body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def __decide_h_extension(self):
"\n Decides which language 'owns' how many .h files\n\n :returns: The report with divided header files\n "
report = self.__report
h_files = report['C']['.h']
if (h_files > 0):
c_files = (sum(report['C'].values()) - h_files)
cpp_files =... | 5,973,847,334,793,231,000 | Decides which language 'owns' how many .h files
:returns: The report with divided header files | gitScrabber/scrabTasks/file/languageDetector.py | __decide_h_extension | Eyenseo/gitScrabber | python | def __decide_h_extension(self):
"\n Decides which language 'owns' how many .h files\n\n :returns: The report with divided header files\n "
report = self.__report
h_files = report['C']['.h']
if (h_files > 0):
c_files = (sum(report['C'].values()) - h_files)
cpp_files =... |
def __calculate_main_language(self, report):
'\n Calculates the main language (maximum of files extensions)\n\n :param report: The report\n\n :returns: The main language.\n '
max_files = 0
max_lang = None
for language in report:
lang_fiels = sum(report[language].v... | 8,954,739,240,078,890,000 | Calculates the main language (maximum of files extensions)
:param report: The report
:returns: The main language. | gitScrabber/scrabTasks/file/languageDetector.py | __calculate_main_language | Eyenseo/gitScrabber | python | def __calculate_main_language(self, report):
'\n Calculates the main language (maximum of files extensions)\n\n :param report: The report\n\n :returns: The main language.\n '
max_files = 0
max_lang = None
for language in report:
lang_fiels = sum(report[language].v... |
def __calculate_used_languages(self, report):
'\n Calculates the used languages by throwing away the extension counts and\n collapsing them to the language. Only languages that have at least one\n file extension are kept and will appear in the report\n\n :param report: The report\n\n... | 8,194,500,951,750,470,000 | Calculates the used languages by throwing away the extension counts and
collapsing them to the language. Only languages that have at least one
file extension are kept and will appear in the report
:param report: The report
:returns: The used languages. | gitScrabber/scrabTasks/file/languageDetector.py | __calculate_used_languages | Eyenseo/gitScrabber | python | def __calculate_used_languages(self, report):
'\n Calculates the used languages by throwing away the extension counts and\n collapsing them to the language. Only languages that have at least one\n file extension are kept and will appear in the report\n\n :param report: The report\n\n... |
def scrab(self, project, filepath, file):
'\n Counts the files that have an extension of one of the languages\n\n :param project: The project that the scrab task shall analyse\n :param filepath: The filepath to the file that can be analysed\n :param file: The file as str... | 9,044,298,979,763,655,000 | Counts the files that have an extension of one of the languages
:param project: The project that the scrab task shall analyse
:param filepath: The filepath to the file that can be analysed
:param file: The file as string that can be analysed
:returns: Report that contains the scrabbed information of ... | gitScrabber/scrabTasks/file/languageDetector.py | scrab | Eyenseo/gitScrabber | python | def scrab(self, project, filepath, file):
'\n Counts the files that have an extension of one of the languages\n\n :param project: The project that the scrab task shall analyse\n :param filepath: The filepath to the file that can be analysed\n :param file: The file as str... |
def report(self):
'\n Decides which headers files are (probable) from which language,\n calculates the main language and removes redundant / unnecessary\n detailed information from the report\n\n :param report: The complete report this task created\n\n :returns: Report that c... | 3,744,189,683,670,182,400 | Decides which headers files are (probable) from which language,
calculates the main language and removes redundant / unnecessary
detailed information from the report
:param report: The complete report this task created
:returns: Report that contains all scrabbed information
eg.:
LanguageDetec... | gitScrabber/scrabTasks/file/languageDetector.py | report | Eyenseo/gitScrabber | python | def report(self):
'\n Decides which headers files are (probable) from which language,\n calculates the main language and removes redundant / unnecessary\n detailed information from the report\n\n :param report: The complete report this task created\n\n :returns: Report that c... |
def main():
'Hep Mortality Prediction App'
st.markdown(html_temp.format('royalblue'), unsafe_allow_html=True)
menu = ['Home', 'Login', 'SignUp']
sub_menu = ['Plot', 'Prediction']
choice = st.sidebar.selectbox('Menu', menu)
if (choice == 'Home'):
st.subheader('Home')
st.markdown(d... | 1,479,472,205,569,399,300 | Hep Mortality Prediction App | app.py | main | Let-Me-Code/Hepatitis-B-Mortality-Prediction | python | def main():
st.markdown(html_temp.format('royalblue'), unsafe_allow_html=True)
menu = ['Home', 'Login', 'SignUp']
sub_menu = ['Plot', 'Prediction']
choice = st.sidebar.selectbox('Menu', menu)
if (choice == 'Home'):
st.subheader('Home')
st.markdown(descriptive_message_temp, unsaf... |
@property
def num_preds(self):
'int: the number of predictions in this assignment'
return len(self.gt_inds) | 7,780,834,999,563,918,000 | int: the number of predictions in this assignment | mmdet3d/models/dense_heads/assigner/assign_result.py | num_preds | yangzilongdmgy/merge_monster_3d | python | @property
def num_preds(self):
return len(self.gt_inds) |
def set_extra_property(self, key, value):
'Set user-defined new property.'
assert (key not in self.info)
self._extra_properties[key] = value | 393,492,990,254,824,600 | Set user-defined new property. | mmdet3d/models/dense_heads/assigner/assign_result.py | set_extra_property | yangzilongdmgy/merge_monster_3d | python | def set_extra_property(self, key, value):
assert (key not in self.info)
self._extra_properties[key] = value |
def get_extra_property(self, key):
'Get user-defined property.'
return self._extra_properties.get(key, None) | -7,626,049,926,330,966,000 | Get user-defined property. | mmdet3d/models/dense_heads/assigner/assign_result.py | get_extra_property | yangzilongdmgy/merge_monster_3d | python | def get_extra_property(self, key):
return self._extra_properties.get(key, None) |
@property
def info(self):
'dict: a dictionary of info about the object'
basic_info = {'num_gts': self.num_gts, 'num_preds': self.num_preds, 'gt_inds': self.gt_inds, 'max_overlaps': self.max_overlaps, 'labels': self.labels}
basic_info.update(self._extra_properties)
return basic_info | 8,762,152,943,817,003,000 | dict: a dictionary of info about the object | mmdet3d/models/dense_heads/assigner/assign_result.py | info | yangzilongdmgy/merge_monster_3d | python | @property
def info(self):
basic_info = {'num_gts': self.num_gts, 'num_preds': self.num_preds, 'gt_inds': self.gt_inds, 'max_overlaps': self.max_overlaps, 'labels': self.labels}
basic_info.update(self._extra_properties)
return basic_info |
def __nice__(self):
'str: a "nice" summary string describing this assign result'
parts = []
parts.append(f'num_gts={self.num_gts!r}')
if (self.gt_inds is None):
parts.append(f'gt_inds={self.gt_inds!r}')
else:
parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}')
if (self.m... | -2,866,129,337,503,404,000 | str: a "nice" summary string describing this assign result | mmdet3d/models/dense_heads/assigner/assign_result.py | __nice__ | yangzilongdmgy/merge_monster_3d | python | def __nice__(self):
parts = []
parts.append(f'num_gts={self.num_gts!r}')
if (self.gt_inds is None):
parts.append(f'gt_inds={self.gt_inds!r}')
else:
parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}')
if (self.max_overlaps is None):
parts.append(f'max_overlaps={s... |
@classmethod
def random(cls, **kwargs):
'Create random AssignResult for tests or debugging.\n\n Args:\n num_preds: number of predicted boxes\n num_gts: number of true boxes\n p_ignore (float): probability of a predicted box assinged to an\n ignored truth\n ... | 1,650,000,623,902,313,500 | Create random AssignResult for tests or debugging.
Args:
num_preds: number of predicted boxes
num_gts: number of true boxes
p_ignore (float): probability of a predicted box assinged to an
ignored truth
p_assigned (float): probability of a predicted box not being
assigned
p_use_label... | mmdet3d/models/dense_heads/assigner/assign_result.py | random | yangzilongdmgy/merge_monster_3d | python | @classmethod
def random(cls, **kwargs):
'Create random AssignResult for tests or debugging.\n\n Args:\n num_preds: number of predicted boxes\n num_gts: number of true boxes\n p_ignore (float): probability of a predicted box assinged to an\n ignored truth\n ... |
def add_gt_(self, gt_labels):
'Add ground truth as assigned results.\n\n Args:\n gt_labels (torch.Tensor): Labels of gt boxes\n '
self_inds = torch.arange(1, (len(gt_labels) + 1), dtype=torch.long, device=gt_labels.device)
self.gt_inds = torch.cat([self_inds, self.gt_inds])
self... | 6,881,752,854,443,798,000 | Add ground truth as assigned results.
Args:
gt_labels (torch.Tensor): Labels of gt boxes | mmdet3d/models/dense_heads/assigner/assign_result.py | add_gt_ | yangzilongdmgy/merge_monster_3d | python | def add_gt_(self, gt_labels):
'Add ground truth as assigned results.\n\n Args:\n gt_labels (torch.Tensor): Labels of gt boxes\n '
self_inds = torch.arange(1, (len(gt_labels) + 1), dtype=torch.long, device=gt_labels.device)
self.gt_inds = torch.cat([self_inds, self.gt_inds])
self... |
def glue(self, pos):
'\n Behaves like simple line port, but for folded interface suggests\n connection to the middle point of a port.\n '
if self.is_folded():
px = ((self.start.x + self.end.x) / 2)
py = ((self.start.y + self.end.y) / 2)
d = distance_point_point((px, ... | 8,054,494,464,208,342,000 | Behaves like simple line port, but for folded interface suggests
connection to the middle point of a port. | gaphor/diagram/classes/interface.py | glue | 987Frogh/Makehuman | python | def glue(self, pos):
'\n Behaves like simple line port, but for folded interface suggests\n connection to the middle point of a port.\n '
if self.is_folded():
px = ((self.start.x + self.end.x) / 2)
py = ((self.start.y + self.end.y) / 2)
d = distance_point_point((px, ... |
def _set_folded(self, folded):
'\n Set folded notation.\n\n :param folded: Folded state, see Folded.* enum.\n '
if (self._folded == folded):
return
self._folded = folded
if (folded == Folded.NONE):
movable = True
else:
if (self._folded == Folded.PROVIDED)... | 614,404,046,103,405,400 | Set folded notation.
:param folded: Folded state, see Folded.* enum. | gaphor/diagram/classes/interface.py | _set_folded | 987Frogh/Makehuman | python | def _set_folded(self, folded):
'\n Set folded notation.\n\n :param folded: Folded state, see Folded.* enum.\n '
if (self._folded == folded):
return
self._folded = folded
if (folded == Folded.NONE):
movable = True
else:
if (self._folded == Folded.PROVIDED)... |
def main(argv):
'\n Main function.\n '
(result_dir, src_dir) = options_script(argv)
run(result_dir, src_dir) | 7,503,100,500,502,827,000 | Main function. | Outils/TRIOXDATA/XTriou/Extract_xdata.py | main | cea-trust-platform/trust-code | python | def main(argv):
'\n \n '
(result_dir, src_dir) = options_script(argv)
run(result_dir, src_dir) |
def assert_matches_stdout(actual, expected_stdout, normalize_fn=(lambda elem: elem), label=''):
'Asserts a PCollection of strings matches the expected stdout elements.\n\n Args:\n actual (beam.PCollection): A PCollection.\n expected (List[str]): A list of stdout elements, one line per element.\n normalize... | -4,696,306,568,593,374,000 | Asserts a PCollection of strings matches the expected stdout elements.
Args:
actual (beam.PCollection): A PCollection.
expected (List[str]): A list of stdout elements, one line per element.
normalize_fn (Function[any]): A function to normalize elements before
comparing them. Can be used to sort lists befor... | sdks/python/apache_beam/examples/snippets/util.py | assert_matches_stdout | DevangiDas/beam | python | def assert_matches_stdout(actual, expected_stdout, normalize_fn=(lambda elem: elem), label=):
'Asserts a PCollection of strings matches the expected stdout elements.\n\n Args:\n actual (beam.PCollection): A PCollection.\n expected (List[str]): A list of stdout elements, one line per element.\n normalize_f... |
def run_shell_commands(commands, **kwargs):
'Runs a list of Notebook-like shell commands.\n\n Lines starting with `#` are ignored as comments.\n Lines starting with `!` are run as commands.\n Variables like `{variable}` are substituted with **kwargs.\n '
for cmd in commands:
cmd = cmd.strip().lstrip... | 8,061,451,941,788,008,000 | Runs a list of Notebook-like shell commands.
Lines starting with `#` are ignored as comments.
Lines starting with `!` are run as commands.
Variables like `{variable}` are substituted with **kwargs. | sdks/python/apache_beam/examples/snippets/util.py | run_shell_commands | DevangiDas/beam | python | def run_shell_commands(commands, **kwargs):
'Runs a list of Notebook-like shell commands.\n\n Lines starting with `#` are ignored as comments.\n Lines starting with `!` are run as commands.\n Variables like `{variable}` are substituted with **kwargs.\n '
for cmd in commands:
cmd = cmd.strip().lstrip... |
def __init__(self, parnames=[], name=''):
'\n :param parnames:\n A list of names of the kernel params, used to alias the intrinsic\n parameter names. This way different instances of the same kernel\n can have different parameter names.\n '
if (len(parnames) == 0):... | 80,885,053,197,826,180 | :param parnames:
A list of names of the kernel params, used to alias the intrinsic
parameter names. This way different instances of the same kernel
can have different parameter names. | prospect/likelihood/kernels.py | __init__ | errai34/prospector | python | def __init__(self, parnames=[], name=):
'\n :param parnames:\n A list of names of the kernel params, used to alias the intrinsic\n parameter names. This way different instances of the same kernel\n can have different parameter names.\n '
if (len(parnames) == 0):
... |
def update(self, **kwargs):
'Take a dictionary of parameters, pick out the properly named\n parameters according to the alias, and put them in the param state\n dictionary.\n '
for k in self.kernel_params:
self.params[k] = kwargs[self.param_alias[k]] | -4,019,182,405,496,869,400 | Take a dictionary of parameters, pick out the properly named
parameters according to the alias, and put them in the param state
dictionary. | prospect/likelihood/kernels.py | update | errai34/prospector | python | def update(self, **kwargs):
'Take a dictionary of parameters, pick out the properly named\n parameters according to the alias, and put them in the param state\n dictionary.\n '
for k in self.kernel_params:
self.params[k] = kwargs[self.param_alias[k]] |
def __call__(self, metric, weights=None, ndim=2, **extras):
'Return a covariance matrix, given a metric. Optionally, multiply\n the output kernel by a weight function to induce non-stationarity.\n '
k = self.construct_kernel(metric)
if (ndim != k.ndim):
k = np.diag(k)
if (weights ... | 1,769,017,840,861,649,200 | Return a covariance matrix, given a metric. Optionally, multiply
the output kernel by a weight function to induce non-stationarity. | prospect/likelihood/kernels.py | __call__ | errai34/prospector | python | def __call__(self, metric, weights=None, ndim=2, **extras):
'Return a covariance matrix, given a metric. Optionally, multiply\n the output kernel by a weight function to induce non-stationarity.\n '
k = self.construct_kernel(metric)
if (ndim != k.ndim):
k = np.diag(k)
if (weights ... |
def construct_kernel(self, metric):
'Construct an exponential squared covariance matrix.\n '
(a, l) = (self.params['amplitude'], self.params['length'])
Sigma = ((a ** 2) * np.exp(((- ((metric[:, None] - metric[None, :]) ** 2)) / (2 * (l ** 2)))))
return Sigma | -3,748,341,603,669,811,000 | Construct an exponential squared covariance matrix. | prospect/likelihood/kernels.py | construct_kernel | errai34/prospector | python | def construct_kernel(self, metric):
'\n '
(a, l) = (self.params['amplitude'], self.params['length'])
Sigma = ((a ** 2) * np.exp(((- ((metric[:, None] - metric[None, :]) ** 2)) / (2 * (l ** 2)))))
return Sigma |
def construct_kernel(self, metric):
'Construct a Matern kernel covariance matrix, for \nu=3/2.\n '
(a, l) = (self.params['amplitude'], self.params['length'])
Sigma = ((np.sqrt(3) * np.abs((metric[:, None] - metric[None, :]))) / l)
Sigma = (((a ** 2) * (1 + Sigma)) * np.exp((- Sigma)))
return ... | -2,407,672,587,236,184,600 | Construct a Matern kernel covariance matrix, for
u=3/2. | prospect/likelihood/kernels.py | construct_kernel | errai34/prospector | python | def construct_kernel(self, metric):
'Construct a Matern kernel covariance matrix, for \nu=3/2.\n '
(a, l) = (self.params['amplitude'], self.params['length'])
Sigma = ((np.sqrt(3) * np.abs((metric[:, None] - metric[None, :]))) / l)
Sigma = (((a ** 2) * (1 + Sigma)) * np.exp((- Sigma)))
return ... |
def print(self):
" Method prints person's data.\n\n :return: None\n "
print('Name: {}, age: {}, phone: {}'.format(self.name, self.age, self.phone)) | 2,257,337,300,328,433,200 | Method prints person's data.
:return: None | person.py | print | jhsaraja/testiprojekti | python | def print(self):
" Method prints person's data.\n\n :return: None\n "
print('Name: {}, age: {}, phone: {}'.format(self.name, self.age, self.phone)) |
def set_name(self, name):
' Method saves a new name for the person.\n\n :param name: new name for the person, string\n :return: None\n '
self.name = name | -8,456,299,319,435,507,000 | Method saves a new name for the person.
:param name: new name for the person, string
:return: None | person.py | set_name | jhsaraja/testiprojekti | python | def set_name(self, name):
' Method saves a new name for the person.\n\n :param name: new name for the person, string\n :return: None\n '
self.name = name |
def get_name(self):
' Method returns the name of the person.\n\n :return: name, string\n '
return self.name | 8,722,847,781,120,407,000 | Method returns the name of the person.
:return: name, string | person.py | get_name | jhsaraja/testiprojekti | python | def get_name(self):
' Method returns the name of the person.\n\n :return: name, string\n '
return self.name |
def set_age(self, age):
' Method saves a new age for the person.\n\n :param age: new age for the person, integer\n :return: None\n '
if (type(age) != int):
print('not valid age {}'.format(age))
return
if (age >= 0):
self.age = age
else:
print('not val... | 2,367,029,125,253,940,000 | Method saves a new age for the person.
:param age: new age for the person, integer
:return: None | person.py | set_age | jhsaraja/testiprojekti | python | def set_age(self, age):
' Method saves a new age for the person.\n\n :param age: new age for the person, integer\n :return: None\n '
if (type(age) != int):
print('not valid age {}'.format(age))
return
if (age >= 0):
self.age = age
else:
print('not val... |
def get_age(self):
' Method returns the age of the person.\n\n :return: age, integer\n '
return self.age | 5,929,410,324,352,048,000 | Method returns the age of the person.
:return: age, integer | person.py | get_age | jhsaraja/testiprojekti | python | def get_age(self):
' Method returns the age of the person.\n\n :return: age, integer\n '
return self.age |
def set_phone(self, phone):
' Method saves a new phone for the person.\n\n :param phone: new phone for the person, string\n :return: None\n '
self.phone = phone | 8,880,604,806,047,877,000 | Method saves a new phone for the person.
:param phone: new phone for the person, string
:return: None | person.py | set_phone | jhsaraja/testiprojekti | python | def set_phone(self, phone):
' Method saves a new phone for the person.\n\n :param phone: new phone for the person, string\n :return: None\n '
self.phone = phone |
def get_phone(self):
' Method returns the phone of the person.\n\n :return: phone, string\n '
return self.phone | -1,529,533,477,153,461,500 | Method returns the phone of the person.
:return: phone, string | person.py | get_phone | jhsaraja/testiprojekti | python | def get_phone(self):
' Method returns the phone of the person.\n\n :return: phone, string\n '
return self.phone |
def get_title(self):
' Method returns the title of the person.\n\n :return: title, string\n '
return self.title | 7,125,931,693,280,901,000 | Method returns the title of the person.
:return: title, string | person.py | get_title | jhsaraja/testiprojekti | python | def get_title(self):
' Method returns the title of the person.\n\n :return: title, string\n '
return self.title |
def set_title(self, title):
' Method saves a new title for the person.\n\n :param title: new title for the person, string\n :return: None\n '
self.title = title | -5,331,485,032,930,876,000 | Method saves a new title for the person.
:param title: new title for the person, string
:return: None | person.py | set_title | jhsaraja/testiprojekti | python | def set_title(self, title):
' Method saves a new title for the person.\n\n :param title: new title for the person, string\n :return: None\n '
self.title = title |
def get_salary(self):
' Method returns the salary of the person.\n\n :return: salary, string\n '
return self.salary | -3,578,107,366,643,422,000 | Method returns the salary of the person.
:return: salary, string | person.py | get_salary | jhsaraja/testiprojekti | python | def get_salary(self):
' Method returns the salary of the person.\n\n :return: salary, string\n '
return self.salary |
def set_salary(self, salary):
' Method saves a new salary for the person.\n\n :param salary: new salary for the person, string\n :return: None\n '
if (salary >= 0):
self.salary = salary | 4,689,736,759,264,431,000 | Method saves a new salary for the person.
:param salary: new salary for the person, string
:return: None | person.py | set_salary | jhsaraja/testiprojekti | python | def set_salary(self, salary):
' Method saves a new salary for the person.\n\n :param salary: new salary for the person, string\n :return: None\n '
if (salary >= 0):
self.salary = salary |
def get_location(self):
' Method returns the location of the person.\n\n :return: location, string\n '
return self.location | 1,266,652,687,538,883,800 | Method returns the location of the person.
:return: location, string | person.py | get_location | jhsaraja/testiprojekti | python | def get_location(self):
' Method returns the location of the person.\n\n :return: location, string\n '
return self.location |
def set_location(self, location):
' Method saves a new location for the person.\n\n :param location: new location for the person, string\n :return: None\n '
self.location = location | 5,467,453,087,817,736,000 | Method saves a new location for the person.
:param location: new location for the person, string
:return: None | person.py | set_location | jhsaraja/testiprojekti | python | def set_location(self, location):
' Method saves a new location for the person.\n\n :param location: new location for the person, string\n :return: None\n '
self.location = location |
def print_businesscard(self):
' Method prints a business card information.\n\n :return: None\n '
print(' Name: {}\n Title: {}\n Phone: {}'.format(self.name, self.title, self.phone)) | -6,489,935,535,142,710,000 | Method prints a business card information.
:return: None | person.py | print_businesscard | jhsaraja/testiprojekti | python | def print_businesscard(self):
' Method prints a business card information.\n\n :return: None\n '
print(' Name: {}\n Title: {}\n Phone: {}'.format(self.name, self.title, self.phone)) |
def get_defaults(lang):
'Get the language-specific defaults, if available in spaCy. This allows\n using lexical attribute getters that depend on static language data, e.g.\n Token.like_num, Token.is_stop, Doc.noun_chunks etc.\n\n lang (unicode): The language code.\n RETURNS (Language.Defaults): The lang... | -7,850,812,653,197,558,000 | Get the language-specific defaults, if available in spaCy. This allows
using lexical attribute getters that depend on static language data, e.g.
Token.like_num, Token.is_stop, Doc.noun_chunks etc.
lang (unicode): The language code.
RETURNS (Language.Defaults): The language defaults. | spacy_stanfordnlp/language.py | get_defaults | mehmetilker/spacy-stanfordnlp | python | def get_defaults(lang):
'Get the language-specific defaults, if available in spaCy. This allows\n using lexical attribute getters that depend on static language data, e.g.\n Token.like_num, Token.is_stop, Doc.noun_chunks etc.\n\n lang (unicode): The language code.\n RETURNS (Language.Defaults): The lang... |
def __init__(self, snlp, meta=None, **kwargs):
'Initialize the Language class.\n\n Instead of "en" etc. we call the language "stanfordnlp_en" to not\n cause conflicts with spaCy\'s built-in languages. Using entry points,\n this also allows serializing and deserializing the language class\n ... | -5,133,790,172,121,754,000 | Initialize the Language class.
Instead of "en" etc. we call the language "stanfordnlp_en" to not
cause conflicts with spaCy's built-in languages. Using entry points,
this also allows serializing and deserializing the language class
and "lang": "stanfordnlp_en" in the meta.json will automatically
instantiate this class... | spacy_stanfordnlp/language.py | __init__ | mehmetilker/spacy-stanfordnlp | python | def __init__(self, snlp, meta=None, **kwargs):
'Initialize the Language class.\n\n Instead of "en" etc. we call the language "stanfordnlp_en" to not\n cause conflicts with spaCy\'s built-in languages. Using entry points,\n this also allows serializing and deserializing the language class\n ... |
def __init__(self, snlp, vocab):
'Initialize the tokenizer.\n\n snlp (stanfordnlp.Pipeline): The initialized StanfordNLP pipeline.\n vocab (spacy.vocab.Vocab): The vocabulary to use.\n RETURNS (Tokenizer): The custom tokenizer.\n '
self.snlp = snlp
self.vocab = vocab | -2,122,144,844,259,570,200 | Initialize the tokenizer.
snlp (stanfordnlp.Pipeline): The initialized StanfordNLP pipeline.
vocab (spacy.vocab.Vocab): The vocabulary to use.
RETURNS (Tokenizer): The custom tokenizer. | spacy_stanfordnlp/language.py | __init__ | mehmetilker/spacy-stanfordnlp | python | def __init__(self, snlp, vocab):
'Initialize the tokenizer.\n\n snlp (stanfordnlp.Pipeline): The initialized StanfordNLP pipeline.\n vocab (spacy.vocab.Vocab): The vocabulary to use.\n RETURNS (Tokenizer): The custom tokenizer.\n '
self.snlp = snlp
self.vocab = vocab |
def __call__(self, text):
'Convert a StanfordNLP Doc to a spaCy Doc.\n\n text (unicode): The text to process.\n RETURNS (spacy.tokens.Doc): The spaCy Doc object.\n '
snlp_doc = self.snlp(text)
text = snlp_doc.text
(tokens, heads) = self.get_tokens_with_heads(snlp_doc)
if (not le... | 621,916,058,141,886,700 | Convert a StanfordNLP Doc to a spaCy Doc.
text (unicode): The text to process.
RETURNS (spacy.tokens.Doc): The spaCy Doc object. | spacy_stanfordnlp/language.py | __call__ | mehmetilker/spacy-stanfordnlp | python | def __call__(self, text):
'Convert a StanfordNLP Doc to a spaCy Doc.\n\n text (unicode): The text to process.\n RETURNS (spacy.tokens.Doc): The spaCy Doc object.\n '
snlp_doc = self.snlp(text)
text = snlp_doc.text
(tokens, heads) = self.get_tokens_with_heads(snlp_doc)
if (not le... |
def get_tokens_with_heads(self, snlp_doc):
'Flatten the tokens in the StanfordNLP Doc and extract the token indices\n of the sentence start tokens to set is_sent_start.\n\n snlp_doc (stanfordnlp.Document): The processed StanfordNLP doc.\n RETURNS (list): The tokens (words).\n '
token... | -4,882,335,766,437,383,000 | Flatten the tokens in the StanfordNLP Doc and extract the token indices
of the sentence start tokens to set is_sent_start.
snlp_doc (stanfordnlp.Document): The processed StanfordNLP doc.
RETURNS (list): The tokens (words). | spacy_stanfordnlp/language.py | get_tokens_with_heads | mehmetilker/spacy-stanfordnlp | python | def get_tokens_with_heads(self, snlp_doc):
'Flatten the tokens in the StanfordNLP Doc and extract the token indices\n of the sentence start tokens to set is_sent_start.\n\n snlp_doc (stanfordnlp.Document): The processed StanfordNLP doc.\n RETURNS (list): The tokens (words).\n '
token... |
def create_socket_pair(self):
'\n Creates a local socket listening on a random port.\n '
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.addCleanup(server.close)
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.addCleanup(client.close)
return (server,... | -8,949,072,660,331,688,000 | Creates a local socket listening on a random port. | tests/test_ws2_32/test_events.py | create_socket_pair | opalmer/pycffiwin32 | python | def create_socket_pair(self):
'\n \n '
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.addCleanup(server.close)
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.addCleanup(client.close)
return (server, client) |
def check_config_status(self):
"Check this subframe's configuration status.\n\n\n By default, incorrectly configured subframes in the database are not returned from\n :any:`Frame.mux_subframes` because they cannot be used in the bus communication.\n You can change this behavior by setting :any:... | -6,809,806,336,883,870,000 | Check this subframe's configuration status.
By default, incorrectly configured subframes in the database are not returned from
:any:`Frame.mux_subframes` because they cannot be used in the bus communication.
You can change this behavior by setting :any:`Database.show_invalid_from_open` to `True`.
When a subframe conf... | nixnet/database/_subframe.py | check_config_status | bigoulours/nixnet-python | python | def check_config_status(self):
"Check this subframe's configuration status.\n\n\n By default, incorrectly configured subframes in the database are not returned from\n :any:`Frame.mux_subframes` because they cannot be used in the bus communication.\n You can change this behavior by setting :any:... |
def find(self, object_class, object_name):
'Finds an object in the database.\n\n This function finds a database object relative to this parent object.\n This object may be a grandparent or great-grandparent.\n\n If this object is a direct parent\n (for example, :any:`Frame<_frame.Frame>`... | 2,359,012,860,875,746,000 | Finds an object in the database.
This function finds a database object relative to this parent object.
This object may be a grandparent or great-grandparent.
If this object is a direct parent
(for example, :any:`Frame<_frame.Frame>` for :any:`Signal<_signal.Signal>`),
the ``object_name`` to search for can be short, a... | nixnet/database/_subframe.py | find | bigoulours/nixnet-python | python | def find(self, object_class, object_name):
'Finds an object in the database.\n\n This function finds a database object relative to this parent object.\n This object may be a grandparent or great-grandparent.\n\n If this object is a direct parent\n (for example, :any:`Frame<_frame.Frame>`... |
@property
def dyn_signals(self):
':any:`DbCollection`: Returns a collection of dynamic :any:`Signal<_signal.Signal>` objects in the subframe.\n\n Those signals are transmitted when the multiplexer signal\n in the frame has the multiplexer value defined in the subframe.\n '
return self._dyn_... | -7,876,208,852,592,375,000 | :any:`DbCollection`: Returns a collection of dynamic :any:`Signal<_signal.Signal>` objects in the subframe.
Those signals are transmitted when the multiplexer signal
in the frame has the multiplexer value defined in the subframe. | nixnet/database/_subframe.py | dyn_signals | bigoulours/nixnet-python | python | @property
def dyn_signals(self):
':any:`DbCollection`: Returns a collection of dynamic :any:`Signal<_signal.Signal>` objects in the subframe.\n\n Those signals are transmitted when the multiplexer signal\n in the frame has the multiplexer value defined in the subframe.\n '
return self._dyn_... |
@property
def frm(self):
':any:`Frame<_frame.Frame>`: Returns the reference to the parent frame.\n\n The parent frame is defined when the subframe is created,\n and you cannot change it afterwards.\n '
handle = _props.get_subframe_frm_ref(self._handle)
return _frame.Frame(_handle=handle... | 747,096,797,756,296,700 | :any:`Frame<_frame.Frame>`: Returns the reference to the parent frame.
The parent frame is defined when the subframe is created,
and you cannot change it afterwards. | nixnet/database/_subframe.py | frm | bigoulours/nixnet-python | python | @property
def frm(self):
':any:`Frame<_frame.Frame>`: Returns the reference to the parent frame.\n\n The parent frame is defined when the subframe is created,\n and you cannot change it afterwards.\n '
handle = _props.get_subframe_frm_ref(self._handle)
return _frame.Frame(_handle=handle... |
@property
def mux_value(self):
'int: Get or set the multiplexer value for this subframe.\n\n This property specifies the multiplexer signal value used when the\n dynamic signals in this subframe are transmitted in the frame.\n Only one subframe is transmitted at a time in the frame.\n\n ... | -2,052,745,770,387,338,800 | int: Get or set the multiplexer value for this subframe.
This property specifies the multiplexer signal value used when the
dynamic signals in this subframe are transmitted in the frame.
Only one subframe is transmitted at a time in the frame.
There also is a multiplexer value for a signal object as a read-only prope... | nixnet/database/_subframe.py | mux_value | bigoulours/nixnet-python | python | @property
def mux_value(self):
'int: Get or set the multiplexer value for this subframe.\n\n This property specifies the multiplexer signal value used when the\n dynamic signals in this subframe are transmitted in the frame.\n Only one subframe is transmitted at a time in the frame.\n\n ... |
@property
def name(self):
'str: Get or set the name of the subframe object.\n\n Lowercase letters, uppercase letters, numbers,\n and the underscore (_) are valid characters for the short name.\n The space ( ), period (.), and other special characters are not supported within the name.\n ... | -6,174,104,265,428,616,000 | str: Get or set the name of the subframe object.
Lowercase letters, uppercase letters, numbers,
and the underscore (_) are valid characters for the short name.
The space ( ), period (.), and other special characters are not supported within the name.
The short name must begin with a letter (uppercase or lowercase) or ... | nixnet/database/_subframe.py | name | bigoulours/nixnet-python | python | @property
def name(self):
'str: Get or set the name of the subframe object.\n\n Lowercase letters, uppercase letters, numbers,\n and the underscore (_) are valid characters for the short name.\n The space ( ), period (.), and other special characters are not supported within the name.\n ... |
@property
def pdu(self):
":any:`Pdu`: Returns the subframe's parent PDU.\n\n This property returns the reference to the subframe's parent PDU.\n The parent PDU is defined when the subframe object is created.\n You cannot change it afterwards.\n "
from nixnet.database import _pdu
... | 4,860,405,005,379,393,000 | :any:`Pdu`: Returns the subframe's parent PDU.
This property returns the reference to the subframe's parent PDU.
The parent PDU is defined when the subframe object is created.
You cannot change it afterwards. | nixnet/database/_subframe.py | pdu | bigoulours/nixnet-python | python | @property
def pdu(self):
":any:`Pdu`: Returns the subframe's parent PDU.\n\n This property returns the reference to the subframe's parent PDU.\n The parent PDU is defined when the subframe object is created.\n You cannot change it afterwards.\n "
from nixnet.database import _pdu
... |
@property
def name_unique_to_cluster(self):
'str: Returns a subframe name unique to the cluster that contains the subframe.\n\n If the single name is not unique within the cluster, the name is <frame-name>.<subframe-name>.\n\n You can pass the name to the `find` function to retrieve the reference to t... | 9,096,425,762,100,041,000 | str: Returns a subframe name unique to the cluster that contains the subframe.
If the single name is not unique within the cluster, the name is <frame-name>.<subframe-name>.
You can pass the name to the `find` function to retrieve the reference to the object,
while the single name is not guaranteed success in `find`
... | nixnet/database/_subframe.py | name_unique_to_cluster | bigoulours/nixnet-python | python | @property
def name_unique_to_cluster(self):
'str: Returns a subframe name unique to the cluster that contains the subframe.\n\n If the single name is not unique within the cluster, the name is <frame-name>.<subframe-name>.\n\n You can pass the name to the `find` function to retrieve the reference to t... |
def log_gaussian(x, mean, sigma):
'\n Computes the log-probability of X=x for a Gaussian of mean=mean and sigma=sigma\n Parameters\n ----------\n x\n mean\n sigma\n\n Returns\n -------\n\n '
log_pdf = ((- ((x - mean) ** 2)) / (2 * (sigma ** 2)))
log_pdf = (log_pdf - np.log((np.sqr... | -581,951,873,479,949,700 | Computes the log-probability of X=x for a Gaussian of mean=mean and sigma=sigma
Parameters
----------
x
mean
sigma
Returns
------- | lstchain/image/pdf.py | log_gaussian | calispac/cta-lstchain | python | def log_gaussian(x, mean, sigma):
'\n Computes the log-probability of X=x for a Gaussian of mean=mean and sigma=sigma\n Parameters\n ----------\n x\n mean\n sigma\n\n Returns\n -------\n\n '
log_pdf = ((- ((x - mean) ** 2)) / (2 * (sigma ** 2)))
log_pdf = (log_pdf - np.log((np.sqr... |
def set_seed(seed: int):
'\n Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if\n installed).\n\n Args:\n seed (:obj:`int`): The seed to set.\n '
random.seed(seed)
np.random.seed(seed)
if is_torch_available():
torch.... | 1,569,534,815,772,305,700 | Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if
installed).
Args:
seed (:obj:`int`): The seed to set. | machine-learning/nlp/bert-text-classification/train.py | set_seed | AJuneSlop/pythoncode-tutorials | python | def set_seed(seed: int):
'\n Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if\n installed).\n\n Args:\n seed (:obj:`int`): The seed to set.\n '
random.seed(seed)
np.random.seed(seed)
if is_torch_available():
torch.... |
def main():
'\n Unit tests\n '
max_depth = 4.0
numFrames = 10
height_ratio = 0.5
sub_sample = 1
reduce_to = 'middle_lower'
print('Program settings:')
print(('\tmax_depth: ' + str(max_depth)))
print(('\tnumFrames: ' + str(numFrames)))
print(('\theight_ratio: ' + str(height_r... | -4,685,269,840,475,023,000 | Unit tests | Camera/camera.py | main | marioliu/AutonomousQuadblade | python | def main():
'\n \n '
max_depth = 4.0
numFrames = 10
height_ratio = 0.5
sub_sample = 1
reduce_to = 'middle_lower'
print('Program settings:')
print(('\tmax_depth: ' + str(max_depth)))
print(('\tnumFrames: ' + str(numFrames)))
print(('\theight_ratio: ' + str(height_ratio)))
... |
def __init__(self, max_depth=4.0, save_images=False, t_buffer=5, output_dir='./Trials/'):
'\n Intitalizes Camera object \n '
self.max_depth = max_depth
self.save_images = save_images
self.clock = time.time()
self.t_buffer = t_buffer
self.output_dir = output_dir
self.data_dir = ... | 559,276,931,801,889,150 | Intitalizes Camera object | Camera/camera.py | __init__ | marioliu/AutonomousQuadblade | python | def __init__(self, max_depth=4.0, save_images=False, t_buffer=5, output_dir='./Trials/'):
'\n \n '
self.max_depth = max_depth
self.save_images = save_images
self.clock = time.time()
self.t_buffer = t_buffer
self.output_dir = output_dir
self.data_dir = path.join(self.output_dir... |
def connect(self):
'\n Establishes connection to R200 camera\n '
logging.info('Cam.py: connecting components')
self.serv = pyrs.Service()
self.dev = self.serv.Device(device_id=0, streams=[pyrs.stream.DepthStream(fps=60), pyrs.stream.ColorStream(fps=60)]) | 8,347,761,966,569,549,000 | Establishes connection to R200 camera | Camera/camera.py | connect | marioliu/AutonomousQuadblade | python | def connect(self):
'\n \n '
logging.info('Cam.py: connecting components')
self.serv = pyrs.Service()
self.dev = self.serv.Device(device_id=0, streams=[pyrs.stream.DepthStream(fps=60), pyrs.stream.ColorStream(fps=60)]) |
def disconnect(self):
'\n Disconnects from R200 camera\n '
self.dev.stop()
self.serv.stop()
logging.info('Cam.py: camera disconnected') | 4,568,159,224,948,574,000 | Disconnects from R200 camera | Camera/camera.py | disconnect | marioliu/AutonomousQuadblade | python | def disconnect(self):
'\n \n '
self.dev.stop()
self.serv.stop()
logging.info('Cam.py: camera disconnected') |
def getFrames(self, frames=5, rgb=False):
'\n Retrieves depth frames (and RGB if true) from R200 input, cleans and averages depth images\n '
self.dev.wait_for_frames()
depth = (self.dev.depth * self.dev.depth_scale)
col = self.dev.color
if (self.save_images and ((time.time() - self.clo... | -6,442,358,346,834,681,000 | Retrieves depth frames (and RGB if true) from R200 input, cleans and averages depth images | Camera/camera.py | getFrames | marioliu/AutonomousQuadblade | python | def getFrames(self, frames=5, rgb=False):
'\n \n '
self.dev.wait_for_frames()
depth = (self.dev.depth * self.dev.depth_scale)
col = self.dev.color
if (self.save_images and ((time.time() - self.clock) > self.t_buffer)):
np.save(path.join(self.data_dir, (str(time.time()) + '_d'))... |
def reduceFrame(self, depth, height_ratio=0.5, sub_sample=0.3, reduce_to='lower'):
'\n Takes in a depth image and rescales it\n\n Args:\n height_ratio: Determines fraction of rows to keep\n sub_sample: Scaling factor for image\n '
if ((height_ratio > 1.0) or (height_ra... | -3,983,497,567,927,168,500 | Takes in a depth image and rescales it
Args:
height_ratio: Determines fraction of rows to keep
sub_sample: Scaling factor for image | Camera/camera.py | reduceFrame | marioliu/AutonomousQuadblade | python | def reduceFrame(self, depth, height_ratio=0.5, sub_sample=0.3, reduce_to='lower'):
'\n Takes in a depth image and rescales it\n\n Args:\n height_ratio: Determines fraction of rows to keep\n sub_sample: Scaling factor for image\n '
if ((height_ratio > 1.0) or (height_ra... |
def parse_options():
'process command line options.'
parser = optparse.OptionParser('usage: %prog [options]')
parser.add_option('--verbose', action='store_true', help='List lock files found and deleted')
(options, args) = parser.parse_args()
return (options, args) | 4,582,584,220,910,883,000 | process command line options. | tools/clean_file_locks.py | parse_options | bopopescu/extra-specs-1 | python | def parse_options():
parser = optparse.OptionParser('usage: %prog [options]')
parser.add_option('--verbose', action='store_true', help='List lock files found and deleted')
(options, args) = parser.parse_args()
return (options, args) |
def main():
'Main loop.'
(options, args) = parse_options()
verbose = options.verbose
if verbose:
LOG.logger.setLevel(logging.DEBUG)
else:
LOG.logger.setLevel(logging.INFO)
LOG.info(('Cleaning stale locks from %s' % FLAGS.lock_path))
utils.cleanup_file_locks()
LOG.info('Fi... | 9,010,034,981,460,363,000 | Main loop. | tools/clean_file_locks.py | main | bopopescu/extra-specs-1 | python | def main():
(options, args) = parse_options()
verbose = options.verbose
if verbose:
LOG.logger.setLevel(logging.DEBUG)
else:
LOG.logger.setLevel(logging.INFO)
LOG.info(('Cleaning stale locks from %s' % FLAGS.lock_path))
utils.cleanup_file_locks()
LOG.info('Finished') |
def cli(self, interface='', output=None):
'parsing mechanism: cli\n\n Function cli() defines the cli type output parsing mechanism which\n typically contains 3 steps: exe\n cuting, transforming, returning\n '
parsed_dict = {}
if (output is None):
if interface:
... | -608,989,924,387,144,300 | parsing mechanism: cli
Function cli() defines the cli type output parsing mechanism which
typically contains 3 steps: exe
cuting, transforming, returning | src/genie/libs/parser/iosxe/show_interface.py | cli | Tristou27/genieparser | python | def cli(self, interface=, output=None):
'parsing mechanism: cli\n\n Function cli() defines the cli type output parsing mechanism which\n typically contains 3 steps: exe\n cuting, transforming, returning\n '
parsed_dict = {}
if (output is None):
if interface:
c... |
def yang(self):
' parsing mechanism: yang\n\n Function yang() defines the yang type output parsing mechanism which\n typically contains 3 steps: executing, transforming, returning\n '
pass | 116,842,370,709,678,300 | parsing mechanism: yang
Function yang() defines the yang type output parsing mechanism which
typically contains 3 steps: executing, transforming, returning | src/genie/libs/parser/iosxe/show_interface.py | yang | Tristou27/genieparser | python | def yang(self):
' parsing mechanism: yang\n\n Function yang() defines the yang type output parsing mechanism which\n typically contains 3 steps: executing, transforming, returning\n '
pass |
def yang(self):
'parsing mechanism: yang\n\n Function yang() defines the yang type output parsing mechanism which\n typically contains 3 steps: executing, transforming, returning\n '
ret = {}
cmd = '<native><interface><Vlan/></interface></native>'
output = self.device.get(('subtree'... | 4,722,897,063,672,879,000 | parsing mechanism: yang
Function yang() defines the yang type output parsing mechanism which
typically contains 3 steps: executing, transforming, returning | src/genie/libs/parser/iosxe/show_interface.py | yang | Tristou27/genieparser | python | def yang(self):
'parsing mechanism: yang\n\n Function yang() defines the yang type output parsing mechanism which\n typically contains 3 steps: executing, transforming, returning\n '
ret = {}
cmd = '<native><interface><Vlan/></interface></native>'
output = self.device.get(('subtree'... |
def _gather(params, indices, validate_indices=None, axis=None, batch_dims=0, name=None):
'gather.'
indices = ops.convert_to_tensor(indices, dtype_hint=np.int32)
if (validate_indices is not None):
raise NotImplementedError('Argument `validate_indices != None` is currently unimplemented.')
if (bat... | 1,254,113,188,679,910,000 | gather. | tensorflow_probability/python/internal/backend/numpy/numpy_array.py | _gather | michalbrys/probability | python | def _gather(params, indices, validate_indices=None, axis=None, batch_dims=0, name=None):
indices = ops.convert_to_tensor(indices, dtype_hint=np.int32)
if (validate_indices is not None):
raise NotImplementedError('Argument `validate_indices != None` is currently unimplemented.')
if (batch_dims <... |
def _args_to_matching_arrays(args_list, dtype_hint=None):
'Converts a list to array using the first element for dtype.\n\n This method is used to match the behavior of `tf.concat`.\n\n Args:\n args_list: A list or tuple of arguments.\n dtype_hint: An optional hint used when converting the args to tensors.\n... | 5,353,915,506,816,408,000 | Converts a list to array using the first element for dtype.
This method is used to match the behavior of `tf.concat`.
Args:
args_list: A list or tuple of arguments.
dtype_hint: An optional hint used when converting the args to tensors.
Returns:
A list of tensors. | tensorflow_probability/python/internal/backend/numpy/numpy_array.py | _args_to_matching_arrays | michalbrys/probability | python | def _args_to_matching_arrays(args_list, dtype_hint=None):
'Converts a list to array using the first element for dtype.\n\n This method is used to match the behavior of `tf.concat`.\n\n Args:\n args_list: A list or tuple of arguments.\n dtype_hint: An optional hint used when converting the args to tensors.\n... |
def _gather_nd(params, indices, batch_dims=0, name=None):
'gather_nd.'
indices = ops.convert_to_tensor(indices, dtype_hint=np.int32)
if (batch_dims < 0):
raise NotImplementedError('Negative `batch_dims` is currently unsupported.')
if ((not JAX_MODE) and (batch_dims > 0)):
raise NotImplem... | -6,853,863,073,649,036,000 | gather_nd. | tensorflow_probability/python/internal/backend/numpy/numpy_array.py | _gather_nd | michalbrys/probability | python | def _gather_nd(params, indices, batch_dims=0, name=None):
indices = ops.convert_to_tensor(indices, dtype_hint=np.int32)
if (batch_dims < 0):
raise NotImplementedError('Negative `batch_dims` is currently unsupported.')
if ((not JAX_MODE) and (batch_dims > 0)):
raise NotImplementedError('... |
def _linspace(start, stop, num, name=None, axis=0):
'Match TF behavior with np.linspace.'
start = ops.convert_to_tensor(start)
if np.issubdtype(start.dtype, np.integer):
start = start.astype(np.float64)
stop = ops.convert_to_tensor(stop, dtype=start.dtype)
num = ops.convert_to_tensor(num, dt... | -7,066,821,717,063,519,000 | Match TF behavior with np.linspace. | tensorflow_probability/python/internal/backend/numpy/numpy_array.py | _linspace | michalbrys/probability | python | def _linspace(start, stop, num, name=None, axis=0):
start = ops.convert_to_tensor(start)
if np.issubdtype(start.dtype, np.integer):
start = start.astype(np.float64)
stop = ops.convert_to_tensor(stop, dtype=start.dtype)
num = ops.convert_to_tensor(num, dtype_hint=np.int32)
if (not np.iss... |
def _one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None):
'One hot.'
if (on_value is None):
on_value = 1
if (off_value is None):
off_value = 0
if (dtype is None):
dtype = utils.common_dtype([on_value, off_value], np.float32)
indices = np.a... | 1,411,797,174,937,303,300 | One hot. | tensorflow_probability/python/internal/backend/numpy/numpy_array.py | _one_hot | michalbrys/probability | python | def _one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None):
if (on_value is None):
on_value = 1
if (off_value is None):
off_value = 0
if (dtype is None):
dtype = utils.common_dtype([on_value, off_value], np.float32)
indices = np.array(indic... |
def _range(start, limit=None, delta=1, dtype=None, name='range'):
'Emulates tf.range.'
dtype = utils.numpy_dtype(dtype)
start = ops.convert_to_tensor(start, dtype=dtype)
limit = (None if (limit is None) else ops.convert_to_tensor(limit, dtype=dtype))
delta = ops.convert_to_tensor(delta, dtype=dtype)... | 5,974,374,142,208,092,000 | Emulates tf.range. | tensorflow_probability/python/internal/backend/numpy/numpy_array.py | _range | michalbrys/probability | python | def _range(start, limit=None, delta=1, dtype=None, name='range'):
dtype = utils.numpy_dtype(dtype)
start = ops.convert_to_tensor(start, dtype=dtype)
limit = (None if (limit is None) else ops.convert_to_tensor(limit, dtype=dtype))
delta = ops.convert_to_tensor(delta, dtype=dtype)
if (dtype is No... |
def _searchsorted(sorted_sequence, values, side='left', out_type=np.int32, name=None):
'Find indices for insertion for list to remain sorted.'
if JAX_MODE:
try:
func = _searchsorted_vmap_sides[side]
except KeyError:
raise ValueError(("'%s' is an invalid value for keyword ... | -3,334,490,459,446,652,000 | Find indices for insertion for list to remain sorted. | tensorflow_probability/python/internal/backend/numpy/numpy_array.py | _searchsorted | michalbrys/probability | python | def _searchsorted(sorted_sequence, values, side='left', out_type=np.int32, name=None):
if JAX_MODE:
try:
func = _searchsorted_vmap_sides[side]
except KeyError:
raise ValueError(("'%s' is an invalid value for keyword 'side'" % side))
sorted_sequence_2d = np.reshap... |
def _split(value, num_or_size_splits, axis=0, num=None, name='split'):
'Map tf.split -> np.split.'
indices_or_sections = np.array(num_or_size_splits)
if (indices_or_sections.ndim == 1):
if any(((idx == (- 1)) for idx in indices_or_sections)):
total_splits = sum((idx for idx in indices_or... | 6,905,526,348,121,598,000 | Map tf.split -> np.split. | tensorflow_probability/python/internal/backend/numpy/numpy_array.py | _split | michalbrys/probability | python | def _split(value, num_or_size_splits, axis=0, num=None, name='split'):
indices_or_sections = np.array(num_or_size_splits)
if (indices_or_sections.ndim == 1):
if any(((idx == (- 1)) for idx in indices_or_sections)):
total_splits = sum((idx for idx in indices_or_sections if (idx != (- 1))... |
@staticmethod
def run(path, code=None, params=None, **meta):
'Check code with pycodestyle.\n\n :return list: List of errors.\n '
parser = get_parser()
for option in parser.option_list:
if (option.dest and (option.dest in params)):
value = params[option.dest]
if ... | 7,664,685,662,998,880,000 | Check code with pycodestyle.
:return list: List of errors. | vimfiles/bundle/vim-python/submodules/pylama/pylama/lint/pylama_pycodestyle.py | run | BHills15/vimrc | python | @staticmethod
def run(path, code=None, params=None, **meta):
'Check code with pycodestyle.\n\n :return list: List of errors.\n '
parser = get_parser()
for option in parser.option_list:
if (option.dest and (option.dest in params)):
value = params[option.dest]
if ... |
def init_file(self, filename, lines, expected, line_offset):
'Prepare storage for errors.'
super(_PycodestyleReport, self).init_file(filename, lines, expected, line_offset)
self.errors = [] | -8,009,620,792,537,842,000 | Prepare storage for errors. | vimfiles/bundle/vim-python/submodules/pylama/pylama/lint/pylama_pycodestyle.py | init_file | BHills15/vimrc | python | def init_file(self, filename, lines, expected, line_offset):
super(_PycodestyleReport, self).init_file(filename, lines, expected, line_offset)
self.errors = [] |
def error(self, line_number, offset, text, check):
'Save errors.'
code = super(_PycodestyleReport, self).error(line_number, offset, text, check)
if code:
self.errors.append(dict(text=text, type=code.replace('E', 'C'), col=(offset + 1), lnum=line_number)) | -7,287,559,401,521,401,000 | Save errors. | vimfiles/bundle/vim-python/submodules/pylama/pylama/lint/pylama_pycodestyle.py | error | BHills15/vimrc | python | def error(self, line_number, offset, text, check):
code = super(_PycodestyleReport, self).error(line_number, offset, text, check)
if code:
self.errors.append(dict(text=text, type=code.replace('E', 'C'), col=(offset + 1), lnum=line_number)) |
def get_file_results(self):
'Get errors.\n\n :return list: List of errors.\n\n '
return self.errors | 6,514,165,612,194,767,000 | Get errors.
:return list: List of errors. | vimfiles/bundle/vim-python/submodules/pylama/pylama/lint/pylama_pycodestyle.py | get_file_results | BHills15/vimrc | python | def get_file_results(self):
'Get errors.\n\n :return list: List of errors.\n\n '
return self.errors |
def load_conf_from_file(conf_file_path, conf=__conf__):
'\n Load conf file from: conf_file_path\n '
if (os.path.isfile(conf_file_path) == False):
raise AgentConfigError('Missing configuration in {0}'.format(conf_file_path))
try:
content = fileutil.read_file(conf_file_path)
conf... | -1,907,993,394,519,222,800 | Load conf file from: conf_file_path | azurelinuxagent/common/conf.py | load_conf_from_file | vittyvk/WALinuxAgent | python | def load_conf_from_file(conf_file_path, conf=__conf__):
'\n \n '
if (os.path.isfile(conf_file_path) == False):
raise AgentConfigError('Missing configuration in {0}'.format(conf_file_path))
try:
content = fileutil.read_file(conf_file_path)
conf.load(content)
except IOError a... |
@tf_export('block_lstm')
def block_lstm(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None):
"Computes the LSTM cell forward propagation for all the time steps.\n\n This is equivalent to applying LSTMBlockCell in a loop, like so:\n\n ```python\n for x1... | -259,147,601,963,394,300 | Computes the LSTM cell forward propagation for all the time steps.
This is equivalent to applying LSTMBlockCell in a loop, like so:
```python
for x1 in unpack(x):
i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
x1, cs_prev, h_prev, w, wci, wcf, wco, b)
cs_prev = cs1
h_prev = h1
i.append(i1)
cs.append(cs1)
... | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py | block_lstm | JustinACoder/H22-GR3-UnrealAI | python | @tf_export('block_lstm')
def block_lstm(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None):
"Computes the LSTM cell forward propagation for all the time steps.\n\n This is equivalent to applying LSTMBlockCell in a loop, like so:\n\n ```python\n for x1... |
def block_lstm_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function block_lstm\n '
_ctx = (ctx if ctx else _context.context())
if (forget_bias is None... | -8,994,183,612,343,482,000 | This is the slowpath function for Eager mode.
This is for function block_lstm | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py | block_lstm_eager_fallback | JustinACoder/H22-GR3-UnrealAI | python | def block_lstm_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function block_lstm\n '
_ctx = (ctx if ctx else _context.context())
if (forget_bias is None... |
@tf_export('block_lstm_grad')
def block_lstm_grad(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None):
'Computes the LSTM cell backward propagation for the entire time sequence.\n\n This implementation is to be used in conjunction of LSTMBlock.\n\... | -8,757,221,502,674,187,000 | Computes the LSTM cell backward propagation for the entire time sequence.
This implementation is to be used in conjunction of LSTMBlock.
Args:
seq_len_max: A `Tensor` of type `int64`.
Maximum time length actually used by this input. Outputs are padded
with zeros beyond this length.
x: A `Tensor`. Must be ... | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py | block_lstm_grad | JustinACoder/H22-GR3-UnrealAI | python | @tf_export('block_lstm_grad')
def block_lstm_grad(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None):
'Computes the LSTM cell backward propagation for the entire time sequence.\n\n This implementation is to be used in conjunction of LSTMBlock.\n\... |
def block_lstm_grad_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function block_lstm_grad\n '
_ctx = (ctx if ctx else _context.context())
use_pe... | 1,323,251,434,971,964,000 | This is the slowpath function for Eager mode.
This is for function block_lstm_grad | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py | block_lstm_grad_eager_fallback | JustinACoder/H22-GR3-UnrealAI | python | def block_lstm_grad_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function block_lstm_grad\n '
_ctx = (ctx if ctx else _context.context())
use_pe... |
@tf_export('lstm_block_cell')
def lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None):
"Computes the LSTM cell forward propagation for 1 time step.\n\n This implementation uses 1 weight matrix and 1 bias vector, and there's an\n optional peephole con... | -5,341,519,731,373,629,000 | Computes the LSTM cell forward propagation for 1 time step.
This implementation uses 1 weight matrix and 1 bias vector, and there's an
optional peephole connection.
This kernel op implements the following mathematical equations:
```python
xh = [x, h_prev]
[i, f, ci, o] = xh * w + b
f = f + forget_bias
if not use_pe... | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py | lstm_block_cell | JustinACoder/H22-GR3-UnrealAI | python | @tf_export('lstm_block_cell')
def lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None):
"Computes the LSTM cell forward propagation for 1 time step.\n\n This implementation uses 1 weight matrix and 1 bias vector, and there's an\n optional peephole con... |
def lstm_block_cell_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function lstm_block_cell\n '
_ctx = (ctx if ctx else _context.context())
if (forget_bias is None):
... | 5,588,209,277,027,045,000 | This is the slowpath function for Eager mode.
This is for function lstm_block_cell | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py | lstm_block_cell_eager_fallback | JustinACoder/H22-GR3-UnrealAI | python | def lstm_block_cell_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function lstm_block_cell\n '
_ctx = (ctx if ctx else _context.context())
if (forget_bias is None):
... |
@tf_export('lstm_block_cell_grad')
def lstm_block_cell_grad(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None):
'Computes the LSTM cell backward propagation for 1 timestep.\n\n This implementation is to be used in conjunction of LSTMBlockCell.\n\n Args:\n x:... | 5,906,303,798,717,116,000 | Computes the LSTM cell backward propagation for 1 timestep.
This implementation is to be used in conjunction of LSTMBlockCell.
Args:
x: A `Tensor`. Must be one of the following types: `half`, `float32`.
The input to the LSTM cell, shape (batch_size, num_inputs).
cs_prev: A `Tensor`. Must have the same type as... | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py | lstm_block_cell_grad | JustinACoder/H22-GR3-UnrealAI | python | @tf_export('lstm_block_cell_grad')
def lstm_block_cell_grad(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None):
'Computes the LSTM cell backward propagation for 1 timestep.\n\n This implementation is to be used in conjunction of LSTMBlockCell.\n\n Args:\n x:... |
def lstm_block_cell_grad_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function lstm_block_cell_grad\n '
_ctx = (ctx if ctx else _context.context())
use_peephole... | -1,909,998,256,456,007,400 | This is the slowpath function for Eager mode.
This is for function lstm_block_cell_grad | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py | lstm_block_cell_grad_eager_fallback | JustinACoder/H22-GR3-UnrealAI | python | def lstm_block_cell_grad_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function lstm_block_cell_grad\n '
_ctx = (ctx if ctx else _context.context())
use_peephole... |
def get_or_create_session_key(self):
'\n Get or create the session key from the request object.\n\n When not present yet, this initializes the session for the user.\n As a result, the request then returns session cookie to the user\n via session middleware.\n '
session_key = s... | 9,179,766,036,658,682,000 | Get or create the session key from the request object.
When not present yet, this initializes the session for the user.
As a result, the request then returns session cookie to the user
via session middleware. | sqrl/sqrl.py | get_or_create_session_key | JamesonNetworks/django-sqrl | python | def get_or_create_session_key(self):
'\n Get or create the session key from the request object.\n\n When not present yet, this initializes the session for the user.\n As a result, the request then returns session cookie to the user\n via session middleware.\n '
session_key = s... |
@property
def nut(self):
'\n Cached property for getting :obj:`.models.SQRLNut`.\n\n When accessed for the first time, this property either replaces or creates\n new :obj:`.models.SQRLNut` by using :meth:`.managers.SQRLNutManager.replace_or_create`.\n All the data for the creation of the... | 3,638,656,951,657,241,000 | Cached property for getting :obj:`.models.SQRLNut`.
When accessed for the first time, this property either replaces or creates
new :obj:`.models.SQRLNut` by using :meth:`.managers.SQRLNutManager.replace_or_create`.
All the data for the creation of the nut is created by using :meth:`.generate_nut_kwargs`. | sqrl/sqrl.py | nut | JamesonNetworks/django-sqrl | python | @property
def nut(self):
'\n Cached property for getting :obj:`.models.SQRLNut`.\n\n When accessed for the first time, this property either replaces or creates\n new :obj:`.models.SQRLNut` by using :meth:`.managers.SQRLNutManager.replace_or_create`.\n All the data for the creation of the... |
def generate_nut_kwargs(self):
'\n Generate kwargs which can be used to create new :obj:`.models.SQRLNut`.\n\n Returns\n -------\n dict\n All required kwargs to instantiate and create :obj:`.models.SQRLNut`.\n '
randomness = generate_randomness(64)
l = (len(rand... | 2,031,614,310,051,996,000 | Generate kwargs which can be used to create new :obj:`.models.SQRLNut`.
Returns
-------
dict
All required kwargs to instantiate and create :obj:`.models.SQRLNut`. | sqrl/sqrl.py | generate_nut_kwargs | JamesonNetworks/django-sqrl | python | def generate_nut_kwargs(self):
'\n Generate kwargs which can be used to create new :obj:`.models.SQRLNut`.\n\n Returns\n -------\n dict\n All required kwargs to instantiate and create :obj:`.models.SQRLNut`.\n '
randomness = generate_randomness(64)
l = (len(rand... |
def get_sqrl_url(self):
'\n Get the server URL of where SQRL client will make first request.\n\n This method should be customized when a custom namespace should be used\n by the SQRL client when generating on the fly per-site public-private keypair.\n For example this can be used when a ... | 3,748,251,528,209,340,400 | Get the server URL of where SQRL client will make first request.
This method should be customized when a custom namespace should be used
by the SQRL client when generating on the fly per-site public-private keypair.
For example this can be used when a web site is a SAAS in which different
"sub-sites" are determined te... | sqrl/sqrl.py | get_sqrl_url | JamesonNetworks/django-sqrl | python | def get_sqrl_url(self):
'\n Get the server URL of where SQRL client will make first request.\n\n This method should be customized when a custom namespace should be used\n by the SQRL client when generating on the fly per-site public-private keypair.\n For example this can be used when a ... |
def get_sqrl_url_params(self):
'\n Get SQRL url params to be added as querystring params in the SQRL url.\n\n By default this only adds ``nut=<nut>``.\n\n Returns\n -------\n str\n URLEncoded querystring params\n '
qd = QueryDict('', mutable=True)
qd.upda... | 8,559,213,639,511,226,000 | Get SQRL url params to be added as querystring params in the SQRL url.
By default this only adds ``nut=<nut>``.
Returns
-------
str
URLEncoded querystring params | sqrl/sqrl.py | get_sqrl_url_params | JamesonNetworks/django-sqrl | python | def get_sqrl_url_params(self):
'\n Get SQRL url params to be added as querystring params in the SQRL url.\n\n By default this only adds ``nut=<nut>``.\n\n Returns\n -------\n str\n URLEncoded querystring params\n '
qd = QueryDict(, mutable=True)
qd.update... |
@property
def url(self):
'\n Property for getting only server-side SQRL auth view URL.\n\n This does not include the full domain within the URL.\n The URL is always relative to the current domain of the site.\n '
return '{url}?{params}'.format(url=self.get_sqrl_url(), params=self.get... | -2,513,625,284,204,591,000 | Property for getting only server-side SQRL auth view URL.
This does not include the full domain within the URL.
The URL is always relative to the current domain of the site. | sqrl/sqrl.py | url | JamesonNetworks/django-sqrl | python | @property
def url(self):
'\n Property for getting only server-side SQRL auth view URL.\n\n This does not include the full domain within the URL.\n The URL is always relative to the current domain of the site.\n '
return '{url}?{params}'.format(url=self.get_sqrl_url(), params=self.get... |
@property
def sqrl_url(self):
'\n Property for getting full SQRL auth view URL including SQRL scheme and full domain with port.\n '
return '{scheme}://{host}{url}'.format(scheme=('sqrl' if self.request.is_secure() else 'qrl'), host=self.request.get_host(), url=self.url) | -4,712,704,604,675,991,000 | Property for getting full SQRL auth view URL including SQRL scheme and full domain with port. | sqrl/sqrl.py | sqrl_url | JamesonNetworks/django-sqrl | python | @property
def sqrl_url(self):
'\n \n '
return '{scheme}://{host}{url}'.format(scheme=('sqrl' if self.request.is_secure() else 'qrl'), host=self.request.get_host(), url=self.url) |
def count_flops_attn(model, _x, y):
'\n A counter for the `thop` package to count the operations in an\n attention operation.\n Meant to be used like:\n macs, params = thop.profile(\n model,\n inputs=(inputs, timestamps),\n custom_ops={QKVAttention: QKVAttention.coun... | 5,236,202,715,761,533,000 | A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
) | diff_dalle/unet.py | count_flops_attn | AranKomat/Diff-DALLE | python | def count_flops_attn(model, _x, y):
'\n A counter for the `thop` package to count the operations in an\n attention operation.\n Meant to be used like:\n macs, params = thop.profile(\n model,\n inputs=(inputs, timestamps),\n custom_ops={QKVAttention: QKVAttention.coun... |
@abstractmethod
def forward(self, x, emb):
'\n Apply the module to `x` given `emb` timestep embeddings.\n ' | 774,829,112,089,547,400 | Apply the module to `x` given `emb` timestep embeddings. | diff_dalle/unet.py | forward | AranKomat/Diff-DALLE | python | @abstractmethod
def forward(self, x, emb):
'\n \n ' |
@abstractmethod
def forward(self, x, y):
'\n Apply the module to `x` given `y`.\n ' | -9,143,765,492,867,446,000 | Apply the module to `x` given `y`. | diff_dalle/unet.py | forward | AranKomat/Diff-DALLE | python | @abstractmethod
def forward(self, x, y):
'\n \n ' |
def forward(self, x, emb):
'\n Apply the block to a Tensor, conditioned on a timestep embedding.\n\n :param x: an [N x C x ...] Tensor of features.\n :param emb: an [N x emb_channels] Tensor of timestep embeddings.\n :return: an [N x C x ...] Tensor of outputs.\n '
return chec... | 8,049,035,836,621,033,000 | Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs. | diff_dalle/unet.py | forward | AranKomat/Diff-DALLE | python | def forward(self, x, emb):
'\n Apply the block to a Tensor, conditioned on a timestep embedding.\n\n :param x: an [N x C x ...] Tensor of features.\n :param emb: an [N x emb_channels] Tensor of timestep embeddings.\n :return: an [N x C x ...] Tensor of outputs.\n '
return chec... |
def forward(self, qkv, y):
'\n Apply QKV attention.\n\n :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n '
(bs, width, length) = qkv.shape
if (y is None):
assert ((width % (3 * self.n_heads)) == 0)
... | -546,739,622,385,842,200 | Apply QKV attention.
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention. | diff_dalle/unet.py | forward | AranKomat/Diff-DALLE | python | def forward(self, qkv, y):
'\n Apply QKV attention.\n\n :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n '
(bs, width, length) = qkv.shape
if (y is None):
assert ((width % (3 * self.n_heads)) == 0)
... |
def forward(self, qkv):
'\n Apply QKV attention.\n\n :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n '
(bs, width, length) = qkv.shape
assert ((width % (3 * self.n_heads)) == 0)
ch = (width // (3 * self.... | -4,963,406,732,217,807,000 | Apply QKV attention.
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention. | diff_dalle/unet.py | forward | AranKomat/Diff-DALLE | python | def forward(self, qkv):
'\n Apply QKV attention.\n\n :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n '
(bs, width, length) = qkv.shape
assert ((width % (3 * self.n_heads)) == 0)
ch = (width // (3 * self.... |
def convert_to_fp16(self):
'\n Convert the torso of the model to float16.\n '
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
self.output_blocks.apply(convert_module_to_f16)
if hasattr(self, 'text_encoder'):
self.text_encoder.apply(... | -6,390,348,050,961,245,000 | Convert the torso of the model to float16. | diff_dalle/unet.py | convert_to_fp16 | AranKomat/Diff-DALLE | python | def convert_to_fp16(self):
'\n \n '
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
self.output_blocks.apply(convert_module_to_f16)
if hasattr(self, 'text_encoder'):
self.text_encoder.apply(convert_module_to_f16_2) |
def convert_to_fp32(self):
'\n Convert the torso of the model to float32.\n '
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
self.output_blocks.apply(convert_module_to_f32)
if hasattr(self, 'text_encoder'):
self.text_encoder.apply(... | -1,808,874,455,012,511,700 | Convert the torso of the model to float32. | diff_dalle/unet.py | convert_to_fp32 | AranKomat/Diff-DALLE | python | def convert_to_fp32(self):
'\n \n '
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
self.output_blocks.apply(convert_module_to_f32)
if hasattr(self, 'text_encoder'):
self.text_encoder.apply(convert_module_to_f32) |
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