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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/urllib3/connectionpool.py | python | HTTPConnectionPool._put_conn | (self, conn) | Put a connection back into the pool.
:param conn:
Connection object for the current host and port as returned by
:meth:`._new_conn` or :meth:`._get_conn`.
If the pool is already full, the connection is closed and discarded
because we exceeded maxsize. If connections are discarded frequently,
then maxsize should be increased.
If the pool is closed, then the connection will be closed and discarded. | Put a connection back into the pool. | [
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"""
Put a connection back into the pool.
:param conn:
Connection object for the current host and port as returned by
:meth:`._new_conn` or :meth:`._get_conn`.
If the pool is already full, the connection is closed and discarded
because we exceeded maxsize. If connections are discarded frequently,
then maxsize should be increased.
If the pool is closed, then the connection will be closed and discarded.
"""
try:
self.pool.put(conn, block=False)
return # Everything is dandy, done.
except AttributeError:
# self.pool is None.
pass
except queue.Full:
# This should never happen if self.block == True
log.warning("Connection pool is full, discarding connection: %s", self.host)
# Connection never got put back into the pool, close it.
if conn:
conn.close() | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | amalgamation/python/mxnet_predict.py | python | Predictor.forward | (self, **kwargs) | Perform forward to get the output.
Parameters
----------
**kwargs
Keyword arguments of input variable name to data.
Examples
--------
>>> predictor.forward(data=mydata)
>>> out = predictor.get_output(0) | Perform forward to get the output. | [
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"""Perform forward to get the output.
Parameters
----------
**kwargs
Keyword arguments of input variable name to data.
Examples
--------
>>> predictor.forward(data=mydata)
>>> out = predictor.get_output(0)
"""
for k, v in kwargs.items():
if not isinstance(v, np.ndarray):
raise ValueError("Expect numpy ndarray as input")
v = np.asarray(v, dtype=np.float32, order='C')
_check_call(_LIB.MXPredSetInput(
self.handle, c_str(k),
v.ctypes.data_as(mx_float_p),
mx_uint(v.size)))
_check_call(_LIB.MXPredForward(self.handle)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | IdleEvent.__init__ | (self, *args, **kwargs) | __init__(self) -> IdleEvent
Constructor | __init__(self) -> IdleEvent | [
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"""
__init__(self) -> IdleEvent
Constructor
"""
_core_.IdleEvent_swiginit(self,_core_.new_IdleEvent(*args, **kwargs)) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/gslib/util.py | python | LookUpGsutilVersion | (gsutil_api, url_str) | Looks up the gsutil version of the specified gsutil tarball URL.
Version is specified in the metadata field set on that object.
Args:
gsutil_api: gsutil Cloud API to use when retrieving gsutil tarball.
url_str: tarball URL to retrieve (such as 'gs://pub/gsutil.tar.gz').
Returns:
Version string if URL is a cloud URL containing x-goog-meta-gsutil-version
metadata, else None. | Looks up the gsutil version of the specified gsutil tarball URL. | [
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"""Looks up the gsutil version of the specified gsutil tarball URL.
Version is specified in the metadata field set on that object.
Args:
gsutil_api: gsutil Cloud API to use when retrieving gsutil tarball.
url_str: tarball URL to retrieve (such as 'gs://pub/gsutil.tar.gz').
Returns:
Version string if URL is a cloud URL containing x-goog-meta-gsutil-version
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"""
url = StorageUrlFromString(url_str)
if url.IsCloudUrl():
obj = gsutil_api.GetObjectMetadata(url.bucket_name, url.object_name,
provider=url.scheme,
fields=['metadata'])
if obj.metadata and obj.metadata.additionalProperties:
for prop in obj.metadata.additionalProperties:
if prop.key == 'gsutil_version':
return prop.value | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/telemetry/third_party/pyserial/serial/serialposix.py | python | PosixSerial.open | (self) | Open port with current settings. This may throw a SerialException
if the port cannot be opened. | Open port with current settings. This may throw a SerialException
if the port cannot be opened. | [
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"""Open port with current settings. This may throw a SerialException
if the port cannot be opened."""
if self._port is None:
raise SerialException("Port must be configured before it can be used.")
if self._isOpen:
raise SerialException("Port is already open.")
self.fd = None
# open
try:
self.fd = os.open(self.portstr, os.O_RDWR|os.O_NOCTTY|os.O_NONBLOCK)
except IOError, msg:
self.fd = None
raise SerialException(msg.errno, "could not open port %s: %s" % (self._port, msg))
#~ fcntl.fcntl(self.fd, FCNTL.F_SETFL, 0) # set blocking
try:
self._reconfigurePort()
except:
try:
os.close(self.fd)
except:
# ignore any exception when closing the port
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pass
self.fd = None
raise
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||
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/nn/layer/basic.py | python | Flatten.__init__ | (self) | Initialize Flatten. | Initialize Flatten. | [
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"""Initialize Flatten."""
super(Flatten, self).__init__() | [
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google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/service.py | python | RpcController.Reset | (self) | Resets the RpcController to its initial state.
After the RpcController has been reset, it may be reused in
a new call. Must not be called while an RPC is in progress. | Resets the RpcController to its initial state. | [
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"""Resets the RpcController to its initial state.
After the RpcController has been reset, it may be reused in
a new call. Must not be called while an RPC is in progress.
"""
raise NotImplementedError | [
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||
RegrowthStudios/SoACode-Public | c3ddd69355b534d5e70e2e6d0c489b4e93ab1ffe | utils/git-hooks/cpplint/cpplint.py | python | CheckCStyleCast | (filename, linenum, line, raw_line, cast_type, pattern,
error) | return True | Checks for a C-style cast by looking for the pattern.
This also handles sizeof(type) warnings, due to similarity of content.
Args:
filename: The name of the current file.
linenum: The number of the line to check.
line: The line of code to check.
raw_line: The raw line of code to check, with comments.
cast_type: The string for the C++ cast to recommend. This is either
reinterpret_cast, static_cast, or const_cast, depending.
pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
True if an error was emitted.
False otherwise. | Checks for a C-style cast by looking for the pattern. | [
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error):
"""Checks for a C-style cast by looking for the pattern.
This also handles sizeof(type) warnings, due to similarity of content.
Args:
filename: The name of the current file.
linenum: The number of the line to check.
line: The line of code to check.
raw_line: The raw line of code to check, with comments.
cast_type: The string for the C++ cast to recommend. This is either
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pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
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"""
match = Search(pattern, line)
if not match:
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# e.g., sizeof(int)
sizeof_match = Match(r'.*sizeof\s*$', line[0:match.start(1) - 1])
if sizeof_match:
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return True
remainder = line[match.end(0):]
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# eg, void *(*foo)(int) = ...
# The > is for MockCallback<...> ...
#
# Right now, this will only catch cases where there's a single argument, and
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# arguments with some unnamed.
function_match = Match(r'\s*(\)|=|(const)?\s*(;|\{|throw\(\)|>))', remainder)
if function_match:
if (not function_match.group(3) or
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return True
# At this point, all that should be left is actual casts.
error(filename, linenum, 'readability/casting', 4,
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(cast_type, match.group(1)))
return True | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/reduce4circleGUI.py | python | MainWindow.menu_sort_by_pt_number | (self) | sort survey table by pt number (with the maximum counts in the scan)
:return: | sort survey table by pt number (with the maximum counts in the scan)
:return: | [
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"""
sort survey table by pt number (with the maximum counts in the scan)
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"""
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/cookielib.py | python | is_third_party | (request) | RFC 2965, section 3.3.6:
An unverifiable transaction is to a third-party host if its request-
host U does not domain-match the reach R of the request-host O in the
origin transaction. | [] | def is_third_party(request):
"""
RFC 2965, section 3.3.6:
An unverifiable transaction is to a third-party host if its request-
host U does not domain-match the reach R of the request-host O in the
origin transaction.
"""
req_host = request_host(request)
if not domain_match(req_host, reach(request.get_origin_req_host())):
return True
else:
return False | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/contextlib.py | python | AbstractContextManager.__enter__ | (self) | return self | Return `self` upon entering the runtime context. | Return `self` upon entering the runtime context. | [
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"""Return `self` upon entering the runtime context."""
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|
ucsb-seclab/difuze | bb59a12ff87ad5ae45d9c60e349891bf80d72877 | helper_scripts/components/bear_parse_headers.py | python | BearParseHeaders.setup | (self) | return None | Perform setup.
:return: Error msg or none | Perform setup.
:return: Error msg or none | [
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"""
Perform setup.
:return: Error msg or none
"""
if not os.path.exists(self.c2xml_path):
return "Provided c2xml path:" + str(self.c2xml_path) + " does not exist."
if not os.path.isdir(self.kernel_src_dir) or not os.path.isdir(os.path.join(self.kernel_src_dir, 'include')):
return "Provided kernel src directory is invalid. " \
"The base directory is not present or it does not contain include folder:" + \
str(self.kernel_src_dir) + ", " + os.path.join(self.kernel_src_dir, 'include')
if self.hdr_file_list is None:
return "No file specified to output hdr file list."
return None | [
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protocolbuffers/protobuf | b5ab0b7a18b7336c60130f4ddb2d97c51792f896 | python/google/protobuf/descriptor_pool.py | python | DescriptorPool.FindExtensionByName | (self, full_name) | return scope.extensions_by_name[extension_name] | Loads the named extension descriptor from the pool.
Args:
full_name (str): The full name of the extension descriptor to load.
Returns:
FieldDescriptor: The field descriptor for the named extension.
Raises:
KeyError: if the extension cannot be found in the pool. | Loads the named extension descriptor from the pool. | [
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Args:
full_name (str): The full name of the extension descriptor to load.
Returns:
FieldDescriptor: The field descriptor for the named extension.
Raises:
KeyError: if the extension cannot be found in the pool.
"""
full_name = _NormalizeFullyQualifiedName(full_name)
try:
# The proto compiler does not give any link between the FileDescriptor
# and top-level extensions unless the FileDescriptorProto is added to
# the DescriptorDatabase, but this can impact memory usage.
# So we registered these extensions by name explicitly.
return self._toplevel_extensions[full_name]
except KeyError:
pass
message_name, _, extension_name = full_name.rpartition('.')
try:
# Most extensions are nested inside a message.
scope = self.FindMessageTypeByName(message_name)
except KeyError:
# Some extensions are defined at file scope.
scope = self._FindFileContainingSymbolInDb(full_name)
return scope.extensions_by_name[extension_name] | [
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|
gnina/gnina | b9ae032f52fc7a8153987bde09c0efa3620d8bb6 | caffe/python/caffe/pycaffe.py | python | _Net_set_input_arrays | (self, data, labels) | return self._set_input_arrays(data, labels) | Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.) | Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.) | [
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] | def _Net_set_input_arrays(self, data, labels):
"""
Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.)
"""
if labels.ndim == 1:
labels = np.ascontiguousarray(labels[:, np.newaxis, np.newaxis,
np.newaxis])
return self._set_input_arrays(data, labels) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/lmbrwaflib/msvs.py | python | msvs_generator.get_compatible_platform_to_toolset_maps | (self, msvs_version, restricted_platforms) | return compatible_platforms_map, ms_toolset_to_platform_map | :param msvs_version:
:param platform_toolset:
:return: | [] | def get_compatible_platform_to_toolset_maps(self, msvs_version, restricted_platforms):
"""
:param msvs_version:
:param platform_toolset:
:return:
"""
# Go through the list of enabled platforms and track which ones 'compatible' toolsets apply to the current toolset
compatible_platforms_map = {}
ms_toolset_to_platform_map = {}
enabled_platforms = self.get_all_target_platforms()
for enabled_platform in enabled_platforms:
# Is this an msvs compatible platform at all?
msvs_attributes = enabled_platform.attributes.get('msvs', None)
if not msvs_attributes:
continue
if restricted_platforms:
# If there is a platform restriction, then check if the platforms name or any of its aliases
# conform to the list of restricted platforms
if enabled_platform.platform not in restricted_platforms and \
enabled_platform.aliases.intersection(restricted_platforms):
continue
# Get this platform's toolset name
platform_toolset_name = msvs_attributes.get('toolset_name', None)
if not platform_toolset_name:
continue
toolset_properties_file = self.get_msbuild_toolset_properties_file_path(msvs_version, platform_toolset_name)
if not os.path.isfile(toolset_properties_file):
continue
compatible_platforms_map[enabled_platform.platform] = enabled_platform
if platform_toolset_name not in ms_toolset_to_platform_map:
ms_toolset_to_platform_map[platform_toolset_name] = []
ms_toolset_to_platform_map[platform_toolset_name].append(enabled_platform)
return compatible_platforms_map, ms_toolset_to_platform_map | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_txt.py | python | FileLoadEvent.GetProgress | (self) | return self._prog | Get the current progress of the load | Get the current progress of the load | [
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"current",
"progress",
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] | def GetProgress(self):
"""Get the current progress of the load"""
return self._prog | [
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|
BVLC/caffe | 9b891540183ddc834a02b2bd81b31afae71b2153 | scripts/cpp_lint.py | python | _ClassifyInclude | (fileinfo, include, is_system) | return _OTHER_HEADER | Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER | Figures out what kind of header 'include' is. | [
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"out",
"what",
"kind",
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"header",
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"is",
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] | def _ClassifyInclude(fileinfo, include, is_system):
"""Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER
"""
# This is a list of all standard c++ header files, except
# those already checked for above.
is_cpp_h = include in _CPP_HEADERS
if is_system:
if is_cpp_h:
return _CPP_SYS_HEADER
else:
return _C_SYS_HEADER
# If the target file and the include we're checking share a
# basename when we drop common extensions, and the include
# lives in . , then it's likely to be owned by the target file.
target_dir, target_base = (
os.path.split(_DropCommonSuffixes(fileinfo.RepositoryName())))
include_dir, include_base = os.path.split(_DropCommonSuffixes(include))
if target_base == include_base and (
include_dir == target_dir or
include_dir == os.path.normpath(target_dir + '/../public')):
return _LIKELY_MY_HEADER
# If the target and include share some initial basename
# component, it's possible the target is implementing the
# include, so it's allowed to be first, but we'll never
# complain if it's not there.
target_first_component = _RE_FIRST_COMPONENT.match(target_base)
include_first_component = _RE_FIRST_COMPONENT.match(include_base)
if (target_first_component and include_first_component and
target_first_component.group(0) ==
include_first_component.group(0)):
return _POSSIBLE_MY_HEADER
return _OTHER_HEADER | [
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|
KhronosGroup/SPIRV-LLVM | 1eb85593f3fe2c39379b9a9b088d51eda4f42b8b | utils/llvm-build/llvmbuild/main.py | python | LLVMProjectInfo.write_cmake_fragment | (self, output_path, enabled_optional_components) | write_cmake_fragment(output_path) -> None
Generate a CMake fragment which includes all of the collated LLVMBuild
information in a format that is easily digestible by a CMake. The exact
contents of this are closely tied to how the CMake configuration
integrates LLVMBuild, see CMakeLists.txt in the top-level. | write_cmake_fragment(output_path) -> None | [
"write_cmake_fragment",
"(",
"output_path",
")",
"-",
">",
"None"
] | def write_cmake_fragment(self, output_path, enabled_optional_components):
"""
write_cmake_fragment(output_path) -> None
Generate a CMake fragment which includes all of the collated LLVMBuild
information in a format that is easily digestible by a CMake. The exact
contents of this are closely tied to how the CMake configuration
integrates LLVMBuild, see CMakeLists.txt in the top-level.
"""
dependencies = list(self.get_fragment_dependencies())
# Write out the CMake fragment.
make_install_dir(os.path.dirname(output_path))
f = open(output_path, 'w')
# Write the header.
header_fmt = '\
#===-- %s - LLVMBuild Configuration for LLVM %s-*- CMake -*--===#'
header_name = os.path.basename(output_path)
header_pad = '-' * (80 - len(header_fmt % (header_name, '')))
header_string = header_fmt % (header_name, header_pad)
f.write("""\
%s
#
# The LLVM Compiler Infrastructure
#
# This file is distributed under the University of Illinois Open Source
# License. See LICENSE.TXT for details.
#
#===------------------------------------------------------------------------===#
#
# This file contains the LLVMBuild project information in a format easily
# consumed by the CMake based build system.
#
# This file is autogenerated by llvm-build, do not edit!
#
#===------------------------------------------------------------------------===#
""" % header_string)
# Write the dependency information in the best way we can.
f.write("""
# LLVMBuild CMake fragment dependencies.
#
# CMake has no builtin way to declare that the configuration depends on
# a particular file. However, a side effect of configure_file is to add
# said input file to CMake's internal dependency list. So, we use that
# and a dummy output file to communicate the dependency information to
# CMake.
#
# FIXME: File a CMake RFE to get a properly supported version of this
# feature.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/_pyio.py | python | IOBase.truncate | (self, pos=None) | Truncate file to size bytes.
Size defaults to the current IO position as reported by tell(). Return
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vnpy/vnpy | f50f2535ed39dd33272e0985ed40c7078e4c19f6 | vnpy/event/engine.py | python | EventEngine.put | (self, event: Event) | Put an event object into event queue. | Put an event object into event queue. | [
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stitchEm/stitchEm | 0f399501d41ab77933677f2907f41f80ceb704d7 | lib/bindings/samples/server/output/output.py | python | WriterOutput._load_preset | (self, preset=None, preserve=False) | Creates configuration object based on the default preset and the given one if present
Args:
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Args:
preset:
"""
if SETTINGS.ptv is not None:
preset_ptv = PTV.from_file(SETTINGS.ptv)
self.ptv = PTV(preset_ptv[self.name])
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self.ptv = PTV.from_file(self._get_preset_filepath(defaults.SYSTEM_PRESETS_DIR_PATH,
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if isinstance(preset, str) or isinstance(preset, unicode):
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self.ptv.merge(self.preset)
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self.ptv.merge(self.additional_preset)
if self.ptv["channel_layout"] == "amb_wxyz":
self.ptv["audio_bitrate"] = self.ptv["ambisonic_audio_bitrate"] | [
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||
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/boost/boost_1_68_0/tools/build/src/build/targets.py | python | TargetRegistry.main_target_sources | (self, sources, main_target_name, no_renaming=0) | return result | Return the list of sources to use, if main target rule is invoked
with 'sources'. If there are any objects in 'sources', they are treated
as main target instances, and the name of such targets are adjusted to
be '<name_of_this_target>__<name_of_source_target>'. Such renaming
is disabled is non-empty value is passed for 'no-renaming' parameter. | Return the list of sources to use, if main target rule is invoked
with 'sources'. If there are any objects in 'sources', they are treated
as main target instances, and the name of such targets are adjusted to
be '<name_of_this_target>__<name_of_source_target>'. Such renaming
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assert is_iterable_typed(sources, basestring)
assert isinstance(main_target_name, basestring)
assert isinstance(no_renaming, (int, bool))
result = []
for t in sources:
t = b2.util.jam_to_value_maybe(t)
if isinstance (t, AbstractTarget):
name = t.name ()
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t.rename (name)
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p.mark_targets_as_explicit([name])
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/dataview.py | python | DataViewTreeStore.AppendItem | (*args, **kwargs) | return _dataview.DataViewTreeStore_AppendItem(*args, **kwargs) | AppendItem(self, DataViewItem parent, String text, Icon icon=wxNullIcon,
wxClientData data=None) -> DataViewItem | AppendItem(self, DataViewItem parent, String text, Icon icon=wxNullIcon,
wxClientData data=None) -> DataViewItem | [
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"""
AppendItem(self, DataViewItem parent, String text, Icon icon=wxNullIcon,
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"""
return _dataview.DataViewTreeStore_AppendItem(*args, **kwargs) | [
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|
Ardour/ardour | a63a18a3387b90c0920d9b1668d2a50bd6302b83 | tools/misc.py | python | copy_attrs | (orig, dest, names, only_if_set=False) | copy class attributes from an object to another | copy class attributes from an object to another | [
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copy class attributes from an object to another
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for a in Utils.to_list(names):
u = getattr(orig, a, ())
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/indexes/multi.py | python | MultiIndex._to_safe_for_reshape | (self) | return self.set_levels([i._to_safe_for_reshape() for i in self.levels]) | convert to object if we are a categorical | convert to object if we are a categorical | [
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""" convert to object if we are a categorical """
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raspberrypi/tools | 13474ee775d0c5ec8a7da4fb0a9fa84187abfc87 | arm-bcm2708/gcc-linaro-arm-linux-gnueabihf-raspbian-x64/share/gdb/python/gdb/printing.py | python | register_pretty_printer | (obj, printer, replace=False) | Register pretty-printer PRINTER with OBJ.
The printer is added to the front of the search list, thus one can override
an existing printer if one needs to. Use a different name when overriding
an existing printer, otherwise an exception will be raised; multiple
printers with the same name are disallowed.
Arguments:
obj: Either an objfile, progspace, or None (in which case the printer
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printer: Either a function of one argument (old way) or any object
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replace: If True replace any existing copy of the printer.
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Returns:
Nothing.
Raises:
TypeError: A problem with the type of the printer.
ValueError: The printer's name contains a semicolon ";".
RuntimeError: A printer with the same name is already registered.
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If printer is an object, __call__ is a method of two arguments:
self, and the value to be pretty-printed. See PrettyPrinter. | Register pretty-printer PRINTER with OBJ. | [
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"""Register pretty-printer PRINTER with OBJ.
The printer is added to the front of the search list, thus one can override
an existing printer if one needs to. Use a different name when overriding
an existing printer, otherwise an exception will be raised; multiple
printers with the same name are disallowed.
Arguments:
obj: Either an objfile, progspace, or None (in which case the printer
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printer: Either a function of one argument (old way) or any object
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# attribute named __foo__ for pretty-printers-as-objects.
# If printer has both, we use `name'.
if not hasattr(printer, "__name__") and not hasattr(printer, "name"):
raise TypeError("printer missing attribute: name")
if hasattr(printer, "name") and not hasattr(printer, "enabled"):
raise TypeError("printer missing attribute: enabled")
if not hasattr(printer, "__call__"):
raise TypeError("printer missing attribute: __call__")
if obj is None:
if gdb.parameter("verbose"):
gdb.write("Registering global %s pretty-printer ...\n" % name)
obj = gdb
else:
if gdb.parameter("verbose"):
gdb.write("Registering %s pretty-printer for %s ...\n" %
(printer.name, obj.filename))
if hasattr(printer, "name"):
if not isinstance(printer.name, basestring):
raise TypeError("printer name is not a string")
# If printer provides a name, make sure it doesn't contain ";".
# Semicolon is used by the info/enable/disable pretty-printer commands
# to delimit subprinters.
if printer.name.find(";") >= 0:
raise ValueError("semicolon ';' in printer name")
# Also make sure the name is unique.
# Alas, we can't do the same for functions and __name__, they could
# all have a canonical name like "lookup_function".
# PERF: gdb records printers in a list, making this inefficient.
i = 0
for p in obj.pretty_printers:
if hasattr(p, "name") and p.name == printer.name:
if replace:
del obj.pretty_printers[i]
break
else:
raise RuntimeError("pretty-printer already registered: %s" %
printer.name)
i = i + 1
obj.pretty_printers.insert(0, printer) | [
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||
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Arch/importIFClegacy.py | python | IfcWriter.addExtrudedEllipse | (self,data,extrusion,placement=None,color=None) | return exp | addExtrudedEllipse(data,extrusion,[placement,color]): makes an extruded ellipse
from the given data (center,radiusx,radiusy) and the given extrusion vector | addExtrudedEllipse(data,extrusion,[placement,color]): makes an extruded ellipse
from the given data (center,radiusx,radiusy) and the given extrusion vector | [
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"""addExtrudedEllipse(data,extrusion,[placement,color]): makes an extruded ellipse
from the given data (center,radiusx,radiusy) and the given extrusion vector"""
cir = self.addProfile("IfcEllipseProfileDef",[data[1],data[2]])
if not placement:
placement = self.addPlacement(origin=data[0],local=False)
exp = self.addExtrusion(cir,extrusion,placement)
if color:
self.addColor(color,exp)
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|
eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/mozbuild/mozbuild/makeutil.py | python | Rule.add_dependencies | (self, deps) | return self | Add dependencies to the rule. | Add dependencies to the rule. | [
"Add",
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"the",
"rule",
"."
] | def add_dependencies(self, deps):
'''Add dependencies to the rule.'''
assert isinstance(deps, Iterable) and not isinstance(deps, StringTypes)
self._dependencies.update(deps)
return self | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/grit/grit/format/policy_templates/writers/admx_writer.py | python | ADMXWriter._AddListPolicy | (self, parent, key, name) | Generates ADMX XML elements for a List-Policy and adds them to the
passed parent element. | Generates ADMX XML elements for a List-Policy and adds them to the
passed parent element. | [
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] | def _AddListPolicy(self, parent, key, name):
'''Generates ADMX XML elements for a List-Policy and adds them to the
passed parent element.
'''
attributes = {
# The ID must be in sync with ID of the corresponding element in the ADML
# file.
'id': name + 'Desc',
'valuePrefix': '',
'key': key + '\\' + name,
}
self.AddElement(parent, 'list', attributes) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/cython/Cython/Compiler/Nodes.py | python | ParallelStatNode.analyse_sharing_attributes | (self, env) | Analyse the privates for this block and set them in self.privates.
This should be called in a post-order fashion during the
analyse_expressions phase | Analyse the privates for this block and set them in self.privates.
This should be called in a post-order fashion during the
analyse_expressions phase | [
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] | def analyse_sharing_attributes(self, env):
"""
Analyse the privates for this block and set them in self.privates.
This should be called in a post-order fashion during the
analyse_expressions phase
"""
for entry, (pos, op) in self.assignments.items():
if self.is_prange and not self.is_parallel:
# closely nested prange in a with parallel block, disallow
# assigning to privates in the with parallel block (we
# consider it too implicit and magicky for users)
if entry in self.parent.assignments:
error(pos, "Cannot assign to private of outer parallel block")
continue
if not self.is_prange and op:
# Again possible, but considered to magicky
error(pos, "Reductions not allowed for parallel blocks")
continue
# By default all variables should have the same values as if
# executed sequentially
lastprivate = True
self.propagate_var_privatization(entry, pos, op, lastprivate) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | ConfigBase.IsRecordingDefaults | (*args, **kwargs) | return _misc_.ConfigBase_IsRecordingDefaults(*args, **kwargs) | IsRecordingDefaults(self) -> bool
Are we currently recording default values? | IsRecordingDefaults(self) -> bool | [
"IsRecordingDefaults",
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] | def IsRecordingDefaults(*args, **kwargs):
"""
IsRecordingDefaults(self) -> bool
Are we currently recording default values?
"""
return _misc_.ConfigBase_IsRecordingDefaults(*args, **kwargs) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | DC.GetSizeTuple | (*args, **kwargs) | return _gdi_.DC_GetSizeTuple(*args, **kwargs) | GetSizeTuple() -> (width, height)
This gets the horizontal and vertical resolution in device units. It
can be used to scale graphics to fit the page. For example, if *maxX*
and *maxY* represent the maximum horizontal and vertical 'pixel' values
used in your application, the following code will scale the graphic to
fit on the printer page::
w, h = dc.GetSize()
scaleX = maxX*1.0 / w
scaleY = maxY*1.0 / h
dc.SetUserScale(min(scaleX,scaleY),min(scaleX,scaleY)) | GetSizeTuple() -> (width, height) | [
"GetSizeTuple",
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] | def GetSizeTuple(*args, **kwargs):
"""
GetSizeTuple() -> (width, height)
This gets the horizontal and vertical resolution in device units. It
can be used to scale graphics to fit the page. For example, if *maxX*
and *maxY* represent the maximum horizontal and vertical 'pixel' values
used in your application, the following code will scale the graphic to
fit on the printer page::
w, h = dc.GetSize()
scaleX = maxX*1.0 / w
scaleY = maxY*1.0 / h
dc.SetUserScale(min(scaleX,scaleY),min(scaleX,scaleY))
"""
return _gdi_.DC_GetSizeTuple(*args, **kwargs) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/datetime.py | python | _is_leap | (year) | return year % 4 == 0 and (year % 100 != 0 or year % 400 == 0) | year -> 1 if leap year, else 0. | year -> 1 if leap year, else 0. | [
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] | def _is_leap(year):
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return year % 4 == 0 and (year % 100 != 0 or year % 400 == 0) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Window.Create | (*args, **kwargs) | return _core_.Window_Create(*args, **kwargs) | Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, long style=0, String name=PanelNameStr) -> bool
Create the GUI part of the Window for 2-phase creation mode. | Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, long style=0, String name=PanelNameStr) -> bool | [
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"""
Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, long style=0, String name=PanelNameStr) -> bool
Create the GUI part of the Window for 2-phase creation mode.
"""
return _core_.Window_Create(*args, **kwargs) | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/utils/qt/qappthreadcall.py | python | QAppThreadCall._ensure_self_on_qapp_thread | (self) | Assuming the QApplication instance exists, ensure this object is on
that thread | Assuming the QApplication instance exists, ensure this object is on
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"""Assuming the QApplication instance exists, ensure this object is on
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if self._moved_to_app:
return
self.moveToThread(QApplication.instance().thread())
self._moved_to_app = True | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | DC.GetPixelPoint | (*args, **kwargs) | return _gdi_.DC_GetPixelPoint(*args, **kwargs) | GetPixelPoint(self, Point pt) -> Colour | GetPixelPoint(self, Point pt) -> Colour | [
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] | def GetPixelPoint(*args, **kwargs):
"""GetPixelPoint(self, Point pt) -> Colour"""
return _gdi_.DC_GetPixelPoint(*args, **kwargs) | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/model_fitting/model_fitting_view.py | python | ModelFittingView.set_selected_y_parameter | (self, y_parameter: str) | Sets the selected Y parameter. | Sets the selected Y parameter. | [
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] | def set_selected_y_parameter(self, y_parameter: str) -> None:
"""Sets the selected Y parameter."""
self.model_fitting_data_selector.set_selected_y_parameter(y_parameter) | [
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||
hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/gluon/contrib/estimator/estimator.py | python | Estimator.fit | (self, train_data,
val_data=None,
epochs=None,
event_handlers=None,
batches=None,
batch_axis=0) | Trains the model with a given :py:class:`DataLoader` for a specified
number of epochs or batches. The batch size is inferred from the
data loader's batch_size.
Parameters
----------
train_data : DataLoader
Training data loader with data and labels.
val_data : DataLoader, default None
Validation data loader with data and labels.
epochs : int, default None
Number of epochs to iterate on the training data.
You can only specify one and only one type of iteration(epochs or batches).
event_handlers : EventHandler or list of EventHandler
List of :py:class:`EventHandlers` to apply during training.
batches : int, default None
Number of batches to iterate on the training data.
You can only specify one and only one type of iteration(epochs or batches).
batch_axis : int, default 0
Batch axis to split the training data into devices. | Trains the model with a given :py:class:`DataLoader` for a specified
number of epochs or batches. The batch size is inferred from the
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val_data=None,
epochs=None,
event_handlers=None,
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batch_axis=0):
"""Trains the model with a given :py:class:`DataLoader` for a specified
number of epochs or batches. The batch size is inferred from the
data loader's batch_size.
Parameters
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train_data : DataLoader
Training data loader with data and labels.
val_data : DataLoader, default None
Validation data loader with data and labels.
epochs : int, default None
Number of epochs to iterate on the training data.
You can only specify one and only one type of iteration(epochs or batches).
event_handlers : EventHandler or list of EventHandler
List of :py:class:`EventHandlers` to apply during training.
batches : int, default None
Number of batches to iterate on the training data.
You can only specify one and only one type of iteration(epochs or batches).
batch_axis : int, default 0
Batch axis to split the training data into devices.
"""
if not isinstance(train_data, gluon.data.DataLoader):
raise ValueError("Estimator only support input as Gluon DataLoader. Alternatively, you "
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"Refer to gluon.data.dataloader")
# must specify one and only one of epochs or batches
if (not epochs) == (not batches):
raise ValueError(
"Fit only support exactly one type of iteration, "
"train by number of epochs or number of batches."
"Please specify one and only one of: epochs or batches.")
self.max_epoch = epochs
self.max_batch = batches
# provide default handlers
event_handlers = self._prepare_default_handlers(val_data, event_handlers)
train_begin, epoch_begin, batch_begin, \
batch_end, epoch_end, train_end = self._categorize_handlers(event_handlers)
# pass a reference to all event handlers
estimator_ref = self
# training begin
for handler in train_begin:
handler.train_begin(estimator_ref)
while True:
# epoch begin
for handler in epoch_begin:
handler.epoch_begin(estimator_ref)
for i, batch in enumerate(train_data):
data, label = self._get_data_and_label(batch, self.context, batch_axis)
batch_size = batch[0].shape[0]
# batch begin
for handler in batch_begin:
handler.batch_begin(estimator_ref, batch=batch)
with autograd.record():
pred = [self.net(x) for x in data]
loss = [self.loss[0](y_hat, y) for y_hat, y in zip(pred, label)]
for l in loss:
l.backward()
self.trainer.step(batch_size)
# batch end
batch_end_result = []
for handler in batch_end:
batch_end_result.append(handler.batch_end(estimator_ref, batch=batch,
pred=pred, label=label, loss=loss))
# if any handler signaled to stop
if any(batch_end_result):
break
# epoch end
epoch_end_result = []
for handler in epoch_end:
epoch_end_result.append(handler.epoch_end(estimator_ref))
# if any handler signaled to stop
if any(epoch_end_result):
break
# train end
for handler in train_end:
handler.train_end(estimator_ref) | [
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||
RoboJackets/robocup-software | bce13ce53ddb2ecb9696266d980722c34617dc15 | util/run-cmake-format.py | python | run_format | (args, file_queue, lock, return_codes) | Takes filenames out of queue and runs clang-format on them. | Takes filenames out of queue and runs clang-format on them. | [
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] | def run_format(args, file_queue, lock, return_codes):
"""Takes filenames out of queue and runs clang-format on them."""
while True:
name = file_queue.get()
invocation = get_format_invocation(name, args.cmake_format_binary, args.check)
proc = subprocess.Popen(
invocation, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
output, err = proc.communicate()
with lock:
return_codes.append(proc.returncode)
sys.stdout.write(" ".join(invocation) + "\n" + output.decode("utf-8"))
if len(err) > 0:
sys.stdout.flush()
sys.stderr.write(err.decode("utf-8"))
file_queue.task_done() | [
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||
ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | build/upload.py | python | AppendOptionalArgsToSSHCommandline | (cmdline, port, ssh_key) | Given optional port and ssh key values, append valid OpenSSH
commandline arguments to the list cmdline if the values are not None. | Given optional port and ssh key values, append valid OpenSSH
commandline arguments to the list cmdline if the values are not None. | [
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] | def AppendOptionalArgsToSSHCommandline(cmdline, port, ssh_key):
"""Given optional port and ssh key values, append valid OpenSSH
commandline arguments to the list cmdline if the values are not None."""
if port is not None:
cmdline.append("-P%d" % port)
if ssh_key is not None:
# Don't interpret ~ paths - ssh can handle that on its own
if not ssh_key.startswith('~'):
ssh_key = WindowsPathToMsysPath(ssh_key)
cmdline.extend(["-o", "IdentityFile=%s" % ssh_key]) | [
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||
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/logging_ops.py | python | get_summary_op | () | return summary_op | Returns a single Summary op that would run all summaries.
Either existing one from `SUMMARY_OP` collection or merges all existing
summaries.
Returns:
If no summaries were collected, returns None. Otherwise returns a scalar
`Tensor` of type `string` containing the serialized `Summary` protocol
buffer resulting from the merging. | Returns a single Summary op that would run all summaries. | [
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"would",
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] | def get_summary_op():
"""Returns a single Summary op that would run all summaries.
Either existing one from `SUMMARY_OP` collection or merges all existing
summaries.
Returns:
If no summaries were collected, returns None. Otherwise returns a scalar
`Tensor` of type `string` containing the serialized `Summary` protocol
buffer resulting from the merging.
"""
summary_op = ops.get_collection(ops.GraphKeys.SUMMARY_OP)
if summary_op is not None:
if summary_op:
summary_op = summary_op[0]
else:
summary_op = None
if summary_op is None:
summary_op = merge_all_summaries()
if summary_op is not None:
ops.add_to_collection(ops.GraphKeys.SUMMARY_OP, summary_op)
return summary_op | [
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|
mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/util/runcommand.py | python | RunCommand.execute | (self) | return error_code, output | Execute 'cmd' and return err_code and output. | Execute 'cmd' and return err_code and output. | [
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] | def execute(self):
"""Execute 'cmd' and return err_code and output."""
self._process = subprocess.Popen(self._cmd_list(), stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, **self._preexec_kargs)
output, _ = self._process.communicate()
error_code = self._process.returncode
return error_code, output | [
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|
Caffe-MPI/Caffe-MPI.github.io | df5992af571a2a19981b69635115c393f18d1c76 | scripts/cpp_lint.py | python | CheckForHeaderGuard | (filename, lines, error) | Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found. | Checks that the file contains a header guard. | [
"Checks",
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] | def CheckForHeaderGuard(filename, lines, error):
"""Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
cppvar = GetHeaderGuardCPPVariable(filename)
ifndef = None
ifndef_linenum = 0
define = None
endif = None
endif_linenum = 0
for linenum, line in enumerate(lines):
linesplit = line.split()
if len(linesplit) >= 2:
# find the first occurrence of #ifndef and #define, save arg
if not ifndef and linesplit[0] == '#ifndef':
# set ifndef to the header guard presented on the #ifndef line.
ifndef = linesplit[1]
ifndef_linenum = linenum
if not define and linesplit[0] == '#define':
define = linesplit[1]
# find the last occurrence of #endif, save entire line
if line.startswith('#endif'):
endif = line
endif_linenum = linenum
if not ifndef:
error(filename, 0, 'build/header_guard', 5,
'No #ifndef header guard found, suggested CPP variable is: %s' %
cppvar)
return
if not define:
error(filename, 0, 'build/header_guard', 5,
'No #define header guard found, suggested CPP variable is: %s' %
cppvar)
return
# The guard should be PATH_FILE_H_, but we also allow PATH_FILE_H__
# for backward compatibility.
if ifndef != cppvar:
error_level = 0
if ifndef != cppvar + '_':
error_level = 5
ParseNolintSuppressions(filename, lines[ifndef_linenum], ifndef_linenum,
error)
error(filename, ifndef_linenum, 'build/header_guard', error_level,
'#ifndef header guard has wrong style, please use: %s' % cppvar)
if define != ifndef:
error(filename, 0, 'build/header_guard', 5,
'#ifndef and #define don\'t match, suggested CPP variable is: %s' %
cppvar)
return
if endif != ('#endif // %s' % cppvar):
error_level = 0
if endif != ('#endif // %s' % (cppvar + '_')):
error_level = 5
ParseNolintSuppressions(filename, lines[endif_linenum], endif_linenum,
error)
error(filename, endif_linenum, 'build/header_guard', error_level,
'#endif line should be "#endif // %s"' % cppvar) | [
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||
eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/build/unix/build-clang/tooltool.py | python | process_command | (options, args) | I know how to take a list of program arguments and
start doing the right thing with them | I know how to take a list of program arguments and
start doing the right thing with them | [
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] | def process_command(options, args):
""" I know how to take a list of program arguments and
start doing the right thing with them"""
cmd = args[0]
cmd_args = args[1:]
log.debug("processing '%s' command with args '%s'" % (cmd, '", "'.join(cmd_args)))
log.debug("using options: %s" % options)
if cmd == 'list':
return list_manifest(options['manifest'])
if cmd == 'validate':
return validate_manifest(options['manifest'])
elif cmd == 'add':
return add_files(options['manifest'], options['algorithm'], cmd_args)
elif cmd == 'fetch':
if not options.has_key('base_url') or options.get('base_url') is None:
log.critical('fetch command requires url option')
return False
return fetch_files(options['manifest'], options['base_url'], options['overwrite'], cmd_args)
else:
log.critical('command "%s" is not implemented' % cmd)
return False | [
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||
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/mplgraphicsview.py | python | MyNavigationToolbar.release_zoom | (self, event) | return | override zoom released method
Parameters
----------
event
Returns
------- | override zoom released method
Parameters
----------
event | [
"override",
"zoom",
"released",
"method",
"Parameters",
"----------",
"event"
] | def release_zoom(self, event):
"""
override zoom released method
Parameters
----------
event
Returns
-------
"""
self.canvas_zoom_released.emit()
NavigationToolbar2.release_zoom(self, event)
return | [
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|
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/train/summary/_writer_pool.py | python | _pack_data | (datadict, wall_time) | return result | Pack data according to which plugin. | Pack data according to which plugin. | [
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] | def _pack_data(datadict, wall_time):
"""Pack data according to which plugin."""
result, summaries, step = [], [], None
for plugin, datalist in datadict.items():
for data in datalist:
if plugin == PluginEnum.GRAPH.value:
result.append([plugin, package_graph_event(data.get('value')).SerializeToString()])
elif plugin in (PluginEnum.TRAIN_LINEAGE.value, PluginEnum.EVAL_LINEAGE.value,
PluginEnum.CUSTOM_LINEAGE_DATA.value, PluginEnum.DATASET_GRAPH.value):
result.append([plugin, serialize_to_lineage_event(plugin, data.get('value'))])
elif plugin in (PluginEnum.SCALAR.value, PluginEnum.TENSOR.value, PluginEnum.HISTOGRAM.value,
PluginEnum.IMAGE.value, PluginEnum.LANDSCAPE.value):
summaries.append({'_type': plugin.title(), 'name': data.get('tag'), 'data': data.get('value')})
step = data.get('step')
if 'export_option' in data:
result.append([WriterPluginEnum.EXPORTER.value, data])
if summaries:
result.append(
[WriterPluginEnum.SUMMARY.value, package_summary_event(summaries, step, wall_time).SerializeToString()])
return result | [
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|
SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/service/read/gamedata.py | python | load_gamespec | (fileobj, game_version, cachefile_name=None, load_cache=False) | return gamespec | Helper method that loads the contents of a 'empires.dat' gzipped wrapper
file.
If cachefile_name is given, this file is consulted before performing the
load. | Helper method that loads the contents of a 'empires.dat' gzipped wrapper
file. | [
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"loads",
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"of",
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] | def load_gamespec(fileobj, game_version, cachefile_name=None, load_cache=False):
"""
Helper method that loads the contents of a 'empires.dat' gzipped wrapper
file.
If cachefile_name is given, this file is consulted before performing the
load.
"""
# try to use the cached result from a previous run
if cachefile_name and load_cache:
try:
with open(cachefile_name, "rb") as cachefile:
# pickle.load() can fail in many ways, we need to catch all.
# pylint: disable=broad-except
try:
gamespec = pickle.load(cachefile)
info("using cached wrapper: %s", cachefile_name)
return gamespec
except Exception:
warn("could not use cached wrapper:")
import traceback
traceback.print_exc()
warn("we will just skip the cache, no worries.")
except FileNotFoundError:
pass
# read the file ourselves
dbg("reading dat file")
compressed_data = fileobj.read()
fileobj.close()
dbg("decompressing dat file")
# -15: there's no header, window size is 15.
file_data = decompress(compressed_data, -15)
del compressed_data
spam("length of decompressed data: %d", len(file_data))
wrapper = EmpiresDatWrapper()
_, gamespec = wrapper.read(file_data, 0, game_version)
# Remove the list sorrounding the converted data
gamespec = gamespec[0]
if cachefile_name:
dbg("dumping dat file contents to cache file: %s", cachefile_name)
with open(cachefile_name, "wb") as cachefile:
pickle.dump(gamespec, cachefile)
return gamespec | [
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|
gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/physics/sNMR/mrs.py | python | MRS.loadZVector | (self, filename='zkernel.vec') | Load the kernel vertical discretisation (z) vector. | Load the kernel vertical discretisation (z) vector. | [
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"kernel",
"vertical",
"discretisation",
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] | def loadZVector(self, filename='zkernel.vec'):
"""Load the kernel vertical discretisation (z) vector."""
self.z = pg.Vector(filename) | [
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||
paullouisageneau/libdatachannel | 27569ce021bea0df6cfc3e0b99e71d5c2d180089 | pages/tasks.py | python | regenerate | (c) | Automatically regenerate site upon file modification | Automatically regenerate site upon file modification | [
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] | def regenerate(c):
"""Automatically regenerate site upon file modification"""
pelican_run('-r -s {settings_base}'.format(**CONFIG)) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/hmac.py | python | digest | (key, msg, digest) | return outer.digest() | Fast inline implementation of HMAC.
key: bytes or buffer, The key for the keyed hash object.
msg: bytes or buffer, Input message.
digest: A hash name suitable for hashlib.new() for best performance. *OR*
A hashlib constructor returning a new hash object. *OR*
A module supporting PEP 247. | Fast inline implementation of HMAC. | [
"Fast",
"inline",
"implementation",
"of",
"HMAC",
"."
] | def digest(key, msg, digest):
"""Fast inline implementation of HMAC.
key: bytes or buffer, The key for the keyed hash object.
msg: bytes or buffer, Input message.
digest: A hash name suitable for hashlib.new() for best performance. *OR*
A hashlib constructor returning a new hash object. *OR*
A module supporting PEP 247.
"""
if (_hashopenssl is not None and
isinstance(digest, str) and digest in _openssl_md_meths):
return _hashopenssl.hmac_digest(key, msg, digest)
if callable(digest):
digest_cons = digest
elif isinstance(digest, str):
digest_cons = lambda d=b'': _hashlib.new(digest, d)
else:
digest_cons = lambda d=b'': digest.new(d)
inner = digest_cons()
outer = digest_cons()
blocksize = getattr(inner, 'block_size', 64)
if len(key) > blocksize:
key = digest_cons(key).digest()
key = key + b'\x00' * (blocksize - len(key))
inner.update(key.translate(trans_36))
outer.update(key.translate(trans_5C))
inner.update(msg)
outer.update(inner.digest())
return outer.digest() | [
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|
weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | v8_5_1/tools/stats-viewer.py | python | StatsViewer.ComputeCounters | (self) | return groups | Group the counters by the suffix of their name.
Since the same code-level counter (for instance "X") can result in
several variables in the binary counters file that differ only by a
two-character prefix (for instance "c:X" and "t:X") counters are
grouped by suffix and then displayed with custom formatting
depending on their prefix.
Returns:
A mapping from suffixes to a list of counters with that suffix,
sorted by prefix. | Group the counters by the suffix of their name. | [
"Group",
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"by",
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"their",
"name",
"."
] | def ComputeCounters(self):
"""Group the counters by the suffix of their name.
Since the same code-level counter (for instance "X") can result in
several variables in the binary counters file that differ only by a
two-character prefix (for instance "c:X" and "t:X") counters are
grouped by suffix and then displayed with custom formatting
depending on their prefix.
Returns:
A mapping from suffixes to a list of counters with that suffix,
sorted by prefix.
"""
names = {}
for i in xrange(self.data.CountersInUse()):
counter = self.data.Counter(i)
name = counter.Name()
names[name] = counter
# By sorting the keys we ensure that the prefixes always come in the
# same order ("c:" before "t:") which looks more consistent in the
# ui.
sorted_keys = names.keys()
sorted_keys.sort()
# Group together the names whose suffix after a ':' are the same.
groups = {}
for name in sorted_keys:
counter = names[name]
if ":" in name:
name = name[name.find(":")+1:]
if not name in groups:
groups[name] = []
groups[name].append(counter)
return groups | [
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|
neoml-lib/neoml | a0d370fba05269a1b2258cef126f77bbd2054a3e | NeoML/Python/neoml/Dnn/Loss.py | python | FocalLoss.force | (self) | return self._internal.get_force() | Gets the focal force multiplier. | Gets the focal force multiplier. | [
"Gets",
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"""Gets the focal force multiplier.
"""
return self._internal.get_force() | [
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|
microsoft/clang | 86d4513d3e0daa4d5a29b0b1de7c854ca15f9fe5 | tools/scan-build-py/libear/__init__.py | python | Toolset.add_definitions | (self, defines) | part of public interface | part of public interface | [
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""" part of public interface """
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||
NVIDIA/TensorRT | 42805f078052daad1a98bc5965974fcffaad0960 | tools/Polygraphy/polygraphy/backend/trt/calibrator.py | python | Calibrator | (
data_loader, cache=None, BaseClass=None, batch_size=None, quantile=None, regression_cutoff=None, algo=None
) | return CalibratorClass() | Supplies calibration data to TensorRT to calibrate the network for INT8 inference.
Args:
data_loader (Generator -> OrderedDict[str, Union[numpy.ndarray, DeviceView, int]]):
A generator or iterable that yields a dictionary that maps input names to NumPy
arrays, Polygraphy DeviceViews, or GPU pointers.
In case you don't know details about the inputs ahead of time, you can access the
`input_metadata` property in your data loader, which will be set to an ``TensorMetadata`` instance.
Note that this does not work for generators or lists.
The number of calibration batches is controlled by the number of items supplied
by the data loader.
cache (Union[str, file-like]):
Path or file-like object to save/load the calibration cache.
By default, the calibration cache is not saved.
BaseClass (type):
The type of calibrator to inherit from.
Defaults to ``trt.IInt8EntropyCalibrator2``.
batch_size (int):
[DEPRECATED] The size of each batch provided by the data loader.
quantile (float):
The quantile to use for ``trt.IInt8LegacyCalibrator``.
Has no effect for other calibrator types.
Defaults to 0.5.
regression_cutoff (float):
The regression cutoff to use for ``trt.IInt8LegacyCalibrator``.
Has no effect for other calibrator types.
Defaults to 0.5.
algo (trt.CalibrationAlgoType):
Calibration algorithm to use for ``trt.IInt8Calibrator``.
Has no effect for other calibrator types.
Defaults to ``trt.CalibrationAlgoType.MINMAX_CALIBRATION``. | Supplies calibration data to TensorRT to calibrate the network for INT8 inference. | [
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] | def Calibrator(
data_loader, cache=None, BaseClass=None, batch_size=None, quantile=None, regression_cutoff=None, algo=None
):
"""
Supplies calibration data to TensorRT to calibrate the network for INT8 inference.
Args:
data_loader (Generator -> OrderedDict[str, Union[numpy.ndarray, DeviceView, int]]):
A generator or iterable that yields a dictionary that maps input names to NumPy
arrays, Polygraphy DeviceViews, or GPU pointers.
In case you don't know details about the inputs ahead of time, you can access the
`input_metadata` property in your data loader, which will be set to an ``TensorMetadata`` instance.
Note that this does not work for generators or lists.
The number of calibration batches is controlled by the number of items supplied
by the data loader.
cache (Union[str, file-like]):
Path or file-like object to save/load the calibration cache.
By default, the calibration cache is not saved.
BaseClass (type):
The type of calibrator to inherit from.
Defaults to ``trt.IInt8EntropyCalibrator2``.
batch_size (int):
[DEPRECATED] The size of each batch provided by the data loader.
quantile (float):
The quantile to use for ``trt.IInt8LegacyCalibrator``.
Has no effect for other calibrator types.
Defaults to 0.5.
regression_cutoff (float):
The regression cutoff to use for ``trt.IInt8LegacyCalibrator``.
Has no effect for other calibrator types.
Defaults to 0.5.
algo (trt.CalibrationAlgoType):
Calibration algorithm to use for ``trt.IInt8Calibrator``.
Has no effect for other calibrator types.
Defaults to ``trt.CalibrationAlgoType.MINMAX_CALIBRATION``.
"""
BaseClass = util.default(BaseClass, trt.IInt8EntropyCalibrator2)
class CalibratorClass(BaseClass):
"""
Calibrator that supplies calibration data to TensorRT to calibrate the network for INT8 inference.
"""
def __init__(self):
# Must explicitly initialize parent for any trampoline class! Will mysteriously segfault without this.
BaseClass.__init__(self)
self.is_active = False
self.data_loader = data_loader
self._cache = cache
self.device_buffers = OrderedDict()
self.reset()
G_LOGGER.verbose("Created calibrator [cache={:}]".format(self._cache))
self.batch_size = util.default(batch_size, 1)
# The function that constructed this instance
self.make_func = Calibrator
def reset(self, input_metadata=None):
"""
Reset this calibrator for reuse.
The calibrator will clear any dynamic ranges cached from previous calibration runs, and will
attempt to rewind the data loader (note that generators cannot be rewound).
Args:
input_metadata (TensorMetadata):
Mapping of input names to their data types and shapes.
Passed along to the data loader if provided. Generally should not be required
unless using Polygraphy's included `DataLoader` for this calibrator.
"""
if input_metadata is not None:
with contextlib.suppress(AttributeError):
self.data_loader.input_metadata = input_metadata
# Attempt to reset data loader
self.data_loader_iter = iter(self.data_loader)
self.num_batches = 0
# Make sure calibrator will check the cache again when reset.
self.cache_contents = None
self.has_cached_scales = False
def get_batch_size(self):
return self.batch_size
def get_batch(self, names):
if not self.is_active:
G_LOGGER.error(
"Calibrator must be activated prior to use. Please use a context manager. "
"For example:\nwith calibrator:\n\t# Use calibrator here"
)
return None
try:
buffers = next(self.data_loader_iter)
except StopIteration:
if not self.num_batches:
G_LOGGER.error(
"Calibrator data loader provided no data.\nPossible reasons for this include:\n(1) data loader "
"has no data to provide\n(2) data loader was a generator, and the calibrator is being "
"used multiple times (generators cannot be rewound)"
)
return None
else:
self.num_batches += 1
if not util.check_dict_contains(buffers, names, dict_name="calibration data", log_func=G_LOGGER.error):
return None
ptrs = []
for name in names:
buf = buffers[name]
if isinstance(buf, cuda.DeviceView):
ptrs.append(buf.ptr)
elif isinstance(buf, np.ndarray):
if name not in self.device_buffers:
self.device_buffers[name] = cuda.DeviceArray(shape=buf.shape, dtype=buf.dtype)
G_LOGGER.verbose("Allocated: {:}".format(self.device_buffers[name]))
ptrs.append(self.device_buffers[name].copy_from(buf).ptr)
elif isinstance(buf, int):
ptrs.append(buf)
else:
G_LOGGER.error(
"Calibration data loader provided an unrecognized type: {:} for input: {:}.\n"
"Please provide either a NumPy array, Polygraphy DeviceView, or GPU pointer. ".format(
type(buf).__name__, name
)
)
return None
return ptrs
def read_calibration_cache(self):
def load_from_cache():
if self._cache is None or not util.get_file_size(self._cache):
return None
try:
return util.load_file(self._cache, description="calibration cache")
except Exception as err:
G_LOGGER.error(
"Could not read from calibration cache: {:}\nNote: Error was: {:}".format(self._cache, err)
)
return None
# Only attempt to read from the cache once.
if self.has_cached_scales:
return self.cache_contents
self.cache_contents = load_from_cache()
if not self.cache_contents:
if self.cache_contents is not None:
G_LOGGER.warning(
"Calibration cache was provided, but is empty. "
"Will regenerate scales by running calibration.",
mode=LogMode.ONCE,
)
self.cache_contents = None
else:
self.has_cached_scales = True
return self.cache_contents
def write_calibration_cache(self, cache):
self.cache_contents = cache.tobytes()
self.has_cached_scales = True
if self._cache is None:
return
try:
util.save_file(contents=self.cache_contents, dest=self._cache, description="calibration cache")
except Exception as err:
G_LOGGER.error(
"Could not write to calibration cache: {:}.\nNote: Error was: {:}".format(self._cache, err)
)
def __enter__(self):
self.is_active = True
return self
def __exit__(self, exc_type, exc_value, traceback):
self.is_active = False
for device_buffer in self.device_buffers.values():
device_buffer.free()
# IInt8LegacyCalibrator methods
def get_quantile(self):
return util.default(quantile, 0.5)
def get_regression_cutoff(self):
return util.default(regression_cutoff, 0.5)
def read_histogram_cache(self, length):
pass
def write_histogram_cache(self, ptr, length):
pass
# IInt8Calibrator methods
def get_algorithm(self):
return util.default(algo, trt.CalibrationAlgoType.MINMAX_CALIBRATION)
def __repr__(self):
return util.make_repr(
"Calibrator",
data_loader,
cache=cache,
BaseClass=BaseClass,
batch_size=batch_size,
quantile=quantile,
regression_cutoff=regression_cutoff,
algo=algo,
)[0]
return CalibratorClass() | [
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|
carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | PythonAPI/examples/no_rendering_mode.py | python | World._show_nearby_vehicles | (self, vehicles) | Shows nearby vehicles of the hero actor | Shows nearby vehicles of the hero actor | [
"Shows",
"nearby",
"vehicles",
"of",
"the",
"hero",
"actor"
] | def _show_nearby_vehicles(self, vehicles):
"""Shows nearby vehicles of the hero actor"""
info_text = []
if self.hero_actor is not None and len(vehicles) > 1:
location = self.hero_transform.location
vehicle_list = [x[0] for x in vehicles if x[0].id != self.hero_actor.id]
def distance(v): return location.distance(v.get_location())
for n, vehicle in enumerate(sorted(vehicle_list, key=distance)):
if n > 15:
break
vehicle_type = get_actor_display_name(vehicle, truncate=22)
info_text.append('% 5d %s' % (vehicle.id, vehicle_type))
self._hud.add_info('NEARBY VEHICLES', info_text) | [
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||
NVIDIA/TensorRT | 42805f078052daad1a98bc5965974fcffaad0960 | tools/Polygraphy/polygraphy/tools/script.py | python | Script.append_preimport | (self, line) | Append a line to the pre-import prefix of the script.
Args:
line (str): The line to append. | Append a line to the pre-import prefix of the script. | [
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"""
Append a line to the pre-import prefix of the script.
Args:
line (str): The line to append.
"""
line = ensure_safe(line).unwrap()
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | FileHistory.UseMenu | (*args, **kwargs) | return _misc_.FileHistory_UseMenu(*args, **kwargs) | UseMenu(self, Menu menu) | UseMenu(self, Menu menu) | [
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"""UseMenu(self, Menu menu)"""
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|
bundy-dns/bundy | 3d41934996b82b0cd2fe22dd74d2abc1daba835d | src/lib/python/bundy/xfrin/diff.py | python | Diff.__init__ | (self, ds_client, zone, replace=False, journaling=False,
single_update_mode=False) | Initializes the diff to a ready state. It checks the zone exists
in the datasource and if not, NoSuchZone is raised. This also creates
a transaction in the data source.
The ds_client is the datasource client containing the zone. Zone is
bundy.dns.Name object representing the name of the zone (its apex).
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applying the diff.
If journaling is True, the history of subsequent updates will be
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data source support the journaling. If the data source allows
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will still continue applying the diffs with disabling journaling.
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deletions can be done in any order. The first addition and the
first deletion still have to be the new and old SOA records,
respectively. Once apply() or commit() has been called, this
requirement is renewed (since the diff object is essentialy reset).
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first, and then the additions. With the previously mentioned
restrictions, this means that the actual update looks like a single
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restrictions, this class does not do any checking of data; it is
the caller's responsibility to keep the data 'sane', and this class
does not presume to have any knowledge of DNS zone content sanity.
For instance, though it enforces the SOA to be deleted first, and
added first, it does no checks on the SERIAL value.
You can also expect bundy.datasrc.Error or bundy.datasrc.NotImplemented
exceptions. | Initializes the diff to a ready state. It checks the zone exists
in the datasource and if not, NoSuchZone is raised. This also creates
a transaction in the data source. | [
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single_update_mode=False):
"""
Initializes the diff to a ready state. It checks the zone exists
in the datasource and if not, NoSuchZone is raised. This also creates
a transaction in the data source.
The ds_client is the datasource client containing the zone. Zone is
bundy.dns.Name object representing the name of the zone (its apex).
If replace is True, the content of the whole zone is wiped out before
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If journaling is True, the history of subsequent updates will be
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data source support the journaling. If the data source allows
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will still continue applying the diffs with disabling journaling.
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If so, the additions and deletions are kept separately, and applied
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first deletion still have to be the new and old SOA records,
respectively. Once apply() or commit() has been called, this
requirement is renewed (since the diff object is essentialy reset).
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first, and then the additions. With the previously mentioned
restrictions, this means that the actual update looks like a single
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restrictions, this class does not do any checking of data; it is
the caller's responsibility to keep the data 'sane', and this class
does not presume to have any knowledge of DNS zone content sanity.
For instance, though it enforces the SOA to be deleted first, and
added first, it does no checks on the SERIAL value.
You can also expect bundy.datasrc.Error or bundy.datasrc.NotImplemented
exceptions.
"""
try:
self.__updater = ds_client.get_updater(zone, replace, journaling)
except bundy.datasrc.NotImplemented as ex:
if not journaling:
raise ex
self.__updater = ds_client.get_updater(zone, replace, False)
logger.info(LIBXFRIN_NO_JOURNAL, zone, ds_client)
if self.__updater is None:
# The no such zone case
raise NoSuchZone("Zone " + str(zone) +
" does not exist in the data source " +
str(ds_client))
self.__single_update_mode = single_update_mode
if single_update_mode:
self.__additions = []
self.__deletions = []
else:
self.__buffer = [] | [
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||
miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/training/saver.py | python | generate_checkpoint_state_proto | (save_dir,
model_checkpoint_path,
all_model_checkpoint_paths=None) | return coord_checkpoint_proto | Generates a checkpoint state proto.
Args:
save_dir: Directory where the model was saved.
model_checkpoint_path: The checkpoint file.
all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted
checkpoints, sorted from oldest to newest. If this is a non-empty list,
the last element must be equal to model_checkpoint_path. These paths
are also saved in the CheckpointState proto.
Returns:
CheckpointState proto with model_checkpoint_path and
all_model_checkpoint_paths updated to either absolute paths or
relative paths to the current save_dir. | Generates a checkpoint state proto. | [
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"a",
"checkpoint",
"state",
"proto",
"."
] | def generate_checkpoint_state_proto(save_dir,
model_checkpoint_path,
all_model_checkpoint_paths=None):
"""Generates a checkpoint state proto.
Args:
save_dir: Directory where the model was saved.
model_checkpoint_path: The checkpoint file.
all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted
checkpoints, sorted from oldest to newest. If this is a non-empty list,
the last element must be equal to model_checkpoint_path. These paths
are also saved in the CheckpointState proto.
Returns:
CheckpointState proto with model_checkpoint_path and
all_model_checkpoint_paths updated to either absolute paths or
relative paths to the current save_dir.
"""
if all_model_checkpoint_paths is None:
all_model_checkpoint_paths = []
if (not all_model_checkpoint_paths or
all_model_checkpoint_paths[-1] != model_checkpoint_path):
logging.info("%s is not in all_model_checkpoint_paths. Manually adding it.",
model_checkpoint_path)
all_model_checkpoint_paths.append(model_checkpoint_path)
# Relative paths need to be rewritten to be relative to the "save_dir"
# if model_checkpoint_path already contains "save_dir".
if not os.path.isabs(save_dir):
if not os.path.isabs(model_checkpoint_path):
model_checkpoint_path = os.path.relpath(model_checkpoint_path, save_dir)
for i in range(len(all_model_checkpoint_paths)):
p = all_model_checkpoint_paths[i]
if not os.path.isabs(p):
all_model_checkpoint_paths[i] = os.path.relpath(p, save_dir)
coord_checkpoint_proto = CheckpointState(
model_checkpoint_path=model_checkpoint_path,
all_model_checkpoint_paths=all_model_checkpoint_paths)
return coord_checkpoint_proto | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PGProperty.SetWasModified | (*args, **kwargs) | return _propgrid.PGProperty_SetWasModified(*args, **kwargs) | SetWasModified(self, bool set=True) | SetWasModified(self, bool set=True) | [
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"(",
"self",
"bool",
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"=",
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")"
] | def SetWasModified(*args, **kwargs):
"""SetWasModified(self, bool set=True)"""
return _propgrid.PGProperty_SetWasModified(*args, **kwargs) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/random.py | python | Random.weibullvariate | (self, alpha, beta) | return alpha * (-_log(u)) ** (1.0/beta) | Weibull distribution.
alpha is the scale parameter and beta is the shape parameter. | Weibull distribution. | [
"Weibull",
"distribution",
"."
] | def weibullvariate(self, alpha, beta):
"""Weibull distribution.
alpha is the scale parameter and beta is the shape parameter.
"""
# Jain, pg. 499; bug fix courtesy Bill Arms
u = 1.0 - self.random()
return alpha * (-_log(u)) ** (1.0/beta) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/pydoc.py | python | writedocs | (dir, pkgpath='', done=None) | return | Write out HTML documentation for all modules in a directory tree. | Write out HTML documentation for all modules in a directory tree. | [
"Write",
"out",
"HTML",
"documentation",
"for",
"all",
"modules",
"in",
"a",
"directory",
"tree",
"."
] | def writedocs(dir, pkgpath='', done=None):
"""Write out HTML documentation for all modules in a directory tree."""
if done is None: done = {}
for importer, modname, ispkg in pkgutil.walk_packages([dir], pkgpath):
writedoc(modname)
return | [
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|
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/lite/schema/upgrade_schema.py | python | Converter._PerformUpgrade | (self, data) | Manipulate the `data` (parsed JSON) based on changes in format.
This incrementally will upgrade from version to version within data.
Args:
data: Dictionary representing the TensorFlow data. This will be upgraded
in place. | Manipulate the `data` (parsed JSON) based on changes in format. | [
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"data",
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] | def _PerformUpgrade(self, data):
"""Manipulate the `data` (parsed JSON) based on changes in format.
This incrementally will upgrade from version to version within data.
Args:
data: Dictionary representing the TensorFlow data. This will be upgraded
in place.
"""
while data["version"] < self._new_version:
self._upgrade_dispatch[data["version"]](data)
data["version"] += 1 | [
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||
Cisco-Talos/moflow | ed71dfb0540d9e0d7a4c72f0881b58958d573728 | BAP-0.7-moflow/libtracewrap/libtrace/protobuf/python/ez_setup.py | python | use_setuptools | (
version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=os.curdir,
download_delay=15
) | return do_download() | Automatically find/download setuptools and make it available on sys.path
`version` should be a valid setuptools version number that is available
as an egg for download under the `download_base` URL (which should end with
a '/'). `to_dir` is the directory where setuptools will be downloaded, if
it is not already available. If `download_delay` is specified, it should
be the number of seconds that will be paused before initiating a download,
should one be required. If an older version of setuptools is installed,
this routine will print a message to ``sys.stderr`` and raise SystemExit in
an attempt to abort the calling script. | Automatically find/download setuptools and make it available on sys.path | [
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"find",
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"download",
"setuptools",
"and",
"make",
"it",
"available",
"on",
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"path"
] | def use_setuptools(
version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=os.curdir,
download_delay=15
):
"""Automatically find/download setuptools and make it available on sys.path
`version` should be a valid setuptools version number that is available
as an egg for download under the `download_base` URL (which should end with
a '/'). `to_dir` is the directory where setuptools will be downloaded, if
it is not already available. If `download_delay` is specified, it should
be the number of seconds that will be paused before initiating a download,
should one be required. If an older version of setuptools is installed,
this routine will print a message to ``sys.stderr`` and raise SystemExit in
an attempt to abort the calling script.
"""
was_imported = 'pkg_resources' in sys.modules or 'setuptools' in sys.modules
def do_download():
egg = download_setuptools(version, download_base, to_dir, download_delay)
sys.path.insert(0, egg)
import setuptools; setuptools.bootstrap_install_from = egg
try:
import pkg_resources
except ImportError:
return do_download()
try:
pkg_resources.require("setuptools>="+version); return
except pkg_resources.VersionConflict, e:
if was_imported:
print >>sys.stderr, (
"The required version of setuptools (>=%s) is not available, and\n"
"can't be installed while this script is running. Please install\n"
" a more recent version first, using 'easy_install -U setuptools'."
"\n\n(Currently using %r)"
) % (version, e.args[0])
sys.exit(2)
except pkg_resources.DistributionNotFound:
pass
del pkg_resources, sys.modules['pkg_resources'] # reload ok
return do_download() | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/metrics_impl.py | python | _sparse_false_negative_at_k | (labels,
predictions_idx,
class_id=None,
weights=None) | Calculates false negatives for recall@k.
If `class_id` is specified, calculate binary true positives for `class_id`
only.
If `class_id` is not specified, calculate metrics for `k` predicted vs
`n` label classes, where `n` is the 2nd dimension of `labels_sparse`.
Args:
labels: `int64` `Tensor` or `SparseTensor` with shape
[D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
target classes for the associated prediction. Commonly, N=1 and `labels`
has shape [batch_size, num_labels]. [D1, ... DN] must match
`predictions_idx`.
predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
match `labels`.
class_id: Class for which we want binary metrics.
weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
`labels`. If the latter, it must be broadcastable to `labels` (i.e., all
dimensions must be either `1`, or the same as the corresponding `labels`
dimension).
Returns:
A [D1, ... DN] `Tensor` of false negative counts. | Calculates false negatives for recall@k. | [
"Calculates",
"false",
"negatives",
"for",
"recall@k",
"."
] | def _sparse_false_negative_at_k(labels,
predictions_idx,
class_id=None,
weights=None):
"""Calculates false negatives for recall@k.
If `class_id` is specified, calculate binary true positives for `class_id`
only.
If `class_id` is not specified, calculate metrics for `k` predicted vs
`n` label classes, where `n` is the 2nd dimension of `labels_sparse`.
Args:
labels: `int64` `Tensor` or `SparseTensor` with shape
[D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
target classes for the associated prediction. Commonly, N=1 and `labels`
has shape [batch_size, num_labels]. [D1, ... DN] must match
`predictions_idx`.
predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
match `labels`.
class_id: Class for which we want binary metrics.
weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
`labels`. If the latter, it must be broadcastable to `labels` (i.e., all
dimensions must be either `1`, or the same as the corresponding `labels`
dimension).
Returns:
A [D1, ... DN] `Tensor` of false negative counts.
"""
with ops.name_scope(None, 'false_negatives',
(predictions_idx, labels, weights)):
labels, predictions_idx = _maybe_select_class_id(labels, predictions_idx,
class_id)
fn = sets.set_size(
sets.set_difference(predictions_idx, labels, aminusb=False))
fn = math_ops.cast(fn, dtypes.float64)
if weights is not None:
with ops.control_dependencies((weights_broadcast_ops.assert_broadcastable(
weights, fn),)):
weights = math_ops.cast(weights, dtypes.float64)
fn = math_ops.multiply(fn, weights)
return fn | [
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||
klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/base/android/jni_generator/jni_registration_generator.py | python | _Generate | (java_file_paths,
srcjar_path,
proxy_opts,
header_path=None,
namespace='') | Generates files required to perform JNI registration.
Generates a srcjar containing a single class, GEN_JNI, that contains all
native method declarations.
Optionally generates a header file that provides functions
(RegisterMainDexNatives and RegisterNonMainDexNatives) to perform
JNI registration.
Args:
java_file_paths: A list of java file paths.
srcjar_path: Path to the GEN_JNI srcjar.
header_path: If specified, generates a header file in this location.
namespace: If specified, sets the namespace for the generated header file. | Generates files required to perform JNI registration. | [
"Generates",
"files",
"required",
"to",
"perform",
"JNI",
"registration",
"."
] | def _Generate(java_file_paths,
srcjar_path,
proxy_opts,
header_path=None,
namespace=''):
"""Generates files required to perform JNI registration.
Generates a srcjar containing a single class, GEN_JNI, that contains all
native method declarations.
Optionally generates a header file that provides functions
(RegisterMainDexNatives and RegisterNonMainDexNatives) to perform
JNI registration.
Args:
java_file_paths: A list of java file paths.
srcjar_path: Path to the GEN_JNI srcjar.
header_path: If specified, generates a header file in this location.
namespace: If specified, sets the namespace for the generated header file.
"""
# Without multiprocessing, script takes ~13 seconds for chrome_public_apk
# on a z620. With multiprocessing, takes ~2 seconds.
pool = multiprocessing.Pool()
results = []
for d in pool.imap_unordered(
functools.partial(_DictForPath, use_proxy_hash=proxy_opts.use_hash),
java_file_paths):
if d:
results.append(d)
pool.close()
# Sort to make output deterministic.
results.sort(key=lambda d: d['FULL_CLASS_NAME'])
combined_dict = {}
for key in MERGEABLE_KEYS:
combined_dict[key] = ''.join(d.get(key, '') for d in results)
if header_path:
combined_dict['HEADER_GUARD'] = \
os.path.splitext(header_path)[0].replace('/', '_').upper() + '_'
combined_dict['NAMESPACE'] = namespace
header_content = CreateFromDict(combined_dict, proxy_opts.use_hash)
with build_utils.AtomicOutput(header_path, mode='w') as f:
f.write(header_content)
with build_utils.AtomicOutput(srcjar_path) as f:
with zipfile.ZipFile(f, 'w') as srcjar:
if proxy_opts.use_hash:
# J/N.java
build_utils.AddToZipHermetic(
srcjar,
'%s.java' % jni_generator.ProxyHelpers.GetQualifiedClass(True),
data=CreateProxyJavaFromDict(combined_dict, proxy_opts))
# org/chromium/base/natives/GEN_JNI.java
build_utils.AddToZipHermetic(
srcjar,
'%s.java' % jni_generator.ProxyHelpers.GetQualifiedClass(False),
data=CreateProxyJavaFromDict(
combined_dict, proxy_opts, forwarding=True))
else:
# org/chromium/base/natives/GEN_JNI.java
build_utils.AddToZipHermetic(
srcjar,
'%s.java' % jni_generator.ProxyHelpers.GetQualifiedClass(False),
data=CreateProxyJavaFromDict(combined_dict, proxy_opts)) | [
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||
RegrowthStudios/SoACode-Public | c3ddd69355b534d5e70e2e6d0c489b4e93ab1ffe | utils/git-hooks/pep8.py | python | whitespace_around_keywords | (logical_line) | r"""
Avoid extraneous whitespace around keywords.
Okay: True and False
E271: True and False
E272: True and False
E273: True and\tFalse
E274: True\tand False | r"""
Avoid extraneous whitespace around keywords. | [
"r",
"Avoid",
"extraneous",
"whitespace",
"around",
"keywords",
"."
] | def whitespace_around_keywords(logical_line):
r"""
Avoid extraneous whitespace around keywords.
Okay: True and False
E271: True and False
E272: True and False
E273: True and\tFalse
E274: True\tand False
"""
for match in KEYWORD_REGEX.finditer(logical_line):
before, after = match.groups()
if '\t' in before:
yield match.start(1), "E274 tab before keyword"
elif len(before) > 1:
yield match.start(1), "E272 multiple spaces before keyword"
if '\t' in after:
yield match.start(2), "E273 tab after keyword"
elif len(after) > 1:
yield match.start(2), "E271 multiple spaces after keyword" | [
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||
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/post/opensbp_pre.py | python | insert | (filename, docname) | called when freecad imports a file
This insert expects parse to return a list of strings
each string will become a separate path | called when freecad imports a file
This insert expects parse to return a list of strings
each string will become a separate path | [
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'''called when freecad imports a file
This insert expects parse to return a list of strings
each string will become a separate path'''
gfile = pythonopen(filename)
gcode = gfile.read()
gfile.close()
gcode = parse(gcode)
doc = FreeCAD.getDocument(docname)
for subpath in gcode:
obj = doc.addObject("Path::Feature", "Path")
path = Path.Path(subpath)
obj.Path = path | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributed/algorithms/join.py | python | Join.__exit__ | (
self,
type: Optional[Type[BaseException]],
value: Optional[BaseException],
traceback: Optional[TracebackType]
) | r"""
Repeatedly runs the main hooks until all processes join; then, runs
the post-hooks.
Raises:
RuntimeError
If ``throw_on_early_termination=True``. | r"""
Repeatedly runs the main hooks until all processes join; then, runs
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Raises:
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"""
if not self._enable or type:
return # propagate the exception directly if one was raised
all_procs_joined = False
is_last_joiner = True
i = 0
WARN_THRESHOLD = 1000
warnings.simplefilter("once")
while not all_procs_joined:
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# Shadow the all-reduce in non-joined processes
num_nonjoined_procs = self._get_num_nonjoined_procs()
if num_nonjoined_procs == 0:
all_procs_joined = True
else:
if self._throw_on_early_termination:
self._notify_procs_to_terminate()
# Run main hooks
for join_hook in self._join_hooks:
join_hook.main_hook()
is_last_joiner = False
i += 1
# Run post-hooks
for join_hook in self._join_hooks:
join_hook.post_hook(is_last_joiner) | [
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||
cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Alignment/MuonAlignmentAlgorithms/scripts/plotscripts.py | python | set_palette | (name=None, ncontours=999) | Set a color palette from a given RGB list
stops, red, green and blue should all be lists of the same length
see set_decent_colors for an example | Set a color palette from a given RGB list
stops, red, green and blue should all be lists of the same length
see set_decent_colors for an example | [
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"""Set a color palette from a given RGB list
stops, red, green and blue should all be lists of the same length
see set_decent_colors for an example"""
if name == "halfgray":
stops = [0.00, 0.34, 0.61, 0.84, 1.00]
red = map(lambda x: 1. - (1.-x)/2., [1.00, 0.84, 0.61, 0.34, 0.00])
green = map(lambda x: 1. - (1.-x)/2., [1.00, 0.84, 0.61, 0.34, 0.00])
blue = map(lambda x: 1. - (1.-x)/2., [1.00, 0.84, 0.61, 0.34, 0.00])
elif name == "gray":
stops = [0.00, 0.34, 0.61, 0.84, 1.00]
red = [1.00, 0.84, 0.61, 0.34, 0.00]
green = [1.00, 0.84, 0.61, 0.34, 0.00]
blue = [1.00, 0.84, 0.61, 0.34, 0.00]
elif name == "blues":
stops = [0.00, 0.34, 0.61, 0.84, 1.00]
red = [1.00, 0.84, 0.61, 0.34, 0.00]
green = [1.00, 0.84, 0.61, 0.34, 0.00]
blue = [1.00, 1.00, 1.00, 1.00, 1.00]
elif name == "reds":
stops = [0.00, 0.34, 0.61, 0.84, 1.00]
red = [1.00, 1.00, 1.00, 1.00, 1.00]
green = [1.00, 0.84, 0.61, 0.34, 0.00]
blue = [1.00, 0.84, 0.61, 0.34, 0.00]
elif name == "antigray":
stops = [0.00, 0.34, 0.61, 0.84, 1.00]
red = [1.00, 0.84, 0.61, 0.34, 0.00]
green = [1.00, 0.84, 0.61, 0.34, 0.00]
blue = [1.00, 0.84, 0.61, 0.34, 0.00]
red.reverse()
green.reverse()
blue.reverse()
elif name == "fire":
stops = [0.00, 0.20, 0.80, 1.00]
red = [1.00, 1.00, 1.00, 0.50]
green = [1.00, 1.00, 0.00, 0.00]
blue = [0.20, 0.00, 0.00, 0.00]
elif name == "antifire":
stops = [0.00, 0.20, 0.80, 1.00]
red = [0.50, 1.00, 1.00, 1.00]
green = [0.00, 0.00, 1.00, 1.00]
blue = [0.00, 0.00, 0.00, 0.20]
else:
# default palette, looks cool
stops = [0.00, 0.34, 0.61, 0.84, 1.00]
red = [0.00, 0.00, 0.87, 1.00, 0.51]
green = [0.00, 0.81, 1.00, 0.20, 0.00]
blue = [0.51, 1.00, 0.12, 0.00, 0.00]
s = array.array('d', stops)
r = array.array('d', red)
g = array.array('d', green)
b = array.array('d', blue)
npoints = len(s)
ROOT.TColor.CreateGradientColorTable(npoints, s, r, g, b, ncontours)
ROOT.gStyle.SetNumberContours(ncontours) | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/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
usually is a dictionary). | [
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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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|
fifengine/fifengine | 4b62c42e85bec19893cef8e63e6855927cff2c47 | engine/python/fife/extensions/pychan/widgets/containers.py | python | Container.addChild | (self, widget) | Adds a child widget to the container.
This makes the childs widgets visible state the same as the containers.
i.e. if the containter is visible the child will be as well and if the
container widget is hidden so will the child. The child however WILL
be shown when you show the container widget. If you want the child to
be hidden when you show the container widget you must call child.hide(). | Adds a child widget to the container.
This makes the childs widgets visible state the same as the containers.
i.e. if the containter is visible the child will be as well and if the
container widget is hidden so will the child. The child however WILL
be shown when you show the container widget. If you want the child to
be hidden when you show the container widget you must call child.hide(). | [
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container widget is hidden so will the child. The child however WILL
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"""
widget.parent = self
if widget.max_size[0] > self.max_size[0] or widget.max_size[1] > self.max_size[1]:
widget.max_size = self.max_size
self.children.append(widget)
self.real_widget.add(widget.real_widget)
# add all to the manager
def _add(added_widget):
if not added_widget._added:
get_manager().addWidget(added_widget)
if added_widget._top_added:
get_manager().removeTopWidget(added_widget)
widget.deepApply(_add) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/sans/command_interface/ISISCommandInterface.py | python | set_save | (save_algorithms, save_as_zero_error_free) | Mainly internally used by BatchMode. Provides the save settings.
@param save_algorithms: A list of SaveType enums.
@param save_as_zero_error_free: True if a zero error correction should be performed. | Mainly internally used by BatchMode. Provides the save settings. | [
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"""
Mainly internally used by BatchMode. Provides the save settings.
@param save_algorithms: A list of SaveType enums.
@param save_as_zero_error_free: True if a zero error correction should be performed.
"""
save_command = NParameterCommand(command_id=NParameterCommandId.SAVE, values=[save_algorithms,
save_as_zero_error_free])
director.add_command(save_command) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | InputStream.readlines | (*args, **kwargs) | return _core_.InputStream_readlines(*args, **kwargs) | readlines(self, int sizehint=-1) -> PyObject | readlines(self, int sizehint=-1) -> PyObject | [
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|
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | tools/codestyle/docstring_checker.py | python | DocstringChecker.one_line | (self, node) | return True | one_line checks if docstring (len < 40) is on one line.
Args:
node (astroid.node): The node visiting.
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doc = node.doc
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|
freeorion/freeorion | c266a40eccd3a99a17de8fe57c36ef6ba3771665 | default/python/universe_generation/planets.py | python | calc_planet_type | (star_type, orbit, planet_size) | Calculate planet type randomly for a potential new planet.
TODO: take into account star type and orbit number for determining planet type. | Calculate planet type randomly for a potential new planet. | [
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] | def calc_planet_type(star_type, orbit, planet_size):
"""
Calculate planet type randomly for a potential new planet.
TODO: take into account star type and orbit number for determining planet type.
"""
# check specified planet size to determine if we want a planet at all
if planet_size in planet_sizes:
# if yes, determine planet type based on planet size...
if planet_size == fo.planetSize.gasGiant:
return fo.planetType.gasGiant
elif planet_size == fo.planetSize.asteroids:
return fo.planetType.asteroids
else:
return random.choice(planet_types_real)
else:
return fo.planetType.unknown | [
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||
adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/site_compare/command_line.py | python | Command.__init__ | (self, names, helptext, validator=None, impl=None) | Initializes Command from names and helptext, plus optional callables.
Args:
names: command name, or list of synonyms
helptext: brief string description of the command
validator: callable for custom argument validation
Should raise ParseError if it wants
impl: callable to be invoked when command is called | Initializes Command from names and helptext, plus optional callables. | [
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"""Initializes Command from names and helptext, plus optional callables.
Args:
names: command name, or list of synonyms
helptext: brief string description of the command
validator: callable for custom argument validation
Should raise ParseError if it wants
impl: callable to be invoked when command is called
"""
self.names = names
self.validator = validator
self.helptext = helptext
self.impl = impl
self.args = []
self.required_groups = []
self.arg_dict = {}
self.positional_args = []
self.cmdline = None | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/thumbnailctrl.py | python | ScrolledThumbnail.SetPopupMenu | (self, menu) | Sets the thumbnails popup menu when at least one thumbnail is selected.
:param `menu`: an instance of :class:`Menu`. | Sets the thumbnails popup menu when at least one thumbnail is selected. | [
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"""
Sets the thumbnails popup menu when at least one thumbnail is selected.
:param `menu`: an instance of :class:`Menu`.
"""
self._pmenu = menu | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/image_ops.py | python | _ImageDimensions | (images, static_only=True) | Returns the dimensions of an image tensor.
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]`
static_only: Boolean, whether to return only static shape.
Returns:
list of integers `[batch, height, width, channels]`, when static shape is
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list of integer scalar tensors `[batch, height, width, channels]`, when
static shape is not fully defined. | Returns the dimensions of an image tensor. | [
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"""Returns the dimensions of an image tensor.
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]`
static_only: Boolean, whether to return only static shape.
Returns:
list of integers `[batch, height, width, channels]`, when static shape is
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list of integer scalar tensors `[batch, height, width, channels]`, when
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"""
# A simple abstraction to provide names for each dimension. This abstraction
# should make it simpler to switch dimensions in the future (e.g. if we ever
# want to switch height and width.)
if static_only or images.get_shape().is_fully_defined():
return images.get_shape().as_list()
else:
return array_ops.unpack(array_ops.shape(images)) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/fps/connection.py | python | FPSConnection.get_payment_instruction | (self, action, response, **kw) | return self.get_object(action, kw, response) | Gets the payment instruction of a token. | Gets the payment instruction of a token. | [
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Gets the payment instruction of a token.
"""
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/rgw/module.py | python | Module.config_notify | (self) | This method is called whenever one of our config options is changed. | This method is called whenever one of our config options is changed. | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pip/vendor/html5lib/inputstream.py | python | EncodingBytes.jumpTo | (self, bytes) | Look for the next sequence of bytes matching a given sequence. If
a match is found advance the position to the last byte of the match | Look for the next sequence of bytes matching a given sequence. If
a match is found advance the position to the last byte of the match | [
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newPosition = self[self.position:].find(bytes)
if newPosition > -1:
# XXX: This is ugly, but I can't see a nicer way to fix this.
if self._position == -1:
self._position = 0
self._position += (newPosition + len(bytes) - 1)
return True
else:
raise StopIteration | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | SplashScreen.GetSplashStyle | (*args, **kwargs) | return _windows_.SplashScreen_GetSplashStyle(*args, **kwargs) | GetSplashStyle(self) -> long | GetSplashStyle(self) -> long | [
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"""GetSplashStyle(self) -> long"""
return _windows_.SplashScreen_GetSplashStyle(*args, **kwargs) | [
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|
krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/plan/cspace.py | python | MotionPlan.__init__ | (self,space,type=None,**options) | Initializes a plan with a given CSpace and a given type.
Optionally, planner options can be set via keyword arguments.
Valid values for type are:
* 'prm': the Probabilistic Roadmap algorithm
* 'rrt': the Rapidly Exploring Random Trees algorithm
* 'sbl': the Single-Query Bidirectional Lazy planner
* 'sblprt': the probabilistic roadmap of trees (PRT) algorithm with SBL as the inter-root planner.
* 'rrt*': the RRT* algorithm for optimal motion planning
* 'prm*': the PRM* algorithm for optimal motion planning
* 'lazyprm*': the Lazy-PRM* algorithm for optimal motion planning
* 'lazyrrg*': the Lazy-RRG* algorithm for optimal motion planning
* 'fmm': the fast marching method algorithm for resolution-complete optimal motion planning
* 'fmm*': an anytime fast marching method algorithm for optimal motion planning
(this list may be out-of-date; the most current documentation
is listed in src/motionplanning.h) | Initializes a plan with a given CSpace and a given type.
Optionally, planner options can be set via keyword arguments.
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* 'prm': the Probabilistic Roadmap algorithm
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"""
if space.cspace is None:
space.setup()
if type != None:
motionplanning.setPlanType(type)
if len(options) > 0:
MotionPlan.setOptions(**options)
self.space = space
self.planOptions = motionplanning.getPlanJSONString()
self.planner = motionplanning.PlannerInterface(space.cspace)
self.edgeCost=None
self.terminalCost=None | [
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||
natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/rnn_cell.py | python | EmbeddingWrapper.__init__ | (self, cell, embedding_classes, embedding_size, initializer=None) | Create a cell with an added input embedding.
Args:
cell: an RNNCell, an embedding will be put before its inputs.
embedding_classes: integer, how many symbols will be embedded.
embedding_size: integer, the size of the vectors we embed into.
initializer: an initializer to use when creating the embedding;
if None, the initializer from variable scope or a default one is used.
Raises:
TypeError: if cell is not an RNNCell.
ValueError: if embedding_classes is not positive. | Create a cell with an added input embedding. | [
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"""Create a cell with an added input embedding.
Args:
cell: an RNNCell, an embedding will be put before its inputs.
embedding_classes: integer, how many symbols will be embedded.
embedding_size: integer, the size of the vectors we embed into.
initializer: an initializer to use when creating the embedding;
if None, the initializer from variable scope or a default one is used.
Raises:
TypeError: if cell is not an RNNCell.
ValueError: if embedding_classes is not positive.
"""
if not isinstance(cell, RNNCell):
raise TypeError("The parameter cell is not RNNCell.")
if embedding_classes <= 0 or embedding_size <= 0:
raise ValueError("Both embedding_classes and embedding_size must be > 0: "
"%d, %d." % (embedding_classes, embedding_size))
self._cell = cell
self._embedding_classes = embedding_classes
self._embedding_size = embedding_size
self._initializer = initializer | [
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||
netket/netket | 0d534e54ecbf25b677ea72af6b85947979420652 | netket/hilbert/qubit.py | python | Qubit.__init__ | (self, N: int = 1, graph: Optional[AbstractGraph] = None) | r"""Initializes a qubit hilbert space.
Args:
N: Number of qubits.
graph: (deprecated) a graph from which to extract the number of sites.
Examples:
Simple spin hilbert space.
>>> from netket.hilbert import Qubit
>>> hi = Qubit(N=100)
>>> print(hi.size)
100 | r"""Initializes a qubit hilbert space. | [
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] | def __init__(self, N: int = 1, graph: Optional[AbstractGraph] = None):
r"""Initializes a qubit hilbert space.
Args:
N: Number of qubits.
graph: (deprecated) a graph from which to extract the number of sites.
Examples:
Simple spin hilbert space.
>>> from netket.hilbert import Qubit
>>> hi = Qubit(N=100)
>>> print(hi.size)
100
"""
N = graph_to_N_depwarn(N=N, graph=graph)
super().__init__([0.0, 1.0], N) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/Inelastic/Direct/PropertyManager.py | python | PropertyManager._init_private_properties | (self,prop_dict) | helper method used to define all private dictionaries at once
during __init__ procedure | helper method used to define all private dictionaries at once
during __init__ procedure | [
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""" helper method used to define all private dictionaries at once
during __init__ procedure
"""
class_decor = '_'+type(self).__name__+'__'
for key,val in prop_dict.items():
new_key = class_decor+key
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/ops.py | python | _get_opstr | (op, cls) | return {operator.add: '+',
radd: '+',
operator.mul: '*',
rmul: '*',
operator.sub: '-',
rsub: '-',
operator.truediv: '/',
rtruediv: '/',
operator.floordiv: '//',
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rand_: '&',
operator.or_: '|',
ror_: '|',
operator.xor: '^',
rxor: '^',
divmod: None,
rdivmod: None}[op] | Find the operation string, if any, to pass to numexpr for this
operation.
Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None | Find the operation string, if any, to pass to numexpr for this
operation. | [
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"""
Find the operation string, if any, to pass to numexpr for this
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Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None
"""
# numexpr is available for non-sparse classes
subtyp = getattr(cls, '_subtyp', '')
use_numexpr = 'sparse' not in subtyp
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|
randomascii/blogstuff | 07074af1c2df6e61d30bb4fb7704e4166d0dc9ed | ChromiumBuildAnalysis/analyze_chrome.py | python | ReadTargets | (log, show_all) | return targets_dict.values() | Reads all targets from .ninja_log file |log_file|, sorted by duration.
The result is a list of Target objects. | Reads all targets from .ninja_log file |log_file|, sorted by duration. | [
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"""Reads all targets from .ninja_log file |log_file|, sorted by duration.
The result is a list of Target objects."""
header = log.readline()
assert header == '# ninja log v5\n', \
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targets_dict = {}
last_end_seen = 0.0
for line in log:
parts = line.strip().split('\t')
if len(parts) != 5:
# If ninja.exe is rudely halted then the .ninja_log file may be
# corrupt. Silently continue.
continue
start, end, _, name, cmdhash = parts # Ignore restat.
# Convert from integral milliseconds to float seconds.
start = int(start) / 1000.0
end = int(end) / 1000.0
if not show_all and end < last_end_seen:
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# This has to be done by comparing end times because records are
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# times are guaranteed to be in order, but start times are not.
targets_dict = {}
target = None
if cmdhash in targets_dict:
target = targets_dict[cmdhash]
if not show_all and (target.start != start or target.end != end):
# If several builds in a row just run one or two build steps then
# the end times may not go backwards so the last build may not be
# detected as such. However in many cases there will be a build step
# repeated in the two builds and the changed start/stop points for
# that command, identified by the hash, can be used to detect and
# reset the target dictionary.
targets_dict = {}
target = None
if not target:
targets_dict[cmdhash] = target = Target(start, end)
last_end_seen = end
target.targets.append(name)
return targets_dict.values() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/extern/pygments/lexers/text.py | python | YamlLexer.parse_plain_scalar_indent | (token_class) | return callback | Process indentation spaces in a plain scalar. | Process indentation spaces in a plain scalar. | [
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"."
] | def parse_plain_scalar_indent(token_class):
"""Process indentation spaces in a plain scalar."""
def callback(lexer, match, context):
text = match.group()
if len(text) <= context.indent:
context.stack.pop()
context.stack.pop()
return
if text:
yield match.start(), token_class, text
context.pos = match.end()
return callback | [
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naver/sling | 5671cd445a2caae0b4dd0332299e4cfede05062c | webkit/Tools/Scripts/webkitpy/thirdparty/mod_pywebsocket/standalone.py | python | WebSocketRequestHandler.log_error | (self, *args) | Override BaseHTTPServer.log_error. | Override BaseHTTPServer.log_error. | [
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] | def log_error(self, *args):
"""Override BaseHTTPServer.log_error."""
# Despite the name, this method is for warnings than for errors.
# For example, HTTP status code is logged by this method.
self._logger.warning('%s - %s',
self.address_string(),
args[0] % args[1:]) | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | lldb/third_party/Python/module/pexpect-4.6/pexpect/screen.py | python | screen.crlf | (self) | This advances the cursor with CRLF properties.
The cursor will line wrap and the screen may scroll. | This advances the cursor with CRLF properties.
The cursor will line wrap and the screen may scroll. | [
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'''This advances the cursor with CRLF properties.
The cursor will line wrap and the screen may scroll.
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projectchrono/chrono | 92015a8a6f84ef63ac8206a74e54a676251dcc89 | src/demos/python/chrono-tensorflow/PPO/policy.py | python | Policy._restore_model | (self) | restore saved model.
if multiprocessing on gpu enable dynamic memory allocation | restore saved model.
if multiprocessing on gpu enable dynamic memory allocation | [
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""" restore saved model.
if multiprocessing on gpu enable dynamic memory allocation """
tf.reset_default_graph()
if self.multiGPU :
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
else:
self.sess = tf.Session()
# import graph from file, initialize variables, get variables and operations needed, initialize saver, restore checkpoint
loader = tf.train.import_meta_graph("./savedmodel/"+self.env_name+"/Policy/trained_variables.ckpt.meta")
self.sess.run(tf.global_variables_initializer())
self.g = tf.get_default_graph()
self.obs_ph = self.g.get_tensor_by_name('obs:0')
self.act_ph = self.g.get_tensor_by_name('act:0')
self.means = self.g.get_tensor_by_name('means/BiasAdd:0')
self.log_vars = self.g.get_tensor_by_name('log_vars:0')
self.advantages_ph = self.g.get_tensor_by_name('advantages:0')
self.beta_ph = self.g.get_tensor_by_name('beta:0')
self.eta_ph = self.g.get_tensor_by_name('eta:0')
self.lr_ph = self.g.get_tensor_by_name('lr:0')
self.old_log_vars_ph = self.g.get_tensor_by_name('old_log_vars:0')
self.old_means_ph = self.g.get_tensor_by_name('old_means:0')
self.sampled_act = self.g.get_tensor_by_name('sampledact:0')
self.loss = self.g.get_tensor_by_name('loss:0')
self.train_op = self.g.get_operation_by_name('train_op')
self.entropy = self.g.get_tensor_by_name('entropy:0')
self.kl = self.g.get_tensor_by_name('kl:0')
self.logp = self.g.get_tensor_by_name('logp:0')
self.logp_old = self.g.get_tensor_by_name('logp_old:0')
self.lr = 9e-4 / np.sqrt(int(np.sqrt(self.obs_dim * 10 * self.act_dim * 10)))
self.saver = tf.train.Saver()
loader.restore(self.sess, tf.train.latest_checkpoint("./savedmodel/"+self.env_name+"/Policy")) | [
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||
pristineio/webrtc-mirror | 7a5bcdffaab90a05bc1146b2b1ea71c004e54d71 | webrtc/rtc_tools/py_event_log_analyzer/rtp_analyzer.py | python | RTPStatistics.ComputeBandwidth | (self) | Computes bandwidth averaged over several consecutive packets.
The number of consecutive packets used in the average is
BANDWIDTH_SMOOTHING_WINDOW_SIZE. Averaging is done with
numpy.correlate. | Computes bandwidth averaged over several consecutive packets. | [
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] | def ComputeBandwidth(self):
"""Computes bandwidth averaged over several consecutive packets.
The number of consecutive packets used in the average is
BANDWIDTH_SMOOTHING_WINDOW_SIZE. Averaging is done with
numpy.correlate.
"""
start_ms = self.data_points[0].real_send_time_ms
stop_ms = self.data_points[-1].real_send_time_ms
(self.bandwidth_kbps, _) = numpy.histogram(
[point.real_send_time_ms for point in self.data_points],
bins=numpy.arange(start_ms, stop_ms,
RTPStatistics.PLOT_RESOLUTION_MS),
weights=[point.size * 8 / RTPStatistics.PLOT_RESOLUTION_MS
for point in self.data_points]
)
correlate_filter = (numpy.ones(
RTPStatistics.BANDWIDTH_SMOOTHING_WINDOW_SIZE) /
RTPStatistics.BANDWIDTH_SMOOTHING_WINDOW_SIZE)
self.smooth_bw_kbps = numpy.correlate(self.bandwidth_kbps, correlate_filter) | [
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||
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/ao/ns/_numeric_suite.py | python | compare_weights | (
float_dict: Dict[str, Any], quantized_dict: Dict[str, Any]
) | return weight_dict | r"""Compare the weights of the float module with its corresponding quantized
module. Return a dict with key corresponding to module names and each entry being
a dictionary with two keys 'float' and 'quantized', containing the float and
quantized weights. This dict can be used to compare and compute the quantization
error of the weights of float and quantized models.
Example usage::
wt_compare_dict = compare_weights(
float_model.state_dict(), qmodel.state_dict())
for key in wt_compare_dict:
print(
key,
compute_error(
wt_compare_dict[key]['float'],
wt_compare_dict[key]['quantized'].dequantize()
)
)
Args:
float_dict: state dict of the float model
quantized_dict: state dict of the quantized model
Return:
weight_dict: dict with key corresponding to module names and each entry being
a dictionary with two keys 'float' and 'quantized', containing the float and
quantized weights | r"""Compare the weights of the float module with its corresponding quantized
module. Return a dict with key corresponding to module names and each entry being
a dictionary with two keys 'float' and 'quantized', containing the float and
quantized weights. This dict can be used to compare and compute the quantization
error of the weights of float and quantized models. | [
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float_dict: Dict[str, Any], quantized_dict: Dict[str, Any]
) -> Dict[str, Dict[str, torch.Tensor]]:
r"""Compare the weights of the float module with its corresponding quantized
module. Return a dict with key corresponding to module names and each entry being
a dictionary with two keys 'float' and 'quantized', containing the float and
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Example usage::
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for key in wt_compare_dict:
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float_dict: state dict of the float model
quantized_dict: state dict of the quantized model
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weight_dict: dict with key corresponding to module names and each entry being
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quantized weights
"""
torch._C._log_api_usage_once("quantization_api._numeric_suite.compare_weights")
weight_dict: Dict[str, Dict] = {}
for key in quantized_dict:
match_key = _find_match(float_dict, key, "weight")
if match_key is not None:
weight_dict[key] = {}
weight_dict[key]["float"] = float_dict[match_key]
weight_dict[key]["quantized"] = quantized_dict[key]
continue
# For matching "fc.weight" and "fc._packed_params._packed_params"
match_key = _find_match(float_dict, key, "_packed_params")
if match_key is not None:
weight_dict[key] = {}
weight_dict[key]["float"] = float_dict[match_key]
weight_dict[key]["quantized"] = quantized_dict[key][0]
# For LSTM
split_str = key.split(".")
if split_str[-1] == "param" and split_str[-3] == "_all_weight_values":
layer = split_str[-2]
module_name = ".".join(split_str[:-3])
float_weight_ih_key = module_name + ".weight_ih_l" + layer
float_weight_hh_key = module_name + ".weight_hh_l" + layer
if float_weight_ih_key in float_dict and float_weight_hh_key in float_dict:
weight_dict[key] = {}
weight_dict[key]["float"] = float_dict[float_weight_ih_key]
weight_dict[key]["quantized"] = (
quantized_dict[key].__getstate__()[0][4][0].__getstate__()[0][0]
)
weight_dict[key]["float"] = float_dict[float_weight_hh_key]
weight_dict[key]["quantized"] = (
quantized_dict[key].__getstate__()[0][4][1].__getstate__()[0][0]
)
return weight_dict | [
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] | https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/ao/ns/_numeric_suite.py#L52-L118 |
|
intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | clang/utils/check_cfc/check_cfc.py | python | path_without_wrapper | () | return remove_dir_from_path(path, scriptdir) | Returns the PATH variable modified to remove the path to this program. | Returns the PATH variable modified to remove the path to this program. | [
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] | def path_without_wrapper():
"""Returns the PATH variable modified to remove the path to this program."""
scriptdir = get_main_dir()
path = os.environ['PATH']
return remove_dir_from_path(path, scriptdir) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/propgrid.py | python | PropertyGrid.GetSelectionBackgroundColour | (*args, **kwargs) | return _propgrid.PropertyGrid_GetSelectionBackgroundColour(*args, **kwargs) | GetSelectionBackgroundColour(self) -> Colour | GetSelectionBackgroundColour(self) -> Colour | [
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] | def GetSelectionBackgroundColour(*args, **kwargs):
"""GetSelectionBackgroundColour(self) -> Colour"""
return _propgrid.PropertyGrid_GetSelectionBackgroundColour(*args, **kwargs) | [
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|
zachriggle/ida-splode | a4aee3be415b318a0e051a523ebd0a8d6d5e0026 | py/idasplode/name.py | python | MakeOffset | (x, sign=True) | return hexstr | Make integer x into an IDA-styled offset string
>>> MakeOffset(0)
0
>>> MakeOffset(0xd0)
0D0h
>>> MakeOffset(0x1234)
1234h | Make integer x into an IDA-styled offset string | [
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] | def MakeOffset(x, sign=True):
"""Make integer x into an IDA-styled offset string
>>> MakeOffset(0)
0
>>> MakeOffset(0xd0)
0D0h
>>> MakeOffset(0x1234)
1234h
"""
if sign and x == 0:
return ""
hexstr = "%X" % x
if hexstr[0] in ('A','B','C','D','E','F'):
hexstr = '0' + hexstr
if x > 9:
hexstr += 'h'
if sign and x >= 0:
return "+%s" % hexstr
return hexstr | [
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|
bareos/bareos | 56a10bb368b0a81e977bb51304033fe49d59efb0 | core/src/plugins/filed/python/pyfiles/BareosFdPluginBaseclass.py | python | BareosFdPluginBaseclass.start_backup_job | (self) | return bRC_OK | Start of Backup Job. Called just before backup job really start.
Overload this to arrange whatever you have to do at this time. | Start of Backup Job. Called just before backup job really start.
Overload this to arrange whatever you have to do at this time. | [
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] | def start_backup_job(self):
"""
Start of Backup Job. Called just before backup job really start.
Overload this to arrange whatever you have to do at this time.
"""
return bRC_OK | [
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|
y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/sgicc.py | python | generate | (env) | Add Builders and construction variables for gcc to an Environment. | Add Builders and construction variables for gcc to an Environment. | [
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] | def generate(env):
"""Add Builders and construction variables for gcc to an Environment."""
cc.generate(env)
env['CXX'] = 'CC'
env['SHOBJSUFFIX'] = '.o'
env['STATIC_AND_SHARED_OBJECTS_ARE_THE_SAME'] = 1 | [
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