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def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def deeplift_grad(module, grad_input, grad_output):
"""The backward hook which computes the deeplift
gradient for an nn.Module
"""
# first, get the module type
module_type = module.__class__.__name__
# first, check the module is supported
if module_type in op_handler:
if op_handler[module_type].__name__ not in ['passthrough', 'linear_1d']:
return op_handler[module_type](module, grad_input, grad_output)
else:
print('Warning: unrecognized nn.Module: {}'.format(module_type))
return grad_input |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def open_pager(self):
"Open the selected item with the system's pager"
data = self.get_selected_item()
if data['type'] == 'Submission':
text = '\n\n'.join((data['permalink'], data['text']))
self.term.open_pager(text)
elif data['type'] == 'Comment':
text = '\n\n'.join((data['permalink'], data['body']))
self.term.open_pager(text)
else:
self.term.flash() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def add_interim_values(module, input, output):
"""The forward hook used to save interim tensors, detached
from the graph. Used to calculate the multipliers
"""
try:
del module.x
except AttributeError:
pass
try:
del module.y
except AttributeError:
pass
module_type = module.__class__.__name__
if module_type in op_handler:
func_name = op_handler[module_type].__name__
# First, check for cases where we don't need to save the x and y tensors
if func_name == 'passthrough':
pass
else:
# check only the 0th input varies
for i in range(len(input)):
if i != 0 and type(output) is tuple:
assert input[i] == output[i], "Only the 0th input may vary!"
# if a new method is added, it must be added here too. This ensures tensors
# are only saved if necessary
if func_name in ['maxpool', 'nonlinear_1d']:
# only save tensors if necessary
if type(input) is tuple:
setattr(module, 'x', torch.nn.Parameter(input[0].detach()))
else:
setattr(module, 'x', torch.nn.Parameter(input.detach()))
if type(output) is tuple:
setattr(module, 'y', torch.nn.Parameter(output[0].detach()))
else:
setattr(module, 'y', torch.nn.Parameter(output.detach()))
if module_type in failure_case_modules:
input[0].register_hook(deeplift_tensor_grad) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def add_comment(self):
"""
Submit a reply to the selected item.
Selected item:
Submission - add a top level comment
Comment - add a comment reply
"""
data = self.get_selected_item()
if data['type'] == 'Submission':
body = data['text']
reply = data['object'].add_comment
elif data['type'] == 'Comment':
body = data['body']
reply = data['object'].reply
else:
self.term.flash()
return
# Construct the text that will be displayed in the editor file.
# The post body will be commented out and added for reference
lines = ['# |' + line for line in body.split('\n')]
content = '\n'.join(lines)
comment_info = docs.COMMENT_FILE.format(
author=data['author'],
type=data['type'].lower(),
content=content)
with self.term.open_editor(comment_info) as comment:
if not comment:
self.term.show_notification('Canceled')
return
with self.term.loader('Posting', delay=0):
reply(comment)
# Give reddit time to process the submission
time.sleep(2.0)
if self.term.loader.exception is None:
self.refresh_content()
else:
raise TemporaryFileError() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def get_target_input(module, input, output):
"""A forward hook which saves the tensor - attached to its graph.
Used if we want to explain the interim outputs of a model
"""
try:
del module.target_input
except AttributeError:
pass
setattr(module, 'target_input', input) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def delete_comment(self):
"Delete the selected comment"
if self.get_selected_item()['type'] == 'Comment':
self.delete_item()
else:
self.term.flash() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def deeplift_tensor_grad(grad):
return_grad = complex_module_gradients[-1]
del complex_module_gradients[-1]
return return_grad |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def comment_urlview(self):
data = self.get_selected_item()
comment = data.get('body') or data.get('text') or data.get('url_full')
if comment:
self.term.open_urlview(comment)
else:
self.term.flash() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def passthrough(module, grad_input, grad_output):
"""No change made to gradients"""
return None |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _draw_item(self, win, data, inverted):
if data['type'] == 'MoreComments':
return self._draw_more_comments(win, data)
elif data['type'] == 'HiddenComment':
return self._draw_more_comments(win, data)
elif data['type'] == 'Comment':
return self._draw_comment(win, data, inverted)
else:
return self._draw_submission(win, data) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def maxpool(module, grad_input, grad_output):
pool_to_unpool = {
'MaxPool1d': torch.nn.functional.max_unpool1d,
'MaxPool2d': torch.nn.functional.max_unpool2d,
'MaxPool3d': torch.nn.functional.max_unpool3d
}
pool_to_function = {
'MaxPool1d': torch.nn.functional.max_pool1d,
'MaxPool2d': torch.nn.functional.max_pool2d,
'MaxPool3d': torch.nn.functional.max_pool3d
}
delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
dup0 = [2] + [1 for i in delta_in.shape[1:]]
# we also need to check if the output is a tuple
y, ref_output = torch.chunk(module.y, 2)
cross_max = torch.max(y, ref_output)
diffs = torch.cat([cross_max - ref_output, y - cross_max], 0)
# all of this just to unpool the outputs
with torch.no_grad():
_, indices = pool_to_function[module.__class__.__name__](
module.x, module.kernel_size, module.stride, module.padding,
module.dilation, module.ceil_mode, True)
xmax_pos, rmax_pos = torch.chunk(pool_to_unpool[module.__class__.__name__](
grad_output[0] * diffs, indices, module.kernel_size, module.stride,
module.padding, list(module.x.shape)), 2)
org_input_shape = grad_input[0].shape # for the maxpool 1d
grad_input = [None for _ in grad_input]
grad_input[0] = torch.where(torch.abs(delta_in) < 1e-7, torch.zeros_like(delta_in),
(xmax_pos + rmax_pos) / delta_in).repeat(dup0)
if module.__class__.__name__ == 'MaxPool1d':
complex_module_gradients.append(grad_input[0])
# the grad input that is returned doesn't matter, since it will immediately be
# be overridden by the grad in the complex_module_gradient
grad_input[0] = torch.ones(org_input_shape)
return tuple(grad_input) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _draw_comment(self, win, data, inverted):
n_rows, n_cols = win.getmaxyx()
n_cols -= 1
# Handle the case where the window is not large enough to fit the text.
valid_rows = range(0, n_rows)
offset = 0 if not inverted else -(data['n_rows'] - n_rows)
# If there isn't enough space to fit the comment body on the screen,
# replace the last line with a notification.
split_body = data['split_body']
if data['n_rows'] > n_rows:
# Only when there is a single comment on the page and not inverted
if not inverted and len(self._subwindows) == 0:
cutoff = data['n_rows'] - n_rows + 1
split_body = split_body[:-cutoff]
split_body.append('(Not enough space to display)')
row = offset
if row in valid_rows:
attr = curses.A_BOLD
attr |= (Color.BLUE if not data['is_author'] else Color.GREEN)
self.term.add_line(win, '{author} '.format(**data), row, 1, attr)
if data['flair']:
attr = curses.A_BOLD | Color.YELLOW
self.term.add_line(win, '{flair} '.format(**data), attr=attr)
text, attr = self.term.get_arrow(data['likes'])
self.term.add_line(win, text, attr=attr)
self.term.add_line(win, ' {score} {created} '.format(**data))
if data['gold']:
text, attr = self.term.guilded
self.term.add_line(win, text, attr=attr)
if data['stickied']:
text, attr = '[stickied]', Color.GREEN
self.term.add_line(win, text, attr=attr)
if data['saved']:
text, attr = '[saved]', Color.GREEN
self.term.add_line(win, text, attr=attr)
for row, text in enumerate(split_body, start=offset+1):
if row in valid_rows:
self.term.add_line(win, text, row, 1)
# Unfortunately vline() doesn't support custom color so we have to
# build it one segment at a time.
attr = Color.get_level(data['level'])
x = 0
for y in range(n_rows):
self.term.addch(win, y, x, self.term.vline, attr)
return attr | self.term.vline |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def linear_1d(module, grad_input, grad_output):
"""No change made to gradients."""
return None |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _draw_more_comments(self, win, data):
n_rows, n_cols = win.getmaxyx()
n_cols -= 1
self.term.add_line(win, '{body}'.format(**data), 0, 1)
self.term.add_line(
win, ' [{count}]'.format(**data), attr=curses.A_BOLD)
attr = Color.get_level(data['level'])
self.term.addch(win, 0, 0, self.term.vline, attr)
return attr | self.term.vline |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def nonlinear_1d(module, grad_input, grad_output):
delta_out = module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2):]
delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
dup0 = [2] + [1 for i in delta_in.shape[1:]]
# handles numerical instabilities where delta_in is very small by
# just taking the gradient in those cases
grads = [None for _ in grad_input]
grads[0] = torch.where(torch.abs(delta_in.repeat(dup0)) < 1e-6, grad_input[0],
grad_output[0] * (delta_out / delta_in).repeat(dup0))
return tuple(grads) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def wait_time_gen():
count = 0
while True:
rand = random.randrange(round(interval.total_seconds()))
tmp = round(start + interval.total_seconds() * count + rand - loop.time())
yield tmp
count += 1 |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def every_day(job, loop=None):
return every(job, timedelta=timedelta(days=1), loop=loop) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def every_week(job, loop=None):
return every(job, timedelta=timedelta(days=7), loop=loop) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _nearest_weekday(weekday):
return datetime.now() + timedelta(days=(weekday - datetime.now().weekday()) % 7) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _every_weekday(job, weekday, loop=None):
return every(job, timedelta=timedelta(days=7), start_at=_nearest_weekday(weekday), loop=loop) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_dummy_request():
from rasa.nlu.emulators.no_emulator import NoEmulator
em = NoEmulator()
norm = em.normalise_request_json({"text": ["arb text"]})
assert norm == {"text": "arb text", "time": None}
norm = em.normalise_request_json({"text": ["arb text"], "time": "1499279161658"})
assert norm == {"text": "arb text", "time": "1499279161658"} |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self):
ApiCli.__init__(self)
self.path = "v1/account/sources/"
self.method = "GET" |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_dummy_response():
from rasa.nlu.emulators.no_emulator import NoEmulator
em = NoEmulator()
data = {"intent": "greet", "text": "hi", "entities": {}, "confidence": 1.0}
assert em.normalise_response_json(data) == data |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self, root, transforms=None):
super().__init__(root=root)
self.transforms = transforms
self._flow_list = []
self._image_list = [] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _read_img(self, file_name):
img = Image.open(file_name)
if img.mode != "RGB":
img = img.convert("RGB")
return img |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _read_flow(self, file_name):
# Return the flow or a tuple with the flow and the valid_flow_mask if _has_builtin_flow_mask is True
pass |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __getitem__(self, index):
img1 = self._read_img(self._image_list[index][0])
img2 = self._read_img(self._image_list[index][1])
if self._flow_list: # it will be empty for some dataset when split="test"
flow = self._read_flow(self._flow_list[index])
if self._has_builtin_flow_mask:
flow, valid_flow_mask = flow
else:
valid_flow_mask = None
else:
flow = valid_flow_mask = None
if self.transforms is not None:
img1, img2, flow, valid_flow_mask = self.transforms(img1, img2, flow, valid_flow_mask)
if self._has_builtin_flow_mask or valid_flow_mask is not None:
# The `or valid_flow_mask is not None` part is here because the mask can be generated within a transform
return img1, img2, flow, valid_flow_mask
else:
return img1, img2, flow |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __len__(self):
return len(self._image_list) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __rmul__(self, v):
return torch.utils.data.ConcatDataset([self] * v) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self, root, split="train", pass_name="clean", transforms=None):
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "test"))
verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both"))
passes = ["clean", "final"] if pass_name == "both" else [pass_name]
root = Path(root) / "Sintel"
flow_root = root / "training" / "flow"
for pass_name in passes:
split_dir = "training" if split == "train" else split
image_root = root / split_dir / pass_name
for scene in os.listdir(image_root):
image_list = sorted(glob(str(image_root / scene / "*.png")))
for i in range(len(image_list) - 1):
self._image_list += [[image_list[i], image_list[i + 1]]]
if split == "train":
self._flow_list += sorted(glob(str(flow_root / scene / "*.flo"))) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __getitem__(self, index):
"""Return example at given index.
Args:
index(int): The index of the example to retrieve
Returns:
tuple: A 3-tuple with ``(img1, img2, flow)``.
The flow is a numpy array of shape (2, H, W) and the images are PIL images.
``flow`` is None if ``split="test"``.
If a valid flow mask is generated within the ``transforms`` parameter,
a 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` is returned.
"""
return super().__getitem__(index) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _read_flow(self, file_name):
return _read_flo(file_name) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self, root, split="train", transforms=None):
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "test"))
root = Path(root) / "KittiFlow" / (split + "ing")
images1 = sorted(glob(str(root / "image_2" / "*_10.png")))
images2 = sorted(glob(str(root / "image_2" / "*_11.png")))
if not images1 or not images2:
raise FileNotFoundError(
"Could not find the Kitti flow images. Please make sure the directory structure is correct."
)
for img1, img2 in zip(images1, images2):
self._image_list += [[img1, img2]]
if split == "train":
self._flow_list = sorted(glob(str(root / "flow_occ" / "*_10.png"))) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __getitem__(self, index):
"""Return example at given index.
Args:
index(int): The index of the example to retrieve
Returns:
tuple: A 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` where ``valid_flow_mask``
is a numpy boolean mask of shape (H, W)
indicating which flow values are valid. The flow is a numpy array of
shape (2, H, W) and the images are PIL images. ``flow`` and ``valid_flow_mask`` are None if
``split="test"``.
"""
return super().__getitem__(index) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _read_flow(self, file_name):
return _read_16bits_png_with_flow_and_valid_mask(file_name) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self, root, split="train", transforms=None):
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "val"))
root = Path(root) / "FlyingChairs"
images = sorted(glob(str(root / "data" / "*.ppm")))
flows = sorted(glob(str(root / "data" / "*.flo")))
split_file_name = "FlyingChairs_train_val.txt"
if not os.path.exists(root / split_file_name):
raise FileNotFoundError(
"The FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring)."
)
split_list = np.loadtxt(str(root / split_file_name), dtype=np.int32)
for i in range(len(flows)):
split_id = split_list[i]
if (split == "train" and split_id == 1) or (split == "val" and split_id == 2):
self._flow_list += [flows[i]]
self._image_list += [[images[2 * i], images[2 * i + 1]]] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self, root, split="train", pass_name="clean", camera="left", transforms=None):
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "test"))
split = split.upper()
verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both"))
passes = {
"clean": ["frames_cleanpass"],
"final": ["frames_finalpass"],
"both": ["frames_cleanpass", "frames_finalpass"],
}[pass_name]
verify_str_arg(camera, "camera", valid_values=("left", "right", "both"))
cameras = ["left", "right"] if camera == "both" else [camera]
root = Path(root) / "FlyingThings3D"
directions = ("into_future", "into_past")
for pass_name, camera, direction in itertools.product(passes, cameras, directions):
image_dirs = sorted(glob(str(root / pass_name / split / "*/*")))
image_dirs = sorted(Path(image_dir) / camera for image_dir in image_dirs)
flow_dirs = sorted(glob(str(root / "optical_flow" / split / "*/*")))
flow_dirs = sorted(Path(flow_dir) / direction / camera for flow_dir in flow_dirs)
if not image_dirs or not flow_dirs:
raise FileNotFoundError(
"Could not find the FlyingThings3D flow images. "
"Please make sure the directory structure is correct."
)
for image_dir, flow_dir in zip(image_dirs, flow_dirs):
images = sorted(glob(str(image_dir / "*.png")))
flows = sorted(glob(str(flow_dir / "*.pfm")))
for i in range(len(flows) - 1):
if direction == "into_future":
self._image_list += [[images[i], images[i + 1]]]
self._flow_list += [flows[i]]
elif direction == "into_past":
self._image_list += [[images[i + 1], images[i]]]
self._flow_list += [flows[i + 1]] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _read_flow(self, file_name):
return _read_pfm(file_name) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self, root, split="train", transforms=None):
super().__init__(root=root, transforms=transforms)
verify_str_arg(split, "split", valid_values=("train", "test"))
root = Path(root) / "hd1k"
if split == "train":
# There are 36 "sequences" and we don't want seq i to overlap with seq i + 1, so we need this for loop
for seq_idx in range(36):
flows = sorted(glob(str(root / "hd1k_flow_gt" / "flow_occ" / f"{seq_idx:06d}_*.png")))
images = sorted(glob(str(root / "hd1k_input" / "image_2" / f"{seq_idx:06d}_*.png")))
for i in range(len(flows) - 1):
self._flow_list += [flows[i]]
self._image_list += [[images[i], images[i + 1]]]
else:
images1 = sorted(glob(str(root / "hd1k_challenge" / "image_2" / "*10.png")))
images2 = sorted(glob(str(root / "hd1k_challenge" / "image_2" / "*11.png")))
for image1, image2 in zip(images1, images2):
self._image_list += [[image1, image2]]
if not self._image_list:
raise FileNotFoundError(
"Could not find the HD1K images. Please make sure the directory structure is correct."
) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _read_flow(self, file_name):
return _read_16bits_png_with_flow_and_valid_mask(file_name) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _read_flo(file_name):
"""Read .flo file in Middlebury format"""
# Code adapted from:
# http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy
# Everything needs to be in little Endian according to
# https://vision.middlebury.edu/flow/code/flow-code/README.txt
with open(file_name, "rb") as f:
magic = np.fromfile(f, "c", count=4).tobytes()
if magic != b"PIEH":
raise ValueError("Magic number incorrect. Invalid .flo file")
w = int(np.fromfile(f, "<i4", count=1))
h = int(np.fromfile(f, "<i4", count=1))
data = np.fromfile(f, "<f4", count=2 * w * h)
return data.reshape(h, w, 2).transpose(2, 0, 1) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _read_16bits_png_with_flow_and_valid_mask(file_name):
flow_and_valid = _read_png_16(file_name).to(torch.float32)
flow, valid_flow_mask = flow_and_valid[:2, :, :], flow_and_valid[2, :, :]
flow = (flow - 2 ** 15) / 64 # This conversion is explained somewhere on the kitti archive
valid_flow_mask = valid_flow_mask.bool()
# For consistency with other datasets, we convert to numpy
return flow.numpy(), valid_flow_mask.numpy() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def LogPABotMessage(message):
_pabotlog.info(message) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_engine_module_name():
engine = salt.engines.Engine({}, "foobar.start", {}, {}, {}, {}, name="foobar")
assert engine.name == "foobar" |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def resource_setup(cls):
super(VolumesActionsTest, cls).resource_setup()
# Create a test shared volume for attach/detach tests
cls.volume = cls.create_volume() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_attach_detach_volume_to_instance(self):
"""Test attaching and detaching volume to instance"""
# Create a server
server = self.create_server()
# Volume is attached and detached successfully from an instance
self.volumes_client.attach_volume(self.volume['id'],
instance_uuid=server['id'],
mountpoint='/dev/%s' %
CONF.compute.volume_device_name)
waiters.wait_for_volume_resource_status(self.volumes_client,
self.volume['id'], 'in-use')
self.volumes_client.detach_volume(self.volume['id'])
waiters.wait_for_volume_resource_status(self.volumes_client,
self.volume['id'], 'available') |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_volume_bootable(self):
"""Test setting and retrieving bootable flag of a volume"""
for bool_bootable in [True, False]:
self.volumes_client.set_bootable_volume(self.volume['id'],
bootable=bool_bootable)
fetched_volume = self.volumes_client.show_volume(
self.volume['id'])['volume']
# Get Volume information
# NOTE(masayukig): 'bootable' is "true" or "false" in the current
# cinder implementation. So we need to cast boolean values to str
# and make it lower to compare here.
self.assertEqual(str(bool_bootable).lower(),
fetched_volume['bootable']) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_get_volume_attachment(self):
"""Test getting volume attachments
Attach a volume to a server, and then retrieve volume's attachments
info.
"""
# Create a server
server = self.create_server()
# Verify that a volume's attachment information is retrieved
self.volumes_client.attach_volume(self.volume['id'],
instance_uuid=server['id'],
mountpoint='/dev/%s' %
CONF.compute.volume_device_name)
waiters.wait_for_volume_resource_status(self.volumes_client,
self.volume['id'],
'in-use')
self.addCleanup(waiters.wait_for_volume_resource_status,
self.volumes_client,
self.volume['id'], 'available')
self.addCleanup(self.volumes_client.detach_volume, self.volume['id'])
volume = self.volumes_client.show_volume(self.volume['id'])['volume']
attachment = volume['attachments'][0]
self.assertEqual('/dev/%s' %
CONF.compute.volume_device_name,
attachment['device'])
self.assertEqual(server['id'], attachment['server_id'])
self.assertEqual(self.volume['id'], attachment['id'])
self.assertEqual(self.volume['id'], attachment['volume_id']) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_volume_upload(self):
"""Test uploading volume to create an image"""
# NOTE(gfidente): the volume uploaded in Glance comes from setUpClass,
# it is shared with the other tests. After it is uploaded in Glance,
# there is no way to delete it from Cinder, so we delete it from Glance
# using the Glance images_client and from Cinder via tearDownClass.
image_name = data_utils.rand_name(self.__class__.__name__ + '-Image')
body = self.volumes_client.upload_volume(
self.volume['id'], image_name=image_name,
disk_format=CONF.volume.disk_format)['os-volume_upload_image']
image_id = body["image_id"]
self.addCleanup(test_utils.call_and_ignore_notfound_exc,
self.images_client.delete_image,
image_id)
waiters.wait_for_image_status(self.images_client, image_id, 'active')
waiters.wait_for_volume_resource_status(self.volumes_client,
self.volume['id'], 'available')
image_info = self.images_client.show_image(image_id)
self.assertEqual(image_name, image_info['name'])
self.assertEqual(CONF.volume.disk_format, image_info['disk_format']) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_reserve_unreserve_volume(self):
"""Test reserving and unreserving volume"""
# Mark volume as reserved.
self.volumes_client.reserve_volume(self.volume['id'])
# To get the volume info
body = self.volumes_client.show_volume(self.volume['id'])['volume']
self.assertIn('attaching', body['status'])
# Unmark volume as reserved.
self.volumes_client.unreserve_volume(self.volume['id'])
# To get the volume info
body = self.volumes_client.show_volume(self.volume['id'])['volume']
self.assertIn('available', body['status']) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def setup_loader(request):
setup_loader_modules = {pdbedit: {}}
with pytest.helpers.loader_mock(request, setup_loader_modules) as loader_mock:
yield loader_mock |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_disk_usage_sensor_is_stateless():
sensor = disk_usage.DiskUsage()
ok_([] != sensor.measure()) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_when_no_users_returned_no_data_should_be_returned(verbose):
expected_users = {} if verbose else []
with patch.dict(
pdbedit.__salt__,
{
"cmd.run_all": MagicMock(
return_value={"stdout": "", "stderr": "", "retcode": 0}
)
},
):
actual_users = pdbedit.list_users(verbose=verbose)
assert actual_users == expected_users |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_when_verbose_and_retcode_is_nonzero_output_should_be_had():
expected_stderr = "this is something fnord"
with patch.dict(
pdbedit.__salt__,
{
"cmd.run_all": MagicMock(
return_value={"stdout": "", "stderr": expected_stderr, "retcode": 1}
)
},
), patch("salt.modules.pdbedit.log.error", autospec=True) as fake_error_log:
pdbedit.list_users(verbose=True)
actual_error = fake_error_log.mock_calls[0].args[0]
assert actual_error == expected_stderr |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_when_verbose_and_single_good_output_expected_data_should_be_parsed():
expected_data = {
"roscivs": {
"unix username": "roscivs",
"nt username": "bottia",
"full name": "Roscivs Bottia",
"user sid": "42",
"primary group sid": "99",
"home directory": r"\\samba\roscivs",
"account desc": "separators! xxx so long and thanks for all the fish",
"logoff time": "Sat, 14 Aug 2010 15:06:39 UTC",
"kickoff time": "Sat, 14 Aug 2010 15:06:39 UTC",
"password must change": "never",
}
}
pdb_output = dedent(
r"""
Unix username: roscivs
NT username: bottia
User SID: 42
Primary Group SID: 99
Full Name: Roscivs Bottia
Home Directory: \\samba\roscivs
Account desc: separators! xxx so long and thanks for all the fish
Logoff time: Sat, 14 Aug 2010 15:06:39 UTC
Kickoff time: Sat, 14 Aug 2010 15:06:39 UTC
Password must change: never
"""
).strip()
with patch.dict(
pdbedit.__salt__,
{
"cmd.run_all": MagicMock(
return_value={"stdout": pdb_output, "stderr": "", "retcode": 0}
)
},
):
actual_data = pdbedit.list_users(verbose=True)
assert actual_data == expected_data |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def parse_record(self, metadata, line):
factors = line.split('|')
if len(factors) < 7:
return
registry, cc, type_, start, value, dete, status = factors[:7]
if type_ not in ('ipv4', 'ipv6'):
return
return Record(metadata, start, type_, value, cc) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def setup_args(parser=None):
if parser is None:
parser = ParlaiParser(True, True, 'Check tasks for common errors')
# Get command line arguments
parser.add_argument('-ltim', '--log-every-n-secs', type=float, default=2)
parser.add_argument('-d', '--display-examples', type='bool', default=False)
parser.set_defaults(datatype='train:stream:ordered')
return parser |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def get_list(arg=None):
"""get list of messages"""
frappe.form_dict['limit_start'] = int(frappe.form_dict['limit_start'])
frappe.form_dict['limit_page_length'] = int(frappe.form_dict['limit_page_length'])
frappe.form_dict['user'] = frappe.session['user']
# set all messages as read
frappe.db.begin()
frappe.db.sql("""UPDATE `tabCommunication` set seen = 1
where
communication_type in ('Chat', 'Notification')
and reference_doctype = 'User'
and reference_name = %s""", frappe.session.user)
delete_notification_count_for("Messages")
frappe.local.flags.commit = True
if frappe.form_dict['contact'] == frappe.session['user']:
# return messages
return frappe.db.sql("""select * from `tabCommunication`
where
communication_type in ('Chat', 'Notification')
and reference_doctype ='User'
and (owner=%(contact)s
or reference_name=%(user)s
or owner=reference_name)
order by creation desc
limit %(limit_start)s, %(limit_page_length)s""", frappe.local.form_dict, as_dict=1)
else:
return frappe.db.sql("""select * from `tabCommunication`
where
communication_type in ('Chat', 'Notification')
and reference_doctype ='User'
and ((owner=%(contact)s and reference_name=%(user)s)
or (owner=%(contact)s and reference_name=%(contact)s))
order by creation desc
limit %(limit_start)s, %(limit_page_length)s""", frappe.local.form_dict, as_dict=1) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def report(world, counts, log_time):
report = world.report()
log = {
'missing_text': counts['missing_text'],
'missing_labels': counts['missing_labels'],
'missing_label_candidates': counts['missing_label_candidates'],
'empty_string_label_candidates': counts['empty_string_label_candidates'],
'label_candidates_with_missing_label': counts[
'label_candidates_with_missing_label'
],
'did_not_return_message': counts['did_not_return_message'],
}
text, log = log_time.log(report['exs'], world.num_examples(), log)
return text, log |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def get_active_users():
data = frappe.db.sql("""select name,
(select count(*) from tabSessions where user=tabUser.name
and timediff(now(), lastupdate) < time("01:00:00")) as has_session
from tabUser
where enabled=1 and
ifnull(user_type, '')!='Website User' and
name not in ({})
order by first_name""".format(", ".join(["%s"]*len(STANDARD_USERS))), STANDARD_USERS, as_dict=1)
# make sure current user is at the top, using has_session = 100
users = [d.name for d in data]
if frappe.session.user in users:
data[users.index(frappe.session.user)]["has_session"] = 100
else:
# in case of administrator
data.append({"name": frappe.session.user, "has_session": 100})
return data |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def warn(txt, act, opt):
if opt.get('display_examples'):
print(txt + ":\n" + str(act))
else:
warn_once(txt) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def post(txt, contact, parenttype=None, notify=False, subject=None):
"""post message"""
d = frappe.new_doc('Communication')
d.communication_type = 'Notification' if parenttype else 'Chat'
d.subject = subject
d.content = txt
d.reference_doctype = 'User'
d.reference_name = contact
d.sender = frappe.session.user
d.insert(ignore_permissions=True)
delete_notification_count_for("Messages")
if notify and cint(notify):
if contact==frappe.session.user:
_notify([user.name for user in get_enabled_system_users()], txt)
else:
_notify(contact, txt, subject)
return d |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def verify(opt):
if opt['datatype'] == 'train':
logging.warning("changing datatype from train to train:ordered")
opt['datatype'] = 'train:ordered'
opt.log()
# create repeat label agent and assign it to the specified task
agent = RepeatLabelAgent(opt)
world = create_task(opt, agent)
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
counts = {}
counts['missing_text'] = 0
counts['missing_labels'] = 0
counts['missing_label_candidates'] = 0
counts['empty_string_label_candidates'] = 0
counts['label_candidates_with_missing_label'] = 0
counts['did_not_return_message'] = 0
# Show some example dialogs.
while not world.epoch_done():
world.parley()
act = world.acts[0]
if not isinstance(act, Message):
counts['did_not_return_message'] += 1
if 'text' not in act and 'image' not in act:
warn("warning: missing text field:\n", act, opt)
counts['missing_text'] += 1
if 'labels' not in act and 'eval_labels' not in act:
warn("warning: missing labels/eval_labels field:\n", act, opt)
counts['missing_labels'] += 1
else:
if 'label_candidates' not in act:
counts['missing_label_candidates'] += 1
else:
labels = act.get('labels', act.get('eval_labels'))
is_label_cand = {}
for l in labels:
is_label_cand[l] = False
for c in act['label_candidates']:
if c == '':
warn("warning: empty string label_candidate:\n", act, opt)
counts['empty_string_label_candidates'] += 1
if c in is_label_cand:
if is_label_cand[c] is True:
warn(
"warning: label mentioned twice in candidate_labels:\n",
act,
opt,
)
is_label_cand[c] = True
for _, has in is_label_cand.items():
if has is False:
warn("warning: label missing in candidate_labels:\n", act, opt)
counts['label_candidates_with_missing_label'] += 1
if log_time.time() > log_every_n_secs:
text, log = report(world, counts, log_time)
print(text)
try:
# print dataset size if available
logging.info(
f'Loaded {world.num_episodes()} episodes with a '
f'total of {world.num_examples()} examples'
)
except AttributeError:
pass
counts['exs'] = int(world.report()['exs'])
return counts |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def delete(arg=None):
frappe.get_doc("Communication", frappe.form_dict['name']).delete() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def verify_data(opt):
counts = verify(opt)
print(counts)
return counts |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def setup_args(cls):
return setup_args() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def run(self):
return verify_data(self.opt) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def block_size_filter(entity):
return (
entity.size[0] * 2 >= entity.size[1] * 2
and entity.size[1] <= 16
and entity.size[3] <= 4
) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self, auth_provider):
super(SnapshotsClientJSON, self).__init__(auth_provider)
self.service = CONF.volume.catalog_type
self.build_interval = CONF.volume.build_interval
self.build_timeout = CONF.volume.build_timeout |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def list_snapshots(self, params=None):
"""List all the snapshot."""
url = 'snapshots'
if params:
url += '?%s' % urllib.urlencode(params)
resp, body = self.get(url)
body = json.loads(body)
return resp, body['snapshots'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def list_snapshots_with_detail(self, params=None):
"""List the details of all snapshots."""
url = 'snapshots/detail'
if params:
url += '?%s' % urllib.urlencode(params)
resp, body = self.get(url)
body = json.loads(body)
return resp, body['snapshots'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def get_snapshot(self, snapshot_id):
"""Returns the details of a single snapshot."""
url = "snapshots/%s" % str(snapshot_id)
resp, body = self.get(url)
body = json.loads(body)
return resp, body['snapshot'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def create_snapshot(self, volume_id, **kwargs):
"""
Creates a new snapshot.
volume_id(Required): id of the volume.
force: Create a snapshot even if the volume attached (Default=False)
display_name: Optional snapshot Name.
display_description: User friendly snapshot description.
"""
post_body = {'volume_id': volume_id}
post_body.update(kwargs)
post_body = json.dumps({'snapshot': post_body})
resp, body = self.post('snapshots', post_body)
body = json.loads(body)
return resp, body['snapshot'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def update_snapshot(self, snapshot_id, **kwargs):
"""Updates a snapshot."""
put_body = json.dumps({'snapshot': kwargs})
resp, body = self.put('snapshots/%s' % snapshot_id, put_body)
body = json.loads(body)
return resp, body['snapshot'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _get_snapshot_status(self, snapshot_id):
resp, body = self.get_snapshot(snapshot_id)
status = body['status']
# NOTE(afazekas): snapshot can reach an "error"
# state in a "normal" lifecycle
if (status == 'error'):
raise exceptions.SnapshotBuildErrorException(
snapshot_id=snapshot_id)
return status |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def wait_for_snapshot_status(self, snapshot_id, status):
"""Waits for a Snapshot to reach a given status."""
start_time = time.time()
old_value = value = self._get_snapshot_status(snapshot_id)
while True:
dtime = time.time() - start_time
time.sleep(self.build_interval)
if value != old_value:
LOG.info('Value transition from "%s" to "%s"'
'in %d second(s).', old_value,
value, dtime)
if (value == status):
return value
if dtime > self.build_timeout:
message = ('Time Limit Exceeded! (%ds)'
'while waiting for %s, '
'but we got %s.' %
(self.build_timeout, status, value))
raise exceptions.TimeoutException(message)
time.sleep(self.build_interval)
old_value = value
value = self._get_snapshot_status(snapshot_id) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def delete_snapshot(self, snapshot_id):
"""Delete Snapshot."""
return self.delete("snapshots/%s" % str(snapshot_id)) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def is_resource_deleted(self, id):
try:
self.get_snapshot(id)
except exceptions.NotFound:
return True
return False |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def reset_snapshot_status(self, snapshot_id, status):
"""Reset the specified snapshot's status."""
post_body = json.dumps({'os-reset_status': {"status": status}})
resp, body = self.post('snapshots/%s/action' % snapshot_id, post_body)
return resp, body |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def update_snapshot_status(self, snapshot_id, status, progress):
"""Update the specified snapshot's status."""
post_body = {
'status': status,
'progress': progress
}
post_body = json.dumps({'os-update_snapshot_status': post_body})
url = 'snapshots/%s/action' % str(snapshot_id)
resp, body = self.post(url, post_body)
return resp, body |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def create_snapshot_metadata(self, snapshot_id, metadata):
"""Create metadata for the snapshot."""
put_body = json.dumps({'metadata': metadata})
url = "snapshots/%s/metadata" % str(snapshot_id)
resp, body = self.post(url, put_body)
body = json.loads(body)
return resp, body['metadata'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def get_snapshot_metadata(self, snapshot_id):
"""Get metadata of the snapshot."""
url = "snapshots/%s/metadata" % str(snapshot_id)
resp, body = self.get(url)
body = json.loads(body)
return resp, body['metadata'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def update_snapshot_metadata(self, snapshot_id, metadata):
"""Update metadata for the snapshot."""
put_body = json.dumps({'metadata': metadata})
url = "snapshots/%s/metadata" % str(snapshot_id)
resp, body = self.put(url, put_body)
body = json.loads(body)
return resp, body['metadata'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def update_snapshot_metadata_item(self, snapshot_id, id, meta_item):
"""Update metadata item for the snapshot."""
put_body = json.dumps({'meta': meta_item})
url = "snapshots/%s/metadata/%s" % (str(snapshot_id), str(id))
resp, body = self.put(url, put_body)
body = json.loads(body)
return resp, body['meta'] |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def delete_snapshot_metadata_item(self, snapshot_id, id):
"""Delete metadata item for the snapshot."""
url = "snapshots/%s/metadata/%s" % (str(snapshot_id), str(id))
resp, body = self.delete(url)
return resp, body |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_stmt_simplify():
ib = tvm.tir.ir_builder.create()
A = ib.pointer("float32", name="A")
C = ib.pointer("float32", name="C")
n = te.size_var("n")
with ib.for_range(0, n, name="i") as i:
with ib.if_scope(i < 12):
A[i] = C[i]
body = tvm.tir.LetStmt(n, 10, ib.get())
mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([A, C, n], body))
body = tvm.tir.transform.Simplify()(mod)["main"].body
assert isinstance(body.body, tvm.tir.Store) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_thread_extent_simplify():
ib = tvm.tir.ir_builder.create()
A = ib.pointer("float32", name="A")
C = ib.pointer("float32", name="C")
n = te.size_var("n")
tx = te.thread_axis("threadIdx.x")
ty = te.thread_axis("threadIdx.y")
ib.scope_attr(tx, "thread_extent", n)
ib.scope_attr(tx, "thread_extent", n)
ib.scope_attr(ty, "thread_extent", 1)
with ib.if_scope(tx + ty < 12):
A[tx] = C[tx + ty]
body = tvm.tir.LetStmt(n, 10, ib.get())
mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([A, C, n], body))
body = tvm.tir.transform.Simplify()(mod)["main"].body
assert isinstance(body.body.body.body, tvm.tir.Store) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def test_if_likely():
ib = tvm.tir.ir_builder.create()
A = ib.pointer("float32", name="A")
C = ib.pointer("float32", name="C")
n = te.size_var("n")
tx = te.thread_axis("threadIdx.x")
ty = te.thread_axis("threadIdx.y")
ib.scope_attr(tx, "thread_extent", 32)
ib.scope_attr(ty, "thread_extent", 32)
with ib.if_scope(ib.likely(tx * 32 + ty < n)):
with ib.if_scope(ib.likely(tx * 32 + ty < n)):
A[tx] = C[tx * 32 + ty]
body = ib.get()
mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([A, C, n], body))
body = tvm.tir.transform.Simplify()(mod)["main"].body
assert isinstance(body.body.body, tvm.tir.IfThenElse)
assert not isinstance(body.body.body.then_case, tvm.tir.IfThenElse) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def name(self):
if self._values['name'] is None:
return None
name = str(self._values['name']).strip()
if name == '':
raise F5ModuleError(
"You must specify a name for this module"
)
return name |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def f(i):
start = W[i]
extent = W[i + 1] - W[i]
rv = te.reduce_axis((0, extent))
return te.sum(X[rv + start], axis=rv) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def __init__(self, client):
self.client = client
self.have = None
self.want = Parameters(self.client.module.params)
self.changes = Parameters() |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def cumsum(X):
"""
Y[i] = sum(X[:i])
"""
(m,) = X.shape
s_state = te.placeholder((m + 1,), dtype="int32", name="state")
s_init = te.compute((1,), lambda _: tvm.tir.const(0, "int32"))
s_update = te.compute((m + 1,), lambda l: s_state[l - 1] + X[l - 1])
return tvm.te.scan(s_init, s_update, s_state, inputs=[X], name="cumsum") |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _set_changed_options(self):
changed = {}
for key in Parameters.returnables:
if getattr(self.want, key) is not None:
changed[key] = getattr(self.want, key)
if changed:
self.changes = Parameters(changed) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def sls(n, d):
gg = te.reduce_axis((0, lengths[n]))
indices_idx = length_offsets[n] + gg
data_idx = indices[indices_idx]
data_val = data[data_idx, d]
return te.sum(data_val, axis=gg) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _update_changed_options(self):
changed = {}
for key in Parameters.updatables:
if getattr(self.want, key) is not None:
attr1 = getattr(self.want, key)
attr2 = getattr(self.have, key)
if attr1 != attr2:
changed[key] = attr1
if changed:
self.changes = Parameters(changed)
return True
return False |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _pool_is_licensed(self):
if self.have.state == 'LICENSED':
return True
return False |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def _pool_is_unlicensed_eula_unaccepted(self, current):
if current.state != 'LICENSED' and not self.want.accept_eula:
return True
return False |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def exec_module(self):
changed = False
result = dict()
state = self.want.state
try:
if state == "present":
changed = self.present()
elif state == "absent":
changed = self.absent()
except iControlUnexpectedHTTPError as e:
raise F5ModuleError(str(e))
result.update(**self.changes.to_return())
result.update(dict(changed=changed))
return result |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def exists(self):
collection = self.client.api.cm.shared.licensing.pools_s.get_collection(
requests_params=dict(
params="$filter=name+eq+'{0}'".format(self.want.name)
)
)
if len(collection) == 1:
return True
elif len(collection) == 0:
return False
else:
raise F5ModuleError(
"Multiple license pools with the provided name were found!"
) |
def emit(self, level, message):
raise NotImplementedError('Please implement an emit method') | def should_update(self):
if self._pool_is_licensed():
return False
if self._pool_is_unlicensed_eula_unaccepted():
return False
return True |
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