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convert: avoid wrong lfconvert defaults by moving configitems to core...
convert: avoid wrong lfconvert defaults by moving configitems to core The `hg lfconvert --to-normal` command uses the convert extension internally to work its magic, but that produced devel-warn messages if the convert extension wasn't loaded by the user. The test in fcd2f9b06629 (modified here) wasn't showing the warnings because the convert extension was loaded via $HGRCPATH. Most of the config options default to None/False, but 'hg.usebranchnames' and 'hg.tagsbranch' are supposed to default to True and 'default' respectively. The first iteration of this was to ui.setconfig() inside lfconvert, to force the convert extension to load. But there really is no precedent for doing this, and check-config complained that 'extensions.convert' isn't documented. Yuya suggested this alternative. This partially backs out 0d5a1175d0f9.

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common.py
88 lines | 2.6 KiB | text/x-python | PythonLexer
import inspect
import io
import os
import types
def make_cffi(cls):
"""Decorator to add CFFI versions of each test method."""
try:
import zstd_cffi
except ImportError:
return cls
# If CFFI version is available, dynamically construct test methods
# that use it.
for attr in dir(cls):
fn = getattr(cls, attr)
if not inspect.ismethod(fn) and not inspect.isfunction(fn):
continue
if not fn.__name__.startswith('test_'):
continue
name = '%s_cffi' % fn.__name__
# Replace the "zstd" symbol with the CFFI module instance. Then copy
# the function object and install it in a new attribute.
if isinstance(fn, types.FunctionType):
globs = dict(fn.__globals__)
globs['zstd'] = zstd_cffi
new_fn = types.FunctionType(fn.__code__, globs, name,
fn.__defaults__, fn.__closure__)
new_method = new_fn
else:
globs = dict(fn.__func__.func_globals)
globs['zstd'] = zstd_cffi
new_fn = types.FunctionType(fn.__func__.func_code, globs, name,
fn.__func__.func_defaults,
fn.__func__.func_closure)
new_method = types.UnboundMethodType(new_fn, fn.im_self,
fn.im_class)
setattr(cls, name, new_method)
return cls
class OpCountingBytesIO(io.BytesIO):
def __init__(self, *args, **kwargs):
self._read_count = 0
self._write_count = 0
return super(OpCountingBytesIO, self).__init__(*args, **kwargs)
def read(self, *args):
self._read_count += 1
return super(OpCountingBytesIO, self).read(*args)
def write(self, data):
self._write_count += 1
return super(OpCountingBytesIO, self).write(data)
_source_files = []
def random_input_data():
"""Obtain the raw content of source files.
This is used for generating "random" data to feed into fuzzing, since it is
faster than random content generation.
"""
if _source_files:
return _source_files
for root, dirs, files in os.walk(os.path.dirname(__file__)):
dirs[:] = list(sorted(dirs))
for f in sorted(files):
try:
with open(os.path.join(root, f), 'rb') as fh:
data = fh.read()
if data:
_source_files.append(data)
except OSError:
pass
return _source_files