*outs,
img_metas=img_meta,
cfg=self.test_cfg,
rescale=False,
with_nms=False)[0]
aug_bboxes.append(bbox_outputs.bboxes)
aug_scores.append(bbox_outputs.scores)
if len(bbox_outputs) >= 3:
aug_labels.append(bbox_outputs.labels)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = self.merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas)
merged_labels = torch.cat(aug_labels, dim=0) if aug_labels else None
if merged_bboxes.numel() == 0:
det_bboxes = torch.cat([merged_bboxes, merged_scores[:, None]], -1)
return [
(det_bboxes, merged_labels),
]
det_bboxes, keep_idxs = batched_nms(merged_bboxes, merged_scores,
merged_labels, self.test_cfg.nms)
det_bboxes = det_bboxes[:self.test_cfg.max_per_img]
det_labels = merged_labels[keep_idxs][:self.test_cfg.max_per_img]
if rescale:
_det_bboxes = det_bboxes
else:
_det_bboxes = det_bboxes.clone()
_det_bboxes[:, :4] *= det_bboxes.new_tensor(
img_metas[0][0]['scale_factor'])
results = InstanceData()
results.bboxes = _det_bboxes[:, :4]
results.scores = _det_bboxes[:, 4]
results.labels = det_labels
return [results]
```
###Assistant : Test det bboxes with test time augmentation, can be applied in
DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
etc.
Args:
feats (list[Tensor]): the outer list indicates test-time
augmentations and inner Tensor should have a shape NxCxHxW,
which contains features for all images in the batch.
img_metas (list[list[dict]]): the outer list indicates test-time
augs (multiscale, flip, etc.) and the inner list indicates
images in a batch. each dict has image information.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is ``bboxes`` with shape (n, 5),
where 5 represent (tl_x, tl_y, br_x, br_y, score).
The shape of the second tensor in the tuple is ``labels``
with shape (n,). The length of list should always be 1.
|
2,635 | def pauseProducing(self) -> None:
logger.info("[%s] Pause producing", self.id())
self.state = ConnectionStates.PAUSED
| This is called when both the kernel send buffer and the twisted
tcp connection send buffers have become full.
We don't actually have any control over those sizes, so we buffer some
commands ourselves before knifing the connection due to the remote
failing to keep up.
| 46 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def pauseProducing(self) -> None:
logger.info("[%s] Pause producing", self.id())
self.state = ConnectionStates.PAUSED
```
###Assistant : This is called when both the kernel send buffer and the twisted
tcp connection send buffers have become full.
We don't actually have any control over those sizes, so we buffer some
commands ourselves before knifing the connection due to the remote
failing to keep up.
|
2,636 | def throw(self, typ, val=None, tb=None):
if val is None:
if tb is None:
raise typ
val = typ()
if tb is not None:
val = val.with_traceback(tb)
raise val
| Raise an exception in the coroutine.
Return next yielded value or raise StopIteration.
| 13 | 28 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def throw(self, typ, val=None, tb=None):
if val is None:
if tb is None:
raise typ
val = typ()
if tb is not None:
val = val.with_traceback(tb)
raise val
```
###Assistant : Raise an exception in the coroutine.
Return next yielded value or raise StopIteration.
|
2,637 | def _exit_buffer(self) -> None:
self._buffer_index -= 1
self._check_buffer()
| Leave buffer context, and render content if required. | 8 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _exit_buffer(self) -> None:
self._buffer_index -= 1
self._check_buffer()
```
###Assistant : Leave buffer context, and render content if required.
|
2,638 | def flatten_sensors_data(sensor):
if "temp" in sensor["data"]:
sensor["data"]["temperature"] = sensor["data"]["temp"]["c"]
return sensor
| Deconstruct SwitchBot library temp object C/Fº readings from dictionary. | 9 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def flatten_sensors_data(sensor):
if "temp" in sensor["data"]:
sensor["data"]["temperature"] = sensor["data"]["temp"]["c"]
return sensor
```
###Assistant : Deconstruct SwitchBot library temp object C/Fº readings from dictionary.
|
2,639 | def test_shared_embedding_column_with_non_sequence_categorical(self):
with tf.Graph().as_default():
vocabulary_size = 3
sparse_input_a = tf.compat.v1.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2),
)
sparse_input_b = tf.compat.v1.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2),
)
categorical_column_a = (
tf.feature_column.categorical_column_with_identity(
key="aaa", num_buckets=vocabulary_size
)
)
categorical_column_b = (
tf.feature_column.categorical_column_with_identity(
key="bbb", num_buckets=vocabulary_size
)
)
shared_embedding_columns = tf.feature_column.shared_embeddings(
[categorical_column_a, categorical_column_b], dimension=2
)
sequence_input_layer = ksfc.SequenceFeatures(
shared_embedding_columns
)
with self.assertRaisesRegex(
ValueError,
r"In embedding_column: aaa_shared_embedding\. "
r"categorical_column must "
r"be of type SequenceCategoricalColumn to use "
r"SequenceFeatures\.",
):
_, _ = sequence_input_layer(
{"aaa": sparse_input_a, "bbb": sparse_input_b}
)
| Tests that error is raised for non-sequence shared embedding
column. | 10 | 115 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_shared_embedding_column_with_non_sequence_categorical(self):
with tf.Graph().as_default():
vocabulary_size = 3
sparse_input_a = tf.compat.v1.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2),
)
sparse_input_b = tf.compat.v1.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2),
)
categorical_column_a = (
tf.feature_column.categorical_column_with_identity(
key="aaa", num_buckets=vocabulary_size
)
)
categorical_column_b = (
tf.feature_column.categorical_column_with_identity(
key="bbb", num_buckets=vocabulary_size
)
)
shared_embedding_columns = tf.feature_column.shared_embeddings(
[categorical_column_a, categorical_column_b], dimension=2
)
sequence_input_layer = ksfc.SequenceFeatures(
shared_embedding_columns
)
with self.assertRaisesRegex(
ValueError,
r"In embedding_column: aaa_shared_embedding\. "
r"categorical_column must "
r"be of type SequenceCategoricalColumn to use "
r"SequenceFeatures\.",
):
_, _ = sequence_input_layer(
{"aaa": sparse_input_a, "bbb": sparse_input_b}
)
```
###Assistant : Tests that error is raised for non-sequence shared embedding
column.
|
2,640 | def get_next_stock_market_days(last_stock_day, n_next_days) -> list:
n_days = 0
l_pred_days = []
years: list = []
holidays: list = []
if isinstance(last_stock_day, datetime):
while n_days < n_next_days:
last_stock_day += timedelta(hours=24)
year = last_stock_day.date().year
if year not in years:
years.append(year)
holidays += us_market_holidays(year)
# Check if it is a weekend
if last_stock_day.date().weekday() > 4:
continue
# Check if it is a holiday
if last_stock_day.strftime("%Y-%m-%d") in holidays:
continue
# Otherwise stock market is open
n_days += 1
l_pred_days.append(last_stock_day)
else:
while n_days < n_next_days:
l_pred_days.append(last_stock_day + 1 + n_days)
n_days += 1
return l_pred_days
| Gets the next stock market day. Checks against weekends and holidays | 11 | 90 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_next_stock_market_days(last_stock_day, n_next_days) -> list:
n_days = 0
l_pred_days = []
years: list = []
holidays: list = []
if isinstance(last_stock_day, datetime):
while n_days < n_next_days:
last_stock_day += timedelta(hours=24)
year = last_stock_day.date().year
if year not in years:
years.append(year)
holidays += us_market_holidays(year)
# Check if it is a weekend
if last_stock_day.date().weekday() > 4:
continue
# Check if it is a holiday
if last_stock_day.strftime("%Y-%m-%d") in holidays:
continue
# Otherwise stock market is open
n_days += 1
l_pred_days.append(last_stock_day)
else:
while n_days < n_next_days:
l_pred_days.append(last_stock_day + 1 + n_days)
n_days += 1
return l_pred_days
```
###Assistant : Gets the next stock market day. Checks against weekends and holidays
|
2,641 | async def test_max_concurrent_in_progress_functions(extra_req_num):
max_req = 10
a = A(max_num_call=max_req)
# Run more than allowed concurrent async functions should trigger rate limiting
res_arr = await asyncio.gather(
*[a.fn1() if i % 2 == 0 else a.fn2() for i in range(max_req + extra_req_num)]
)
fail_cnt = 0
for ok in res_arr:
fail_cnt += 0 if ok else 1
expected_fail_cnt = max(0, extra_req_num)
assert fail_cnt == expected_fail_cnt, (
f"{expected_fail_cnt} out of {max_req + extra_req_num} "
f"concurrent runs should fail with max={max_req} but {fail_cnt}."
)
assert a.num_call_ == 0, "All requests should be done"
@pytest.mark.asyncio
@pytest.mark.parametrize(
"failures",
[
[True, True, True, True, True],
[False, False, False, False, False],
[False, True, False, True, False],
[False, False, False, True, True],
[True, True, False, False, False],
],
) | Test rate limiting for concurrent in-progress requests on StateHead | 9 | 120 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
async def test_max_concurrent_in_progress_functions(extra_req_num):
max_req = 10
a = A(max_num_call=max_req)
# Run more than allowed concurrent async functions should trigger rate limiting
res_arr = await asyncio.gather(
*[a.fn1() if i % 2 == 0 else a.fn2() for i in range(max_req + extra_req_num)]
)
fail_cnt = 0
for ok in res_arr:
fail_cnt += 0 if ok else 1
expected_fail_cnt = max(0, extra_req_num)
assert fail_cnt == expected_fail_cnt, (
f"{expected_fail_cnt} out of {max_req + extra_req_num} "
f"concurrent runs should fail with max={max_req} but {fail_cnt}."
)
assert a.num_call_ == 0, "All requests should be done"
@pytest.mark.asyncio
@pytest.mark.parametrize(
"failures",
[
[True, True, True, True, True],
[False, False, False, False, False],
[False, True, False, True, False],
[False, False, False, True, True],
[True, True, False, False, False],
],
)
```
###Assistant : Test rate limiting for concurrent in-progress requests on StateHead
|
2,642 | def transpose_qkv(X, num_heads):
# Shape of input `X`:
# (`batch_size`, no. of queries or key-value pairs, `num_hiddens`).
# Shape of output `X`:
# (`batch_size`, no. of queries or key-value pairs, `num_heads`,
# `num_hiddens` / `num_heads`)
X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)
# Shape of output `X`:
# (`batch_size`, `num_heads`, no. of queries or key-value pairs,
# `num_hiddens` / `num_heads`)
X = X.transpose(0, 2, 1, 3)
# Shape of `output`:
# (`batch_size` * `num_heads`, no. of queries or key-value pairs,
# `num_hiddens` / `num_heads`)
return X.reshape(-1, X.shape[2], X.shape[3])
| Transposition for parallel computation of multiple attention heads.
Defined in :numref:`sec_multihead-attention` | 11 | 87 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def transpose_qkv(X, num_heads):
# Shape of input `X`:
# (`batch_size`, no. of queries or key-value pairs, `num_hiddens`).
# Shape of output `X`:
# (`batch_size`, no. of queries or key-value pairs, `num_heads`,
# `num_hiddens` / `num_heads`)
X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)
# Shape of output `X`:
# (`batch_size`, `num_heads`, no. of queries or key-value pairs,
# `num_hiddens` / `num_heads`)
X = X.transpose(0, 2, 1, 3)
# Shape of `output`:
# (`batch_size` * `num_heads`, no. of queries or key-value pairs,
# `num_hiddens` / `num_heads`)
return X.reshape(-1, X.shape[2], X.shape[3])
```
###Assistant : Transposition for parallel computation of multiple attention heads.
Defined in :numref:`sec_multihead-attention`
|
2,643 | def current_state(self, session=NEW_SESSION) -> str:
return (
session.query(TaskInstance.state)
.filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id == self.task_id,
TaskInstance.run_id == self.run_id,
)
.scalar()
)
|
Get the very latest state from the database, if a session is passed,
we use and looking up the state becomes part of the session, otherwise
a new session is used.
:param session: SQLAlchemy ORM Session
| 36 | 21 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def current_state(self, session=NEW_SESSION) -> str:
return (
session.query(TaskInstance.state)
.filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id == self.task_id,
TaskInstance.run_id == self.run_id,
)
.scalar()
)
```
###Assistant :
Get the very latest state from the database, if a session is passed,
we use and looking up the state becomes part of the session, otherwise
a new session is used.
:param session: SQLAlchemy ORM Session
|
2,644 | def house_graph(create_using=None):
description = [
"adjacencylist",
"House Graph",
5,
[[2, 3], [1, 4], [1, 4, 5], [2, 3, 5], [3, 4]],
]
G = make_small_undirected_graph(description, create_using)
return G
|
Returns the House graph (square with triangle on top)
The house graph is a simple undirected graph with
5 nodes and 6 edges [1]_.
Parameters
----------
create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.
Returns
-------
G : networkx Graph
House graph in the form of a square with a triangle on top
References
----------
.. [1] https://mathworld.wolfram.com/HouseGraph.html
| 68 | 28 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def house_graph(create_using=None):
description = [
"adjacencylist",
"House Graph",
5,
[[2, 3], [1, 4], [1, 4, 5], [2, 3, 5], [3, 4]],
]
G = make_small_undirected_graph(description, create_using)
return G
```
###Assistant :
Returns the House graph (square with triangle on top)
The house graph is a simple undirected graph with
5 nodes and 6 edges [1]_.
Parameters
----------
create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.
Returns
-------
G : networkx Graph
House graph in the form of a square with a triangle on top
References
----------
.. [1] https://mathworld.wolfram.com/HouseGraph.html
|
2,645 | def check_header_validity(header):
name, value = header
for part in header:
if type(part) not in HEADER_VALIDATORS:
raise InvalidHeader(
f"Header part ({part!r}) from {{{name!r}: {value!r}}} must be "
f"of type str or bytes, not {type(part)}"
)
_validate_header_part(name, "name", HEADER_VALIDATORS[type(name)][0])
_validate_header_part(value, "value", HEADER_VALIDATORS[type(value)][1])
| Verifies that header parts don't contain leading whitespace
reserved characters, or return characters.
:param header: tuple, in the format (name, value).
| 21 | 40 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def check_header_validity(header):
name, value = header
for part in header:
if type(part) not in HEADER_VALIDATORS:
raise InvalidHeader(
f"Header part ({part!r}) from {{{name!r}: {value!r}}} must be "
f"of type str or bytes, not {type(part)}"
)
_validate_header_part(name, "name", HEADER_VALIDATORS[type(name)][0])
_validate_header_part(value, "value", HEADER_VALIDATORS[type(value)][1])
```
###Assistant : Verifies that header parts don't contain leading whitespace
reserved characters, or return characters.
:param header: tuple, in the format (name, value).
|
2,646 | def test_orderline_query(staff_api_client, permission_manage_orders, fulfilled_order):
order = fulfilled_order
query =
line = order.lines.first()
metadata_key = "md key"
metadata_value = "md value"
line.store_value_in_private_metadata({metadata_key: metadata_value})
line.store_value_in_metadata({metadata_key: metadata_value})
line.save()
staff_api_client.user.user_permissions.add(permission_manage_orders)
response = staff_api_client.post_graphql(query)
content = get_graphql_content(response)
order_data = content["data"]["orders"]["edges"][0]["node"]
first_order_data_line = order_data["lines"][0]
variant_id = graphene.Node.to_global_id("ProductVariant", line.variant.pk)
assert first_order_data_line["thumbnail"] is None
assert first_order_data_line["variant"]["id"] == variant_id
assert first_order_data_line["quantity"] == line.quantity
assert first_order_data_line["unitPrice"]["currency"] == line.unit_price.currency
assert first_order_data_line["metadata"] == [
{"key": metadata_key, "value": metadata_value}
]
assert first_order_data_line["privateMetadata"] == [
{"key": metadata_key, "value": metadata_value}
]
expected_unit_price = Money(
amount=str(first_order_data_line["unitPrice"]["gross"]["amount"]),
currency="USD",
)
assert first_order_data_line["totalPrice"]["currency"] == line.unit_price.currency
assert expected_unit_price == line.unit_price.gross
expected_total_price = Money(
amount=str(first_order_data_line["totalPrice"]["gross"]["amount"]),
currency="USD",
)
assert expected_total_price == line.unit_price.gross * line.quantity
allocation = line.allocations.first()
allocation_id = graphene.Node.to_global_id("Allocation", allocation.pk)
warehouse_id = graphene.Node.to_global_id(
"Warehouse", allocation.stock.warehouse.pk
)
assert first_order_data_line["allocations"] == [
{
"id": allocation_id,
"quantity": allocation.quantity_allocated,
"warehouse": {"id": warehouse_id},
}
]
|
query OrdersQuery {
orders(first: 1) {
edges {
node {
lines {
thumbnail(size: 540) {
url
}
variant {
id
}
quantity
allocations {
id
quantity
warehouse {
id
}
}
unitPrice {
currency
gross {
amount
}
}
totalPrice {
currency
gross {
amount
}
}
metadata {
key
value
}
privateMetadata {
key
value
}
}
}
}
}
}
| 62 | 129 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_orderline_query(staff_api_client, permission_manage_orders, fulfilled_order):
order = fulfilled_order
query =
line = order.lines.first()
metadata_key = "md key"
metadata_value = "md value"
line.store_value_in_private_metadata({metadata_key: metadata_value})
line.store_value_in_metadata({metadata_key: metadata_value})
line.save()
staff_api_client.user.user_permissions.add(permission_manage_orders)
response = staff_api_client.post_graphql(query)
content = get_graphql_content(response)
order_data = content["data"]["orders"]["edges"][0]["node"]
first_order_data_line = order_data["lines"][0]
variant_id = graphene.Node.to_global_id("ProductVariant", line.variant.pk)
assert first_order_data_line["thumbnail"] is None
assert first_order_data_line["variant"]["id"] == variant_id
assert first_order_data_line["quantity"] == line.quantity
assert first_order_data_line["unitPrice"]["currency"] == line.unit_price.currency
assert first_order_data_line["metadata"] == [
{"key": metadata_key, "value": metadata_value}
]
assert first_order_data_line["privateMetadata"] == [
{"key": metadata_key, "value": metadata_value}
]
expected_unit_price = Money(
amount=str(first_order_data_line["unitPrice"]["gross"]["amount"]),
currency="USD",
)
assert first_order_data_line["totalPrice"]["currency"] == line.unit_price.currency
assert expected_unit_price == line.unit_price.gross
expected_total_price = Money(
amount=str(first_order_data_line["totalPrice"]["gross"]["amount"]),
currency="USD",
)
assert expected_total_price == line.unit_price.gross * line.quantity
allocation = line.allocations.first()
allocation_id = graphene.Node.to_global_id("Allocation", allocation.pk)
warehouse_id = graphene.Node.to_global_id(
"Warehouse", allocation.stock.warehouse.pk
)
assert first_order_data_line["allocations"] == [
{
"id": allocation_id,
"quantity": allocation.quantity_allocated,
"warehouse": {"id": warehouse_id},
}
]
```
###Assistant :
query OrdersQuery {
orders(first: 1) {
edges {
node {
lines {
thumbnail(size: 540) {
url
}
variant {
id
}
quantity
allocations {
id
quantity
warehouse {
id
}
}
unitPrice {
currency
gross {
amount
}
}
totalPrice {
currency
gross {
amount
}
}
metadata {
key
value
}
privateMetadata {
key
value
}
}
}
}
}
}
|
2,647 | def in4_pseudoheader(proto, u, plen):
# type: (int, IP, int) -> bytes
if u.len is not None:
if u.ihl is None:
olen = sum(len(x) for x in u.options)
ihl = 5 + olen // 4 + (1 if olen % 4 else 0)
else:
ihl = u.ihl
ln = max(u.len - 4 * ihl, 0)
else:
ln = plen
# Filter out IPOption_LSRR and IPOption_SSRR
sr_options = [opt for opt in u.options if isinstance(opt, IPOption_LSRR) or
isinstance(opt, IPOption_SSRR)]
len_sr_options = len(sr_options)
if len_sr_options == 1 and len(sr_options[0].routers):
# The checksum must be computed using the final
# destination address
u.dst = sr_options[0].routers[-1]
elif len_sr_options > 1:
message = "Found %d Source Routing Options! "
message += "Falling back to IP.dst for checksum computation."
warning(message, len_sr_options)
return struct.pack("!4s4sHH",
inet_pton(socket.AF_INET, u.src),
inet_pton(socket.AF_INET, u.dst),
proto,
ln)
| IPv4 Pseudo Header as defined in RFC793 as bytes
:param proto: value of upper layer protocol
:param u: IP layer instance
:param plen: the length of the upper layer and payload
| 31 | 132 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def in4_pseudoheader(proto, u, plen):
# type: (int, IP, int) -> bytes
if u.len is not None:
if u.ihl is None:
olen = sum(len(x) for x in u.options)
ihl = 5 + olen // 4 + (1 if olen % 4 else 0)
else:
ihl = u.ihl
ln = max(u.len - 4 * ihl, 0)
else:
ln = plen
# Filter out IPOption_LSRR and IPOption_SSRR
sr_options = [opt for opt in u.options if isinstance(opt, IPOption_LSRR) or
isinstance(opt, IPOption_SSRR)]
len_sr_options = len(sr_options)
if len_sr_options == 1 and len(sr_options[0].routers):
# The checksum must be computed using the final
# destination address
u.dst = sr_options[0].routers[-1]
elif len_sr_options > 1:
message = "Found %d Source Routing Options! "
message += "Falling back to IP.dst for checksum computation."
warning(message, len_sr_options)
return struct.pack("!4s4sHH",
inet_pton(socket.AF_INET, u.src),
inet_pton(socket.AF_INET, u.dst),
proto,
ln)
```
###Assistant : IPv4 Pseudo Header as defined in RFC793 as bytes
:param proto: value of upper layer protocol
:param u: IP layer instance
:param plen: the length of the upper layer and payload
|
2,648 | def doc_resample_fillna(method, refer_to, params=None, overwrite_template_params=False):
action = f"fill missing values in each group independently using {method} method"
params_substitution = "limit : int\n"
if params:
params_substitution = (
params
if overwrite_template_params
else format_string(
"{params}\n{params_substitution}",
params=params,
params_substitution=params_substitution,
)
)
build_rules = "- QueryCompiler contains unsampled data with missing values filled."
return doc_resample(
action=action,
extra_params=params_substitution,
build_rules=build_rules,
refer_to=refer_to,
)
doc_dt = partial(
doc_qc_method,
template=,
one_column_method=True,
refer_to_module_name="Series.dt",
)
doc_dt_timestamp = partial(doc_dt, dt_type="datetime")
doc_dt_interval = partial(doc_dt, dt_type="interval")
doc_dt_period = partial(doc_dt, dt_type="period")
doc_dt_round = partial(
doc_qc_method,
template=,
one_column_method=True,
refer_to_module_name="Series.dt",
)
doc_str_method = partial(
doc_qc_method,
template=,
one_column_method=True,
refer_to_module_name="Series.str",
)
|
Build decorator which adds docstring for the resample fillna query compiler method.
Parameters
----------
method : str
Fillna method name.
refer_to : str
Method name in ``modin.pandas.resample.Resampler`` module to refer to for
more information about parameters and output format.
params : str, optional
Method parameters in the NumPy docstyle format to substitute
to the docstring template.
overwrite_template_params : bool, default: False
If `params` is specified indicates whether to overwrite method parameters in
the docstring template or append then at the end.
Returns
-------
callable
Get {prop} for each {dt_type} value.
{params}
Returns
-------
BaseQueryCompiler
New QueryCompiler with the same shape as `self`, where each element is
{prop} for the corresponding {dt_type} value.
Perform {refer_to} operation on the underlying time-series data to the specified `freq`.
Parameters
----------
freq : str
ambiguous : {{"raise", "infer", "NaT"}} or bool mask, default: "raise"
nonexistent : {{"raise", "shift_forward", "shift_backward", "NaT"}} or timedelta, default: "raise"
Returns
-------
BaseQueryCompiler
New QueryCompiler with performed {refer_to} operation on every element.
Apply "{refer_to}" function to each string value in QueryCompiler.
{params}
Returns
-------
BaseQueryCompiler
New QueryCompiler containing the result of execution of the "{refer_to}" function
against each string element.
| 189 | 91 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def doc_resample_fillna(method, refer_to, params=None, overwrite_template_params=False):
action = f"fill missing values in each group independently using {method} method"
params_substitution = "limit : int\n"
if params:
params_substitution = (
params
if overwrite_template_params
else format_string(
"{params}\n{params_substitution}",
params=params,
params_substitution=params_substitution,
)
)
build_rules = "- QueryCompiler contains unsampled data with missing values filled."
return doc_resample(
action=action,
extra_params=params_substitution,
build_rules=build_rules,
refer_to=refer_to,
)
doc_dt = partial(
doc_qc_method,
template=,
one_column_method=True,
refer_to_module_name="Series.dt",
)
doc_dt_timestamp = partial(doc_dt, dt_type="datetime")
doc_dt_interval = partial(doc_dt, dt_type="interval")
doc_dt_period = partial(doc_dt, dt_type="period")
doc_dt_round = partial(
doc_qc_method,
template=,
one_column_method=True,
refer_to_module_name="Series.dt",
)
doc_str_method = partial(
doc_qc_method,
template=,
one_column_method=True,
refer_to_module_name="Series.str",
)
```
###Assistant :
Build decorator which adds docstring for the resample fillna query compiler method.
Parameters
----------
method : str
Fillna method name.
refer_to : str
Method name in ``modin.pandas.resample.Resampler`` module to refer to for
more information about parameters and output format.
params : str, optional
Method parameters in the NumPy docstyle format to substitute
to the docstring template.
overwrite_template_params : bool, default: False
If `params` is specified indicates whether to overwrite method parameters in
the docstring template or append then at the end.
Returns
-------
callable
Get {prop} for each {dt_type} value.
{params}
Returns
-------
BaseQueryCompiler
New QueryCompiler with the same shape as `self`, where each element is
{prop} for the corresponding {dt_type} value.
Perform {refer_to} operation on the underlying time-series data to the specified `freq`.
Parameters
----------
freq : str
ambiguous : {{"raise", "infer", "NaT"}} or bool mask, default: "raise"
nonexistent : {{"raise", "shift_forward", "shift_backward", "NaT"}} or timedelta, default: "raise"
Returns
-------
BaseQueryCompiler
New QueryCompiler with performed {refer_to} operation on every element.
Apply "{refer_to}" function to each string value in QueryCompiler.
{params}
Returns
-------
BaseQueryCompiler
New QueryCompiler containing the result of execution of the "{refer_to}" function
against each string element.
|
2,649 | def delete_events(ref_type, ref_name):
events = (
frappe.db.sql_list(
,
(ref_type, ref_name),
)
or []
)
if events:
frappe.delete_doc("Event", events, for_reload=True)
| SELECT
distinct `tabEvent`.name
from
`tabEvent`, `tabEvent Participants`
where
`tabEvent`.name = `tabEvent Participants`.parent
and `tabEvent Participants`.reference_doctype = %s
and `tabEvent Participants`.reference_docname = %s
| 22 | 19 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def delete_events(ref_type, ref_name):
events = (
frappe.db.sql_list(
,
(ref_type, ref_name),
)
or []
)
if events:
frappe.delete_doc("Event", events, for_reload=True)
```
###Assistant : SELECT
distinct `tabEvent`.name
from
`tabEvent`, `tabEvent Participants`
where
`tabEvent`.name = `tabEvent Participants`.parent
and `tabEvent Participants`.reference_doctype = %s
and `tabEvent Participants`.reference_docname = %s
|
2,650 | def check_started(self) -> ReplicaStartupStatus:
status, version = self._actor.check_ready()
if status == ReplicaStartupStatus.SUCCEEDED:
# Re-assign DeploymentVersion if start / update / recover succeeded
# by reading re-computed version in RayServeReplica
if version is not None:
self._version = version
return status
| Check if the replica has started. If so, transition to RUNNING.
Should handle the case where the replica has already stopped.
Returns:
status: Most recent state of replica by
querying actor obj ref
| 33 | 39 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def check_started(self) -> ReplicaStartupStatus:
status, version = self._actor.check_ready()
if status == ReplicaStartupStatus.SUCCEEDED:
# Re-assign DeploymentVersion if start / update / recover succeeded
# by reading re-computed version in RayServeReplica
if version is not None:
self._version = version
return status
```
###Assistant : Check if the replica has started. If so, transition to RUNNING.
Should handle the case where the replica has already stopped.
Returns:
status: Most recent state of replica by
querying actor obj ref
|
2,651 | async def test_battery_low(hass, utcnow):
helper = await setup_test_component(
hass, create_battery_level_sensor, suffix="battery"
)
state = await helper.async_update(
ServicesTypes.BATTERY_SERVICE,
{
CharacteristicsTypes.BATTERY_LEVEL: 1,
CharacteristicsTypes.STATUS_LO_BATT: 0,
},
)
assert state.attributes["icon"] == "mdi:battery-10"
state = await helper.async_update(
ServicesTypes.BATTERY_SERVICE,
{
CharacteristicsTypes.BATTERY_LEVEL: 1,
CharacteristicsTypes.STATUS_LO_BATT: 1,
},
)
assert state.attributes["icon"] == "mdi:battery-alert"
| Test reading the state of a HomeKit battery's low state. | 10 | 44 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
async def test_battery_low(hass, utcnow):
helper = await setup_test_component(
hass, create_battery_level_sensor, suffix="battery"
)
state = await helper.async_update(
ServicesTypes.BATTERY_SERVICE,
{
CharacteristicsTypes.BATTERY_LEVEL: 1,
CharacteristicsTypes.STATUS_LO_BATT: 0,
},
)
assert state.attributes["icon"] == "mdi:battery-10"
state = await helper.async_update(
ServicesTypes.BATTERY_SERVICE,
{
CharacteristicsTypes.BATTERY_LEVEL: 1,
CharacteristicsTypes.STATUS_LO_BATT: 1,
},
)
assert state.attributes["icon"] == "mdi:battery-alert"
```
###Assistant : Test reading the state of a HomeKit battery's low state.
|
2,652 | def _hyab(self, y_true, y_pred):
delta = y_true - y_pred
root = K.sqrt(K.clip(K.pow(delta[..., 0:1], 2), self._epsilon, None))
delta_norm = frobenius_norm(delta[..., 1:3])
return root + delta_norm
| Compute the HyAB distance between true and predicted images.
Parameters
----------
y_true: :class:`plaidml.tile.Value`
The ground truth batch of images in standard or Hunt-adjusted L*A*B* color space
y_pred: :class:`plaidml.tile.Value`
The predicted batch of images in in standard or Hunt-adjusted L*A*B* color space
Returns
-------
:class:`plaidml.tile.Value`
image tensor containing the per-pixel HyAB distances between true and predicted images
| 56 | 24 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _hyab(self, y_true, y_pred):
delta = y_true - y_pred
root = K.sqrt(K.clip(K.pow(delta[..., 0:1], 2), self._epsilon, None))
delta_norm = frobenius_norm(delta[..., 1:3])
return root + delta_norm
```
###Assistant : Compute the HyAB distance between true and predicted images.
Parameters
----------
y_true: :class:`plaidml.tile.Value`
The ground truth batch of images in standard or Hunt-adjusted L*A*B* color space
y_pred: :class:`plaidml.tile.Value`
The predicted batch of images in in standard or Hunt-adjusted L*A*B* color space
Returns
-------
:class:`plaidml.tile.Value`
image tensor containing the per-pixel HyAB distances between true and predicted images
|
2,653 | def test_dry_run(self, run_cleanup_mock, dry_run_arg, expected):
args = self.parser.parse_args(
[
'db',
'clean',
'--clean-before-timestamp',
'2021-01-01',
*dry_run_arg,
]
)
db_command.cleanup_tables(args)
run_cleanup_mock.assert_called_once_with(
table_names=None,
dry_run=expected,
clean_before_timestamp=pendulum.parse('2021-01-01 00:00:00Z'),
verbose=False,
confirm=True,
skip_archive=False,
)
|
When tz included in the string then default timezone should not be used.
| 13 | 26 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_dry_run(self, run_cleanup_mock, dry_run_arg, expected):
args = self.parser.parse_args(
[
'db',
'clean',
'--clean-before-timestamp',
'2021-01-01',
*dry_run_arg,
]
)
db_command.cleanup_tables(args)
run_cleanup_mock.assert_called_once_with(
table_names=None,
dry_run=expected,
clean_before_timestamp=pendulum.parse('2021-01-01 00:00:00Z'),
verbose=False,
confirm=True,
skip_archive=False,
)
```
###Assistant :
When tz included in the string then default timezone should not be used.
|
2,654 | def async_dismiss_setup_message(hass, entry_id):
persistent_notification.async_dismiss(hass, entry_id)
| Dismiss persistent notification and remove QR code. | 7 | 5 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def async_dismiss_setup_message(hass, entry_id):
persistent_notification.async_dismiss(hass, entry_id)
```
###Assistant : Dismiss persistent notification and remove QR code.
|
2,655 | def get_containing_app_config(self, object_name):
self.check_apps_ready()
candidates = []
for app_config in self.app_configs.values():
if object_name.startswith(app_config.name):
subpath = object_name[len(app_config.name) :]
if subpath == "" or subpath[0] == ".":
candidates.append(app_config)
if candidates:
return sorted(candidates, key=lambda ac: -len(ac.name))[0]
|
Look for an app config containing a given object.
object_name is the dotted Python path to the object.
Return the app config for the inner application in case of nesting.
Return None if the object isn't in any registered app config.
| 41 | 33 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_containing_app_config(self, object_name):
self.check_apps_ready()
candidates = []
for app_config in self.app_configs.values():
if object_name.startswith(app_config.name):
subpath = object_name[len(app_config.name) :]
if subpath == "" or subpath[0] == ".":
candidates.append(app_config)
if candidates:
return sorted(candidates, key=lambda ac: -len(ac.name))[0]
```
###Assistant :
Look for an app config containing a given object.
object_name is the dotted Python path to the object.
Return the app config for the inner application in case of nesting.
Return None if the object isn't in any registered app config.
|
2,656 | def _track_variables(self, value):
for val in tf.nest.flatten(value):
if isinstance(val, tf.Variable):
self._track_variable(val)
elif tf_utils.is_extension_type(val):
# Manually expand extension types to track resource variables.
nested_vals = tf_utils.type_spec_from_value(val)._to_components(
val
)
self._track_variables(nested_vals)
| Tracks `Variable`s including `Variable`s in `CompositeTensor`s. | 6 | 28 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _track_variables(self, value):
for val in tf.nest.flatten(value):
if isinstance(val, tf.Variable):
self._track_variable(val)
elif tf_utils.is_extension_type(val):
# Manually expand extension types to track resource variables.
nested_vals = tf_utils.type_spec_from_value(val)._to_components(
val
)
self._track_variables(nested_vals)
```
###Assistant : Tracks `Variable`s including `Variable`s in `CompositeTensor`s.
|
2,657 | def _create_vhost_v2(self, node):
addrs = set()
for param in node.parameters:
addr = obj.Addr.fromstring(param)
if addr:
addrs.add(addr)
is_ssl = False
# Exclusion to match the behavior in get_virtual_hosts_v2
sslengine = node.find_directives("SSLEngine", exclude=False)
if sslengine:
for directive in sslengine:
if directive.parameters[0].lower() == "on":
is_ssl = True
break
# "SSLEngine on" might be set outside of <VirtualHost>
# Treat vhosts with port 443 as ssl vhosts
for addr in addrs:
if addr.get_port() == "443":
is_ssl = True
enabled = apache_util.included_in_paths(node.filepath, self.parsed_paths)
macro = False
# Check if the VirtualHost is contained in a mod_macro block
if node.find_ancestors("Macro"):
macro = True
vhost = obj.VirtualHost(
node.filepath, None, addrs, is_ssl, enabled, modmacro=macro, node=node
)
self._populate_vhost_names_v2(vhost)
return vhost
| Used by get_virtual_hosts_v2 to create vhost objects using ParserNode
interfaces.
:param interfaces.BlockNode node: The BlockNode object of VirtualHost block
:returns: newly created vhost
:rtype: :class:`~certbot_apache.obj.VirtualHost`
| 25 | 111 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _create_vhost_v2(self, node):
addrs = set()
for param in node.parameters:
addr = obj.Addr.fromstring(param)
if addr:
addrs.add(addr)
is_ssl = False
# Exclusion to match the behavior in get_virtual_hosts_v2
sslengine = node.find_directives("SSLEngine", exclude=False)
if sslengine:
for directive in sslengine:
if directive.parameters[0].lower() == "on":
is_ssl = True
break
# "SSLEngine on" might be set outside of <VirtualHost>
# Treat vhosts with port 443 as ssl vhosts
for addr in addrs:
if addr.get_port() == "443":
is_ssl = True
enabled = apache_util.included_in_paths(node.filepath, self.parsed_paths)
macro = False
# Check if the VirtualHost is contained in a mod_macro block
if node.find_ancestors("Macro"):
macro = True
vhost = obj.VirtualHost(
node.filepath, None, addrs, is_ssl, enabled, modmacro=macro, node=node
)
self._populate_vhost_names_v2(vhost)
return vhost
```
###Assistant : Used by get_virtual_hosts_v2 to create vhost objects using ParserNode
interfaces.
:param interfaces.BlockNode node: The BlockNode object of VirtualHost block
:returns: newly created vhost
:rtype: :class:`~certbot_apache.obj.VirtualHost`
|
2,658 | def insert_on(self, path, loc=None, replace=False):
loc = loc or self.location
if not loc:
return
nloc = _normalize_cached(loc)
bdir = os.path.dirname(nloc)
npath = [(p and _normalize_cached(p) or p) for p in path]
for p, item in enumerate(npath):
if item == nloc:
if replace:
break
else:
# don't modify path (even removing duplicates) if
# found and not replace
return
elif item == bdir and self.precedence == EGG_DIST:
# if it's an .egg, give it precedence over its directory
# UNLESS it's already been added to sys.path and replace=False
if (not replace) and nloc in npath[p:]:
return
if path is sys.path:
self.check_version_conflict()
path.insert(p, loc)
npath.insert(p, nloc)
break
else:
if path is sys.path:
self.check_version_conflict()
if replace:
path.insert(0, loc)
else:
path.append(loc)
return
# p is the spot where we found or inserted loc; now remove duplicates
while True:
try:
np = npath.index(nloc, p + 1)
except ValueError:
break
else:
del npath[np], path[np]
# ha!
p = np
return
| Ensure self.location is on path
If replace=False (default):
- If location is already in path anywhere, do nothing.
- Else:
- If it's an egg and its parent directory is on path,
insert just ahead of the parent.
- Else: add to the end of path.
If replace=True:
- If location is already on path anywhere (not eggs)
or higher priority than its parent (eggs)
do nothing.
- Else:
- If it's an egg and its parent directory is on path,
insert just ahead of the parent,
removing any lower-priority entries.
- Else: add it to the front of path.
| 100 | 154 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def insert_on(self, path, loc=None, replace=False):
loc = loc or self.location
if not loc:
return
nloc = _normalize_cached(loc)
bdir = os.path.dirname(nloc)
npath = [(p and _normalize_cached(p) or p) for p in path]
for p, item in enumerate(npath):
if item == nloc:
if replace:
break
else:
# don't modify path (even removing duplicates) if
# found and not replace
return
elif item == bdir and self.precedence == EGG_DIST:
# if it's an .egg, give it precedence over its directory
# UNLESS it's already been added to sys.path and replace=False
if (not replace) and nloc in npath[p:]:
return
if path is sys.path:
self.check_version_conflict()
path.insert(p, loc)
npath.insert(p, nloc)
break
else:
if path is sys.path:
self.check_version_conflict()
if replace:
path.insert(0, loc)
else:
path.append(loc)
return
# p is the spot where we found or inserted loc; now remove duplicates
while True:
try:
np = npath.index(nloc, p + 1)
except ValueError:
break
else:
del npath[np], path[np]
# ha!
p = np
return
```
###Assistant : Ensure self.location is on path
If replace=False (default):
- If location is already in path anywhere, do nothing.
- Else:
- If it's an egg and its parent directory is on path,
insert just ahead of the parent.
- Else: add to the end of path.
If replace=True:
- If location is already on path anywhere (not eggs)
or higher priority than its parent (eggs)
do nothing.
- Else:
- If it's an egg and its parent directory is on path,
insert just ahead of the parent,
removing any lower-priority entries.
- Else: add it to the front of path.
|
2,659 | def serving_output(self, output):
pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = (
tf.convert_to_tensor(output.cross_attentions)
if self.config.output_attentions and self.config.add_cross_attention
else None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=output.hidden_states,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
@add_start_docstrings(
,
XGLM_START_DOCSTRING,
) |
The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
| 20 | 47 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def serving_output(self, output):
pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = (
tf.convert_to_tensor(output.cross_attentions)
if self.config.output_attentions and self.config.add_cross_attention
else None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=output.hidden_states,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
@add_start_docstrings(
,
XGLM_START_DOCSTRING,
)
```
###Assistant :
The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
|
2,660 | def _apply_func_to_list_of_partitions(cls, func, partitions, **kwargs):
preprocessed_map_func = cls.preprocess_func(func)
key_futures = RayWrapper.materialize(
[
partition.apply(preprocessed_map_func, **kwargs)
for partition in partitions
]
)
gpu_managers = [partition.get_gpu_manager() for partition in partitions]
return cls._create_partitions(key_futures, gpu_managers)
|
Apply `func` to a list of remote partitions from `partitions`.
Parameters
----------
func : callable
The function to apply.
partitions : np.ndarray
NumPy array with partitions.
**kwargs : dict
Additional keywords arguments to be passed in `func`.
Returns
-------
np.ndarray
A NumPy array of ``cuDFOnRayDataframePartition`` objects.
Notes
-----
This preprocesses the `func` first before applying it to the partitions.
| 59 | 30 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _apply_func_to_list_of_partitions(cls, func, partitions, **kwargs):
preprocessed_map_func = cls.preprocess_func(func)
key_futures = RayWrapper.materialize(
[
partition.apply(preprocessed_map_func, **kwargs)
for partition in partitions
]
)
gpu_managers = [partition.get_gpu_manager() for partition in partitions]
return cls._create_partitions(key_futures, gpu_managers)
```
###Assistant :
Apply `func` to a list of remote partitions from `partitions`.
Parameters
----------
func : callable
The function to apply.
partitions : np.ndarray
NumPy array with partitions.
**kwargs : dict
Additional keywords arguments to be passed in `func`.
Returns
-------
np.ndarray
A NumPy array of ``cuDFOnRayDataframePartition`` objects.
Notes
-----
This preprocesses the `func` first before applying it to the partitions.
|
2,661 | def _execute_impl(self, *args, **kwargs) -> ObjectRef:
return self._deployment_function_handle.remote(
*self._bound_args, **self._bound_kwargs
)
| Executor of DeploymentNode getting called each time on dag.execute.
The execute implementation is recursive, that is, the method nodes will
receive whatever this method returns. We return a handle here so method
node can directly call upon.
| 37 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _execute_impl(self, *args, **kwargs) -> ObjectRef:
return self._deployment_function_handle.remote(
*self._bound_args, **self._bound_kwargs
)
```
###Assistant : Executor of DeploymentNode getting called each time on dag.execute.
The execute implementation is recursive, that is, the method nodes will
receive whatever this method returns. We return a handle here so method
node can directly call upon.
|
2,662 | def save_img(path, x, data_format=None, file_format=None, scale=True, **kwargs):
if data_format is None:
data_format = backend.image_data_format()
img = array_to_img(x, data_format=data_format, scale=scale)
if img.mode == 'RGBA' and (file_format == 'jpg' or file_format == 'jpeg'):
warnings.warn('The JPG format does not support '
'RGBA images, converting to RGB.')
img = img.convert('RGB')
img.save(path, format=file_format, **kwargs)
@keras_export('keras.utils.load_img', 'keras.preprocessing.image.load_img') | Saves an image stored as a Numpy array to a path or file object.
Args:
path: Path or file object.
x: Numpy array.
data_format: Image data format, either "channels_first" or
"channels_last".
file_format: Optional file format override. If omitted, the format to use
is determined from the filename extension. If a file object was used
instead of a filename, this parameter should always be used.
scale: Whether to rescale image values to be within `[0, 255]`.
**kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
| 82 | 51 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def save_img(path, x, data_format=None, file_format=None, scale=True, **kwargs):
if data_format is None:
data_format = backend.image_data_format()
img = array_to_img(x, data_format=data_format, scale=scale)
if img.mode == 'RGBA' and (file_format == 'jpg' or file_format == 'jpeg'):
warnings.warn('The JPG format does not support '
'RGBA images, converting to RGB.')
img = img.convert('RGB')
img.save(path, format=file_format, **kwargs)
@keras_export('keras.utils.load_img', 'keras.preprocessing.image.load_img')
```
###Assistant : Saves an image stored as a Numpy array to a path or file object.
Args:
path: Path or file object.
x: Numpy array.
data_format: Image data format, either "channels_first" or
"channels_last".
file_format: Optional file format override. If omitted, the format to use
is determined from the filename extension. If a file object was used
instead of a filename, this parameter should always be used.
scale: Whether to rescale image values to be within `[0, 255]`.
**kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
|
2,663 | def log_commenting_changes(self, changes, revision):
for comment in changes["new_comments"]:
comment.log_create(page_revision=revision, user=self.request.user)
for comment in changes["edited_comments"]:
comment.log_edit(page_revision=revision, user=self.request.user)
for comment in changes["resolved_comments"]:
comment.log_resolve(page_revision=revision, user=self.request.user)
for comment in changes["deleted_comments"]:
comment.log_delete(page_revision=revision, user=self.request.user)
for comment, replies in changes["new_replies"]:
for reply in replies:
reply.log_create(page_revision=revision, user=self.request.user)
for comment, replies in changes["edited_replies"]:
for reply in replies:
reply.log_edit(page_revision=revision, user=self.request.user)
for comment, replies in changes["deleted_replies"]:
for reply in replies:
reply.log_delete(page_revision=revision, user=self.request.user)
|
Generates log entries for any changes made to comments or replies.
| 11 | 61 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def log_commenting_changes(self, changes, revision):
for comment in changes["new_comments"]:
comment.log_create(page_revision=revision, user=self.request.user)
for comment in changes["edited_comments"]:
comment.log_edit(page_revision=revision, user=self.request.user)
for comment in changes["resolved_comments"]:
comment.log_resolve(page_revision=revision, user=self.request.user)
for comment in changes["deleted_comments"]:
comment.log_delete(page_revision=revision, user=self.request.user)
for comment, replies in changes["new_replies"]:
for reply in replies:
reply.log_create(page_revision=revision, user=self.request.user)
for comment, replies in changes["edited_replies"]:
for reply in replies:
reply.log_edit(page_revision=revision, user=self.request.user)
for comment, replies in changes["deleted_replies"]:
for reply in replies:
reply.log_delete(page_revision=revision, user=self.request.user)
```
###Assistant :
Generates log entries for any changes made to comments or replies.
|
2,664 | def __new__(cls, stylename, **kwargs):
# The "class" should have the _style_list attribute, which is a mapping
# of style names to style classes.
_list = stylename.replace(" ", "").split(",")
_name = _list[0].lower()
try:
_cls = cls._style_list[_name]
except KeyError as err:
raise ValueError(f"Unknown style: {stylename}") from err
try:
_args_pair = [cs.split("=") for cs in _list[1:]]
_args = {k: float(v) for k, v in _args_pair}
except ValueError as err:
raise ValueError(f"Incorrect style argument: {stylename}") from err
return _cls(**{**_args, **kwargs})
| Return the instance of the subclass with the given style name. | 11 | 76 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def __new__(cls, stylename, **kwargs):
# The "class" should have the _style_list attribute, which is a mapping
# of style names to style classes.
_list = stylename.replace(" ", "").split(",")
_name = _list[0].lower()
try:
_cls = cls._style_list[_name]
except KeyError as err:
raise ValueError(f"Unknown style: {stylename}") from err
try:
_args_pair = [cs.split("=") for cs in _list[1:]]
_args = {k: float(v) for k, v in _args_pair}
except ValueError as err:
raise ValueError(f"Incorrect style argument: {stylename}") from err
return _cls(**{**_args, **kwargs})
```
###Assistant : Return the instance of the subclass with the given style name.
|
2,665 | def losses(self):
collected_losses = []
for layer in self._flatten_layers():
# If any eager losses are present, we assume the model to be part of
# an eager training loop (either a custom one or the one used when
# `run_eagerly=True`) and so we always return just the eager losses.
if layer._eager_losses:
# Filter placeholder losses that may have been added by revived
# layers. (see base_layer_utils for details).
if (
layer._eager_losses[0]
is not base_layer_utils.REVIVED_LOSS_PLACEHOLDER
):
collected_losses.extend(layer._eager_losses)
else:
collected_losses.extend(layer._losses)
for regularizer in layer._callable_losses:
loss_tensor = regularizer()
if loss_tensor is not None:
collected_losses.append(loss_tensor)
return collected_losses
| List of losses added using the `add_loss()` API.
Variable regularization tensors are created when this property is
accessed, so it is eager safe: accessing `losses` under a
`tf.GradientTape` will propagate gradients back to the corresponding
variables.
Examples:
>>> class MyLayer(tf.keras.layers.Layer):
... def call(self, inputs):
... self.add_loss(tf.abs(tf.reduce_mean(inputs)))
... return inputs
>>> l = MyLayer()
>>> l(np.ones((10, 1)))
>>> l.losses
[1.0]
>>> inputs = tf.keras.Input(shape=(10,))
>>> x = tf.keras.layers.Dense(10)(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Activity regularization.
>>> len(model.losses)
0
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
>>> len(model.losses)
1
>>> inputs = tf.keras.Input(shape=(10,))
>>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
>>> x = d(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Weight regularization.
>>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
>>> model.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]
Returns:
A list of tensors.
| 128 | 93 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def losses(self):
collected_losses = []
for layer in self._flatten_layers():
# If any eager losses are present, we assume the model to be part of
# an eager training loop (either a custom one or the one used when
# `run_eagerly=True`) and so we always return just the eager losses.
if layer._eager_losses:
# Filter placeholder losses that may have been added by revived
# layers. (see base_layer_utils for details).
if (
layer._eager_losses[0]
is not base_layer_utils.REVIVED_LOSS_PLACEHOLDER
):
collected_losses.extend(layer._eager_losses)
else:
collected_losses.extend(layer._losses)
for regularizer in layer._callable_losses:
loss_tensor = regularizer()
if loss_tensor is not None:
collected_losses.append(loss_tensor)
return collected_losses
```
###Assistant : List of losses added using the `add_loss()` API.
Variable regularization tensors are created when this property is
accessed, so it is eager safe: accessing `losses` under a
`tf.GradientTape` will propagate gradients back to the corresponding
variables.
Examples:
>>> class MyLayer(tf.keras.layers.Layer):
... def call(self, inputs):
... self.add_loss(tf.abs(tf.reduce_mean(inputs)))
... return inputs
>>> l = MyLayer()
>>> l(np.ones((10, 1)))
>>> l.losses
[1.0]
>>> inputs = tf.keras.Input(shape=(10,))
>>> x = tf.keras.layers.Dense(10)(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Activity regularization.
>>> len(model.losses)
0
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
>>> len(model.losses)
1
>>> inputs = tf.keras.Input(shape=(10,))
>>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
>>> x = d(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Weight regularization.
>>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
>>> model.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]
Returns:
A list of tensors.
|
2,666 | def fit(self, X, y, Xy=None):
self._validate_params()
X, y = self._validate_data(X, y, y_numeric=True, multi_output=True)
_normalize = _deprecate_normalize(
self.normalize, default=True, estimator_name=self.__class__.__name__
)
alpha = getattr(self, "alpha", 0.0)
if hasattr(self, "n_nonzero_coefs"):
alpha = 0.0 # n_nonzero_coefs parametrization takes priority
max_iter = self.n_nonzero_coefs
else:
max_iter = self.max_iter
if self.jitter is not None:
rng = check_random_state(self.random_state)
noise = rng.uniform(high=self.jitter, size=len(y))
y = y + noise
self._fit(
X,
y,
max_iter=max_iter,
alpha=alpha,
fit_path=self.fit_path,
normalize=_normalize,
Xy=Xy,
)
return self
| Fit the model using X, y as training data.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Xy : array-like of shape (n_samples,) or (n_samples, n_targets), \
default=None
Xy = np.dot(X.T, y) that can be precomputed. It is useful
only when the Gram matrix is precomputed.
Returns
-------
self : object
Returns an instance of self.
| 70 | 71 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def fit(self, X, y, Xy=None):
self._validate_params()
X, y = self._validate_data(X, y, y_numeric=True, multi_output=True)
_normalize = _deprecate_normalize(
self.normalize, default=True, estimator_name=self.__class__.__name__
)
alpha = getattr(self, "alpha", 0.0)
if hasattr(self, "n_nonzero_coefs"):
alpha = 0.0 # n_nonzero_coefs parametrization takes priority
max_iter = self.n_nonzero_coefs
else:
max_iter = self.max_iter
if self.jitter is not None:
rng = check_random_state(self.random_state)
noise = rng.uniform(high=self.jitter, size=len(y))
y = y + noise
self._fit(
X,
y,
max_iter=max_iter,
alpha=alpha,
fit_path=self.fit_path,
normalize=_normalize,
Xy=Xy,
)
return self
```
###Assistant : Fit the model using X, y as training data.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Xy : array-like of shape (n_samples,) or (n_samples, n_targets), \
default=None
Xy = np.dot(X.T, y) that can be precomputed. It is useful
only when the Gram matrix is precomputed.
Returns
-------
self : object
Returns an instance of self.
|
2,667 | def _decode_block_string(block_string):
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
# Check stride
cond_1 = ('s' in options and len(options['s']) == 1)
cond_2 = ((len(options['s']) == 2) and (options['s'][0] == options['s'][1]))
assert (cond_1 or cond_2)
return BlockArgs(kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
stride=[int(options['s'][0])])
| Gets a block through a string notation of arguments. | 9 | 73 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _decode_block_string(block_string):
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
# Check stride
cond_1 = ('s' in options and len(options['s']) == 1)
cond_2 = ((len(options['s']) == 2) and (options['s'][0] == options['s'][1]))
assert (cond_1 or cond_2)
return BlockArgs(kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
stride=[int(options['s'][0])])
```
###Assistant : Gets a block through a string notation of arguments.
|
2,668 | def copy(a, order='K', subok=False):
return array(a, order=order, subok=subok, copy=True)
# Basic operations
|
Return an array copy of the given object.
Parameters
----------
a : array_like
Input data.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the copy. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible. (Note that this function and :meth:`ndarray.copy` are very
similar, but have different default values for their order=
arguments.)
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise the
returned array will be forced to be a base-class array (defaults to False).
.. versionadded:: 1.19.0
Returns
-------
arr : ndarray
Array interpretation of `a`.
See Also
--------
ndarray.copy : Preferred method for creating an array copy
Notes
-----
This is equivalent to:
>>> np.array(a, copy=True) #doctest: +SKIP
Examples
--------
Create an array x, with a reference y and a copy z:
>>> x = np.array([1, 2, 3])
>>> y = x
>>> z = np.copy(x)
Note that, when we modify x, y changes, but not z:
>>> x[0] = 10
>>> x[0] == y[0]
True
>>> x[0] == z[0]
False
Note that, np.copy clears previously set WRITEABLE=False flag.
>>> a = np.array([1, 2, 3])
>>> a.flags["WRITEABLE"] = False
>>> b = np.copy(a)
>>> b.flags["WRITEABLE"]
True
>>> b[0] = 3
>>> b
array([3, 2, 3])
Note that np.copy is a shallow copy and will not copy object
elements within arrays. This is mainly important for arrays
containing Python objects. The new array will contain the
same object which may lead to surprises if that object can
be modified (is mutable):
>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> b = np.copy(a)
>>> b[2][0] = 10
>>> a
array([1, 'm', list([10, 3, 4])], dtype=object)
To ensure all elements within an ``object`` array are copied,
use `copy.deepcopy`:
>>> import copy
>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> c = copy.deepcopy(a)
>>> c[2][0] = 10
>>> c
array([1, 'm', list([10, 3, 4])], dtype=object)
>>> a
array([1, 'm', list([2, 3, 4])], dtype=object)
| 340 | 12 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def copy(a, order='K', subok=False):
return array(a, order=order, subok=subok, copy=True)
# Basic operations
```
###Assistant :
Return an array copy of the given object.
Parameters
----------
a : array_like
Input data.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the copy. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible. (Note that this function and :meth:`ndarray.copy` are very
similar, but have different default values for their order=
arguments.)
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise the
returned array will be forced to be a base-class array (defaults to False).
.. versionadded:: 1.19.0
Returns
-------
arr : ndarray
Array interpretation of `a`.
See Also
--------
ndarray.copy : Preferred method for creating an array copy
Notes
-----
This is equivalent to:
>>> np.array(a, copy=True) #doctest: +SKIP
Examples
--------
Create an array x, with a reference y and a copy z:
>>> x = np.array([1, 2, 3])
>>> y = x
>>> z = np.copy(x)
Note that, when we modify x, y changes, but not z:
>>> x[0] = 10
>>> x[0] == y[0]
True
>>> x[0] == z[0]
False
Note that, np.copy clears previously set WRITEABLE=False flag.
>>> a = np.array([1, 2, 3])
>>> a.flags["WRITEABLE"] = False
>>> b = np.copy(a)
>>> b.flags["WRITEABLE"]
True
>>> b[0] = 3
>>> b
array([3, 2, 3])
Note that np.copy is a shallow copy and will not copy object
elements within arrays. This is mainly important for arrays
containing Python objects. The new array will contain the
same object which may lead to surprises if that object can
be modified (is mutable):
>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> b = np.copy(a)
>>> b[2][0] = 10
>>> a
array([1, 'm', list([10, 3, 4])], dtype=object)
To ensure all elements within an ``object`` array are copied,
use `copy.deepcopy`:
>>> import copy
>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> c = copy.deepcopy(a)
>>> c[2][0] = 10
>>> c
array([1, 'm', list([10, 3, 4])], dtype=object)
>>> a
array([1, 'm', list([2, 3, 4])], dtype=object)
|
2,669 | def test_context_placement_group():
driver_code =
proc = run_string_as_driver_nonblocking(driver_code)
|
import ray
from ray.data.context import DatasetContext
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
from ray._private.test_utils import placement_group_assert_no_leak
ray.init(num_cpus=1)
context = DatasetContext.get_current()
# This placement group will take up all cores of the local cluster.
placement_group = ray.util.placement_group(
name="core_hog",
strategy="SPREAD",
bundles=[
{"CPU": 1},
],
)
ray.get(placement_group.ready())
context.scheduling_strategy = PlacementGroupSchedulingStrategy(placement_group)
pipe = ray.data.range(100, parallelism=2) \
.window(blocks_per_window=1) \
.map(lambda x: x + 1)
assert pipe.take_all() == list(range(1, 101))
placement_group_assert_no_leak([placement_group])
ray.shutdown()
| 64 | 7 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_context_placement_group():
driver_code =
proc = run_string_as_driver_nonblocking(driver_code)
```
###Assistant :
import ray
from ray.data.context import DatasetContext
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
from ray._private.test_utils import placement_group_assert_no_leak
ray.init(num_cpus=1)
context = DatasetContext.get_current()
# This placement group will take up all cores of the local cluster.
placement_group = ray.util.placement_group(
name="core_hog",
strategy="SPREAD",
bundles=[
{"CPU": 1},
],
)
ray.get(placement_group.ready())
context.scheduling_strategy = PlacementGroupSchedulingStrategy(placement_group)
pipe = ray.data.range(100, parallelism=2) \
.window(blocks_per_window=1) \
.map(lambda x: x + 1)
assert pipe.take_all() == list(range(1, 101))
placement_group_assert_no_leak([placement_group])
ray.shutdown()
|
2,670 | def reorder_categories(self, new_categories, ordered=None):
if set(self.dtype.categories) != set(new_categories):
raise ValueError(
"items in new_categories are not the same as in old categories"
)
return self.set_categories(new_categories, ordered=ordered)
|
Reorder categories as specified in new_categories.
`new_categories` need to include all old categories and no new category
items.
Parameters
----------
new_categories : Index-like
The categories in new order.
ordered : bool, optional
Whether or not the categorical is treated as a ordered categorical.
If not given, do not change the ordered information.
Returns
-------
cat : Categorical
Categorical with reordered categories.
Raises
------
ValueError
If the new categories do not contain all old category items or any
new ones
See Also
--------
rename_categories : Rename categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
| 114 | 25 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def reorder_categories(self, new_categories, ordered=None):
if set(self.dtype.categories) != set(new_categories):
raise ValueError(
"items in new_categories are not the same as in old categories"
)
return self.set_categories(new_categories, ordered=ordered)
```
###Assistant :
Reorder categories as specified in new_categories.
`new_categories` need to include all old categories and no new category
items.
Parameters
----------
new_categories : Index-like
The categories in new order.
ordered : bool, optional
Whether or not the categorical is treated as a ordered categorical.
If not given, do not change the ordered information.
Returns
-------
cat : Categorical
Categorical with reordered categories.
Raises
------
ValueError
If the new categories do not contain all old category items or any
new ones
See Also
--------
rename_categories : Rename categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
|
2,671 | def test_get_member_list_no_permission_former_member_with_at_token(self):
# create a room, invite the user and the user joins
room_id = self.helper.create_room_as("@alice:red")
self.helper.invite(room_id, "@alice:red", self.user_id)
self.helper.join(room_id, self.user_id)
# sync to get an at token
channel = self.make_request("GET", "/sync")
self.assertEqual(200, channel.code)
sync_token = channel.json_body["next_batch"]
# check that the user can see the member list to start with
channel = self.make_request(
"GET", "/rooms/%s/members?at=%s" % (room_id, sync_token)
)
self.assertEqual(200, channel.code, msg=channel.result["body"])
# ban the user (Note: the user is actually allowed to see this event and
# state so that they know they're banned!)
self.helper.change_membership(room_id, "@alice:red", self.user_id, "ban")
# invite a third user and let them join
self.helper.invite(room_id, "@alice:red", "@bob:red")
self.helper.join(room_id, "@bob:red")
# now, with the original user, sync again to get a new at token
channel = self.make_request("GET", "/sync")
self.assertEqual(200, channel.code)
sync_token = channel.json_body["next_batch"]
# check the user can no longer see the updated member list
channel = self.make_request(
"GET", "/rooms/%s/members?at=%s" % (room_id, sync_token)
)
self.assertEqual(403, channel.code, msg=channel.result["body"])
|
Tests that a former member of the room can not get the member list
(in the case that they use an at token).
| 23 | 150 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_get_member_list_no_permission_former_member_with_at_token(self):
# create a room, invite the user and the user joins
room_id = self.helper.create_room_as("@alice:red")
self.helper.invite(room_id, "@alice:red", self.user_id)
self.helper.join(room_id, self.user_id)
# sync to get an at token
channel = self.make_request("GET", "/sync")
self.assertEqual(200, channel.code)
sync_token = channel.json_body["next_batch"]
# check that the user can see the member list to start with
channel = self.make_request(
"GET", "/rooms/%s/members?at=%s" % (room_id, sync_token)
)
self.assertEqual(200, channel.code, msg=channel.result["body"])
# ban the user (Note: the user is actually allowed to see this event and
# state so that they know they're banned!)
self.helper.change_membership(room_id, "@alice:red", self.user_id, "ban")
# invite a third user and let them join
self.helper.invite(room_id, "@alice:red", "@bob:red")
self.helper.join(room_id, "@bob:red")
# now, with the original user, sync again to get a new at token
channel = self.make_request("GET", "/sync")
self.assertEqual(200, channel.code)
sync_token = channel.json_body["next_batch"]
# check the user can no longer see the updated member list
channel = self.make_request(
"GET", "/rooms/%s/members?at=%s" % (room_id, sync_token)
)
self.assertEqual(403, channel.code, msg=channel.result["body"])
```
###Assistant :
Tests that a former member of the room can not get the member list
(in the case that they use an at token).
|
2,672 | def _keep_original_ws(s, tag_s):
return ''.join(
c if tag_c == " " and c.isspace() else tag_c
for c, tag_c in zip(s, tag_s)
)
| Replace whitespace with the original whitespace characters in `s` | 9 | 22 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _keep_original_ws(s, tag_s):
return ''.join(
c if tag_c == " " and c.isspace() else tag_c
for c, tag_c in zip(s, tag_s)
)
```
###Assistant : Replace whitespace with the original whitespace characters in `s`
|
2,673 | def unregister_cmap(name):
cmap = _colormaps.get(name, None)
_colormaps.unregister(name)
return cmap
|
Remove a colormap recognized by :func:`get_cmap`.
You may not remove built-in colormaps.
If the named colormap is not registered, returns with no error, raises
if you try to de-register a default colormap.
.. warning::
Colormap names are currently a shared namespace that may be used
by multiple packages. Use `unregister_cmap` only if you know you
have registered that name before. In particular, do not
unregister just in case to clean the name before registering a
new colormap.
Parameters
----------
name : str
The name of the colormap to be un-registered
Returns
-------
ColorMap or None
If the colormap was registered, return it if not return `None`
Raises
------
ValueError
If you try to de-register a default built-in colormap.
| 118 | 9 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def unregister_cmap(name):
cmap = _colormaps.get(name, None)
_colormaps.unregister(name)
return cmap
```
###Assistant :
Remove a colormap recognized by :func:`get_cmap`.
You may not remove built-in colormaps.
If the named colormap is not registered, returns with no error, raises
if you try to de-register a default colormap.
.. warning::
Colormap names are currently a shared namespace that may be used
by multiple packages. Use `unregister_cmap` only if you know you
have registered that name before. In particular, do not
unregister just in case to clean the name before registering a
new colormap.
Parameters
----------
name : str
The name of the colormap to be un-registered
Returns
-------
ColorMap or None
If the colormap was registered, return it if not return `None`
Raises
------
ValueError
If you try to de-register a default built-in colormap.
|
2,674 | def _get_extraction_protocol_with_magic_number(f) -> Optional[str]:
magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)
f.seek(0)
for i in range(MAGIC_NUMBER_MAX_LENGTH):
compression = MAGIC_NUMBER_TO_COMPRESSION_PROTOCOL.get(magic_number[: MAGIC_NUMBER_MAX_LENGTH - i])
if compression is not None: # TODO(QL): raise an error for .tar.gz files as in _get_extraction_protocol
return compression
compression = MAGIC_NUMBER_TO_UNSUPPORTED_COMPRESSION_PROTOCOL.get(magic_number[: MAGIC_NUMBER_MAX_LENGTH - i])
if compression is not None:
raise NotImplementedError(f"Compression protocol '{compression}' not implemented.")
| read the magic number from a file-like object and return the compression protocol | 13 | 53 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _get_extraction_protocol_with_magic_number(f) -> Optional[str]:
magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)
f.seek(0)
for i in range(MAGIC_NUMBER_MAX_LENGTH):
compression = MAGIC_NUMBER_TO_COMPRESSION_PROTOCOL.get(magic_number[: MAGIC_NUMBER_MAX_LENGTH - i])
if compression is not None: # TODO(QL): raise an error for .tar.gz files as in _get_extraction_protocol
return compression
compression = MAGIC_NUMBER_TO_UNSUPPORTED_COMPRESSION_PROTOCOL.get(magic_number[: MAGIC_NUMBER_MAX_LENGTH - i])
if compression is not None:
raise NotImplementedError(f"Compression protocol '{compression}' not implemented.")
```
###Assistant : read the magic number from a file-like object and return the compression protocol
|
2,675 | def _get_veths(net_data):
if isinstance(net_data, dict):
net_data = list(net_data.items())
nics = salt.utils.odict.OrderedDict()
current_nic = salt.utils.odict.OrderedDict()
no_names = True
for item in net_data:
if item and isinstance(item, dict):
item = list(item.items())[0]
# skip LXC configuration comment lines, and play only with tuples conf
elif isinstance(item, str):
# deal with reflection of commented lxc configs
sitem = item.strip()
if sitem.startswith("#") or not sitem:
continue
elif "=" in item:
item = tuple(a.strip() for a in item.split("=", 1))
if item[0] == "lxc.network.type":
current_nic = salt.utils.odict.OrderedDict()
if item[0] == "lxc.network.name":
no_names = False
nics[item[1].strip()] = current_nic
current_nic[item[0].strip()] = item[1].strip()
# if not ethernet card name has been collected, assuming we collected
# data for eth0
if no_names and current_nic:
nics[DEFAULT_NIC] = current_nic
return nics
|
Parse the nic setup inside lxc conf tuples back to a dictionary indexed by
network interface
| 16 | 118 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _get_veths(net_data):
if isinstance(net_data, dict):
net_data = list(net_data.items())
nics = salt.utils.odict.OrderedDict()
current_nic = salt.utils.odict.OrderedDict()
no_names = True
for item in net_data:
if item and isinstance(item, dict):
item = list(item.items())[0]
# skip LXC configuration comment lines, and play only with tuples conf
elif isinstance(item, str):
# deal with reflection of commented lxc configs
sitem = item.strip()
if sitem.startswith("#") or not sitem:
continue
elif "=" in item:
item = tuple(a.strip() for a in item.split("=", 1))
if item[0] == "lxc.network.type":
current_nic = salt.utils.odict.OrderedDict()
if item[0] == "lxc.network.name":
no_names = False
nics[item[1].strip()] = current_nic
current_nic[item[0].strip()] = item[1].strip()
# if not ethernet card name has been collected, assuming we collected
# data for eth0
if no_names and current_nic:
nics[DEFAULT_NIC] = current_nic
return nics
```
###Assistant :
Parse the nic setup inside lxc conf tuples back to a dictionary indexed by
network interface
|
2,676 | def start_stdout_logging() -> None:
if '_stdout_' in _handlers:
return
handler = StreamHandler(sys.stdout)
handler.setFormatter(_StdoutFormatter())
_handlers['_stdout_'] = handler
_root_logger.addHandler(handler)
|
Register the stdout handler.
This function should be invoked on importing nni.
It is safe to call it multiple times.
| 20 | 17 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def start_stdout_logging() -> None:
if '_stdout_' in _handlers:
return
handler = StreamHandler(sys.stdout)
handler.setFormatter(_StdoutFormatter())
_handlers['_stdout_'] = handler
_root_logger.addHandler(handler)
```
###Assistant :
Register the stdout handler.
This function should be invoked on importing nni.
It is safe to call it multiple times.
|
2,677 | def get_staffing_plan_detail(designation, company, offer_date):
detail = frappe.db.sql(
,
(designation, company, offer_date),
as_dict=1,
)
return frappe._dict(detail[0]) if (detail and detail[0].parent) else None
@frappe.whitelist() |
SELECT DISTINCT spd.parent,
sp.from_date as from_date,
sp.to_date as to_date,
sp.name,
sum(spd.vacancies) as vacancies,
spd.designation
FROM `tabStaffing Plan Detail` spd, `tabStaffing Plan` sp
WHERE
sp.docstatus=1
AND spd.designation=%s
AND sp.company=%s
AND spd.parent = sp.name
AND %s between sp.from_date and sp.to_date
| 38 | 22 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_staffing_plan_detail(designation, company, offer_date):
detail = frappe.db.sql(
,
(designation, company, offer_date),
as_dict=1,
)
return frappe._dict(detail[0]) if (detail and detail[0].parent) else None
@frappe.whitelist()
```
###Assistant :
SELECT DISTINCT spd.parent,
sp.from_date as from_date,
sp.to_date as to_date,
sp.name,
sum(spd.vacancies) as vacancies,
spd.designation
FROM `tabStaffing Plan Detail` spd, `tabStaffing Plan` sp
WHERE
sp.docstatus=1
AND spd.designation=%s
AND sp.company=%s
AND spd.parent = sp.name
AND %s between sp.from_date and sp.to_date
|
2,678 | def test_display_name(self) -> None:
evaluator = self._get_evaluator({"body": "foo bar baz"})
condition = {
"kind": "contains_display_name",
}
# Blank names are skipped.
self.assertFalse(evaluator.matches(condition, "@user:test", ""))
# Check a display name that doesn't match.
self.assertFalse(evaluator.matches(condition, "@user:test", "not found"))
# Check a display name which matches.
self.assertTrue(evaluator.matches(condition, "@user:test", "foo"))
# A display name that matches, but not a full word does not result in a match.
self.assertFalse(evaluator.matches(condition, "@user:test", "ba"))
# A display name should not be interpreted as a regular expression.
self.assertFalse(evaluator.matches(condition, "@user:test", "ba[rz]"))
# A display name with spaces should work fine.
self.assertTrue(evaluator.matches(condition, "@user:test", "foo bar"))
| Check for a matching display name in the body of the event. | 12 | 94 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_display_name(self) -> None:
evaluator = self._get_evaluator({"body": "foo bar baz"})
condition = {
"kind": "contains_display_name",
}
# Blank names are skipped.
self.assertFalse(evaluator.matches(condition, "@user:test", ""))
# Check a display name that doesn't match.
self.assertFalse(evaluator.matches(condition, "@user:test", "not found"))
# Check a display name which matches.
self.assertTrue(evaluator.matches(condition, "@user:test", "foo"))
# A display name that matches, but not a full word does not result in a match.
self.assertFalse(evaluator.matches(condition, "@user:test", "ba"))
# A display name should not be interpreted as a regular expression.
self.assertFalse(evaluator.matches(condition, "@user:test", "ba[rz]"))
# A display name with spaces should work fine.
self.assertTrue(evaluator.matches(condition, "@user:test", "foo bar"))
```
###Assistant : Check for a matching display name in the body of the event.
|
2,679 | def test_readlink_not_a_link(file, source):
with pytest.raises(SaltInvocationError) as exc:
file.readlink(path=source)
assert "A valid link was not specified" in exc.value.message
|
Test readlink where the path is not a link
Should throw a SaltInvocationError
| 13 | 17 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_readlink_not_a_link(file, source):
with pytest.raises(SaltInvocationError) as exc:
file.readlink(path=source)
assert "A valid link was not specified" in exc.value.message
```
###Assistant :
Test readlink where the path is not a link
Should throw a SaltInvocationError
|
2,680 | def get_periodic_data(entry, filters):
periodic_data = {}
for d in entry:
period = get_period(d.posting_date, filters)
bal_qty = 0
# if period against item does not exist yet, instantiate it
# insert existing balance dict against period, and add/subtract to it
if periodic_data.get(d.item_code) and not periodic_data.get(d.item_code).get(period):
previous_balance = periodic_data[d.item_code]["balance"].copy()
periodic_data[d.item_code][period] = previous_balance
if d.voucher_type == "Stock Reconciliation":
if periodic_data.get(d.item_code) and periodic_data.get(d.item_code).get("balance").get(
d.warehouse
):
bal_qty = periodic_data[d.item_code]["balance"][d.warehouse]
qty_diff = d.qty_after_transaction - bal_qty
else:
qty_diff = d.actual_qty
if filters["value_quantity"] == "Quantity":
value = qty_diff
else:
value = d.stock_value_difference
# period-warehouse wise balance
periodic_data.setdefault(d.item_code, {}).setdefault("balance", {}).setdefault(d.warehouse, 0.0)
periodic_data.setdefault(d.item_code, {}).setdefault(period, {}).setdefault(d.warehouse, 0.0)
periodic_data[d.item_code]["balance"][d.warehouse] += value
periodic_data[d.item_code][period][d.warehouse] = periodic_data[d.item_code]["balance"][
d.warehouse
]
return periodic_data
| Structured as:
Item 1
- Balance (updated and carried forward):
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
- Jun 2021 (sum of warehouse quantities used in report)
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
- Jul 2021 (sum of warehouse quantities used in report)
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
Item 2
- Balance (updated and carried forward):
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
- Jun 2021 (sum of warehouse quantities used in report)
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
- Jul 2021 (sum of warehouse quantities used in report)
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
| 118 | 106 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_periodic_data(entry, filters):
periodic_data = {}
for d in entry:
period = get_period(d.posting_date, filters)
bal_qty = 0
# if period against item does not exist yet, instantiate it
# insert existing balance dict against period, and add/subtract to it
if periodic_data.get(d.item_code) and not periodic_data.get(d.item_code).get(period):
previous_balance = periodic_data[d.item_code]["balance"].copy()
periodic_data[d.item_code][period] = previous_balance
if d.voucher_type == "Stock Reconciliation":
if periodic_data.get(d.item_code) and periodic_data.get(d.item_code).get("balance").get(
d.warehouse
):
bal_qty = periodic_data[d.item_code]["balance"][d.warehouse]
qty_diff = d.qty_after_transaction - bal_qty
else:
qty_diff = d.actual_qty
if filters["value_quantity"] == "Quantity":
value = qty_diff
else:
value = d.stock_value_difference
# period-warehouse wise balance
periodic_data.setdefault(d.item_code, {}).setdefault("balance", {}).setdefault(d.warehouse, 0.0)
periodic_data.setdefault(d.item_code, {}).setdefault(period, {}).setdefault(d.warehouse, 0.0)
periodic_data[d.item_code]["balance"][d.warehouse] += value
periodic_data[d.item_code][period][d.warehouse] = periodic_data[d.item_code]["balance"][
d.warehouse
]
return periodic_data
```
###Assistant : Structured as:
Item 1
- Balance (updated and carried forward):
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
- Jun 2021 (sum of warehouse quantities used in report)
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
- Jul 2021 (sum of warehouse quantities used in report)
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
Item 2
- Balance (updated and carried forward):
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
- Jun 2021 (sum of warehouse quantities used in report)
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
- Jul 2021 (sum of warehouse quantities used in report)
- Warehouse A : bal_qty/value
- Warehouse B : bal_qty/value
|
2,681 | def shuffle(*arrays, random_state=None, n_samples=None):
return resample(
*arrays, replace=False, n_samples=n_samples, random_state=random_state
)
| Shuffle arrays or sparse matrices in a consistent way.
This is a convenience alias to ``resample(*arrays, replace=False)`` to do
random permutations of the collections.
Parameters
----------
*arrays : sequence of indexable data-structures
Indexable data-structures can be arrays, lists, dataframes or scipy
sparse matrices with consistent first dimension.
random_state : int, RandomState instance or None, default=None
Determines random number generation for shuffling
the data.
Pass an int for reproducible results across multiple function calls.
See :term:`Glossary <random_state>`.
n_samples : int, default=None
Number of samples to generate. If left to None this is
automatically set to the first dimension of the arrays. It should
not be larger than the length of arrays.
Returns
-------
shuffled_arrays : sequence of indexable data-structures
Sequence of shuffled copies of the collections. The original arrays
are not impacted.
See Also
--------
resample : Resample arrays or sparse matrices in a consistent way.
Examples
--------
It is possible to mix sparse and dense arrays in the same run::
>>> import numpy as np
>>> X = np.array([[1., 0.], [2., 1.], [0., 0.]])
>>> y = np.array([0, 1, 2])
>>> from scipy.sparse import coo_matrix
>>> X_sparse = coo_matrix(X)
>>> from sklearn.utils import shuffle
>>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
>>> X
array([[0., 0.],
[2., 1.],
[1., 0.]])
>>> X_sparse
<3x2 sparse matrix of type '<... 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Row format>
>>> X_sparse.toarray()
array([[0., 0.],
[2., 1.],
[1., 0.]])
>>> y
array([2, 1, 0])
>>> shuffle(y, n_samples=2, random_state=0)
array([0, 1])
| 248 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def shuffle(*arrays, random_state=None, n_samples=None):
return resample(
*arrays, replace=False, n_samples=n_samples, random_state=random_state
)
```
###Assistant : Shuffle arrays or sparse matrices in a consistent way.
This is a convenience alias to ``resample(*arrays, replace=False)`` to do
random permutations of the collections.
Parameters
----------
*arrays : sequence of indexable data-structures
Indexable data-structures can be arrays, lists, dataframes or scipy
sparse matrices with consistent first dimension.
random_state : int, RandomState instance or None, default=None
Determines random number generation for shuffling
the data.
Pass an int for reproducible results across multiple function calls.
See :term:`Glossary <random_state>`.
n_samples : int, default=None
Number of samples to generate. If left to None this is
automatically set to the first dimension of the arrays. It should
not be larger than the length of arrays.
Returns
-------
shuffled_arrays : sequence of indexable data-structures
Sequence of shuffled copies of the collections. The original arrays
are not impacted.
See Also
--------
resample : Resample arrays or sparse matrices in a consistent way.
Examples
--------
It is possible to mix sparse and dense arrays in the same run::
>>> import numpy as np
>>> X = np.array([[1., 0.], [2., 1.], [0., 0.]])
>>> y = np.array([0, 1, 2])
>>> from scipy.sparse import coo_matrix
>>> X_sparse = coo_matrix(X)
>>> from sklearn.utils import shuffle
>>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
>>> X
array([[0., 0.],
[2., 1.],
[1., 0.]])
>>> X_sparse
<3x2 sparse matrix of type '<... 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Row format>
>>> X_sparse.toarray()
array([[0., 0.],
[2., 1.],
[1., 0.]])
>>> y
array([2, 1, 0])
>>> shuffle(y, n_samples=2, random_state=0)
array([0, 1])
|
2,682 | def check_changes(self, args, results): # type: (SanityConfig, Results) -> None
integration_targets = list(walk_integration_targets())
module_targets = list(walk_module_targets())
integration_targets_by_name = dict((target.name, target) for target in integration_targets)
module_names_by_path = dict((target.path, target.module) for target in module_targets)
disabled_targets = []
unstable_targets = []
unsupported_targets = []
for command in [command for command in args.metadata.change_description.focused_command_targets if 'integration' in command]:
for target in args.metadata.change_description.focused_command_targets[command]:
if self.DISABLED in integration_targets_by_name[target].aliases:
disabled_targets.append(target)
elif self.UNSTABLE in integration_targets_by_name[target].aliases:
unstable_targets.append(target)
elif self.UNSUPPORTED in integration_targets_by_name[target].aliases:
unsupported_targets.append(target)
untested_modules = []
for path in args.metadata.change_description.no_integration_paths:
module = module_names_by_path.get(path)
if module:
untested_modules.append(module)
comments = [
self.format_comment(self.TEMPLATE_DISABLED, disabled_targets),
self.format_comment(self.TEMPLATE_UNSTABLE, unstable_targets),
self.format_comment(self.TEMPLATE_UNSUPPORTED, unsupported_targets),
self.format_comment(self.TEMPLATE_UNTESTED, untested_modules),
]
comments = [comment for comment in comments if comment]
labels = dict(
needs_tests=bool(untested_modules),
disabled_tests=bool(disabled_targets),
unstable_tests=bool(unstable_targets),
unsupported_tests=bool(unsupported_targets),
)
results.comments += comments
results.labels.update(labels)
| Check changes and store results in the provided result dictionary. | 10 | 118 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def check_changes(self, args, results): # type: (SanityConfig, Results) -> None
integration_targets = list(walk_integration_targets())
module_targets = list(walk_module_targets())
integration_targets_by_name = dict((target.name, target) for target in integration_targets)
module_names_by_path = dict((target.path, target.module) for target in module_targets)
disabled_targets = []
unstable_targets = []
unsupported_targets = []
for command in [command for command in args.metadata.change_description.focused_command_targets if 'integration' in command]:
for target in args.metadata.change_description.focused_command_targets[command]:
if self.DISABLED in integration_targets_by_name[target].aliases:
disabled_targets.append(target)
elif self.UNSTABLE in integration_targets_by_name[target].aliases:
unstable_targets.append(target)
elif self.UNSUPPORTED in integration_targets_by_name[target].aliases:
unsupported_targets.append(target)
untested_modules = []
for path in args.metadata.change_description.no_integration_paths:
module = module_names_by_path.get(path)
if module:
untested_modules.append(module)
comments = [
self.format_comment(self.TEMPLATE_DISABLED, disabled_targets),
self.format_comment(self.TEMPLATE_UNSTABLE, unstable_targets),
self.format_comment(self.TEMPLATE_UNSUPPORTED, unsupported_targets),
self.format_comment(self.TEMPLATE_UNTESTED, untested_modules),
]
comments = [comment for comment in comments if comment]
labels = dict(
needs_tests=bool(untested_modules),
disabled_tests=bool(disabled_targets),
unstable_tests=bool(unstable_targets),
unsupported_tests=bool(unsupported_targets),
)
results.comments += comments
results.labels.update(labels)
```
###Assistant : Check changes and store results in the provided result dictionary.
|
2,683 | def test_overlap_first(business_client, setup_before_upload, show_overlap_first):
c = business_client
config = dict(
title='test_overlap_first',
is_published=True,
maximum_annotations=1,
show_overlap_first=show_overlap_first,
sampling="Uniform sampling",
label_config=
)
project = make_project(config, business_client.user)
annotation_result = json.dumps([{
'from_name': 'text_class',
'to_name': 'text',
'type': 'choices',
'value': {'choices': ['class_A']}
}])
num_tasks = 1000
overlap_cohort_percentage = 1
# set up tasks overlap
setup_after_upload = True
if setup_before_upload:
r = c.patch(
f'/api/projects/{project.id}/',
data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}),
content_type='application/json'
)
assert r.status_code == 200
setup_after_upload = False
# create tasks
tasks = []
for i in range(num_tasks):
tasks.append({'data': {'text': f'this is {str(i)}'}})
r = business_client.post(
f'/api/projects/{project.id}/tasks/bulk/', data=json.dumps(tasks), content_type='application/json')
assert r.status_code == 201
if setup_after_upload:
r = c.patch(
f'/api/projects/{project.id}/',
data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}),
content_type='application/json'
)
assert r.status_code == 200
expected_tasks_with_overlap = int(overlap_cohort_percentage / 100. * num_tasks)
assert Task.objects.filter(Q(project_id=project.id) & Q(overlap__gt=1)).count() == expected_tasks_with_overlap
|
<View>
<Text name="text" value="$text"></Text>
<Choices name="text_class" choice="single">
<Choice value="class_A"></Choice>
<Choice value="class_B"></Choice>
</Choices>
</View> | 13 | 122 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_overlap_first(business_client, setup_before_upload, show_overlap_first):
c = business_client
config = dict(
title='test_overlap_first',
is_published=True,
maximum_annotations=1,
show_overlap_first=show_overlap_first,
sampling="Uniform sampling",
label_config=
)
project = make_project(config, business_client.user)
annotation_result = json.dumps([{
'from_name': 'text_class',
'to_name': 'text',
'type': 'choices',
'value': {'choices': ['class_A']}
}])
num_tasks = 1000
overlap_cohort_percentage = 1
# set up tasks overlap
setup_after_upload = True
if setup_before_upload:
r = c.patch(
f'/api/projects/{project.id}/',
data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}),
content_type='application/json'
)
assert r.status_code == 200
setup_after_upload = False
# create tasks
tasks = []
for i in range(num_tasks):
tasks.append({'data': {'text': f'this is {str(i)}'}})
r = business_client.post(
f'/api/projects/{project.id}/tasks/bulk/', data=json.dumps(tasks), content_type='application/json')
assert r.status_code == 201
if setup_after_upload:
r = c.patch(
f'/api/projects/{project.id}/',
data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}),
content_type='application/json'
)
assert r.status_code == 200
expected_tasks_with_overlap = int(overlap_cohort_percentage / 100. * num_tasks)
assert Task.objects.filter(Q(project_id=project.id) & Q(overlap__gt=1)).count() == expected_tasks_with_overlap
```
###Assistant :
<View>
<Text name="text" value="$text"></Text>
<Choices name="text_class" choice="single">
<Choice value="class_A"></Choice>
<Choice value="class_B"></Choice>
</Choices>
</View>
|
2,684 | def svd_flip(u, v, u_based_decision=True):
if u_based_decision:
# columns of u, rows of v
max_abs_cols = np.argmax(np.abs(u), axis=0)
signs = np.sign(u[max_abs_cols, range(u.shape[1])])
u *= signs
v *= signs[:, np.newaxis]
else:
# rows of v, columns of u
max_abs_rows = np.argmax(np.abs(v), axis=1)
signs = np.sign(v[range(v.shape[0]), max_abs_rows])
u *= signs
v *= signs[:, np.newaxis]
return u, v
| Sign correction to ensure deterministic output from SVD.
Adjusts the columns of u and the rows of v such that the loadings in the
columns in u that are largest in absolute value are always positive.
Parameters
----------
u : ndarray
Parameters u and v are the output of `linalg.svd` or
:func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
dimensions so one can compute `np.dot(u * s, v)`.
v : ndarray
Parameters u and v are the output of `linalg.svd` or
:func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
dimensions so one can compute `np.dot(u * s, v)`.
The input v should really be called vt to be consistent with scipy's
output.
u_based_decision : bool, default=True
If True, use the columns of u as the basis for sign flipping.
Otherwise, use the rows of v. The choice of which variable to base the
decision on is generally algorithm dependent.
Returns
-------
u_adjusted : ndarray
Array u with adjusted columns and the same dimensions as u.
v_adjusted : ndarray
Array v with adjusted rows and the same dimensions as v.
| 171 | 54 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def svd_flip(u, v, u_based_decision=True):
if u_based_decision:
# columns of u, rows of v
max_abs_cols = np.argmax(np.abs(u), axis=0)
signs = np.sign(u[max_abs_cols, range(u.shape[1])])
u *= signs
v *= signs[:, np.newaxis]
else:
# rows of v, columns of u
max_abs_rows = np.argmax(np.abs(v), axis=1)
signs = np.sign(v[range(v.shape[0]), max_abs_rows])
u *= signs
v *= signs[:, np.newaxis]
return u, v
```
###Assistant : Sign correction to ensure deterministic output from SVD.
Adjusts the columns of u and the rows of v such that the loadings in the
columns in u that are largest in absolute value are always positive.
Parameters
----------
u : ndarray
Parameters u and v are the output of `linalg.svd` or
:func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
dimensions so one can compute `np.dot(u * s, v)`.
v : ndarray
Parameters u and v are the output of `linalg.svd` or
:func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
dimensions so one can compute `np.dot(u * s, v)`.
The input v should really be called vt to be consistent with scipy's
output.
u_based_decision : bool, default=True
If True, use the columns of u as the basis for sign flipping.
Otherwise, use the rows of v. The choice of which variable to base the
decision on is generally algorithm dependent.
Returns
-------
u_adjusted : ndarray
Array u with adjusted columns and the same dimensions as u.
v_adjusted : ndarray
Array v with adjusted rows and the same dimensions as v.
|
2,685 | def extra_action_out_fn(self) -> Dict[str, TensorType]:
extra_fetches = {}
# Action-logp and action-prob.
if self._sampled_action_logp is not None:
extra_fetches[SampleBatch.ACTION_PROB] = self._sampled_action_prob
extra_fetches[SampleBatch.ACTION_LOGP] = self._sampled_action_logp
# Action-dist inputs.
if self._dist_inputs is not None:
extra_fetches[SampleBatch.ACTION_DIST_INPUTS] = self._dist_inputs
return extra_fetches
| Extra values to fetch and return from compute_actions().
By default we return action probability/log-likelihood info
and action distribution inputs (if present).
Returns:
Dict[str, TensorType]: An extra fetch-dict to be passed to and
returned from the compute_actions() call.
| 37 | 36 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def extra_action_out_fn(self) -> Dict[str, TensorType]:
extra_fetches = {}
# Action-logp and action-prob.
if self._sampled_action_logp is not None:
extra_fetches[SampleBatch.ACTION_PROB] = self._sampled_action_prob
extra_fetches[SampleBatch.ACTION_LOGP] = self._sampled_action_logp
# Action-dist inputs.
if self._dist_inputs is not None:
extra_fetches[SampleBatch.ACTION_DIST_INPUTS] = self._dist_inputs
return extra_fetches
```
###Assistant : Extra values to fetch and return from compute_actions().
By default we return action probability/log-likelihood info
and action distribution inputs (if present).
Returns:
Dict[str, TensorType]: An extra fetch-dict to be passed to and
returned from the compute_actions() call.
|
2,686 | async def cleanup_finished_triggers(self):
for trigger_id, details in list(self.triggers.items()):
if details["task"].done():
# Check to see if it exited for good reasons
saved_exc = None
try:
result = details["task"].result()
except (asyncio.CancelledError, SystemExit, KeyboardInterrupt):
# These are "expected" exceptions and we stop processing here
# If we don't, then the system requesting a trigger be removed -
# which turns into CancelledError - results in a failure.
del self.triggers[trigger_id]
continue
except BaseException as e:
# This is potentially bad, so log it.
self.log.exception("Trigger %s exited with error %s", details["name"], e)
saved_exc = e
else:
# See if they foolishly returned a TriggerEvent
if isinstance(result, TriggerEvent):
self.log.error(
"Trigger %s returned a TriggerEvent rather than yielding it", details["name"]
)
# See if this exited without sending an event, in which case
# any task instances depending on it need to be failed
if details["events"] == 0:
self.log.error(
"Trigger %s exited without sending an event. Dependent tasks will be failed.",
details["name"],
)
self.failed_triggers.append((trigger_id, saved_exc))
del self.triggers[trigger_id]
await asyncio.sleep(0)
|
Go through all trigger tasks (coroutines) and clean up entries for
ones that have exited, optionally warning users if the exit was
not normal.
| 24 | 162 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
async def cleanup_finished_triggers(self):
for trigger_id, details in list(self.triggers.items()):
if details["task"].done():
# Check to see if it exited for good reasons
saved_exc = None
try:
result = details["task"].result()
except (asyncio.CancelledError, SystemExit, KeyboardInterrupt):
# These are "expected" exceptions and we stop processing here
# If we don't, then the system requesting a trigger be removed -
# which turns into CancelledError - results in a failure.
del self.triggers[trigger_id]
continue
except BaseException as e:
# This is potentially bad, so log it.
self.log.exception("Trigger %s exited with error %s", details["name"], e)
saved_exc = e
else:
# See if they foolishly returned a TriggerEvent
if isinstance(result, TriggerEvent):
self.log.error(
"Trigger %s returned a TriggerEvent rather than yielding it", details["name"]
)
# See if this exited without sending an event, in which case
# any task instances depending on it need to be failed
if details["events"] == 0:
self.log.error(
"Trigger %s exited without sending an event. Dependent tasks will be failed.",
details["name"],
)
self.failed_triggers.append((trigger_id, saved_exc))
del self.triggers[trigger_id]
await asyncio.sleep(0)
```
###Assistant :
Go through all trigger tasks (coroutines) and clean up entries for
ones that have exited, optionally warning users if the exit was
not normal.
|
2,687 | def get_global_travel_time(self):
gtt = 0
for entity in self.tripinfo:
gtt += self.get_duration(entity, default=0.0)
for entity in self.personinfo:
gtt += self.get_duration(entity, default=0.0)
return gtt
###########################################################################
# ROUTING
|
Returns the global travel time computed from SUMO tripinfo data.
The functions process_tripinfo_file() needs to be called in advance
to initialize the data structures required.
| 25 | 26 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_global_travel_time(self):
gtt = 0
for entity in self.tripinfo:
gtt += self.get_duration(entity, default=0.0)
for entity in self.personinfo:
gtt += self.get_duration(entity, default=0.0)
return gtt
###########################################################################
# ROUTING
```
###Assistant :
Returns the global travel time computed from SUMO tripinfo data.
The functions process_tripinfo_file() needs to be called in advance
to initialize the data structures required.
|
2,688 | def post(self, url, data=None, json=None, **kwargs):
r
return self.request("POST", url, data=data, json=json, **kwargs)
| Sends a POST request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary, list of tuples, bytes, or file-like
object to send in the body of the :class:`Request`.
:param json: (optional) json to send in the body of the :class:`Request`.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:rtype: requests.Response
| 55 | 13 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def post(self, url, data=None, json=None, **kwargs):
r
return self.request("POST", url, data=data, json=json, **kwargs)
```
###Assistant : Sends a POST request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary, list of tuples, bytes, or file-like
object to send in the body of the :class:`Request`.
:param json: (optional) json to send in the body of the :class:`Request`.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:rtype: requests.Response
|
2,689 | def __getitem__(self, idx):
# type: (int) -> HPackHdrEntry
assert idx >= 0
if idx > type(self)._static_entries_last_idx:
idx -= type(self)._static_entries_last_idx + 1
if idx >= len(self._dynamic_table):
raise KeyError(
'EINVAL: idx: out-of-bound read: {}; maximum index: {}'.format(idx, len(self._dynamic_table)) # noqa: E501
)
return self._dynamic_table[idx]
return type(self)._static_entries[idx]
| Gets an element from the header tables (static or dynamic indifferently)
:param int idx: the index number of the entry to retrieve. If the index
value is superior to the last index of the static entry table, then the
dynamic entry type is requested, following the procedure described in
RFC 7541 par2.3.3
:return: HPackHdrEntry: the entry defined at this requested index. If the entry does not exist, KeyError is # noqa: E501
raised
:raises: KeyError, AssertionError
| 76 | 44 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def __getitem__(self, idx):
# type: (int) -> HPackHdrEntry
assert idx >= 0
if idx > type(self)._static_entries_last_idx:
idx -= type(self)._static_entries_last_idx + 1
if idx >= len(self._dynamic_table):
raise KeyError(
'EINVAL: idx: out-of-bound read: {}; maximum index: {}'.format(idx, len(self._dynamic_table)) # noqa: E501
)
return self._dynamic_table[idx]
return type(self)._static_entries[idx]
```
###Assistant : Gets an element from the header tables (static or dynamic indifferently)
:param int idx: the index number of the entry to retrieve. If the index
value is superior to the last index of the static entry table, then the
dynamic entry type is requested, following the procedure described in
RFC 7541 par2.3.3
:return: HPackHdrEntry: the entry defined at this requested index. If the entry does not exist, KeyError is # noqa: E501
raised
:raises: KeyError, AssertionError
|
2,690 | def test_pagination(self):
parent = Parent.objects.create(name="anything")
for i in range(1, 31):
Child.objects.create(name="name %s" % i, parent=parent)
Child.objects.create(name="filtered %s" % i, parent=parent)
request = self.factory.get("/child/")
request.user = self.superuser
# Test default queryset
m = ChildAdmin(Child, custom_site)
cl = m.get_changelist_instance(request)
self.assertEqual(cl.queryset.count(), 60)
self.assertEqual(cl.paginator.count, 60)
self.assertEqual(list(cl.paginator.page_range), [1, 2, 3, 4, 5, 6])
# Test custom queryset
m = FilteredChildAdmin(Child, custom_site)
cl = m.get_changelist_instance(request)
self.assertEqual(cl.queryset.count(), 30)
self.assertEqual(cl.paginator.count, 30)
self.assertEqual(list(cl.paginator.page_range), [1, 2, 3])
|
Regression tests for #12893: Pagination in admins changelist doesn't
use queryset set by modeladmin.
| 14 | 67 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def test_pagination(self):
parent = Parent.objects.create(name="anything")
for i in range(1, 31):
Child.objects.create(name="name %s" % i, parent=parent)
Child.objects.create(name="filtered %s" % i, parent=parent)
request = self.factory.get("/child/")
request.user = self.superuser
# Test default queryset
m = ChildAdmin(Child, custom_site)
cl = m.get_changelist_instance(request)
self.assertEqual(cl.queryset.count(), 60)
self.assertEqual(cl.paginator.count, 60)
self.assertEqual(list(cl.paginator.page_range), [1, 2, 3, 4, 5, 6])
# Test custom queryset
m = FilteredChildAdmin(Child, custom_site)
cl = m.get_changelist_instance(request)
self.assertEqual(cl.queryset.count(), 30)
self.assertEqual(cl.paginator.count, 30)
self.assertEqual(list(cl.paginator.page_range), [1, 2, 3])
```
###Assistant :
Regression tests for #12893: Pagination in admins changelist doesn't
use queryset set by modeladmin.
|
2,691 | def transform(self, X):
if self.solver == "lsqr":
raise NotImplementedError(
"transform not implemented for 'lsqr' solver (use 'svd' or 'eigen')."
)
check_is_fitted(self)
X = self._validate_data(X, reset=False)
if self.solver == "svd":
X_new = np.dot(X - self.xbar_, self.scalings_)
elif self.solver == "eigen":
X_new = np.dot(X, self.scalings_)
return X_new[:, : self._max_components]
| Project data to maximize class separation.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
X_new : ndarray of shape (n_samples, n_components) or \
(n_samples, min(rank, n_components))
Transformed data. In the case of the 'svd' solver, the shape
is (n_samples, min(rank, n_components)).
| 46 | 47 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def transform(self, X):
if self.solver == "lsqr":
raise NotImplementedError(
"transform not implemented for 'lsqr' solver (use 'svd' or 'eigen')."
)
check_is_fitted(self)
X = self._validate_data(X, reset=False)
if self.solver == "svd":
X_new = np.dot(X - self.xbar_, self.scalings_)
elif self.solver == "eigen":
X_new = np.dot(X, self.scalings_)
return X_new[:, : self._max_components]
```
###Assistant : Project data to maximize class separation.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
Returns
-------
X_new : ndarray of shape (n_samples, n_components) or \
(n_samples, min(rank, n_components))
Transformed data. In the case of the 'svd' solver, the shape
is (n_samples, min(rank, n_components)).
|
2,692 | async def test_heater_cooler_hvac_mode_vs_hvac_action(hass, utcnow):
helper = await setup_test_component(hass, create_heater_cooler_service)
# Simulate that current temperature is above target temp
# Heating might be on, but hvac_action currently 'off'
await helper.async_update(
ServicesTypes.HEATER_COOLER,
{
CharacteristicsTypes.TEMPERATURE_CURRENT: 22,
CharacteristicsTypes.TEMPERATURE_HEATING_THRESHOLD: 21,
CharacteristicsTypes.CURRENT_HEATER_COOLER_STATE: CurrentHeaterCoolerStateValues.IDLE,
CharacteristicsTypes.TARGET_HEATER_COOLER_STATE: TargetHeaterCoolerStateValues.HEAT,
CharacteristicsTypes.SWING_MODE: SwingModeValues.DISABLED,
},
)
state = await helper.poll_and_get_state()
assert state.state == "heat"
assert state.attributes["hvac_action"] == "idle"
# Simulate that current temperature is below target temp
# Heating might be on and hvac_action currently 'heat'
await helper.async_update(
ServicesTypes.HEATER_COOLER,
{
CharacteristicsTypes.TEMPERATURE_CURRENT: 19,
CharacteristicsTypes.TEMPERATURE_HEATING_THRESHOLD: 21,
CharacteristicsTypes.CURRENT_HEATER_COOLER_STATE: CurrentHeaterCoolerStateValues.HEATING,
CharacteristicsTypes.TARGET_HEATER_COOLER_STATE: TargetHeaterCoolerStateValues.HEAT,
CharacteristicsTypes.SWING_MODE: SwingModeValues.DISABLED,
},
)
state = await helper.poll_and_get_state()
assert state.state == "heat"
assert state.attributes["hvac_action"] == "heating"
| Check that we haven't conflated hvac_mode and hvac_action. | 8 | 101 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
async def test_heater_cooler_hvac_mode_vs_hvac_action(hass, utcnow):
helper = await setup_test_component(hass, create_heater_cooler_service)
# Simulate that current temperature is above target temp
# Heating might be on, but hvac_action currently 'off'
await helper.async_update(
ServicesTypes.HEATER_COOLER,
{
CharacteristicsTypes.TEMPERATURE_CURRENT: 22,
CharacteristicsTypes.TEMPERATURE_HEATING_THRESHOLD: 21,
CharacteristicsTypes.CURRENT_HEATER_COOLER_STATE: CurrentHeaterCoolerStateValues.IDLE,
CharacteristicsTypes.TARGET_HEATER_COOLER_STATE: TargetHeaterCoolerStateValues.HEAT,
CharacteristicsTypes.SWING_MODE: SwingModeValues.DISABLED,
},
)
state = await helper.poll_and_get_state()
assert state.state == "heat"
assert state.attributes["hvac_action"] == "idle"
# Simulate that current temperature is below target temp
# Heating might be on and hvac_action currently 'heat'
await helper.async_update(
ServicesTypes.HEATER_COOLER,
{
CharacteristicsTypes.TEMPERATURE_CURRENT: 19,
CharacteristicsTypes.TEMPERATURE_HEATING_THRESHOLD: 21,
CharacteristicsTypes.CURRENT_HEATER_COOLER_STATE: CurrentHeaterCoolerStateValues.HEATING,
CharacteristicsTypes.TARGET_HEATER_COOLER_STATE: TargetHeaterCoolerStateValues.HEAT,
CharacteristicsTypes.SWING_MODE: SwingModeValues.DISABLED,
},
)
state = await helper.poll_and_get_state()
assert state.state == "heat"
assert state.attributes["hvac_action"] == "heating"
```
###Assistant : Check that we haven't conflated hvac_mode and hvac_action.
|
2,693 | def not_in_timeout(cls, last_triggered, timeout):
return (
last_triggered is None
or timeout is None
or (time.time() - last_triggered > timeout)
)
| Checks if current error lies not in timeout after last trigger (potential reset of connection). | 15 | 20 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def not_in_timeout(cls, last_triggered, timeout):
return (
last_triggered is None
or timeout is None
or (time.time() - last_triggered > timeout)
)
```
###Assistant : Checks if current error lies not in timeout after last trigger (potential reset of connection).
|
2,694 | def get_rescored_finished(self, n_best=None):
# if we never actually finished, force one
if not self.finished:
self.outputs[-1][0] = self.eos
self.finished.append(
_HypothesisTail(
timestep=len(self.outputs) - 1,
hypid=0,
score=self.all_scores[-1][0],
tokenid=self.outputs[-1][0],
token_score=self.token_scores[0, -1]
if self.token_scores is not None
else None,
token_rank=self.token_ranks[0, -1]
if self.token_ranks is not None
else None,
)
)
rescored_finished = []
for finished_item in self.finished:
current_length = finished_item.timestep + 1
# these weights are from Google NMT paper
length_penalty = math.pow((1 + current_length) / 6, self.length_penalty)
rescored_finished.append(
_HypothesisTail(
timestep=finished_item.timestep,
hypid=finished_item.hypid,
score=finished_item.score / length_penalty,
tokenid=finished_item.tokenid,
token_score=finished_item.token_score,
token_rank=finished_item.token_rank,
)
)
# Note: beam size is almost always pretty small, so sorting is cheap enough
srted = sorted(rescored_finished, key=attrgetter('score'), reverse=True)
if n_best is not None:
srted = srted[:n_best]
n_best_list = []
for hyp in srted:
hyp_data = self._get_hyp_from_finished(hyp)
token_ids = self._get_pretty_hypothesis(hyp_data)
token_metadata = (
self._get_pretty_token_metadata(hyp_data) if self.verbose else None
)
n_best_list.append((token_ids, hyp.score, token_metadata))
# check that there is at least one finished candidate
# and assert that each of them contains only one EOS
assert (
len(n_best_list) >= 1
), f'TreeSearch returned {len(n_best_list)} candidates, must be >= 1'
for (pred, score, _) in n_best_list:
assert (pred == self.eos).sum() == 1, (
f'TreeSearch returned a finalized hypo with multiple end tokens '
f'with score {score.item():.2f}'
)
return n_best_list
|
Return finished hypotheses according to adjusted scores.
Score adjustment is done according to the Google NMT paper, which
penalizes long utterances.
:param n_best:
number of finalized hypotheses to return
:return:
list of (tokens, score, token_metadata) 3-tuples, in sorted order, where:
- tokens is a tensor of token ids
- score is the adjusted log probability of the entire utterance
- token_metadata dictionary:
token_logprobs -> a tensor of conditional log probabilities of tokens
token_ranks -> a tensor of ranks of tokens in vocabulator, by probability, when sampled
| 86 | 201 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def get_rescored_finished(self, n_best=None):
# if we never actually finished, force one
if not self.finished:
self.outputs[-1][0] = self.eos
self.finished.append(
_HypothesisTail(
timestep=len(self.outputs) - 1,
hypid=0,
score=self.all_scores[-1][0],
tokenid=self.outputs[-1][0],
token_score=self.token_scores[0, -1]
if self.token_scores is not None
else None,
token_rank=self.token_ranks[0, -1]
if self.token_ranks is not None
else None,
)
)
rescored_finished = []
for finished_item in self.finished:
current_length = finished_item.timestep + 1
# these weights are from Google NMT paper
length_penalty = math.pow((1 + current_length) / 6, self.length_penalty)
rescored_finished.append(
_HypothesisTail(
timestep=finished_item.timestep,
hypid=finished_item.hypid,
score=finished_item.score / length_penalty,
tokenid=finished_item.tokenid,
token_score=finished_item.token_score,
token_rank=finished_item.token_rank,
)
)
# Note: beam size is almost always pretty small, so sorting is cheap enough
srted = sorted(rescored_finished, key=attrgetter('score'), reverse=True)
if n_best is not None:
srted = srted[:n_best]
n_best_list = []
for hyp in srted:
hyp_data = self._get_hyp_from_finished(hyp)
token_ids = self._get_pretty_hypothesis(hyp_data)
token_metadata = (
self._get_pretty_token_metadata(hyp_data) if self.verbose else None
)
n_best_list.append((token_ids, hyp.score, token_metadata))
# check that there is at least one finished candidate
# and assert that each of them contains only one EOS
assert (
len(n_best_list) >= 1
), f'TreeSearch returned {len(n_best_list)} candidates, must be >= 1'
for (pred, score, _) in n_best_list:
assert (pred == self.eos).sum() == 1, (
f'TreeSearch returned a finalized hypo with multiple end tokens '
f'with score {score.item():.2f}'
)
return n_best_list
```
###Assistant :
Return finished hypotheses according to adjusted scores.
Score adjustment is done according to the Google NMT paper, which
penalizes long utterances.
:param n_best:
number of finalized hypotheses to return
:return:
list of (tokens, score, token_metadata) 3-tuples, in sorted order, where:
- tokens is a tensor of token ids
- score is the adjusted log probability of the entire utterance
- token_metadata dictionary:
token_logprobs -> a tensor of conditional log probabilities of tokens
token_ranks -> a tensor of ranks of tokens in vocabulator, by probability, when sampled
|
2,695 | def quantile(self, q=0.5, **kwargs):
return self._downsample("quantile", q=q, **kwargs)
|
Return value at the given quantile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Returns
-------
DataFrame or Series
Quantile of values within each group.
See Also
--------
Series.quantile
Return a series, where the index is q and the values are the quantiles.
DataFrame.quantile
Return a DataFrame, where the columns are the columns of self,
and the values are the quantiles.
DataFrameGroupBy.quantile
Return a DataFrame, where the coulmns are groupby columns,
and the values are its quantiles.
| 80 | 8 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def quantile(self, q=0.5, **kwargs):
return self._downsample("quantile", q=q, **kwargs)
```
###Assistant :
Return value at the given quantile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Returns
-------
DataFrame or Series
Quantile of values within each group.
See Also
--------
Series.quantile
Return a series, where the index is q and the values are the quantiles.
DataFrame.quantile
Return a DataFrame, where the columns are the columns of self,
and the values are the quantiles.
DataFrameGroupBy.quantile
Return a DataFrame, where the coulmns are groupby columns,
and the values are its quantiles.
|
2,696 | def current_columns(self):
return copy.deepcopy(self.custcols) #deepcopy to prevent users from changing it
|
Return the currently defined custom columns
Return the currently defined custom columns including the ones that haven't
yet been created. It is a dict of dicts defined as follows:
custcols[lookup_name] = {
'label': lookup_name,
'name': column_heading,
'datatype': datatype,
'display': display,
'normalized': None,
'colnum': an integer used internally,
'is_multiple': is_multiple,
}
Columns that already exist will have additional attributes that this class
doesn't use. See calibre.library.field_metadata.add_custom_field() for the
complete list.
| 69 | 11 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def current_columns(self):
return copy.deepcopy(self.custcols) #deepcopy to prevent users from changing it
```
###Assistant :
Return the currently defined custom columns
Return the currently defined custom columns including the ones that haven't
yet been created. It is a dict of dicts defined as follows:
custcols[lookup_name] = {
'label': lookup_name,
'name': column_heading,
'datatype': datatype,
'display': display,
'normalized': None,
'colnum': an integer used internally,
'is_multiple': is_multiple,
}
Columns that already exist will have additional attributes that this class
doesn't use. See calibre.library.field_metadata.add_custom_field() for the
complete list.
|
2,697 | def transform_vector(self, vector):
return Vector(
(vector.x + self.offset[0]) * self.scale[0],
(vector.y + self.offset[1]) * self.scale[1],
)
|
Transforms the given vector into the coordinate space of the final image.
Use this to find out where a point on the source image would end up in the
final image after cropping/resizing has been performed.
Returns a new vector.
| 40 | 16 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def transform_vector(self, vector):
return Vector(
(vector.x + self.offset[0]) * self.scale[0],
(vector.y + self.offset[1]) * self.scale[1],
)
```
###Assistant :
Transforms the given vector into the coordinate space of the final image.
Use this to find out where a point on the source image would end up in the
final image after cropping/resizing has been performed.
Returns a new vector.
|
2,698 | def _load_from_file(module_path):
from imp import PY_SOURCE, load_module
imported = None
if module_path:
with open(module_path, 'r') as openfile:
imported = load_module("mod", openfile, module_path, ('imported', 'r', PY_SOURCE))
return imported
|
Load a python module from its absolute filesystem path
| 9 | 27 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _load_from_file(module_path):
from imp import PY_SOURCE, load_module
imported = None
if module_path:
with open(module_path, 'r') as openfile:
imported = load_module("mod", openfile, module_path, ('imported', 'r', PY_SOURCE))
return imported
```
###Assistant :
Load a python module from its absolute filesystem path
|
2,699 | def _alter_column_type_sql(self, table, old_field, new_field, new_type):
if not hasattr(old_field, "dim") or not hasattr(new_field, "dim"):
return super()._alter_column_type_sql(table, old_field, new_field, new_type)
if old_field.dim == 2 and new_field.dim == 3:
sql_alter = self.sql_alter_column_to_3d
elif old_field.dim == 3 and new_field.dim == 2:
sql_alter = self.sql_alter_column_to_2d
else:
sql_alter = self.sql_alter_column_type
return (
(
sql_alter
% {
"column": self.quote_name(new_field.column),
"type": new_type,
},
[],
),
[],
)
|
Special case when dimension changed.
| 5 | 60 | Python |
###User : Below is a Python method which does a task. Create a documentation for the below code :
```Python
def _alter_column_type_sql(self, table, old_field, new_field, new_type):
if not hasattr(old_field, "dim") or not hasattr(new_field, "dim"):
return super()._alter_column_type_sql(table, old_field, new_field, new_type)
if old_field.dim == 2 and new_field.dim == 3:
sql_alter = self.sql_alter_column_to_3d
elif old_field.dim == 3 and new_field.dim == 2:
sql_alter = self.sql_alter_column_to_2d
else:
sql_alter = self.sql_alter_column_type
return (
(
sql_alter
% {
"column": self.quote_name(new_field.column),
"type": new_type,
},
[],
),
[],
)
```
###Assistant :
Special case when dimension changed.
|
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