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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Wm.wm_colormapwindows | (self, *wlist) | return map(self._nametowidget, self.tk.call(args)) | Store list of window names (WLIST) into WM_COLORMAPWINDOWS property
of this widget. This list contains windows whose colormaps differ from their
parents. Return current list of widgets if WLIST is empty. | Store list of window names (WLIST) into WM_COLORMAPWINDOWS property
of this widget. This list contains windows whose colormaps differ from their
parents. Return current list of widgets if WLIST is empty. | [
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"""Store list of window names (WLIST) into WM_COLORMAPWINDOWS property
of this widget. This list contains windows whose colormaps differ from their
parents. Return current list of widgets if WLIST is empty."""
if len(wlist) > 1:
wlist = (wlist,) # Tk needs a list of windows here
args = ('wm', 'colormapwindows', self._w) + wlist
return map(self._nametowidget, self.tk.call(args)) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/dynamodb/layer2.py | python | Layer2.update_throughput | (self, table, read_units, write_units) | Update the ProvisionedThroughput for the Amazon DynamoDB Table.
:type table: :class:`boto.dynamodb.table.Table`
:param table: The Table object whose throughput is being updated.
:type read_units: int
:param read_units: The new value for ReadCapacityUnits.
:type write_units: int
:param write_units: The new value for WriteCapacityUnits. | Update the ProvisionedThroughput for the Amazon DynamoDB Table. | [
"Update",
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"Table",
"."
] | def update_throughput(self, table, read_units, write_units):
"""
Update the ProvisionedThroughput for the Amazon DynamoDB Table.
:type table: :class:`boto.dynamodb.table.Table`
:param table: The Table object whose throughput is being updated.
:type read_units: int
:param read_units: The new value for ReadCapacityUnits.
:type write_units: int
:param write_units: The new value for WriteCapacityUnits.
"""
response = self.layer1.update_table(table.name,
{'ReadCapacityUnits': read_units,
'WriteCapacityUnits': write_units})
table.update_from_response(response) | [
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abforce/xposed_art_n | ec3fbe417d74d4664cec053d91dd4e3881176374 | tools/cpplint.py | python | CheckComment | (comment, filename, linenum, error) | Checks for common mistakes in TODO comments.
Args:
comment: The text of the comment from the line in question.
filename: The name of the current file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks for common mistakes in TODO comments. | [
"Checks",
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"mistakes",
"in",
"TODO",
"comments",
"."
] | def CheckComment(comment, filename, linenum, error):
"""Checks for common mistakes in TODO comments.
Args:
comment: The text of the comment from the line in question.
filename: The name of the current file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
match = _RE_PATTERN_TODO.match(comment)
if match:
# One whitespace is correct; zero whitespace is handled elsewhere.
leading_whitespace = match.group(1)
if len(leading_whitespace) > 1:
error(filename, linenum, 'whitespace/todo', 2,
'Too many spaces before TODO')
username = match.group(2)
if not username:
error(filename, linenum, 'readability/todo', 2,
'Missing username in TODO; it should look like '
'"// TODO(my_username): Stuff."')
middle_whitespace = match.group(3)
# Comparisons made explicit for correctness -- pylint: disable-msg=C6403
if middle_whitespace != ' ' and middle_whitespace != '':
error(filename, linenum, 'whitespace/todo', 2,
'TODO(my_username) should be followed by a space') | [
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google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | ortools/sat/samples/stop_after_n_solutions_sample_sat.py | python | StopAfterNSolutionsSampleSat | () | Showcases calling the solver to search for small number of solutions. | Showcases calling the solver to search for small number of solutions. | [
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"for",
"small",
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"."
] | def StopAfterNSolutionsSampleSat():
"""Showcases calling the solver to search for small number of solutions."""
# Creates the model.
model = cp_model.CpModel()
# Creates the variables.
num_vals = 3
x = model.NewIntVar(0, num_vals - 1, 'x')
y = model.NewIntVar(0, num_vals - 1, 'y')
z = model.NewIntVar(0, num_vals - 1, 'z')
# Create a solver and solve.
solver = cp_model.CpSolver()
solution_printer = VarArraySolutionPrinterWithLimit([x, y, z], 5)
# Enumerate all solutions.
solver.parameters.enumerate_all_solutions = True
# Solve.
status = solver.Solve(model, solution_printer)
print('Status = %s' % solver.StatusName(status))
print('Number of solutions found: %i' % solution_printer.solution_count())
assert solution_printer.solution_count() == 5 | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Alignment/MuonAlignment/python/svgfig.py | python | Ticks.SVG | (self, trans=None) | return output | Apply the transformation "trans" and return an SVG object. | Apply the transformation "trans" and return an SVG object. | [
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] | def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if isinstance(trans, str): trans = totrans(trans)
self.last_ticks, self.last_miniticks = self.interpret()
tickmarks = Path([], **self.attr)
minitickmarks = Path([], **self.attr)
output = SVG("g")
if (self.arrow_start != False and self.arrow_start != None) or (self.arrow_end != False and self.arrow_end != None):
defs = SVG("defs")
if self.arrow_start != False and self.arrow_start != None:
if isinstance(self.arrow_start, SVG):
defs.append(self.arrow_start)
elif isinstance(self.arrow_start, str):
defs.append(make_marker(self.arrow_start, "arrow_start"))
else:
raise TypeError("arrow_start must be False/None or an id string for the new marker")
if self.arrow_end != False and self.arrow_end != None:
if isinstance(self.arrow_end, SVG):
defs.append(self.arrow_end)
elif isinstance(self.arrow_end, str):
defs.append(make_marker(self.arrow_end, "arrow_end"))
else:
raise TypeError("arrow_end must be False/None or an id string for the new marker")
output.append(defs)
eps = _epsilon * (self.high - self.low)
for t, label in self.last_ticks.items():
(X, Y), (xhatx, xhaty), (yhatx, yhaty), angle = self.orient_tickmark(t, trans)
if (not self.arrow_start or abs(t - self.low) > eps) and (not self.arrow_end or abs(t - self.high) > eps):
tickmarks.d.append(("M", X - yhatx*self.tick_start, Y - yhaty*self.tick_start, True))
tickmarks.d.append(("L", X - yhatx*self.tick_end, Y - yhaty*self.tick_end, True))
angle = (angle - math.pi/2.)*180./math.pi + self.text_angle
########### a HACK! ############ (to be removed when Inkscape handles baselines)
if _hacks["inkscape-text-vertical-shift"]:
if self.text_start > 0:
X += math.cos(angle*math.pi/180. + math.pi/2.) * 2.
Y += math.sin(angle*math.pi/180. + math.pi/2.) * 2.
else:
X += math.cos(angle*math.pi/180. + math.pi/2.) * 2. * 2.5
Y += math.sin(angle*math.pi/180. + math.pi/2.) * 2. * 2.5
########### end hack ###########
if label != "":
output.append(SVG("text", label, transform="translate(%g, %g) rotate(%g)" % \
(X - yhatx*self.text_start, Y - yhaty*self.text_start, angle), **self.text_attr))
for t in self.last_miniticks:
skip = False
for tt in self.last_ticks.keys():
if abs(t - tt) < eps:
skip = True
break
if not skip:
(X, Y), (xhatx, xhaty), (yhatx, yhaty), angle = self.orient_tickmark(t, trans)
if (not self.arrow_start or abs(t - self.low) > eps) and (not self.arrow_end or abs(t - self.high) > eps):
minitickmarks.d.append(("M", X - yhatx*self.minitick_start, Y - yhaty*self.minitick_start, True))
minitickmarks.d.append(("L", X - yhatx*self.minitick_end, Y - yhaty*self.minitick_end, True))
output.prepend(tickmarks.SVG(trans))
output.prepend(minitickmarks.SVG(trans))
return output | [
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mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Node/FS.py | python | Base.__lt__ | (self, other) | return str(self) < str(other) | less than operator used by sorting on py3 | less than operator used by sorting on py3 | [
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""" less than operator used by sorting on py3"""
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/debug/cli/command_parser.py | python | validate_slicing_string | (slicing_string) | return bool(re.search(r"^\[(\d|,|\s|:)+\]$", slicing_string)) | Validate a slicing string.
Check if the input string contains only brackets, digits, commas and
colons that are valid characters in numpy-style array slicing.
Args:
slicing_string: (str) Input slicing string to be validated.
Returns:
(bool) True if and only if the slicing string is valid. | Validate a slicing string. | [
"Validate",
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"slicing",
"string",
"."
] | def validate_slicing_string(slicing_string):
"""Validate a slicing string.
Check if the input string contains only brackets, digits, commas and
colons that are valid characters in numpy-style array slicing.
Args:
slicing_string: (str) Input slicing string to be validated.
Returns:
(bool) True if and only if the slicing string is valid.
"""
return bool(re.search(r"^\[(\d|,|\s|:)+\]$", slicing_string)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/targets/base.py | python | _load_global_helpers | () | Execute once to install special symbols into the LLVM symbol table. | Execute once to install special symbols into the LLVM symbol table. | [
"Execute",
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"install",
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"symbols",
"into",
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"LLVM",
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"table",
"."
] | def _load_global_helpers():
"""
Execute once to install special symbols into the LLVM symbol table.
"""
# This is Py_None's real C name
ll.add_symbol("_Py_NoneStruct", id(None))
# Add Numba C helper functions
for c_helpers in (_helperlib.c_helpers, _dynfunc.c_helpers):
for py_name, c_address in c_helpers.items():
c_name = "numba_" + py_name
ll.add_symbol(c_name, c_address)
# Add Numpy C helpers (npy_XXX)
for c_name, c_address in _helperlib.npymath_exports.items():
ll.add_symbol(c_name, c_address)
# Add all built-in exception classes
for obj in utils.builtins.__dict__.values():
if isinstance(obj, type) and issubclass(obj, BaseException):
ll.add_symbol("PyExc_%s" % (obj.__name__), id(obj)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | Process.Kill | (*args, **kwargs) | return _misc_.Process_Kill(*args, **kwargs) | Kill(int pid, int sig=SIGTERM, int flags=KILL_NOCHILDREN) -> int | Kill(int pid, int sig=SIGTERM, int flags=KILL_NOCHILDREN) -> int | [
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"""Kill(int pid, int sig=SIGTERM, int flags=KILL_NOCHILDREN) -> int"""
return _misc_.Process_Kill(*args, **kwargs) | [
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johmathe/shotdetect | 1ecf93a695c96fd7601a41ab5834f1117b9d7d50 | tools/cpplint.py | python | _FunctionState.Begin | (self, function_name) | Start analyzing function body.
Args:
function_name: The name of the function being tracked. | Start analyzing function body. | [
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] | def Begin(self, function_name):
"""Start analyzing function body.
Args:
function_name: The name of the function being tracked.
"""
self.in_a_function = True
self.lines_in_function = 0
self.current_function = function_name | [
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ValveSoftware/source-sdk-2013 | 0d8dceea4310fde5706b3ce1c70609d72a38efdf | sp/src/thirdparty/protobuf-2.3.0/python/mox.py | python | MockObject.__call__ | (self, *params, **named_params) | return mock_method(*params, **named_params) | Provide custom logic for mocking classes that are callable. | Provide custom logic for mocking classes that are callable. | [
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] | def __call__(self, *params, **named_params):
"""Provide custom logic for mocking classes that are callable."""
# Verify the class we are mocking is callable
callable = self._class_to_mock.__dict__.get('__call__', None)
if callable is None:
raise TypeError('Not callable')
# Because the call is happening directly on this object instead of a method,
# the call on the mock method is made right here
mock_method = self._CreateMockMethod('__call__')
return mock_method(*params, **named_params) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/Context.py | python | Context.execute | (self) | Execute the command. Redefine this method in subclasses. | Execute the command. Redefine this method in subclasses. | [
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] | def execute(self):
"""
Execute the command. Redefine this method in subclasses.
"""
global g_module
self.recurse([os.path.dirname(g_module.root_path)]) | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBSymbol.GetStartAddress | (self) | return _lldb.SBSymbol_GetStartAddress(self) | GetStartAddress(self) -> SBAddress | GetStartAddress(self) -> SBAddress | [
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"""GetStartAddress(self) -> SBAddress"""
return _lldb.SBSymbol_GetStartAddress(self) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/auibar.py | python | AuiToolBar.SetMarginsSize | (self, size) | Set the values to be used as margins for the toolbar.
:param Size `size`: the margin size (an instance of :class:`Size`). | Set the values to be used as margins for the toolbar. | [
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"""
Set the values to be used as margins for the toolbar.
:param Size `size`: the margin size (an instance of :class:`Size`).
"""
self.SetMargins(size.x, size.x, size.y, size.y) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/webapp2/webapp2.py | python | WSGIApplication.clear_globals | (self) | Clears global variables. See :meth:`set_globals`. | Clears global variables. See :meth:`set_globals`. | [
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"""Clears global variables. See :meth:`set_globals`."""
if _local is not None: # pragma: no cover
_local.__release_local__()
else: # pragma: no cover
WSGIApplication.app = WSGIApplication.active_instance = None
WSGIApplication.request = None | [
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NVIDIA/TensorRT | 42805f078052daad1a98bc5965974fcffaad0960 | samples/python/efficientnet/build_engine.py | python | EngineCalibrator.get_batch | (self, names) | Overrides from trt.IInt8EntropyCalibrator2.
Get the next batch to use for calibration, as a list of device memory pointers.
:param names: The names of the inputs, if useful to define the order of inputs.
:return: A list of int-casted memory pointers. | Overrides from trt.IInt8EntropyCalibrator2.
Get the next batch to use for calibration, as a list of device memory pointers.
:param names: The names of the inputs, if useful to define the order of inputs.
:return: A list of int-casted memory pointers. | [
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"""
Overrides from trt.IInt8EntropyCalibrator2.
Get the next batch to use for calibration, as a list of device memory pointers.
:param names: The names of the inputs, if useful to define the order of inputs.
:return: A list of int-casted memory pointers.
"""
if not self.image_batcher:
return None
try:
batch, _ = next(self.batch_generator)
log.info("Calibrating image {} / {}".format(self.image_batcher.image_index, self.image_batcher.num_images))
cuda.memcpy_htod(self.batch_allocation, np.ascontiguousarray(batch))
return [int(self.batch_allocation)]
except StopIteration:
log.info("Finished calibration batches")
return None | [
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brave/brave-core | ceaa3de4735789d355b6fa80c21d4709e2c1d0e8 | script/lib/transifex.py | python | get_auth | () | return auth | Creates an HTTPBasicAuth object given the Transifex information | Creates an HTTPBasicAuth object given the Transifex information | [
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"""Creates an HTTPBasicAuth object given the Transifex information"""
username = get_env_var('TRANSIFEX_USERNAME')
password = get_env_var('TRANSIFEX_PASSWORD')
transifex_api_key = get_env_var('TRANSIFEX_API_KEY')
auth = None
if transifex_api_key:
api_key_username = "api:" + transifex_api_key
auth = requests.auth.HTTPBasicAuth(api_key_username, '')
else:
auth = requests.auth.HTTPBasicAuth(username, password)
return auth | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | lldb/packages/Python/lldbsuite/support/seven.py | python | hexlify | (data) | return bitcast_to_string(binascii.hexlify(bitcast_to_bytes(data))) | Hex-encode string data. The result if always a string. | Hex-encode string data. The result if always a string. | [
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"""Hex-encode string data. The result if always a string."""
return bitcast_to_string(binascii.hexlify(bitcast_to_bytes(data))) | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/tools/gyp/pylib/gyp/input.py | python | ProcessVariablesAndConditionsInDict | (the_dict, phase, variables_in,
build_file, the_dict_key=None) | Handle all variable and command expansion and conditional evaluation.
This function is the public entry point for all variable expansions and
conditional evaluations. The variables_in dictionary will not be modified
by this function. | Handle all variable and command expansion and conditional evaluation. | [
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] | def ProcessVariablesAndConditionsInDict(the_dict, phase, variables_in,
build_file, the_dict_key=None):
"""Handle all variable and command expansion and conditional evaluation.
This function is the public entry point for all variable expansions and
conditional evaluations. The variables_in dictionary will not be modified
by this function.
"""
# Make a copy of the variables_in dict that can be modified during the
# loading of automatics and the loading of the variables dict.
variables = variables_in.copy()
LoadAutomaticVariablesFromDict(variables, the_dict)
if 'variables' in the_dict:
# Make sure all the local variables are added to the variables
# list before we process them so that you can reference one
# variable from another. They will be fully expanded by recursion
# in ExpandVariables.
for key, value in the_dict['variables'].iteritems():
variables[key] = value
# Handle the associated variables dict first, so that any variable
# references within can be resolved prior to using them as variables.
# Pass a copy of the variables dict to avoid having it be tainted.
# Otherwise, it would have extra automatics added for everything that
# should just be an ordinary variable in this scope.
ProcessVariablesAndConditionsInDict(the_dict['variables'], phase,
variables, build_file, 'variables')
LoadVariablesFromVariablesDict(variables, the_dict, the_dict_key)
for key, value in the_dict.iteritems():
# Skip "variables", which was already processed if present.
if key != 'variables' and type(value) is str:
expanded = ExpandVariables(value, phase, variables, build_file)
if type(expanded) not in (str, int):
raise ValueError(
'Variable expansion in this context permits str and int ' + \
'only, found ' + expanded.__class__.__name__ + ' for ' + key)
the_dict[key] = expanded
# Variable expansion may have resulted in changes to automatics. Reload.
# TODO(mark): Optimization: only reload if no changes were made.
variables = variables_in.copy()
LoadAutomaticVariablesFromDict(variables, the_dict)
LoadVariablesFromVariablesDict(variables, the_dict, the_dict_key)
# Process conditions in this dict. This is done after variable expansion
# so that conditions may take advantage of expanded variables. For example,
# if the_dict contains:
# {'type': '<(library_type)',
# 'conditions': [['_type=="static_library"', { ... }]]},
# _type, as used in the condition, will only be set to the value of
# library_type if variable expansion is performed before condition
# processing. However, condition processing should occur prior to recursion
# so that variables (both automatic and "variables" dict type) may be
# adjusted by conditions sections, merged into the_dict, and have the
# intended impact on contained dicts.
#
# This arrangement means that a "conditions" section containing a "variables"
# section will only have those variables effective in subdicts, not in
# the_dict. The workaround is to put a "conditions" section within a
# "variables" section. For example:
# {'conditions': [['os=="mac"', {'variables': {'define': 'IS_MAC'}}]],
# 'defines': ['<(define)'],
# 'my_subdict': {'defines': ['<(define)']}},
# will not result in "IS_MAC" being appended to the "defines" list in the
# current scope but would result in it being appended to the "defines" list
# within "my_subdict". By comparison:
# {'variables': {'conditions': [['os=="mac"', {'define': 'IS_MAC'}]]},
# 'defines': ['<(define)'],
# 'my_subdict': {'defines': ['<(define)']}},
# will append "IS_MAC" to both "defines" lists.
# Evaluate conditions sections, allowing variable expansions within them
# as well as nested conditionals. This will process a 'conditions' or
# 'target_conditions' section, perform appropriate merging and recursive
# conditional and variable processing, and then remove the conditions section
# from the_dict if it is present.
ProcessConditionsInDict(the_dict, phase, variables, build_file)
# Conditional processing may have resulted in changes to automatics or the
# variables dict. Reload.
variables = variables_in.copy()
LoadAutomaticVariablesFromDict(variables, the_dict)
LoadVariablesFromVariablesDict(variables, the_dict, the_dict_key)
# Recurse into child dicts, or process child lists which may result in
# further recursion into descendant dicts.
for key, value in the_dict.iteritems():
# Skip "variables" and string values, which were already processed if
# present.
if key == 'variables' or type(value) is str:
continue
if type(value) is dict:
# Pass a copy of the variables dict so that subdicts can't influence
# parents.
ProcessVariablesAndConditionsInDict(value, phase, variables,
build_file, key)
elif type(value) is list:
# The list itself can't influence the variables dict, and
# ProcessVariablesAndConditionsInList will make copies of the variables
# dict if it needs to pass it to something that can influence it. No
# copy is necessary here.
ProcessVariablesAndConditionsInList(value, phase, variables,
build_file)
elif type(value) is not int:
raise TypeError('Unknown type ' + value.__class__.__name__ + \
' for ' + key) | [
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/processor/conversion/ror/processor.py | python | RoRProcessor.create_entity_lines | (gamespec, full_data_set) | Sort units/buildings into lines, based on information from techs and civs.
:param full_data_set: GenieObjectContainer instance that
contains all relevant data for the conversion
process.
:type full_data_set: class: ...dataformat.aoc.genie_object_container.GenieObjectContainer | Sort units/buildings into lines, based on information from techs and civs. | [
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] | def create_entity_lines(gamespec, full_data_set):
"""
Sort units/buildings into lines, based on information from techs and civs.
:param full_data_set: GenieObjectContainer instance that
contains all relevant data for the conversion
process.
:type full_data_set: class: ...dataformat.aoc.genie_object_container.GenieObjectContainer
"""
# Search a player civ (egyptians) for the starting units
player_civ_units = gamespec[0]["civs"][1]["units"].get_value()
task_group_ids = set()
villager_unit_ids = set()
for raw_unit in player_civ_units.values():
unit_id = raw_unit["id0"].get_value()
enabled = raw_unit["enabled"].get_value()
entity = full_data_set.genie_units[unit_id]
if not enabled:
# Unlocked by tech
continue
unit_type = entity["unit_type"].get_value()
if unit_type == 70:
if entity.has_member("task_group") and\
entity["task_group"].get_value() > 0:
task_group_id = entity["task_group"].get_value()
villager_unit_ids.add(unit_id)
if task_group_id in task_group_ids:
task_group = full_data_set.task_groups[task_group_id]
task_group.add_unit(entity)
else:
if task_group_id == 1:
line_id = RoRUnitTaskGroup.male_line_id
task_group = RoRUnitTaskGroup(line_id, task_group_id, -1, full_data_set)
task_group.add_unit(entity)
task_group_ids.add(task_group_id)
full_data_set.task_groups.update({task_group_id: task_group})
else:
unit_line = RoRUnitLineGroup(unit_id, -1, full_data_set)
unit_line.add_unit(entity)
full_data_set.unit_lines.update({unit_line.get_id(): unit_line})
full_data_set.unit_ref.update({unit_id: unit_line})
elif unit_type == 80:
building_line = RoRBuildingLineGroup(unit_id, -1, full_data_set)
building_line.add_unit(entity)
full_data_set.building_lines.update({building_line.get_id(): building_line})
full_data_set.unit_ref.update({unit_id: building_line})
# Create the villager task group
villager = RoRVillagerGroup(118, task_group_ids, full_data_set)
full_data_set.unit_lines.update({villager.get_id(): villager})
full_data_set.villager_groups.update({villager.get_id(): villager})
for unit_id in villager_unit_ids:
full_data_set.unit_ref.update({unit_id: villager})
# Other units unlocks through techs
unit_unlocks = full_data_set.unit_unlocks
for unit_unlock in unit_unlocks.values():
line_id = unit_unlock.get_line_id()
unit = full_data_set.genie_units[line_id]
unit_line = RoRUnitLineGroup(line_id, unit_unlock.get_id(), full_data_set)
unit_line.add_unit(unit)
full_data_set.unit_lines.update({unit_line.get_id(): unit_line})
full_data_set.unit_ref.update({line_id: unit_line})
# Check if the tech unlocks other lines
# TODO: Make this cleaner
unlock_effects = unit_unlock.get_effects(effect_type=2)
for unlock_effect in unlock_effects:
line_id = unlock_effect["attr_a"].get_value()
unit = full_data_set.genie_units[line_id]
if line_id not in full_data_set.unit_lines.keys():
unit_line = RoRUnitLineGroup(line_id, unit_unlock.get_id(), full_data_set)
unit_line.add_unit(unit)
full_data_set.unit_lines.update({unit_line.get_id(): unit_line})
full_data_set.unit_ref.update({line_id: unit_line})
# Upgraded units in a line
unit_upgrades = full_data_set.unit_upgrades
for unit_upgrade in unit_upgrades.values():
line_id = unit_upgrade.get_line_id()
target_id = unit_upgrade.get_upgrade_target_id()
unit = full_data_set.genie_units[target_id]
# Find the previous unit in the line
required_techs = unit_upgrade.tech["required_techs"].get_value()
for required_tech in required_techs:
required_tech_id = required_tech.get_value()
if required_tech_id in full_data_set.unit_unlocks.keys():
source_id = full_data_set.unit_unlocks[required_tech_id].get_line_id()
break
if required_tech_id in full_data_set.unit_upgrades.keys():
source_id = full_data_set.unit_upgrades[required_tech_id].get_upgrade_target_id()
break
unit_line = full_data_set.unit_lines[line_id]
unit_line.add_unit(unit, after=source_id)
full_data_set.unit_ref.update({target_id: unit_line})
# Other buildings unlocks through techs
building_unlocks = full_data_set.building_unlocks
for building_unlock in building_unlocks.values():
line_id = building_unlock.get_line_id()
building = full_data_set.genie_units[line_id]
building_line = RoRBuildingLineGroup(line_id, building_unlock.get_id(), full_data_set)
building_line.add_unit(building)
full_data_set.building_lines.update({building_line.get_id(): building_line})
full_data_set.unit_ref.update({line_id: building_line})
# Upgraded buildings through techs
building_upgrades = full_data_set.building_upgrades
for building_upgrade in building_upgrades.values():
line_id = building_upgrade.get_line_id()
target_id = building_upgrade.get_upgrade_target_id()
unit = full_data_set.genie_units[target_id]
# Find the previous unit in the line
required_techs = building_upgrade.tech["required_techs"].get_value()
for required_tech in required_techs:
required_tech_id = required_tech.get_value()
if required_tech_id in full_data_set.building_unlocks.keys():
source_id = full_data_set.building_unlocks[required_tech_id].get_line_id()
break
if required_tech_id in full_data_set.building_upgrades.keys():
source_id = full_data_set.building_upgrades[required_tech_id].get_upgrade_target_id()
break
building_line = full_data_set.building_lines[line_id]
building_line.add_unit(unit, after=source_id)
full_data_set.unit_ref.update({target_id: building_line})
# Upgraded units/buildings through age ups
age_ups = full_data_set.age_upgrades
for age_up in age_ups.values():
effects = age_up.get_effects(effect_type=3)
for effect in effects:
source_id = effect["attr_a"].get_value()
target_id = effect["attr_b"].get_value()
unit = full_data_set.genie_units[target_id]
if source_id in full_data_set.building_lines.keys():
building_line = full_data_set.building_lines[source_id]
building_line.add_unit(unit, after=source_id)
full_data_set.unit_ref.update({target_id: building_line})
elif source_id in full_data_set.unit_lines.keys():
unit_line = full_data_set.unit_lines[source_id]
unit_line.add_unit(unit, after=source_id)
full_data_set.unit_ref.update({target_id: unit_line}) | [
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liulei01/DRBox | b5c76e033c555c9009590ab384e1f7bd3c66c237 | python/caffe/io.py | python | resize_image | (im, new_dims, interp_order=1) | return resized_im.astype(np.float32) | Resize an image array with interpolation.
Parameters
----------
im : (H x W x K) ndarray
new_dims : (height, width) tuple of new dimensions.
interp_order : interpolation order, default is linear.
Returns
-------
im : resized ndarray with shape (new_dims[0], new_dims[1], K) | Resize an image array with interpolation. | [
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] | def resize_image(im, new_dims, interp_order=1):
"""
Resize an image array with interpolation.
Parameters
----------
im : (H x W x K) ndarray
new_dims : (height, width) tuple of new dimensions.
interp_order : interpolation order, default is linear.
Returns
-------
im : resized ndarray with shape (new_dims[0], new_dims[1], K)
"""
if im.shape[-1] == 1 or im.shape[-1] == 3:
im_min, im_max = im.min(), im.max()
if im_max > im_min:
# skimage is fast but only understands {1,3} channel images
# in [0, 1].
im_std = (im - im_min) / (im_max - im_min)
resized_std = resize(im_std, new_dims, order=interp_order)
resized_im = resized_std * (im_max - im_min) + im_min
else:
# the image is a constant -- avoid divide by 0
ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),
dtype=np.float32)
ret.fill(im_min)
return ret
else:
# ndimage interpolates anything but more slowly.
scale = tuple(np.array(new_dims, dtype=float) / np.array(im.shape[:2]))
resized_im = zoom(im, scale + (1,), order=interp_order)
return resized_im.astype(np.float32) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/packaging/version.py | python | _parse_local_version | (local) | return None | Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve"). | Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve"). | [
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] | def _parse_local_version(local):
# type: (str) -> Optional[LocalType]
"""
Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
"""
if local is not None:
return tuple(
part.lower() if not part.isdigit() else int(part)
for part in _local_version_separators.split(local)
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return None | [
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tum-vision/fusenet | a1451be2971b348a01b0f525c2a3a7a0e215a591 | scripts/cpp_lint.py | python | _NamespaceInfo.CheckEnd | (self, filename, clean_lines, linenum, error) | Check end of namespace comments. | Check end of namespace comments. | [
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"""Check end of namespace comments."""
line = clean_lines.raw_lines[linenum]
# Check how many lines is enclosed in this namespace. Don't issue
# warning for missing namespace comments if there aren't enough
# lines. However, do apply checks if there is already an end of
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#
# TODO(unknown): We always want to check end of namespace comments
# if a namespace is large, but sometimes we also want to apply the
# check if a short namespace contained nontrivial things (something
# other than forward declarations). There is currently no logic on
# deciding what these nontrivial things are, so this check is
# triggered by namespace size only, which works most of the time.
if (linenum - self.starting_linenum < 10
and not Match(r'};*\s*(//|/\*).*\bnamespace\b', line)):
return
# Look for matching comment at end of namespace.
#
# Note that we accept C style "/* */" comments for terminating
# namespaces, so that code that terminate namespaces inside
# preprocessor macros can be cpplint clean.
#
# We also accept stuff like "// end of namespace <name>." with the
# period at the end.
#
# Besides these, we don't accept anything else, otherwise we might
# get false negatives when existing comment is a substring of the
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if self.name:
# Named namespace
if not Match((r'};*\s*(//|/\*).*\bnamespace\s+' + re.escape(self.name) +
r'[\*/\.\\\s]*$'),
line):
error(filename, linenum, 'readability/namespace', 5,
'Namespace should be terminated with "// namespace %s"' %
self.name)
else:
# Anonymous namespace
if not Match(r'};*\s*(//|/\*).*\bnamespace[\*/\.\\\s]*$', line):
error(filename, linenum, 'readability/namespace', 5,
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/lite/python/tflite_keras_util.py | python | create_pseudo_output_names | (outputs) | return _create_pseudo_names(outputs, prefix='output_') | Create pseudo output names for a subclassed Model. | Create pseudo output names for a subclassed Model. | [
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tfwu/FaceDetection-ConvNet-3D | f9251c48eb40c5aec8fba7455115c355466555be | python/mxnet/ndarray.py | python | _init_ndarray_module | () | List and add all the ndarray functions to current module. | List and add all the ndarray functions to current module. | [
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] | def _init_ndarray_module():
"""List and add all the ndarray functions to current module."""
plist = ctypes.POINTER(FunctionHandle)()
size = ctypes.c_uint()
check_call(_LIB.MXListFunctions(ctypes.byref(size),
ctypes.byref(plist)))
module_obj = sys.modules[__name__]
for i in range(size.value):
hdl = FunctionHandle(plist[i])
function = _make_ndarray_function(hdl)
# if function name starts with underscore, register as static method of NDArray
if function.__name__.startswith('_'):
setattr(NDArray, function.__name__, staticmethod(function))
else:
fname = function.__name__
fn_obj = getattr(module_obj, fname, None)
if fn_obj is None:
setattr(module_obj, fname, function)
else:
setattr(module_obj, fname + '_internal', function) | [
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baidu/bigflow | 449245016c0df7d1252e85581e588bfc60cefad3 | bigflow_python/python/bigflow/util/utils.py | python | is_ptype | (obj) | return isinstance(obj, ptype.PType) | Return if an object is a PType
Args:
obj (object): object
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bool: True if the object is a PType, False otherwise | Return if an object is a PType | [
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"""
Return if an object is a PType
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obj (object): object
Returns:
bool: True if the object is a PType, False otherwise
"""
return isinstance(obj, ptype.PType) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/externals/joblib/pool.py | python | delete_folder | (folder_path) | Utility function to cleanup a temporary folder if still existing. | Utility function to cleanup a temporary folder if still existing. | [
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"""Utility function to cleanup a temporary folder if still existing."""
try:
if os.path.exists(folder_path):
shutil.rmtree(folder_path)
except WindowsError:
warnings.warn("Failed to clean temporary folder: %s" % folder_path) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/android_platform/development/scripts/symbol.py | python | FindToolchain | () | Look for the latest available toolchain
Args:
None
Returns:
A pair of strings containing toolchain label and target prefix. | Look for the latest available toolchain | [
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] | def FindToolchain():
"""Look for the latest available toolchain
Args:
None
Returns:
A pair of strings containing toolchain label and target prefix.
"""
global TOOLCHAIN_INFO
if TOOLCHAIN_INFO is not None:
return TOOLCHAIN_INFO
## Known toolchains, newer ones in the front.
if ARCH == "arm":
known_toolchains = [
("arm-linux-androideabi-4.6", "arm", "arm-linux-androideabi"),
]
elif ARCH =="x86":
known_toolchains = [
("i686-android-linux-4.4.3", "x86", "i686-android-linux")
]
else:
known_toolchains = []
# Look for addr2line to check for valid toolchain path.
for (label, platform, target) in known_toolchains:
toolchain_info = (label, platform, target);
if os.path.exists(ToolPath("addr2line", toolchain_info)):
TOOLCHAIN_INFO = toolchain_info
return toolchain_info
raise Exception("Could not find tool chain") | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/SANSUtility.py | python | delete_zero_error_free_workspace | (input_workspace_name) | return message, complete | Deletes the zero-error free workspace
@param ws :: The input workspace | Deletes the zero-error free workspace | [
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'''
Deletes the zero-error free workspace
@param ws :: The input workspace
'''
complete = False
message = ""
if input_workspace_name in mtd:
DeleteWorkspace(Workspace=input_workspace_name)
complete = True
else:
message = 'Failed to delete a zero-error free workspace'
return message, complete | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/excel/_odfreader.py | python | ODFReader._get_row_repeat | (self, row) | return int(row.attributes.get((TABLENS, "number-rows-repeated"), 1)) | Return number of times this row was repeated
Repeating an empty row appeared to be a common way
of representing sparse rows in the table. | Return number of times this row was repeated
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"""
Return number of times this row was repeated
Repeating an empty row appeared to be a common way
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"""
from odf.namespaces import TABLENS
return int(row.attributes.get((TABLENS, "number-rows-repeated"), 1)) | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/distro.py | python | LinuxDistribution._parse_distro_release_content | (line) | return distro_info | Parse a line from a distro release file.
Parameters:
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Returns:
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"""
Parse a line from a distro release file.
Parameters:
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or a UTF-8 encoded byte string.
Returns:
A dictionary containing all information items.
"""
if isinstance(line, bytes):
line = line.decode('utf-8')
matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match(
line.strip()[::-1])
distro_info = {}
if matches:
# regexp ensures non-None
distro_info['name'] = matches.group(3)[::-1]
if matches.group(2):
distro_info['version_id'] = matches.group(2)[::-1]
if matches.group(1):
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return distro_info | [
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tensorflow/ngraph-bridge | ea6422491ec75504e78a63db029e7f74ec3479a5 | examples/retrain_ngraph.py | python | export_model | (module_spec, class_count, saved_model_dir) | Exports model for serving.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: The number of classes.
saved_model_dir: Directory in which to save exported model and variables. | Exports model for serving. | [
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"""Exports model for serving.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: The number of classes.
saved_model_dir: Directory in which to save exported model and variables.
"""
# The SavedModel should hold the eval graph.
sess, in_image, _, _, _, _ = build_eval_session(module_spec, class_count)
with sess.graph.as_default() as graph:
tf.saved_model.simple_save(
sess,
saved_model_dir,
inputs={'image': in_image},
outputs={'prediction': graph.get_tensor_by_name('final_result:0')},
legacy_init_op=tf.group(
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osrf/gazebo | f570338107862253229a0514ffea10deab4f4517 | tools/cpplint.py | python | GetHeaderGuardCPPVariable | (filename) | return re.sub(r'[-./\s]', '_', fileinfo.RepositoryName()).upper() + '_' | Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
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"""Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
The CPP variable that should be used as a header guard in the
named file.
"""
# Restores original filename in case that cpplint is invoked from Emacs's
# flymake.
filename = re.sub(r'_flymake\.h$', '.h', filename)
fileinfo = FileInfo(filename)
return re.sub(r'[-./\s]', '_', fileinfo.RepositoryName()).upper() + '_' | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/pyami/installers/ubuntu/installer.py | python | Installer.install | (self) | This is the only method you need to override | This is the only method you need to override | [
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deepmodeling/deepmd-kit | 159e45d248b0429844fb6a8cb3b3a201987c8d79 | deepmd/descriptor/descriptor.py | python | Descriptor.get_feed_dict | (self,
coord_: tf.Tensor,
atype_: tf.Tensor,
natoms: tf.Tensor,
box: tf.Tensor,
mesh: tf.Tensor
) | return feed_dict | Generate the feed_dict for current descriptor
Parameters
----------
coord_ : tf.Tensor
The coordinate of atoms
atype_ : tf.Tensor
The type of atoms
natoms : tf.Tensor
The number of atoms. This tensor has the length of Ntypes + 2
natoms[0]: number of local atoms
natoms[1]: total number of atoms held by this processor
natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
box : tf.Tensor
The box. Can be generated by deepmd.model.make_stat_input
mesh : tf.Tensor
For historical reasons, only the length of the Tensor matters.
if size of mesh == 6, pbc is assumed.
if size of mesh == 0, no-pbc is assumed.
Returns
-------
feed_dict : dict[str, tf.Tensor]
The output feed_dict of current descriptor | Generate the feed_dict for current descriptor | [
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] | def get_feed_dict(self,
coord_: tf.Tensor,
atype_: tf.Tensor,
natoms: tf.Tensor,
box: tf.Tensor,
mesh: tf.Tensor
) -> Dict[str, tf.Tensor]:
"""
Generate the feed_dict for current descriptor
Parameters
----------
coord_ : tf.Tensor
The coordinate of atoms
atype_ : tf.Tensor
The type of atoms
natoms : tf.Tensor
The number of atoms. This tensor has the length of Ntypes + 2
natoms[0]: number of local atoms
natoms[1]: total number of atoms held by this processor
natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
box : tf.Tensor
The box. Can be generated by deepmd.model.make_stat_input
mesh : tf.Tensor
For historical reasons, only the length of the Tensor matters.
if size of mesh == 6, pbc is assumed.
if size of mesh == 0, no-pbc is assumed.
Returns
-------
feed_dict : dict[str, tf.Tensor]
The output feed_dict of current descriptor
"""
feed_dict = {
't_coord:0' :coord_,
't_type:0' :atype_,
't_natoms:0' :natoms,
't_box:0' :box,
't_mesh:0' :mesh
}
return feed_dict | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/propgrid.py | python | PropertyGridInterface.SetPropertyValues | (self,dict_) | Sets property values from dict_, which can be either\ndictionary | Sets property values from dict_, which can be either\ndictionary | [
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""
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" are auto-created. For more info, see AutoFill."
""
"Notes:"
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" * Attributes can be set with entries named @<propname>@<attr>."
""
autofill = False
if dict_ is None:
dict_ = {}
elif hasattr(dict_,'__dict__'):
dict_ = dict_.__dict__
attr_dicts = []
def set_sub_obj(k0,dict_):
for k,v in dict_.iteritems():
if k[0] != '_':
if k.endswith('@attr'):
attr_dicts.append((k[1:-5],v))
else:
try:
self.SetPropertyValue(k,v)
except:
try:
if autofill:
self._AutoFillOne(k0,k,v)
continue
except:
if isinstance(v,dict):
set_sub_obj(k,v)
elif hasattr(v,'__dict__'):
set_sub_obj(k,v.__dict__)
for k,v in attr_dicts:
p = GetPropertyByName(k)
if not p:
raise AssertionError("No such property: '%s'"%k)
for an,av in v.iteritems():
p.SetAttribute(an, av)
cur_page = False
is_manager = isinstance(self,PropertyGridManager)
try:
set_sub_obj(self.GetGrid().GetRoot(),dict_)
except:
import traceback
traceback.print_exc()
self.Refresh() | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/rospy/src/rospy/topics.py | python | _TopicImpl.close | (self) | close I/O | close I/O | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/lonely-pixel-i.py | python | Solution2.findLonelyPixel | (self, picture) | return sum(col.count('B') == 1 == picture[col.index('B')].count('B') \
for col in zip(*picture)) | :type picture: List[List[str]]
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:rtype: int | :type picture: List[List[str]]
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:type picture: List[List[str]]
:type N: int
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"""
return sum(col.count('B') == 1 == picture[col.index('B')].count('B') \
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/run.py | python | MyHandler.exithook | (self) | override SocketIO method - wait for MainThread to shut us down | override SocketIO method - wait for MainThread to shut us down | [
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NicknineTheEagle/TF2-Base | 20459c5a7fbc995b6bf54fa85c2f62a101e9fb64 | src/thirdparty/protobuf-2.3.0/python/google/protobuf/reflection.py | python | _AddInitMethod | (message_descriptor, cls) | Adds an __init__ method to cls. | Adds an __init__ method to cls. | [
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] | def _AddInitMethod(message_descriptor, cls):
"""Adds an __init__ method to cls."""
fields = message_descriptor.fields
def init(self, **kwargs):
self._cached_byte_size = 0
self._cached_byte_size_dirty = False
self._fields = {}
self._is_present_in_parent = False
self._listener = message_listener_mod.NullMessageListener()
self._listener_for_children = _Listener(self)
for field_name, field_value in kwargs.iteritems():
field = _GetFieldByName(message_descriptor, field_name)
if field is None:
raise TypeError("%s() got an unexpected keyword argument '%s'" %
(message_descriptor.name, field_name))
if field.label == _FieldDescriptor.LABEL_REPEATED:
copy = field._default_constructor(self)
if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: # Composite
for val in field_value:
copy.add().MergeFrom(val)
else: # Scalar
copy.extend(field_value)
self._fields[field] = copy
elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE:
copy = field._default_constructor(self)
copy.MergeFrom(field_value)
self._fields[field] = copy
else:
self._fields[field] = field_value
init.__module__ = None
init.__doc__ = None
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may0324/DeepCompression-caffe | 0aff6c1287bda4cfc7f378ed8a16524e1afabd8c | scripts/cpp_lint.py | python | FindEndOfExpressionInLine | (line, startpos, depth, startchar, endchar) | return (-1, depth) | Find the position just after the matching endchar.
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line: a CleansedLines line.
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Args:
line: a CleansedLines line.
startpos: start searching at this position.
depth: nesting level at startpos.
startchar: expression opening character.
endchar: expression closing character.
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"""
for i in xrange(startpos, len(line)):
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depth += 1
elif line[i] == endchar:
depth -= 1
if depth == 0:
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return (-1, depth) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/python_gflags/gflags.py | python | NumericParser.Convert | (self, argument) | return argument | Default implementation: always returns its argument unmodified. | Default implementation: always returns its argument unmodified. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/importlib-metadata/py2/importlib_metadata/__init__.py | python | Distribution.from_name | (cls, name) | Return the Distribution for the given package name.
:param name: The name of the distribution package to search for.
:return: The Distribution instance (or subclass thereof) for the named
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:return: The Distribution instance (or subclass thereof) for the named
package, if found.
:raises PackageNotFoundError: When the named package's distribution
metadata cannot be found.
"""
for resolver in cls._discover_resolvers():
dists = resolver(DistributionFinder.Context(name=name))
dist = next(iter(dists), None)
if dist is not None:
return dist
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/summary/impl/reservoir.py | python | Reservoir.__init__ | (self, size, seed=0) | Creates a new reservoir.
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size: The number of values to keep in the reservoir for each tag. If 0,
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seed: The seed of the random number generator to use when sampling.
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if size < 0 or size != round(size):
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self._buckets = collections.defaultdict(
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# _mutex guards the keys - creating new keys, retreiving by key, etc
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self._mutex = threading.Lock() | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/contrib/imports/mirobridge/mirobridge/mirobridge_interpreter_2_0_3.py | python | MiroInterpreter.do_mythtv_getunwatched | (self, line) | Process MythTV get all un-watched video details | Process MythTV get all un-watched video details | [
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print u"%-20s %-10s %s" % (u"State", u"Size", u"Name")
print u"-" * 70
for item in views.watchableItems:
# Skip any audio file as MythTV Internal player may abort the MythTV Frontend on a MP3
if not item.isVideo:
continue
state = item.get_state()
if not state == u'newly-downloaded':
continue
# Skip any bittorrent video downloads for legal concerns
if filetypes.is_torrent_filename(item.getURL()):
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self.printItems(item)
self.videofiles.append(self._get_item_dict(item))
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/robotsim.py | python | Geometry3D.getCollisionMargin | (self) | return _robotsim.Geometry3D_getCollisionMargin(self) | r"""
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/normal.py | python | Normal.std | (self, name="std") | Standard deviation of this distribution. | Standard deviation of this distribution. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/aui.py | python | AuiGenericTabArt.__init__ | (self, *args, **kwargs) | __init__(self) -> AuiGenericTabArt | __init__(self) -> AuiGenericTabArt | [
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NVIDIA/thrust | 627dccb359a635afdd69e95a6cc59698f23f70e2 | internal/benchmark/compare_benchmark_results.py | python | store_false_multiple | (*destinations) | return store_const_multiple(False, *destinations) | Returns an `argument_action` class that sets multiple argument
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | ToolBarToolBase.GetShortHelp | (*args, **kwargs) | return _controls_.ToolBarToolBase_GetShortHelp(*args, **kwargs) | GetShortHelp(self) -> String | GetShortHelp(self) -> String | [
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casadi/casadi | 8d0f80a4d0fe2054384bfb9748f7a0f6bae540ff | docs/api/extra/doxy2swigX.py | python | Doxy2SWIG_X.generic_parse | (self, node, pad=0) | A Generic parser for arbitrary tags in a node.
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If 0 the node data is not padded with newlines. If 1 it
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | Font.GetDefaultEncoding | (*args, **kwargs) | return _gdi_.Font_GetDefaultEncoding(*args, **kwargs) | GetDefaultEncoding() -> int
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | lldb/third_party/Python/module/pexpect-4.6/pexpect/screen.py | python | screen.cursor_force_position | (self, r, c) | Identical to Cursor Home. | Identical to Cursor Home. | [
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/extras/dcc.py | python | options | (opt) | Add the ``--with-diab-bindir`` command-line options. | Add the ``--with-diab-bindir`` command-line options. | [
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bristolcrypto/SPDZ-2 | 721abfae849625a02ea49aabc534f9cf41ca643f | Compiler/path_oram.py | python | PathORAM.get_children | (self, i, l) | return self.buckets[2*j+1], self.buckets[2*j+2] | Get children of the i-th bucket on level l | Get children of the i-th bucket on level l | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/tweet-counts-per-frequency.py | python | TweetCounts.recordTweet | (self, tweetName, time) | :type tweetName: str
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nsnam/ns-3-dev-git | efdb2e21f45c0a87a60b47c547b68fa140a7b686 | bindings/python/rad_util.py | python | gcf | (a, b, epsilon=1e-16) | return abs(result) | Return the greatest common factor of a and b, using Euclidean algorithm.
Arguments:
a, b -- two numbers
If both numbers are integers return an integer result,
otherwise return a float result.
epsilon -- floats less than this magnitude are considered to be zero
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2
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0.5
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2
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5
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0.5
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0
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ZhouWeikuan/DouDiZhu | 0d84ff6c0bc54dba6ae37955de9ae9307513dc99 | code/frameworks/cocos2d-x/tools/bindings-generator/backup/clang-llvm-3.3-pybinding/cindex.py | python | File.name | (self) | return conf.lib.clang_getCString(conf.lib.clang_getFileName(self)) | Return the complete file and path name of the file. | Return the complete file and path name of the file. | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | grc/gui/StateCache.py | python | StateCache.save_new_state | (self, state) | Save a new state.
Place the new state at the next index and add one to the number of previous states.
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Save a new state.
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PlatformLab/NanoLog | 2a94d70f9d1db4da416053b1b926387fa068a59b | preprocessor/docopt.py | python | parse_expr | (tokens, options) | return [Either(*result)] if len(result) > 1 else result | expr ::= seq ( '|' seq )* ; | expr ::= seq ( '|' seq )* ; | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/utils/_mask.py | python | _get_mask | (X, value_to_mask) | Compute the boolean mask X == missing_values. | Compute the boolean mask X == missing_values. | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/cygprofile/symbolize.py | python | FindFunctions | (addr, unique_addrs, address_map) | return address_map[BinarySearchAddresses(addr, 0, len(unique_addrs) - 1,
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/image/image.py | python | ImageIter.check_valid_image | (self, data) | Checks if the input data is valid | Checks if the input data is valid | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/debug/cli/debugger_cli_common.py | python | CommandHandlerRegistry.get_help | (self, cmd_prefix=None) | Compile help information into a RichTextLines object.
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Args:
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Returns:
A RichTextLines object containing the help information. If cmd_prefix
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if not cmd_prefix:
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help_info = RichTextLines([])
if self._help_intro:
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help_info.extend(self._help_intro)
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for cmd_prefix in sorted_prefixes:
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lines.append("")
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lmb-freiburg/ogn | 974f72ef4bf840d6f6693d22d1843a79223e77ce | scripts/cpp_lint.py | python | PrintCategories | () | Prints a list of all the error-categories used by error messages.
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py | python | DNNLinearCombinedEstimator.__init__ | (self, # _joint_linear_weights pylint: disable=invalid-name
head,
model_dir=None,
linear_feature_columns=None,
linear_optimizer=None,
_joint_linear_weights=False,
dnn_feature_columns=None,
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dnn_dropout=None,
gradient_clip_norm=None,
config=None,
feature_engineering_fn=None,
embedding_lr_multipliers=None,
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input_layer_partitioner=None) | Initializes a DNNLinearCombinedEstimator instance.
Note: New users must set `fix_global_step_increment_bug=True` when creating
an estimator.
Args:
head: A _Head object.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
linear_feature_columns: An iterable containing all the feature columns
used by linear part of the model. All items in the set should be
instances of classes derived from `FeatureColumn`.
linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the linear part of the model. If `None`, will use a FTRL optimizer.
_joint_linear_weights: If True will use a single (possibly partitioned)
variable to store all weights for the linear model. More efficient if
there are many columns, however requires all columns are sparse and
have the 'sum' combiner.
dnn_feature_columns: An iterable containing all the feature columns used
by deep part of the model. All items in the set should be instances of
classes derived from `FeatureColumn`.
dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the deep part of the model. If `None`, will use an Adagrad optimizer.
dnn_hidden_units: List of hidden units per layer. All layers are fully
connected.
dnn_activation_fn: Activation function applied to each layer. If `None`,
will use `tf.nn.relu`.
dnn_dropout: When not None, the probability we will drop out
a given coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
tf.clip_by_global_norm for more details.
config: RunConfig object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
labels which are the output of `input_fn` and returns features and
labels which will be fed into the model.
embedding_lr_multipliers: Optional. A dictionary from `EmbeddingColumn` to
a `float` multiplier. Multiplier will be used to multiply with
learning rate for the embedding variables.
fix_global_step_increment_bug: If `False`, the estimator needs two fit
steps to optimize both linear and dnn parts. If `True`, this bug is
fixed. New users must set this to `True`, but the default value is
`False` for backwards compatibility.
input_layer_partitioner: Optional. Partitioner for input layer.
Raises:
ValueError: If both linear_feature_columns and dnn_features_columns are
empty at the same time. | Initializes a DNNLinearCombinedEstimator instance. | [
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"DNNLinearCombinedEstimator",
"instance",
"."
] | def __init__(self, # _joint_linear_weights pylint: disable=invalid-name
head,
model_dir=None,
linear_feature_columns=None,
linear_optimizer=None,
_joint_linear_weights=False,
dnn_feature_columns=None,
dnn_optimizer=None,
dnn_hidden_units=None,
dnn_activation_fn=None,
dnn_dropout=None,
gradient_clip_norm=None,
config=None,
feature_engineering_fn=None,
embedding_lr_multipliers=None,
fix_global_step_increment_bug=False,
input_layer_partitioner=None):
"""Initializes a DNNLinearCombinedEstimator instance.
Note: New users must set `fix_global_step_increment_bug=True` when creating
an estimator.
Args:
head: A _Head object.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
linear_feature_columns: An iterable containing all the feature columns
used by linear part of the model. All items in the set should be
instances of classes derived from `FeatureColumn`.
linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the linear part of the model. If `None`, will use a FTRL optimizer.
_joint_linear_weights: If True will use a single (possibly partitioned)
variable to store all weights for the linear model. More efficient if
there are many columns, however requires all columns are sparse and
have the 'sum' combiner.
dnn_feature_columns: An iterable containing all the feature columns used
by deep part of the model. All items in the set should be instances of
classes derived from `FeatureColumn`.
dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the deep part of the model. If `None`, will use an Adagrad optimizer.
dnn_hidden_units: List of hidden units per layer. All layers are fully
connected.
dnn_activation_fn: Activation function applied to each layer. If `None`,
will use `tf.nn.relu`.
dnn_dropout: When not None, the probability we will drop out
a given coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
tf.clip_by_global_norm for more details.
config: RunConfig object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
labels which are the output of `input_fn` and returns features and
labels which will be fed into the model.
embedding_lr_multipliers: Optional. A dictionary from `EmbeddingColumn` to
a `float` multiplier. Multiplier will be used to multiply with
learning rate for the embedding variables.
fix_global_step_increment_bug: If `False`, the estimator needs two fit
steps to optimize both linear and dnn parts. If `True`, this bug is
fixed. New users must set this to `True`, but the default value is
`False` for backwards compatibility.
input_layer_partitioner: Optional. Partitioner for input layer.
Raises:
ValueError: If both linear_feature_columns and dnn_features_columns are
empty at the same time.
"""
linear_feature_columns = tuple(linear_feature_columns or [])
dnn_feature_columns = tuple(dnn_feature_columns or [])
if not linear_feature_columns + dnn_feature_columns:
raise ValueError("Either linear_feature_columns or dnn_feature_columns "
"must be defined.")
super(DNNLinearCombinedEstimator, self).__init__(
model_fn=_dnn_linear_combined_model_fn,
model_dir=model_dir,
config=config,
params={
"head": head,
"linear_feature_columns": linear_feature_columns,
"linear_optimizer": linear_optimizer,
"joint_linear_weights": _joint_linear_weights,
"dnn_feature_columns": dnn_feature_columns,
"dnn_optimizer": dnn_optimizer,
"dnn_hidden_units": dnn_hidden_units,
"dnn_activation_fn": dnn_activation_fn,
"dnn_dropout": dnn_dropout,
"gradient_clip_norm": gradient_clip_norm,
"embedding_lr_multipliers": embedding_lr_multipliers,
"fix_global_step_increment_bug": fix_global_step_increment_bug,
"input_layer_partitioner": input_layer_partitioner
},
feature_engineering_fn=feature_engineering_fn) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/ao/quantization/fx/_equalize.py | python | scale_input_observer | (node: Node, modules: Dict[str, nn.Module]) | Scales the following input quantization observer's min/max values by
updating the values with the scaled min/max values calculated by the input
equalization observer | Scales the following input quantization observer's min/max values by
updating the values with the scaled min/max values calculated by the input
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""" Scales the following input quantization observer's min/max values by
updating the values with the scaled min/max values calculated by the input
equalization observer
"""
input_eq_obs = modules[str(node.target)]
assert(isinstance(input_eq_obs, _InputEqualizationObserver))
input_quant_obs_node = node.args[0]
assert(isinstance(input_quant_obs_node, Node))
input_quant_obs = modules[str(input_quant_obs_node.target)]
if not isinstance(input_quant_obs, ObserverBase):
return
min_input_scaled, max_input_scaled = input_eq_obs.calculate_scaled_minmax()
if min_input_scaled is None and max_input_scaled is None:
return
input_quant_obs.min_val = min_input_scaled
input_quant_obs.max_val = max_input_scaled | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/protobuf/python/google/protobuf/internal/cpp_message.py | python | NewCMessage | (full_message_name) | return _net_proto2___python.NewCMessage(full_message_name) | Creates a new C++ protocol message by its name. | Creates a new C++ protocol message by its name. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/requests/sessions.py | python | Session.get_adapter | (self, url) | Returns the appropriate connection adapter for the given URL.
:rtype: requests.adapters.BaseAdapter | Returns the appropriate connection adapter for the given URL. | [
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"""
Returns the appropriate connection adapter for the given URL.
:rtype: requests.adapters.BaseAdapter
"""
for (prefix, adapter) in self.adapters.items():
if url.lower().startswith(prefix.lower()):
return adapter
# Nothing matches :-/
raise InvalidSchema("No connection adapters were found for {!r}".format(url)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | NotifyEvent.Allow | (*args, **kwargs) | return _core_.NotifyEvent_Allow(*args, **kwargs) | Allow(self)
This is the opposite of `Veto`: it explicitly allows the event to be
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the events are allowed anyhow but some are forbidden by default (this
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"""
Allow(self)
This is the opposite of `Veto`: it explicitly allows the event to be
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the events are allowed anyhow but some are forbidden by default (this
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return _core_.NotifyEvent_Allow(*args, **kwargs) | [
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | Statistics.get_key_value | (self, key) | Return the value of a particular statistical counter.
>>> x = Int('x')
>>> s = Then('simplify', 'nlsat').solver()
>>> s.add(x > 0)
>>> s.check()
sat
>>> st = s.statistics()
>>> st.get_key_value('nlsat propagations')
2 | Return the value of a particular statistical counter. | [
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"""Return the value of a particular statistical counter.
>>> x = Int('x')
>>> s = Then('simplify', 'nlsat').solver()
>>> s.add(x > 0)
>>> s.check()
sat
>>> st = s.statistics()
>>> st.get_key_value('nlsat propagations')
2
"""
for idx in range(len(self)):
if key == Z3_stats_get_key(self.ctx.ref(), self.stats, idx):
if Z3_stats_is_uint(self.ctx.ref(), self.stats, idx):
return int(Z3_stats_get_uint_value(self.ctx.ref(), self.stats, idx))
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return Z3_stats_get_double_value(self.ctx.ref(), self.stats, idx)
raise Z3Exception("unknown key") | [
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facebookresearch/ELF | 1f790173095cd910976d9f651b80beb872ec5d12 | rlpytorch/model_loader.py | python | load_env | (envs, num_models=None, overrides=dict(), defaults=dict(), **kwargs) | return env, all_args | Load envs. envs will be specified as environment variables, more specifically, ``game``, ``model_file`` and ``model`` are required.
Returns:
env: dict of
``game`` : game module
``method``: Learning method used
``model_loaders``: loaders for model
all_args: loaded arguments from `ArgsPrivider` | Load envs. envs will be specified as environment variables, more specifically, ``game``, ``model_file`` and ``model`` are required. | [
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''' Load envs. envs will be specified as environment variables, more specifically, ``game``, ``model_file`` and ``model`` are required.
Returns:
env: dict of
``game`` : game module
``method``: Learning method used
``model_loaders``: loaders for model
all_args: loaded arguments from `ArgsPrivider`
'''
game = load_module(envs["game"]).Loader()
model_file = load_module(envs["model_file"])
# TODO This is not good, need to fix.
if len(model_file.Models[envs["model"]]) == 2:
model_class, method_class = model_file.Models[envs["model"]]
sampler_class = Sampler
else:
model_class, method_class, sampler_class = model_file.Models[envs["model"]]
defaults.update(getattr(model_file, "Defaults", dict()))
overrides.update(getattr(model_file, "Overrides", dict()))
method = method_class()
sampler = sampler_class()
mi = ModelInterface()
# You might want multiple models loaded.
if num_models is None:
model_loaders = [ ModelLoader(model_class) ]
else:
model_loaders = [ ModelLoader(model_class, model_idx=i) for i in range(num_models) ]
env = dict(game=game, method=method, sampler=sampler, model_loaders=model_loaders, mi=mi)
env.update(kwargs)
parser = argparse.ArgumentParser()
all_args = ArgsProvider.Load(parser, env, global_defaults=defaults, global_overrides=overrides)
return env, all_args | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/ao/ns/fx/graph_matcher.py | python | _get_name_for_subgraph | (
subgraph_a: NSSubgraph,
gm_a: GraphModule,
base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]],
existing_names: Set[str],
) | return proposed_name | Returns a unique name for a subgraph. This name is based on two things:
1. the name of the set containing the underlying type of the base op in the
subgraph (i.e. 'torch.nn.functional.linear' if this is related to a linear op)
2. the number of previous subgraphs with related underlying type of the base op
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The subgraphs are (linear0, relu0) and (linear1, relu1). If we iterate
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Why are we not just using the node name? Answer: because of two requirements:
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(1) and (2) knowing anything about each other. | Returns a unique name for a subgraph. This name is based on two things:
1. the name of the set containing the underlying type of the base op in the
subgraph (i.e. 'torch.nn.functional.linear' if this is related to a linear op)
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"""
target_type = _get_node_target_type(subgraph_a.base_op_node, gm_a)
target_base_type = None
for base_name, sets_of_related_ops in base_name_to_sets_of_related_ops.items():
if target_type in sets_of_related_ops:
target_base_type = base_name
target_base_name = 'base_op_' + str(target_base_type)
counter = 0
proposed_name = target_base_name + '_' + str(counter)
while proposed_name in existing_names:
counter += 1
proposed_name = target_base_name + '_' + str(counter)
existing_names.add(proposed_name)
return proposed_name | [
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tensorflow/serving | 3b29e18ab57c68604f599d0b3e1f8df417d22427 | tensorflow_serving/apis/prediction_service_pb2_grpc.py | python | PredictionServiceServicer.Classify | (self, request, context) | Classify. | Classify. | [
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | UpdateUIEvent.GetShown | (*args, **kwargs) | return _core_.UpdateUIEvent_GetShown(*args, **kwargs) | GetShown(self) -> bool
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return _core_.UpdateUIEvent_GetShown(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_core.py#L6765-L6771 | |
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/utils/checkpoint.py | python | checkpoint_sequential | (functions, segments, input, **kwargs) | return run_function(end + 1, len(functions) - 1, functions)(input) | r"""A helper function for checkpointing sequential models.
Sequential models execute a list of modules/functions in order
(sequentially). Therefore, we can divide such a model in various segments
and checkpoint each segment. All segments except the last will run in
:func:`torch.no_grad` manner, i.e., not storing the intermediate
activations. The inputs of each checkpointed segment will be saved for
re-running the segment in the backward pass.
See :func:`~torch.utils.checkpoint.checkpoint` on how checkpointing works.
.. warning::
Checkpointing currently only supports :func:`torch.autograd.backward`
and only if its `inputs` argument is not passed. :func:`torch.autograd.grad`
is not supported.
.. warning:
At least one of the inputs needs to have :code:`requires_grad=True` if
grads are needed for model inputs, otherwise the checkpointed part of the
model won't have gradients.
.. warning:
Since PyTorch 1.4, it allows only one Tensor as the input and
intermediate outputs, just like :class:`torch.nn.Sequential`.
Args:
functions: A :class:`torch.nn.Sequential` or the list of modules or
functions (comprising the model) to run sequentially.
segments: Number of chunks to create in the model
input: A Tensor that is input to :attr:`functions`
preserve_rng_state(bool, optional, default=True): Omit stashing and restoring
the RNG state during each checkpoint.
Returns:
Output of running :attr:`functions` sequentially on :attr:`*inputs`
Example:
>>> model = nn.Sequential(...)
>>> input_var = checkpoint_sequential(model, chunks, input_var) | r"""A helper function for checkpointing sequential models. | [
"r",
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] | def checkpoint_sequential(functions, segments, input, **kwargs):
r"""A helper function for checkpointing sequential models.
Sequential models execute a list of modules/functions in order
(sequentially). Therefore, we can divide such a model in various segments
and checkpoint each segment. All segments except the last will run in
:func:`torch.no_grad` manner, i.e., not storing the intermediate
activations. The inputs of each checkpointed segment will be saved for
re-running the segment in the backward pass.
See :func:`~torch.utils.checkpoint.checkpoint` on how checkpointing works.
.. warning::
Checkpointing currently only supports :func:`torch.autograd.backward`
and only if its `inputs` argument is not passed. :func:`torch.autograd.grad`
is not supported.
.. warning:
At least one of the inputs needs to have :code:`requires_grad=True` if
grads are needed for model inputs, otherwise the checkpointed part of the
model won't have gradients.
.. warning:
Since PyTorch 1.4, it allows only one Tensor as the input and
intermediate outputs, just like :class:`torch.nn.Sequential`.
Args:
functions: A :class:`torch.nn.Sequential` or the list of modules or
functions (comprising the model) to run sequentially.
segments: Number of chunks to create in the model
input: A Tensor that is input to :attr:`functions`
preserve_rng_state(bool, optional, default=True): Omit stashing and restoring
the RNG state during each checkpoint.
Returns:
Output of running :attr:`functions` sequentially on :attr:`*inputs`
Example:
>>> model = nn.Sequential(...)
>>> input_var = checkpoint_sequential(model, chunks, input_var)
"""
# Hack for keyword-only parameter in a python 2.7-compliant way
preserve = kwargs.pop('preserve_rng_state', True)
if kwargs:
raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs))
def run_function(start, end, functions):
def forward(input):
for j in range(start, end + 1):
input = functions[j](input)
return input
return forward
if isinstance(functions, torch.nn.Sequential):
functions = list(functions.children())
segment_size = len(functions) // segments
# the last chunk has to be non-volatile
end = -1
for start in range(0, segment_size * (segments - 1), segment_size):
end = start + segment_size - 1
input = checkpoint(run_function(start, end, functions), input,
preserve_rng_state=preserve)
return run_function(end + 1, len(functions) - 1, functions)(input) | [
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Node/FS.py | python | Dir.File | (self, name) | return self.fs.File(name, self) | Looks up or creates a file node named 'name' relative to
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | samples/pubsub/advanced/kwargs_topics.py | python | topic_2.msgDataSpec | (msg=None) | - msg: a text string | - msg: a text string | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib-tk/ttk.py | python | _format_mapdict | (mapdict, script=False) | return _flatten(opts) | Formats mapdict to pass it to tk.call.
E.g. (script=False):
{'expand': [('active', 'selected', 'grey'), ('focus', [1, 2, 3, 4])]}
returns:
('-expand', '{active selected} grey focus {1, 2, 3, 4}') | Formats mapdict to pass it to tk.call. | [
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] | def _format_mapdict(mapdict, script=False):
"""Formats mapdict to pass it to tk.call.
E.g. (script=False):
{'expand': [('active', 'selected', 'grey'), ('focus', [1, 2, 3, 4])]}
returns:
('-expand', '{active selected} grey focus {1, 2, 3, 4}')"""
opts = []
for opt, value in mapdict.iteritems():
opts.extend(("-%s" % opt,
_format_optvalue(_mapdict_values(value), script)))
return _flatten(opts) | [
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greearb/xorp.ct | b8942f69e9cb69a35adcaa58f3dd6b9a16629e6a | xorp/libxorp/callback-gen.py | python | starting_csv | (l, comma = ", ") | return s | Starts a comma separated list. Last element is follow by
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l. | Starts a comma separated list. Last element is follow by
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"""
Starts a comma separated list. Last element is follow by
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l.
"""
s = ''
n = len(l)
for i in range(0,n):
s += "%s%s" % (l[i], comma)
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/config/find_OOM_errors.py | python | count_lines | () | Keep track of the amount of times individual lines occur, in order to
prioritize the errors which occur most frequently. | Keep track of the amount of times individual lines occur, in order to
prioritize the errors which occur most frequently. | [
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] | def count_lines():
"""Keep track of the amount of times individual lines occur, in order to
prioritize the errors which occur most frequently."""
counts = {}
for string,count in blacklist.items():
for line in string.split("\n"):
counts[line] = counts.get(line, 0) + count
lines = []
for k,v in counts.items():
lines.append("{0:6}: {1}".format(v, k))
lines.sort()
countlog = file("../OOM_count_log", "w")
countlog.write("\n".join(lines))
countlog.flush()
countlog.close() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/attrs/attr/_make.py | python | _get_annotations | (cls) | return {} | Get annotations for *cls*. | Get annotations for *cls*. | [
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] | def _get_annotations(cls):
"""
Get annotations for *cls*.
"""
if _has_own_attribute(cls, "__annotations__"):
return cls.__annotations__
return {} | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | amalgamation/python/mxnet_predict.py | python | Predictor.reshape | (self, input_shapes) | Change the input shape of the predictor.
Parameters
----------
input_shapes : dict of str to tuple
The new shape of input data.
Examples
--------
>>> predictor.reshape({'data':data_shape_tuple}) | Change the input shape of the predictor. | [
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] | def reshape(self, input_shapes):
"""Change the input shape of the predictor.
Parameters
----------
input_shapes : dict of str to tuple
The new shape of input data.
Examples
--------
>>> predictor.reshape({'data':data_shape_tuple})
"""
indptr = [0]
sdata = []
keys = []
for k, v in input_shapes.items():
if not isinstance(v, tuple):
raise ValueError("Expect input_shapes to be dict str->tuple")
keys.append(c_str(k))
sdata.extend(v)
indptr.append(len(sdata))
new_handle = PredictorHandle()
_check_call(_LIB.MXPredReshape(
mx_uint(len(indptr) - 1),
c_array(ctypes.c_char_p, keys),
c_array(mx_uint, indptr),
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self.handle = new_handle | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8/tools/release/common_includes.py | python | MakeChangeLogBugReference | (body) | Grep for "BUG=xxxx" lines in the commit message and convert them to
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"""Grep for "BUG=xxxx" lines in the commit message and convert them to
"(issue xxxx)".
"""
crbugs = []
v8bugs = []
def AddIssues(text):
ref = re.match(r"^BUG[ \t]*=[ \t]*(.+)$", text.strip())
if not ref:
return
for bug in ref.group(1).split(","):
bug = bug.strip()
match = re.match(r"^v8:(\d+)$", bug)
if match: v8bugs.append(int(match.group(1)))
else:
match = re.match(r"^(?:chromium:)?(\d+)$", bug)
if match: crbugs.append(int(match.group(1)))
# Add issues to crbugs and v8bugs.
map(AddIssues, body.splitlines())
# Filter duplicates, sort, stringify.
crbugs = map(str, sorted(set(crbugs)))
v8bugs = map(str, sorted(set(v8bugs)))
bug_groups = []
def FormatIssues(prefix, bugs):
if len(bugs) > 0:
plural = "s" if len(bugs) > 1 else ""
bug_groups.append("%sissue%s %s" % (prefix, plural, ", ".join(bugs)))
FormatIssues("", v8bugs)
FormatIssues("Chromium ", crbugs)
if len(bug_groups) > 0:
return "(%s)" % ", ".join(bug_groups)
else:
return "" | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/plistlib.py | python | readPlist | (pathOrFile) | return rootObject | Read a .plist file. 'pathOrFile' may either be a file name or a
(readable) file object. Return the unpacked root object (which
usually is a dictionary). | Read a .plist file. 'pathOrFile' may either be a file name or a
(readable) file object. Return the unpacked root object (which
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"""Read a .plist file. 'pathOrFile' may either be a file name or a
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usually is a dictionary).
"""
didOpen = 0
if isinstance(pathOrFile, (str, unicode)):
pathOrFile = open(pathOrFile)
didOpen = 1
p = PlistParser()
rootObject = p.parse(pathOrFile)
if didOpen:
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return rootObject | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/mailbox.py | python | BabylMessage._explain_to | (self, message) | Copy Babyl-specific state to message insofar as possible. | Copy Babyl-specific state to message insofar as possible. | [
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"""Copy Babyl-specific state to message insofar as possible."""
if isinstance(message, MaildirMessage):
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if 'unseen' in labels:
message.set_subdir('cur')
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message.add_sequence('unseen')
if 'answered' in labels:
message.add_sequence('replied')
elif isinstance(message, BabylMessage):
message.set_visible(self.get_visible())
for label in self.get_labels():
message.add_label(label)
elif isinstance(message, Message):
pass
else:
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googlearchive/tango-examples-c | b57d8c173664de569a7fec703091ff82684a2db5 | third_party/libfreetype/src/tools/docmaker/tohtml.py | python | HtmlFormatter.make_html_items | ( self, items ) | return string.join( lines, '\n' ) | convert a field's content into some valid HTML | convert a field's content into some valid HTML | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/graph_editor/edit.py | python | bypass | (sgv) | return sgv, detached_inputs | Bypass the given subgraph by connecting its inputs to its outputs.
Args:
sgv: the subgraph view to be bypassed. This argument is converted to a
subgraph using the same rules than the function subgraph.make_view.
Returns:
A new subgraph view of the bypassed subgraph.
Note that sgv is also modified in place.
Raises:
StandardError: if sgv cannot be converted to a SubGraphView using
the same rules than the function subgraph.make_view. | Bypass the given subgraph by connecting its inputs to its outputs. | [
"Bypass",
"the",
"given",
"subgraph",
"by",
"connecting",
"its",
"inputs",
"to",
"its",
"outputs",
"."
] | def bypass(sgv):
"""Bypass the given subgraph by connecting its inputs to its outputs.
Args:
sgv: the subgraph view to be bypassed. This argument is converted to a
subgraph using the same rules than the function subgraph.make_view.
Returns:
A new subgraph view of the bypassed subgraph.
Note that sgv is also modified in place.
Raises:
StandardError: if sgv cannot be converted to a SubGraphView using
the same rules than the function subgraph.make_view.
"""
# TODO(fkp): allows to plug sgv.inputs to individual sgv.outputs consumers
sgv = subgraph.make_view(sgv)
sgv_inputs = list(sgv.inputs)
sgv, detached_inputs = detach_inputs(sgv)
reroute.reroute_a2b_ts(sgv_inputs, sgv.outputs)
return sgv, detached_inputs | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/stc.py | python | StyledTextCtrl.SetRectangularSelectionModifier | (*args, **kwargs) | return _stc.StyledTextCtrl_SetRectangularSelectionModifier(*args, **kwargs) | SetRectangularSelectionModifier(self, int modifier)
On GTK+, allow selecting the modifier key to use for mouse-based
rectangular selection. Often the window manager requires Alt+Mouse Drag
for moving windows.
Valid values are SCMOD_CTRL(default), SCMOD_ALT, or SCMOD_SUPER. | SetRectangularSelectionModifier(self, int modifier) | [
"SetRectangularSelectionModifier",
"(",
"self",
"int",
"modifier",
")"
] | def SetRectangularSelectionModifier(*args, **kwargs):
"""
SetRectangularSelectionModifier(self, int modifier)
On GTK+, allow selecting the modifier key to use for mouse-based
rectangular selection. Often the window manager requires Alt+Mouse Drag
for moving windows.
Valid values are SCMOD_CTRL(default), SCMOD_ALT, or SCMOD_SUPER.
"""
return _stc.StyledTextCtrl_SetRectangularSelectionModifier(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/html.py | python | HtmlCell.GetDepth | (*args, **kwargs) | return _html.HtmlCell_GetDepth(*args, **kwargs) | GetDepth(self) -> unsigned int | GetDepth(self) -> unsigned int | [
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"""GetDepth(self) -> unsigned int"""
return _html.HtmlCell_GetDepth(*args, **kwargs) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/distutils/msvc9compiler.py | python | query_vcvarsall | (version, arch="x86") | return result | Launch vcvarsall.bat and read the settings from its environment | Launch vcvarsall.bat and read the settings from its environment | [
"Launch",
"vcvarsall",
".",
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"and",
"read",
"the",
"settings",
"from",
"its",
"environment"
] | def query_vcvarsall(version, arch="x86"):
"""Launch vcvarsall.bat and read the settings from its environment
"""
vcvarsall = find_vcvarsall(version)
interesting = set(("include", "lib", "libpath", "path"))
result = {}
if vcvarsall is None:
raise DistutilsPlatformError("Unable to find vcvarsall.bat")
log.debug("Calling 'vcvarsall.bat %s' (version=%s)", arch, version)
popen = subprocess.Popen('"%s" %s & set' % (vcvarsall, arch),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
try:
stdout, stderr = popen.communicate()
if popen.wait() != 0:
raise DistutilsPlatformError(stderr.decode("mbcs"))
stdout = stdout.decode("mbcs")
for line in stdout.split("\n"):
line = Reg.convert_mbcs(line)
if '=' not in line:
continue
line = line.strip()
key, value = line.split('=', 1)
key = key.lower()
if key in interesting:
if value.endswith(os.pathsep):
value = value[:-1]
result[key] = removeDuplicates(value)
finally:
popen.stdout.close()
popen.stderr.close()
if len(result) != len(interesting):
raise ValueError(str(list(result.keys())))
return result | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_trustregion_constr/canonical_constraint.py | python | CanonicalConstraint.from_PreparedConstraint | (cls, constraint) | Create an instance from `PreparedConstrained` object. | Create an instance from `PreparedConstrained` object. | [
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] | def from_PreparedConstraint(cls, constraint):
"""Create an instance from `PreparedConstrained` object."""
lb, ub = constraint.bounds
cfun = constraint.fun
keep_feasible = constraint.keep_feasible
if np.all(lb == -np.inf) and np.all(ub == np.inf):
return cls.empty(cfun.n)
if np.all(lb == -np.inf) and np.all(ub == np.inf):
return cls.empty(cfun.n)
elif np.all(lb == ub):
return cls._equal_to_canonical(cfun, lb)
elif np.all(lb == -np.inf):
return cls._less_to_canonical(cfun, ub, keep_feasible)
elif np.all(ub == np.inf):
return cls._greater_to_canonical(cfun, lb, keep_feasible)
else:
return cls._interval_to_canonical(cfun, lb, ub, keep_feasible) | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/v8/third_party/jinja2/compiler.py | python | generate | (node, environment, name, filename, stream=None,
defer_init=False) | Generate the python source for a node tree. | Generate the python source for a node tree. | [
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] | def generate(node, environment, name, filename, stream=None,
defer_init=False):
"""Generate the python source for a node tree."""
if not isinstance(node, nodes.Template):
raise TypeError('Can\'t compile non template nodes')
generator = environment.code_generator_class(environment, name, filename,
stream, defer_init)
generator.visit(node)
if stream is None:
return generator.stream.getvalue() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PropertyGridInterface.SetPropertyHelpString | (*args, **kwargs) | return _propgrid.PropertyGridInterface_SetPropertyHelpString(*args, **kwargs) | SetPropertyHelpString(self, PGPropArg id, String helpString) | SetPropertyHelpString(self, PGPropArg id, String helpString) | [
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] | def SetPropertyHelpString(*args, **kwargs):
"""SetPropertyHelpString(self, PGPropArg id, String helpString)"""
return _propgrid.PropertyGridInterface_SetPropertyHelpString(*args, **kwargs) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/model/calibrate.py | python | RobotExtrinsicCalibration.executeTrajectory | (self,
controller : RobotInterfaceBase,
traj : RobotTrajectory,
camera_fn : Callable,
detect_fn : Callable=None,
speed=1.0,
wait=0.0) | Executes a trajectory on a controller, optionally taking images
along the way.
TODO: specify times to actually stop. | Executes a trajectory on a controller, optionally taking images
along the way. | [
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] | def executeTrajectory(self,
controller : RobotInterfaceBase,
traj : RobotTrajectory,
camera_fn : Callable,
detect_fn : Callable=None,
speed=1.0,
wait=0.0) -> None:
"""Executes a trajectory on a controller, optionally taking images
along the way.
TODO: specify times to actually stop.
"""
import time
def wait_for_move():
moving = True
while moving:
controller.beginStep()
moving = controller.isMoving()
controller.endStep()
time.sleep(0.1)
for i in range(len(traj.milestones)):
print("Moving to milestone",i)
controller.beginStep()
controller.moveToPosition(traj.milestones[0],speed)
controller.endStep()
wait_for_move()
if wait != 0:
print("Waiting for",wait,"s")
time.sleep(wait*0.5)
if camera_fn:
print("Taking pictures")
if isinstance(camera_fn,dict):
for (k,fn) in camera_fn.items():
image = fn()
self.add_frame(image,controller.sensedPosition(),camera_id=k,detect_fn=detect_fn)
else:
image = camera_fn()
self.add_frame(image,controller.sensedPosition(),detect_fn=detect_fn)
time.sleep(wait*0.5) | [
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google/iree | 1224bbdbe65b0d1fdf40e7324f60f68beeaf7c76 | integrations/tensorflow/python_projects/iree_tf/iree/tf/support/module_utils.py | python | _IreeFunctionWrapper.get_serialized_values | (self) | Get cxx serialized inputs and outputs for this function. | Get cxx serialized inputs and outputs for this function. | [
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"serialized",
"inputs",
"and",
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"for",
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"."
] | def get_serialized_values(self) -> Tuple[Tuple[str], Tuple[str]]:
"""Get cxx serialized inputs and outputs for this function."""
if hasattr(self._f, "get_serialized_values"):
# TODO: The native ABI does not implement this, and if still needed,
# it should not be implemented this way (maybe a thread local trace
# listener).
return self._f.get_serialized_values()
else:
return ("",), ("",) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/tbe/tensor_move.py | python | _tensor_move_tbe | () | return | TensorMove TBE register | TensorMove TBE register | [
"TensorMove",
"TBE",
"register"
] | def _tensor_move_tbe():
"""TensorMove TBE register"""
return | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/ma/core.py | python | is_mask | (m) | Return True if m is a valid, standard mask.
This function does not check the contents of the input, only that the
type is MaskType. In particular, this function returns False if the
mask has a flexible dtype.
Parameters
----------
m : array_like
Array to test.
Returns
-------
result : bool
True if `m.dtype.type` is MaskType, False otherwise.
See Also
--------
isMaskedArray : Test whether input is an instance of MaskedArray.
Examples
--------
>>> import numpy.ma as ma
>>> m = ma.masked_equal([0, 1, 0, 2, 3], 0)
>>> m
masked_array(data = [-- 1 -- 2 3],
mask = [ True False True False False],
fill_value=999999)
>>> ma.is_mask(m)
False
>>> ma.is_mask(m.mask)
True
Input must be an ndarray (or have similar attributes)
for it to be considered a valid mask.
>>> m = [False, True, False]
>>> ma.is_mask(m)
False
>>> m = np.array([False, True, False])
>>> m
array([False, True, False])
>>> ma.is_mask(m)
True
Arrays with complex dtypes don't return True.
>>> dtype = np.dtype({'names':['monty', 'pithon'],
'formats':[bool, bool]})
>>> dtype
dtype([('monty', '|b1'), ('pithon', '|b1')])
>>> m = np.array([(True, False), (False, True), (True, False)],
dtype=dtype)
>>> m
array([(True, False), (False, True), (True, False)],
dtype=[('monty', '|b1'), ('pithon', '|b1')])
>>> ma.is_mask(m)
False | Return True if m is a valid, standard mask. | [
"Return",
"True",
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"is",
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"valid",
"standard",
"mask",
"."
] | def is_mask(m):
"""
Return True if m is a valid, standard mask.
This function does not check the contents of the input, only that the
type is MaskType. In particular, this function returns False if the
mask has a flexible dtype.
Parameters
----------
m : array_like
Array to test.
Returns
-------
result : bool
True if `m.dtype.type` is MaskType, False otherwise.
See Also
--------
isMaskedArray : Test whether input is an instance of MaskedArray.
Examples
--------
>>> import numpy.ma as ma
>>> m = ma.masked_equal([0, 1, 0, 2, 3], 0)
>>> m
masked_array(data = [-- 1 -- 2 3],
mask = [ True False True False False],
fill_value=999999)
>>> ma.is_mask(m)
False
>>> ma.is_mask(m.mask)
True
Input must be an ndarray (or have similar attributes)
for it to be considered a valid mask.
>>> m = [False, True, False]
>>> ma.is_mask(m)
False
>>> m = np.array([False, True, False])
>>> m
array([False, True, False])
>>> ma.is_mask(m)
True
Arrays with complex dtypes don't return True.
>>> dtype = np.dtype({'names':['monty', 'pithon'],
'formats':[bool, bool]})
>>> dtype
dtype([('monty', '|b1'), ('pithon', '|b1')])
>>> m = np.array([(True, False), (False, True), (True, False)],
dtype=dtype)
>>> m
array([(True, False), (False, True), (True, False)],
dtype=[('monty', '|b1'), ('pithon', '|b1')])
>>> ma.is_mask(m)
False
"""
try:
return m.dtype.type is MaskType
except AttributeError:
return False | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/RemoteConsole/ly_remote_console/ly_remote_console/remote_console_commands.py | python | RemoteConsole.pump | (self) | Pump function that is used by the pump_thread. Listens to receive messages from the socket
and disconnects during an exception. | Pump function that is used by the pump_thread. Listens to receive messages from the socket
and disconnects during an exception. | [
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] | def pump(self):
# type: () -> None
"""
Pump function that is used by the pump_thread. Listens to receive messages from the socket
and disconnects during an exception.
"""
while not self.stop_pump.is_set():
# Sending a NOOP message in order to get log lines
self._send_message(self._create_message(CONSOLE_MESSAGE_MAP['NOOP']))
try:
self._handle_message(self.socket.recv(4096))
except:
self.on_disconnect()
self.stop_pump.set() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListMainWindow.TileBackground | (self, dc) | Tiles the background image to fill all the available area.
:param `dc`: an instance of :class:`DC`.
.. todo:: Support background images also in stretch and centered modes. | Tiles the background image to fill all the available area. | [
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] | def TileBackground(self, dc):
"""
Tiles the background image to fill all the available area.
:param `dc`: an instance of :class:`DC`.
.. todo:: Support background images also in stretch and centered modes.
"""
if not self._backgroundImage:
return
if self._imageStretchStyle != _StyleTile:
# Can we actually do something here (or in OnPaint()) To Handle
# background images that are stretchable or always centered?
# I tried but I get enormous flickering...
return
sz = self.GetClientSize()
w = self._backgroundImage.GetWidth()
h = self._backgroundImage.GetHeight()
x = 0
while x < sz.width:
y = 0
while y < sz.height:
dc.DrawBitmap(self._backgroundImage, x, y, True)
y = y + h
x = x + w | [
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"!=",
"_StyleTile",
":",
"# Can we actually do something here (or in OnPaint()) To Handle",
"# background ima... | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/agw/ultimatelistctrl.py#L7202-L7233 |
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