def parallel_exec_transform_with_prettyprint( # noqa: C901
transform: Codemod,
files: Sequence[str],
*,
jobs: Optional[int] = None,
unified_diff: Optional[int] = None,
include_generated: bool = False,
generated_code_marker: str = _DEFAULT_GENERATED_CODE_MARKER,
format_code: bool = False,
formatter_args: Sequence[str] = (),
show_successes: bool = False,
hide_generated: bool = False,
hide_blacklisted: bool = False,
hide_progress: bool = False,
blacklist_patterns: Sequence[str] = (),
python_version: Optional[str] = None,
repo_root: Optional[str] = None,
) -> ParallelTransformResult:
"""
Given a list of files and an instantiated codemod we should apply to them,
fork and apply the codemod in parallel to all of the files, including any
configured formatter. The ``jobs`` parameter controls the maximum number of
in-flight transforms, and needs to be at least 1. If not included, the number
of jobs will automatically be set to the number of CPU cores. If ``unified_diff``
is set to a number, changes to files will be printed to stdout with
``unified_diff`` lines of context. If it is set to ``None`` or left out, files
themselves will be updated with changes and formatting. If a
``python_version`` is provided, then we will parse each source file using
this version. Otherwise, we will use the version of the currently executing python
binary.
A progress indicator as well as any generated warnings will be printed to stderr.
To supress the interactive progress indicator, set ``hide_progress`` to ``True``.
Files that include the generated code marker will be skipped unless the
``include_generated`` parameter is set to ``True``. Similarly, files that match
a supplied blacklist of regex patterns will be skipped. Warnings for skipping
both blacklisted and generated files will be printed to stderr along with
warnings generated by the codemod unless ``hide_blacklisted`` and
``hide_generated`` are set to ``True``. Files that were successfully codemodded
will not be printed to stderr unless ``show_successes`` is set to ``True``.
To make this API possible, we take an instantiated transform. This is due to
the fact that lambdas are not pickleable and pickling functions is undefined.
This means we're implicitly relying on fork behavior on UNIX-like systems, and
this function will not work on Windows systems. To create a command-line utility
that runs on Windows, please instead see
:func:`~libcst.codemod.exec_transform_with_prettyprint`.
"""
# Ensure that we have no duplicates, otherwise we might get race conditions
# on write.
files = sorted(list({os.path.abspath(f) for f in files}))
total = len(files)
progress = Progress(enabled=not hide_progress, total=total)
# Grab number of cores if we need to
jobs: int = jobs if jobs is not None else cpu_count()
if jobs < 1:
raise Exception("Must have at least one job to process!")
if total == 0:
return ParallelTransformResult(successes=0,
failures=0,
skips=0,
warnings=0)
if repo_root is not None:
# Make sure if there is a root that we have the absolute path to it.
repo_root = os.path.abspath(repo_root)
# Spin up a full repo metadata manager so that we can provide metadata
# like type inference to individual forked processes.
print("Calculating full-repo metadata...", file=sys.stderr)
metadata_manager = FullRepoManager(
repo_root,
files,
transform.get_inherited_dependencies(),
)
metadata_manager.resolve_cache()
transform.context = replace(
transform.context,
metadata_manager=metadata_manager,
)
print("Executing codemod...", file=sys.stderr)
config = ExecutionConfig(
repo_root=repo_root,
unified_diff=unified_diff,
include_generated=include_generated,
generated_code_marker=generated_code_marker,
format_code=format_code,
formatter_args=formatter_args,
blacklist_patterns=blacklist_patterns,
python_version=python_version,
)
if total == 1:
# Simple case, we should not pay for process overhead.
# Let's just use a dummy synchronous pool.
jobs = 1
pool_impl = DummyPool
else:
pool_impl = Pool
# Warm the parser, pre-fork.
parse_module(
"",
config=(PartialParserConfig(python_version=python_version)
if python_version is not None else PartialParserConfig()),
)
successes: int = 0
failures: int = 0
warnings: int = 0
skips: int = 0
with pool_impl(processes=jobs) as p: # type: ignore
args = [{
"transformer": transform,
"filename": filename,
"config": config,
} for filename in files]
try:
for result in p.imap_unordered(_execute_transform_wrap,
args,
chunksize=4):
# Print an execution result, keep track of failures
_print_parallel_result(
result,
progress,
unified_diff=bool(unified_diff),
show_successes=show_successes,
hide_generated=hide_generated,
hide_blacklisted=hide_blacklisted,
)
progress.print(successes + failures + skips)
if isinstance(result.transform_result, TransformFailure):
failures += 1
elif isinstance(result.transform_result, TransformSuccess):
successes += 1
elif isinstance(result.transform_result,
(TransformExit, TransformSkip)):
skips += 1
warnings += len(result.transform_result.warning_messages)
finally:
progress.clear()
# Return whether there was one or more failure.
return ParallelTransformResult(successes=successes,
failures=failures,
skips=skips,
warnings=warnings)
def parallel_exec_transform_with_prettyprint( # noqa: C901
transform: Codemod,
files: Sequence[str],
*,
jobs: Optional[int] = None,
unified_diff: Optional[int] = None,
include_generated: bool = False,
generated_code_marker: str = _DEFAULT_GENERATED_CODE_MARKER,
format_code: bool = False,
formatter_args: Sequence[str] = (),
show_successes: bool = False,
hide_generated: bool = False,
hide_blacklisted: bool = False,
hide_progress: bool = False,
blacklist_patterns: Sequence[str] = (),
python_version: Optional[str] = None,
repo_root: Optional[str] = None,
) -> ParallelTransformResult:
"""
Given a list of files and an instantiated codemod we should apply to them,
fork and apply the codemod in parallel to all of the files, including any
configured formatter. The ``jobs`` parameter controls the maximum number of
in-flight transforms, and needs to be at least 1. If not included, the number
of jobs will automatically be set to the number of CPU cores. If ``unified_diff``
is set to a number, changes to files will be printed to stdout with
``unified_diff`` lines of context. If it is set to ``None`` or left out, files
themselves will be updated with changes and formatting. If a
``python_version`` is provided, then we will parse each source file using
this version. Otherwise, we will use the version of the currently executing python
binary.
A progress indicator as well as any generated warnings will be printed to stderr.
To supress the interactive progress indicator, set ``hide_progress`` to ``True``.
Files that include the generated code marker will be skipped unless the
``include_generated`` parameter is set to ``True``. Similarly, files that match
a supplied blacklist of regex patterns will be skipped. Warnings for skipping
both blacklisted and generated files will be printed to stderr along with
warnings generated by the codemod unless ``hide_blacklisted`` and
``hide_generated`` are set to ``True``. Files that were successfully codemodded
will not be printed to stderr unless ``show_successes`` is set to ``True``.
To make this API possible, we take an instantiated transform. This is due to
the fact that lambdas are not pickleable and pickling functions is undefined.
This means we're implicitly relying on fork behavior on UNIX-like systems, and
this function will not work on Windows systems. To create a command-line utility
that runs on Windows, please instead see
:func:`~libcst.codemod.exec_transform_with_prettyprint`.
"""
# Ensure that we have no duplicates, otherwise we might get race conditions
# on write.
files = sorted(list(set(os.path.abspath(f) for f in files)))
total = len(files)
progress = Progress(enabled=not hide_progress, total=total)
# Grab number of cores if we need to
jobs: int = jobs or cpu_count()
if jobs < 1:
raise Exception("Must have at least one job to process!")
if total == 0:
return ParallelTransformResult(successes=0,
failures=0,
skips=0,
warnings=0)
if repo_root:
# Make sure if there is a root that we have the absolute path to it.
repo_root = os.path.abspath(repo_root)
# Spin up a full repo metadata manager so that we can provide metadata
# like type inference to individual forked processes.
print("Calculating full-repo metadata...", file=sys.stderr)
metadata_manager = FullRepoManager(
repo_root,
files,
transform.get_inherited_dependencies(),
)
metadata_manager.resolve_cache()
transform.context = replace(
transform.context,
metadata_manager=metadata_manager,
)
print("Executing codemod...", file=sys.stderr)
# We place results in this queue inside _parallel_exec_process_stub
# so that we can control when things get printed to the console.
queue = Queue()
if total == 1:
# Simple case, we should not pay for process overhead. Lets still
# use the exec stub however, so we can share code.
progress.print(0)
_parallel_exec_process_stub(
queue,
transform,
files[0],
repo_root,
unified_diff=unified_diff,
include_generated=include_generated,
generated_code_marker=generated_code_marker,
format_code=format_code,
formatter_args=formatter_args,
blacklist_patterns=blacklist_patterns,
python_version=python_version,
)
result = queue.get()
_print_parallel_result(
result,
progress,
unified_diff=bool(unified_diff),
show_successes=show_successes,
hide_generated=hide_generated,
hide_blacklisted=hide_blacklisted,
)
if isinstance(result.transform_result, TransformFailure):
return ParallelTransformResult(
successes=0,
failures=1,
skips=0,
warnings=len(result.transform_result.warning_messages),
)
elif isinstance(result.transform_result,
(TransformSkip, TransformExit)):
return ParallelTransformResult(
successes=0,
failures=0,
skips=1,
warnings=len(result.transform_result.warning_messages),
)
elif isinstance(result.transform_result, TransformSuccess):
return ParallelTransformResult(
successes=1,
failures=0,
skips=0,
warnings=len(result.transform_result.warning_messages),
)
else:
raise Exception("Logic error, unaccounted for result!")
# Warm the parser, pre-fork.
parse_module(
"",
config=(PartialParserConfig(python_version=python_version)
if python_version is not None else PartialParserConfig()),
)
# Complex case, more than one file
successes: int = 0
failures: int = 0
warnings: int = 0
skips: int = 0
pending_processes: List[Process] = []
# Start processes
filename_to_process: Dict[str, Process] = {}
for f in files:
process = Process(
target=_parallel_exec_process_stub,
args=(
queue,
transform,
f,
repo_root,
unified_diff,
include_generated,
generated_code_marker,
format_code,
formatter_args,
blacklist_patterns,
python_version,
),
)
pending_processes.append(process)
filename_to_process[f] = process
# Start the processes, allowing no more than num_processes to be running
# at once.
results_left = len(pending_processes)
joinable_processes: Set[Process] = set()
processes_started = 0
interrupted = False
while results_left > 0 and not interrupted:
while processes_started < jobs and pending_processes:
try:
# Move this process to the joinables
process = pending_processes.pop(0)
joinable_processes.add(process)
# Start it, bookkeep that we did
process.start()
processes_started += 1
except KeyboardInterrupt:
interrupted = True
continue
try:
result = queue.get(block=True, timeout=0.005)
except KeyboardInterrupt:
interrupted = True
continue
except Empty:
progress.print(successes + failures + skips)
continue
# Bookkeep the result, since we know the process that returned this is done.
results_left -= 1
processes_started -= 1
# Print an execution result, keep track of failures
_print_parallel_result(
result,
progress,
unified_diff=bool(unified_diff),
show_successes=show_successes,
hide_generated=hide_generated,
hide_blacklisted=hide_blacklisted,
)
progress.print(successes + failures + skips)
if isinstance(result.transform_result, TransformFailure):
failures += 1
elif isinstance(result.transform_result, TransformSuccess):
successes += 1
elif isinstance(result.transform_result,
(TransformExit, TransformSkip)):
skips += 1
warnings += len(result.transform_result.warning_messages)
# Join the process to free any related resources.
# Remove all references to the process to allow the GC to
# clean up any file handles.
process = filename_to_process.pop(result.filename, None)
if process:
process.join()
joinable_processes.discard(process)
# Now, join on all of them so we don't leave zombies or hang
for p in joinable_processes:
p.join()
# Return whether there was one or more failure.
progress.clear()
# If we caught an interrupt, raise that
if interrupted:
raise KeyboardInterrupt()
return ParallelTransformResult(successes=successes,
failures=failures,
skips=skips,
warnings=warnings)
