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env-llmeval/lib/python3.10/site-packages/joblib/__init__.py

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+"""Joblib is a set of tools to provide **lightweight pipelining in
+Python**. In particular:
+
+1. transparent disk-caching of functions and lazy re-evaluation
+   (memoize pattern)
+
+2. easy simple parallel computing
+
+Joblib is optimized to be **fast** and **robust** on large
+data in particular and has specific optimizations for `numpy` arrays. It is
+**BSD-licensed**.
+
+
+    ==================== ===============================================
+    **Documentation:**       https://joblib.readthedocs.io
+
+    **Download:**            https://pypi.python.org/pypi/joblib#downloads
+
+    **Source code:**         https://github.com/joblib/joblib
+
+    **Report issues:**       https://github.com/joblib/joblib/issues
+    ==================== ===============================================
+
+
+Vision
+--------
+
+The vision is to provide tools to easily achieve better performance and
+reproducibility when working with long running jobs.
+
+ *  **Avoid computing the same thing twice**: code is often rerun again and
+    again, for instance when prototyping computational-heavy jobs (as in
+    scientific development), but hand-crafted solutions to alleviate this
+    issue are error-prone and often lead to unreproducible results.
+
+ *  **Persist to disk transparently**: efficiently persisting
+    arbitrary objects containing large data is hard. Using
+    joblib's caching mechanism avoids hand-written persistence and
+    implicitly links the file on disk to the execution context of
+    the original Python object. As a result, joblib's persistence is
+    good for resuming an application status or computational job, eg
+    after a crash.
+
+Joblib addresses these problems while **leaving your code and your flow
+control as unmodified as possible** (no framework, no new paradigms).
+
+Main features
+------------------
+
+1) **Transparent and fast disk-caching of output value:** a memoize or
+   make-like functionality for Python functions that works well for
+   arbitrary Python objects, including very large numpy arrays. Separate
+   persistence and flow-execution logic from domain logic or algorithmic
+   code by writing the operations as a set of steps with well-defined
+   inputs and  outputs: Python functions. Joblib can save their
+   computation to disk and rerun it only if necessary::
+
+      >>> from joblib import Memory
+      >>> cachedir = 'your_cache_dir_goes_here'
+      >>> mem = Memory(cachedir)
+      >>> import numpy as np
+      >>> a = np.vander(np.arange(3)).astype(float)
+      >>> square = mem.cache(np.square)
+      >>> b = square(a)                                   # doctest: +ELLIPSIS
+      ______________________________________________________________________...
+      [Memory] Calling square...
+      square(array([[0., 0., 1.],
+             [1., 1., 1.],
+             [4., 2., 1.]]))
+      _________________________________________________...square - ...s, 0.0min
+
+      >>> c = square(a)
+      >>> # The above call did not trigger an evaluation
+
+2) **Embarrassingly parallel helper:** to make it easy to write readable
+   parallel code and debug it quickly::
+
+      >>> from joblib import Parallel, delayed
+      >>> from math import sqrt
+      >>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10))
+      [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
+
+
+3) **Fast compressed Persistence**: a replacement for pickle to work
+   efficiently on Python objects containing large data (
+   *joblib.dump* & *joblib.load* ).
+
+..
+    >>> import shutil ; shutil.rmtree(cachedir)
+
+"""
+
+# PEP0440 compatible formatted version, see:
+# https://www.python.org/dev/peps/pep-0440/
+#
+# Generic release markers:
+# X.Y
+# X.Y.Z # For bugfix releases
+#
+# Admissible pre-release markers:
+# X.YaN # Alpha release
+# X.YbN # Beta release
+# X.YrcN # Release Candidate
+# X.Y # Final release
+#
+# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
+# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
+#
+__version__ = '1.4.0'
+
+
+import os
+
+from .memory import Memory
+from .memory import MemorizedResult
+from .memory import register_store_backend
+from .memory import expires_after
+
+from .logger import PrintTime
+from .logger import Logger
+
+from .hashing import hash
+
+from .numpy_pickle import dump
+from .numpy_pickle import load
+
+from .compressor import register_compressor
+
+from .parallel import Parallel
+from .parallel import delayed
+from .parallel import cpu_count
+from .parallel import register_parallel_backend
+from .parallel import parallel_backend
+from .parallel import parallel_config
+from .parallel import effective_n_jobs
+from ._cloudpickle_wrapper import wrap_non_picklable_objects
+
+
+__all__ = ['Memory', 'MemorizedResult', 'PrintTime', 'Logger', 'hash', 'dump',
+           'load', 'Parallel', 'delayed', 'cpu_count', 'effective_n_jobs',
+           'register_parallel_backend', 'parallel_backend', 'expires_after',
+           'register_store_backend', 'register_compressor',
+           'wrap_non_picklable_objects', 'parallel_config']
+
+
+# Workaround issue discovered in intel-openmp 2019.5:
+# https://github.com/ContinuumIO/anaconda-issues/issues/11294
+os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")

env-llmeval/lib/python3.10/site-packages/joblib/_cloudpickle_wrapper.py

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+"""
+Small shim of loky's cloudpickle_wrapper to avoid failure when
+multiprocessing is not available.
+"""
+
+
+from ._multiprocessing_helpers import mp
+
+
+def _my_wrap_non_picklable_objects(obj, keep_wrapper=True):
+    return obj
+
+
+if mp is not None:
+    from .externals.loky import wrap_non_picklable_objects
+else:
+    wrap_non_picklable_objects = _my_wrap_non_picklable_objects
+
+__all__ = ["wrap_non_picklable_objects"]

env-llmeval/lib/python3.10/site-packages/joblib/_dask.py

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+from __future__ import print_function, division, absolute_import
+
+import asyncio
+import concurrent.futures
+import contextlib
+
+import time
+from uuid import uuid4
+import weakref
+
+from .parallel import parallel_config
+from .parallel import AutoBatchingMixin, ParallelBackendBase
+
+from ._utils import (
+    _TracebackCapturingWrapper,
+    _retrieve_traceback_capturing_wrapped_call
+)
+
+try:
+    import dask
+    import distributed
+except ImportError:
+    dask = None
+    distributed = None
+
+if dask is not None and distributed is not None:
+    from dask.utils import funcname
+    from dask.sizeof import sizeof
+    from dask.distributed import (
+        Client,
+        as_completed,
+        get_client,
+        secede,
+        rejoin,
+    )
+    from distributed.utils import thread_state
+
+    try:
+        # asyncio.TimeoutError, Python3-only error thrown by recent versions of
+        # distributed
+        from distributed.utils import TimeoutError as _TimeoutError
+    except ImportError:
+        from tornado.gen import TimeoutError as _TimeoutError
+
+
+def is_weakrefable(obj):
+    try:
+        weakref.ref(obj)
+        return True
+    except TypeError:
+        return False
+
+
+class _WeakKeyDictionary:
+    """A variant of weakref.WeakKeyDictionary for unhashable objects.
+
+    This datastructure is used to store futures for broadcasted data objects
+    such as large numpy arrays or pandas dataframes that are not hashable and
+    therefore cannot be used as keys of traditional python dicts.
+
+    Furthermore using a dict with id(array) as key is not safe because the
+    Python is likely to reuse id of recently collected arrays.
+    """
+
+    def __init__(self):
+        self._data = {}
+
+    def __getitem__(self, obj):
+        ref, val = self._data[id(obj)]
+        if ref() is not obj:
+            # In case of a race condition with on_destroy.
+            raise KeyError(obj)
+        return val
+
+    def __setitem__(self, obj, value):
+        key = id(obj)
+        try:
+            ref, _ = self._data[key]
+            if ref() is not obj:
+                # In case of race condition with on_destroy.
+                raise KeyError(obj)
+        except KeyError:
+            # Insert the new entry in the mapping along with a weakref
+            # callback to automatically delete the entry from the mapping
+            # as soon as the object used as key is garbage collected.
+            def on_destroy(_):
+                del self._data[key]
+            ref = weakref.ref(obj, on_destroy)
+        self._data[key] = ref, value
+
+    def __len__(self):
+        return len(self._data)
+
+    def clear(self):
+        self._data.clear()
+
+
+def _funcname(x):
+    try:
+        if isinstance(x, list):
+            x = x[0][0]
+    except Exception:
+        pass
+    return funcname(x)
+
+
+def _make_tasks_summary(tasks):
+    """Summarize of list of (func, args, kwargs) function calls"""
+    unique_funcs = {func for func, args, kwargs in tasks}
+
+    if len(unique_funcs) == 1:
+        mixed = False
+    else:
+        mixed = True
+    return len(tasks), mixed, _funcname(tasks)
+
+
+class Batch:
+    """dask-compatible wrapper that executes a batch of tasks"""
+    def __init__(self, tasks):
+        # collect some metadata from the tasks to ease Batch calls
+        # introspection when debugging
+        self._num_tasks, self._mixed, self._funcname = _make_tasks_summary(
+            tasks
+        )
+
+    def __call__(self, tasks=None):
+        results = []
+        with parallel_config(backend='dask'):
+            for func, args, kwargs in tasks:
+                results.append(func(*args, **kwargs))
+            return results
+
+    def __repr__(self):
+        descr = f"batch_of_{self._funcname}_{self._num_tasks}_calls"
+        if self._mixed:
+            descr = "mixed_" + descr
+        return descr
+
+
+def _joblib_probe_task():
+    # Noop used by the joblib connector to probe when workers are ready.
+    pass
+
+
+class DaskDistributedBackend(AutoBatchingMixin, ParallelBackendBase):
+    MIN_IDEAL_BATCH_DURATION = 0.2
+    MAX_IDEAL_BATCH_DURATION = 1.0
+    supports_retrieve_callback = True
+    default_n_jobs = -1
+
+    def __init__(self, scheduler_host=None, scatter=None,
+                 client=None, loop=None, wait_for_workers_timeout=10,
+                 **submit_kwargs):
+        super().__init__()
+
+        if distributed is None:
+            msg = ("You are trying to use 'dask' as a joblib parallel backend "
+                   "but dask is not installed. Please install dask "
+                   "to fix this error.")
+            raise ValueError(msg)
+
+        if client is None:
+            if scheduler_host:
+                client = Client(scheduler_host, loop=loop,
+                                set_as_default=False)
+            else:
+                try:
+                    client = get_client()
+                except ValueError as e:
+                    msg = ("To use Joblib with Dask first create a Dask Client"
+                           "\n\n"
+                           "    from dask.distributed import Client\n"
+                           "    client = Client()\n"
+                           "or\n"
+                           "    client = Client('scheduler-address:8786')")
+                    raise ValueError(msg) from e
+
+        self.client = client
+
+        if scatter is not None and not isinstance(scatter, (list, tuple)):
+            raise TypeError("scatter must be a list/tuple, got "
+                            "`%s`" % type(scatter).__name__)
+
+        if scatter is not None and len(scatter) > 0:
+            # Keep a reference to the scattered data to keep the ids the same
+            self._scatter = list(scatter)
+            scattered = self.client.scatter(scatter, broadcast=True)
+            self.data_futures = {id(x): f for x, f in zip(scatter, scattered)}
+        else:
+            self._scatter = []
+            self.data_futures = {}
+        self.wait_for_workers_timeout = wait_for_workers_timeout
+        self.submit_kwargs = submit_kwargs
+        self.waiting_futures = as_completed(
+            [],
+            loop=client.loop,
+            with_results=True,
+            raise_errors=False
+        )
+        self._results = {}
+        self._callbacks = {}
+
+    async def _collect(self):
+        while self._continue:
+            async for future, result in self.waiting_futures:
+                cf_future = self._results.pop(future)
+                callback = self._callbacks.pop(future)
+                if future.status == "error":
+                    typ, exc, tb = result
+                    cf_future.set_exception(exc)
+                else:
+                    cf_future.set_result(result)
+                    callback(result)
+            await asyncio.sleep(0.01)
+
+    def __reduce__(self):
+        return (DaskDistributedBackend, ())
+
+    def get_nested_backend(self):
+        return DaskDistributedBackend(client=self.client), -1
+
+    def configure(self, n_jobs=1, parallel=None, **backend_args):
+        self.parallel = parallel
+        return self.effective_n_jobs(n_jobs)
+
+    def start_call(self):
+        self._continue = True
+        self.client.loop.add_callback(self._collect)
+        self.call_data_futures = _WeakKeyDictionary()
+
+    def stop_call(self):
+        # The explicit call to clear is required to break a cycling reference
+        # to the futures.
+        self._continue = False
+        # wait for the future collection routine (self._backend._collect) to
+        # finish in order to limit asyncio warnings due to aborting _collect
+        # during a following backend termination call
+        time.sleep(0.01)
+        self.call_data_futures.clear()
+
+    def effective_n_jobs(self, n_jobs):
+        effective_n_jobs = sum(self.client.ncores().values())
+        if effective_n_jobs != 0 or not self.wait_for_workers_timeout:
+            return effective_n_jobs
+
+        # If there is no worker, schedule a probe task to wait for the workers
+        # to come up and be available. If the dask cluster is in adaptive mode
+        # task might cause the cluster to provision some workers.
+        try:
+            self.client.submit(_joblib_probe_task).result(
+                timeout=self.wait_for_workers_timeout
+            )
+        except _TimeoutError as e:
+            error_msg = (
+                "DaskDistributedBackend has no worker after {} seconds. "
+                "Make sure that workers are started and can properly connect "
+                "to the scheduler and increase the joblib/dask connection "
+                "timeout with:\n\n"
+                "parallel_config(backend='dask', wait_for_workers_timeout={})"
+            ).format(self.wait_for_workers_timeout,
+                     max(10, 2 * self.wait_for_workers_timeout))
+            raise TimeoutError(error_msg) from e
+        return sum(self.client.ncores().values())
+
+    async def _to_func_args(self, func):
+        itemgetters = dict()
+
+        # Futures that are dynamically generated during a single call to
+        # Parallel.__call__.
+        call_data_futures = getattr(self, 'call_data_futures', None)
+
+        async def maybe_to_futures(args):
+            out = []
+            for arg in args:
+                arg_id = id(arg)
+                if arg_id in itemgetters:
+                    out.append(itemgetters[arg_id])
+                    continue
+
+                f = self.data_futures.get(arg_id, None)
+                if f is None and call_data_futures is not None:
+                    try:
+                        f = await call_data_futures[arg]
+                    except KeyError:
+                        pass
+                    if f is None:
+                        if is_weakrefable(arg) and sizeof(arg) > 1e3:
+                            # Automatically scatter large objects to some of
+                            # the workers to avoid duplicated data transfers.
+                            # Rely on automated inter-worker data stealing if
+                            # more workers need to reuse this data
+                            # concurrently.
+                            # set hash=False - nested scatter calls (i.e
+                            # calling client.scatter inside a dask worker)
+                            # using hash=True often raise CancelledError,
+                            # see dask/distributed#3703
+                            _coro = self.client.scatter(
+                                arg,
+                                asynchronous=True,
+                                hash=False
+                            )
+                            # Centralize the scattering of identical arguments
+                            # between concurrent apply_async callbacks by
+                            # exposing the running coroutine in
+                            # call_data_futures before it completes.
+                            t = asyncio.Task(_coro)
+                            call_data_futures[arg] = t
+
+                            f = await t
+
+                if f is not None:
+                    out.append(f)
+                else:
+                    out.append(arg)
+            return out
+
+        tasks = []
+        for f, args, kwargs in func.items:
+            args = list(await maybe_to_futures(args))
+            kwargs = dict(zip(kwargs.keys(),
+                              await maybe_to_futures(kwargs.values())))
+            tasks.append((f, args, kwargs))
+
+        return (Batch(tasks), tasks)
+
+    def apply_async(self, func, callback=None):
+
+        cf_future = concurrent.futures.Future()
+        cf_future.get = cf_future.result  # achieve AsyncResult API
+
+        async def f(func, callback):
+            batch, tasks = await self._to_func_args(func)
+            key = f'{repr(batch)}-{uuid4().hex}'
+
+            dask_future = self.client.submit(
+                _TracebackCapturingWrapper(batch),
+                tasks=tasks,
+                key=key,
+                **self.submit_kwargs
+            )
+            self.waiting_futures.add(dask_future)
+            self._callbacks[dask_future] = callback
+            self._results[dask_future] = cf_future
+
+        self.client.loop.add_callback(f, func, callback)
+
+        return cf_future
+
+    def retrieve_result_callback(self, out):
+        return _retrieve_traceback_capturing_wrapped_call(out)
+
+    def abort_everything(self, ensure_ready=True):
+        """ Tell the client to cancel any task submitted via this instance
+
+        joblib.Parallel will never access those results
+        """
+        with self.waiting_futures.lock:
+            self.waiting_futures.futures.clear()
+            while not self.waiting_futures.queue.empty():
+                self.waiting_futures.queue.get()
+
+    @contextlib.contextmanager
+    def retrieval_context(self):
+        """Override ParallelBackendBase.retrieval_context to avoid deadlocks.
+
+        This removes thread from the worker's thread pool (using 'secede').
+        Seceding avoids deadlock in nested parallelism settings.
+        """
+        # See 'joblib.Parallel.__call__' and 'joblib.Parallel.retrieve' for how
+        # this is used.
+        if hasattr(thread_state, 'execution_state'):
+            # we are in a worker. Secede to avoid deadlock.
+            secede()
+
+        yield
+
+        if hasattr(thread_state, 'execution_state'):
+            rejoin()

env-llmeval/lib/python3.10/site-packages/joblib/_memmapping_reducer.py

@@ -0,0 +1,657 @@
+"""
+Reducer using memory mapping for numpy arrays
+"""
+# Author: Thomas Moreau <[email protected]>
+# Copyright: 2017, Thomas Moreau
+# License: BSD 3 clause
+
+from mmap import mmap
+import errno
+import os
+import stat
+import threading
+import atexit
+import tempfile
+import time
+import warnings
+import weakref
+from uuid import uuid4
+from multiprocessing import util
+
+from pickle import whichmodule, loads, dumps, HIGHEST_PROTOCOL, PicklingError
+
+try:
+    WindowsError
+except NameError:
+    WindowsError = type(None)
+
+try:
+    import numpy as np
+    from numpy.lib.stride_tricks import as_strided
+except ImportError:
+    np = None
+
+from .numpy_pickle import dump, load, load_temporary_memmap
+from .backports import make_memmap
+from .disk import delete_folder
+from .externals.loky.backend import resource_tracker
+
+# Some system have a ramdisk mounted by default, we can use it instead of /tmp
+# as the default folder to dump big arrays to share with subprocesses.
+SYSTEM_SHARED_MEM_FS = '/dev/shm'
+
+# Minimal number of bytes available on SYSTEM_SHARED_MEM_FS to consider using
+# it as the default folder to dump big arrays to share with subprocesses.
+SYSTEM_SHARED_MEM_FS_MIN_SIZE = int(2e9)
+
+# Folder and file permissions to chmod temporary files generated by the
+# memmapping pool. Only the owner of the Python process can access the
+# temporary files and folder.
+FOLDER_PERMISSIONS = stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR
+FILE_PERMISSIONS = stat.S_IRUSR | stat.S_IWUSR
+
+# Set used in joblib workers, referencing the filenames of temporary memmaps
+# created by joblib to speed up data communication. In child processes, we add
+# a finalizer to these memmaps that sends a maybe_unlink call to the
+# resource_tracker, in order to free main memory as fast as possible.
+JOBLIB_MMAPS = set()
+
+
+def _log_and_unlink(filename):
+    from .externals.loky.backend.resource_tracker import _resource_tracker
+    util.debug(
+        "[FINALIZER CALL] object mapping to {} about to be deleted,"
+        " decrementing the refcount of the file (pid: {})".format(
+            os.path.basename(filename), os.getpid()))
+    _resource_tracker.maybe_unlink(filename, "file")
+
+
+def add_maybe_unlink_finalizer(memmap):
+    util.debug(
+        "[FINALIZER ADD] adding finalizer to {} (id {}, filename {}, pid  {})"
+        "".format(type(memmap), id(memmap), os.path.basename(memmap.filename),
+                  os.getpid()))
+    weakref.finalize(memmap, _log_and_unlink, memmap.filename)
+
+
+def unlink_file(filename):
+    """Wrapper around os.unlink with a retry mechanism.
+
+    The retry mechanism has been implemented primarily to overcome a race
+    condition happening during the finalizer of a np.memmap: when a process
+    holding the last reference to a mmap-backed np.memmap/np.array is about to
+    delete this array (and close the reference), it sends a maybe_unlink
+    request to the resource_tracker. This request can be processed faster than
+    it takes for the last reference of the memmap to be closed, yielding (on
+    Windows) a PermissionError in the resource_tracker loop.
+    """
+    NUM_RETRIES = 10
+    for retry_no in range(1, NUM_RETRIES + 1):
+        try:
+            os.unlink(filename)
+            break
+        except PermissionError:
+            util.debug(
+                '[ResourceTracker] tried to unlink {}, got '
+                'PermissionError'.format(filename)
+            )
+            if retry_no == NUM_RETRIES:
+                raise
+            else:
+                time.sleep(.2)
+        except FileNotFoundError:
+            # In case of a race condition when deleting the temporary folder,
+            # avoid noisy FileNotFoundError exception in the resource tracker.
+            pass
+
+
+resource_tracker._CLEANUP_FUNCS['file'] = unlink_file
+
+
+class _WeakArrayKeyMap:
+    """A variant of weakref.WeakKeyDictionary for unhashable numpy arrays.
+
+    This datastructure will be used with numpy arrays as obj keys, therefore we
+    do not use the __get__ / __set__ methods to avoid any conflict with the
+    numpy fancy indexing syntax.
+    """
+
+    def __init__(self):
+        self._data = {}
+
+    def get(self, obj):
+        ref, val = self._data[id(obj)]
+        if ref() is not obj:
+            # In case of race condition with on_destroy: could never be
+            # triggered by the joblib tests with CPython.
+            raise KeyError(obj)
+        return val
+
+    def set(self, obj, value):
+        key = id(obj)
+        try:
+            ref, _ = self._data[key]
+            if ref() is not obj:
+                # In case of race condition with on_destroy: could never be
+                # triggered by the joblib tests with CPython.
+                raise KeyError(obj)
+        except KeyError:
+            # Insert the new entry in the mapping along with a weakref
+            # callback to automatically delete the entry from the mapping
+            # as soon as the object used as key is garbage collected.
+            def on_destroy(_):
+                del self._data[key]
+            ref = weakref.ref(obj, on_destroy)
+        self._data[key] = ref, value
+
+    def __getstate__(self):
+        raise PicklingError("_WeakArrayKeyMap is not pickleable")
+
+
+###############################################################################
+# Support for efficient transient pickling of numpy data structures
+
+
+def _get_backing_memmap(a):
+    """Recursively look up the original np.memmap instance base if any."""
+    b = getattr(a, 'base', None)
+    if b is None:
+        # TODO: check scipy sparse datastructure if scipy is installed
+        # a nor its descendants do not have a memmap base
+        return None
+
+    elif isinstance(b, mmap):
+        # a is already a real memmap instance.
+        return a
+
+    else:
+        # Recursive exploration of the base ancestry
+        return _get_backing_memmap(b)
+
+
+def _get_temp_dir(pool_folder_name, temp_folder=None):
+    """Get the full path to a subfolder inside the temporary folder.
+
+    Parameters
+    ----------
+    pool_folder_name : str
+        Sub-folder name used for the serialization of a pool instance.
+
+    temp_folder: str, optional
+        Folder to be used by the pool for memmapping large arrays
+        for sharing memory with worker processes. If None, this will try in
+        order:
+
+        - a folder pointed by the JOBLIB_TEMP_FOLDER environment
+          variable,
+        - /dev/shm if the folder exists and is writable: this is a
+          RAMdisk filesystem available by default on modern Linux
+          distributions,
+        - the default system temporary folder that can be
+          overridden with TMP, TMPDIR or TEMP environment
+          variables, typically /tmp under Unix operating systems.
+
+    Returns
+    -------
+    pool_folder : str
+       full path to the temporary folder
+    use_shared_mem : bool
+       whether the temporary folder is written to the system shared memory
+       folder or some other temporary folder.
+    """
+    use_shared_mem = False
+    if temp_folder is None:
+        temp_folder = os.environ.get('JOBLIB_TEMP_FOLDER', None)
+    if temp_folder is None:
+        if os.path.exists(SYSTEM_SHARED_MEM_FS) and hasattr(os, 'statvfs'):
+            try:
+                shm_stats = os.statvfs(SYSTEM_SHARED_MEM_FS)
+                available_nbytes = shm_stats.f_bsize * shm_stats.f_bavail
+                if available_nbytes > SYSTEM_SHARED_MEM_FS_MIN_SIZE:
+                    # Try to see if we have write access to the shared mem
+                    # folder only if it is reasonably large (that is 2GB or
+                    # more).
+                    temp_folder = SYSTEM_SHARED_MEM_FS
+                    pool_folder = os.path.join(temp_folder, pool_folder_name)
+                    if not os.path.exists(pool_folder):
+                        os.makedirs(pool_folder)
+                    use_shared_mem = True
+            except (IOError, OSError):
+                # Missing rights in the /dev/shm partition, fallback to regular
+                # temp folder.
+                temp_folder = None
+    if temp_folder is None:
+        # Fallback to the default tmp folder, typically /tmp
+        temp_folder = tempfile.gettempdir()
+    temp_folder = os.path.abspath(os.path.expanduser(temp_folder))
+    pool_folder = os.path.join(temp_folder, pool_folder_name)
+    return pool_folder, use_shared_mem
+
+
+def has_shareable_memory(a):
+    """Return True if a is backed by some mmap buffer directly or not."""
+    return _get_backing_memmap(a) is not None
+
+
+def _strided_from_memmap(filename, dtype, mode, offset, order, shape, strides,
+                         total_buffer_len, unlink_on_gc_collect):
+    """Reconstruct an array view on a memory mapped file."""
+    if mode == 'w+':
+        # Do not zero the original data when unpickling
+        mode = 'r+'
+
+    if strides is None:
+        # Simple, contiguous memmap
+        return make_memmap(
+            filename, dtype=dtype, shape=shape, mode=mode, offset=offset,
+            order=order, unlink_on_gc_collect=unlink_on_gc_collect
+        )
+    else:
+        # For non-contiguous data, memmap the total enclosing buffer and then
+        # extract the non-contiguous view with the stride-tricks API
+        base = make_memmap(
+            filename, dtype=dtype, shape=total_buffer_len, offset=offset,
+            mode=mode, order=order, unlink_on_gc_collect=unlink_on_gc_collect
+        )
+        return as_strided(base, shape=shape, strides=strides)
+
+
+def _reduce_memmap_backed(a, m):
+    """Pickling reduction for memmap backed arrays.
+
+    a is expected to be an instance of np.ndarray (or np.memmap)
+    m is expected to be an instance of np.memmap on the top of the ``base``
+    attribute ancestry of a. ``m.base`` should be the real python mmap object.
+    """
+    # offset that comes from the striding differences between a and m
+    util.debug('[MEMMAP REDUCE] reducing a memmap-backed array '
+               '(shape, {}, pid: {})'.format(a.shape, os.getpid()))
+    try:
+        from numpy.lib.array_utils import byte_bounds
+    except (ModuleNotFoundError, ImportError):
+        # Backward-compat for numpy < 2.0
+        from numpy import byte_bounds
+    a_start, a_end = byte_bounds(a)
+    m_start = byte_bounds(m)[0]
+    offset = a_start - m_start
+
+    # offset from the backing memmap
+    offset += m.offset
+
+    if m.flags['F_CONTIGUOUS']:
+        order = 'F'
+    else:
+        # The backing memmap buffer is necessarily contiguous hence C if not
+        # Fortran
+        order = 'C'
+
+    if a.flags['F_CONTIGUOUS'] or a.flags['C_CONTIGUOUS']:
+        # If the array is a contiguous view, no need to pass the strides
+        strides = None
+        total_buffer_len = None
+    else:
+        # Compute the total number of items to map from which the strided
+        # view will be extracted.
+        strides = a.strides
+        total_buffer_len = (a_end - a_start) // a.itemsize
+
+    return (_strided_from_memmap,
+            (m.filename, a.dtype, m.mode, offset, order, a.shape, strides,
+             total_buffer_len, False))
+
+
+def reduce_array_memmap_backward(a):
+    """reduce a np.array or a np.memmap from a child process"""
+    m = _get_backing_memmap(a)
+    if isinstance(m, np.memmap) and m.filename not in JOBLIB_MMAPS:
+        # if a is backed by a memmaped file, reconstruct a using the
+        # memmaped file.
+        return _reduce_memmap_backed(a, m)
+    else:
+        # a is either a regular (not memmap-backed) numpy array, or an array
+        # backed by a shared temporary file created by joblib. In the latter
+        # case, in order to limit the lifespan of these temporary files, we
+        # serialize the memmap as a regular numpy array, and decref the
+        # file backing the memmap (done implicitly in a previously registered
+        # finalizer, see ``unlink_on_gc_collect`` for more details)
+        return (
+            loads, (dumps(np.asarray(a), protocol=HIGHEST_PROTOCOL), )
+        )
+
+
+class ArrayMemmapForwardReducer(object):
+    """Reducer callable to dump large arrays to memmap files.
+
+    Parameters
+    ----------
+    max_nbytes: int
+        Threshold to trigger memmapping of large arrays to files created
+        a folder.
+    temp_folder_resolver: callable
+        An callable in charge of resolving a temporary folder name where files
+        for backing memmapped arrays are created.
+    mmap_mode: 'r', 'r+' or 'c'
+        Mode for the created memmap datastructure. See the documentation of
+        numpy.memmap for more details. Note: 'w+' is coerced to 'r+'
+        automatically to avoid zeroing the data on unpickling.
+    verbose: int, optional, 0 by default
+        If verbose > 0, memmap creations are logged.
+        If verbose > 1, both memmap creations, reuse and array pickling are
+        logged.
+    prewarm: bool, optional, False by default.
+        Force a read on newly memmapped array to make sure that OS pre-cache it
+        memory. This can be useful to avoid concurrent disk access when the
+        same data array is passed to different worker processes.
+    """
+
+    def __init__(self, max_nbytes, temp_folder_resolver, mmap_mode,
+                 unlink_on_gc_collect, verbose=0, prewarm=True):
+        self._max_nbytes = max_nbytes
+        self._temp_folder_resolver = temp_folder_resolver
+        self._mmap_mode = mmap_mode
+        self.verbose = int(verbose)
+        if prewarm == "auto":
+            self._prewarm = not self._temp_folder.startswith(
+                SYSTEM_SHARED_MEM_FS
+            )
+        else:
+            self._prewarm = prewarm
+        self._prewarm = prewarm
+        self._memmaped_arrays = _WeakArrayKeyMap()
+        self._temporary_memmaped_filenames = set()
+        self._unlink_on_gc_collect = unlink_on_gc_collect
+
+    @property
+    def _temp_folder(self):
+        return self._temp_folder_resolver()
+
+    def __reduce__(self):
+        # The ArrayMemmapForwardReducer is passed to the children processes: it
+        # needs to be pickled but the _WeakArrayKeyMap need to be skipped as
+        # it's only guaranteed to be consistent with the parent process memory
+        # garbage collection.
+        # Although this reducer is pickled, it is not needed in its destination
+        # process (child processes), as we only use this reducer to send
+        # memmaps from the parent process to the children processes. For this
+        # reason, we can afford skipping the resolver, (which would otherwise
+        # be unpicklable), and pass it as None instead.
+        args = (self._max_nbytes, None, self._mmap_mode,
+                self._unlink_on_gc_collect)
+        kwargs = {
+            'verbose': self.verbose,
+            'prewarm': self._prewarm,
+        }
+        return ArrayMemmapForwardReducer, args, kwargs
+
+    def __call__(self, a):
+        m = _get_backing_memmap(a)
+        if m is not None and isinstance(m, np.memmap):
+            # a is already backed by a memmap file, let's reuse it directly
+            return _reduce_memmap_backed(a, m)
+
+        if (not a.dtype.hasobject and self._max_nbytes is not None and
+                a.nbytes > self._max_nbytes):
+            # check that the folder exists (lazily create the pool temp folder
+            # if required)
+            try:
+                os.makedirs(self._temp_folder)
+                os.chmod(self._temp_folder, FOLDER_PERMISSIONS)
+            except OSError as e:
+                if e.errno != errno.EEXIST:
+                    raise e
+
+            try:
+                basename = self._memmaped_arrays.get(a)
+            except KeyError:
+                # Generate a new unique random filename. The process and thread
+                # ids are only useful for debugging purpose and to make it
+                # easier to cleanup orphaned files in case of hard process
+                # kill (e.g. by "kill -9" or segfault).
+                basename = "{}-{}-{}.pkl".format(
+                    os.getpid(), id(threading.current_thread()), uuid4().hex)
+                self._memmaped_arrays.set(a, basename)
+            filename = os.path.join(self._temp_folder, basename)
+
+            # In case the same array with the same content is passed several
+            # times to the pool subprocess children, serialize it only once
+
+            is_new_memmap = filename not in self._temporary_memmaped_filenames
+
+            # add the memmap to the list of temporary memmaps created by joblib
+            self._temporary_memmaped_filenames.add(filename)
+
+            if self._unlink_on_gc_collect:
+                # Bump reference count of the memmap by 1 to account for
+                # shared usage of the memmap by a child process. The
+                # corresponding decref call will be executed upon calling
+                # resource_tracker.maybe_unlink, registered as a finalizer in
+                # the child.
+                # the incref/decref calls here are only possible when the child
+                # and the parent share the same resource_tracker. It is not the
+                # case for the multiprocessing backend, but it does not matter
+                # because unlinking a memmap from a child process is only
+                # useful to control the memory usage of long-lasting child
+                # processes, while the multiprocessing-based pools terminate
+                # their workers at the end of a map() call.
+                resource_tracker.register(filename, "file")
+
+            if is_new_memmap:
+                # Incref each temporary memmap created by joblib one extra
+                # time.  This means that these memmaps will only be deleted
+                # once an extra maybe_unlink() is called, which is done once
+                # all the jobs have completed (or been canceled) in the
+                # Parallel._terminate_backend() method.
+                resource_tracker.register(filename, "file")
+
+            if not os.path.exists(filename):
+                util.debug(
+                    "[ARRAY DUMP] Pickling new array (shape={}, dtype={}) "
+                    "creating a new memmap at {}".format(
+                        a.shape, a.dtype, filename))
+                for dumped_filename in dump(a, filename):
+                    os.chmod(dumped_filename, FILE_PERMISSIONS)
+
+                if self._prewarm:
+                    # Warm up the data by accessing it. This operation ensures
+                    # that the disk access required to create the memmapping
+                    # file are performed in the reducing process and avoids
+                    # concurrent memmap creation in multiple children
+                    # processes.
+                    load(filename, mmap_mode=self._mmap_mode).max()
+
+            else:
+                util.debug(
+                    "[ARRAY DUMP] Pickling known array (shape={}, dtype={}) "
+                    "reusing memmap file: {}".format(
+                        a.shape, a.dtype, os.path.basename(filename)))
+
+            # The worker process will use joblib.load to memmap the data
+            return (
+                (load_temporary_memmap, (filename, self._mmap_mode,
+                                         self._unlink_on_gc_collect))
+            )
+        else:
+            # do not convert a into memmap, let pickler do its usual copy with
+            # the default system pickler
+            util.debug(
+                '[ARRAY DUMP] Pickling array (NO MEMMAPPING) (shape={}, '
+                ' dtype={}).'.format(a.shape, a.dtype))
+            return (loads, (dumps(a, protocol=HIGHEST_PROTOCOL),))
+
+
+def get_memmapping_reducers(
+        forward_reducers=None, backward_reducers=None,
+        temp_folder_resolver=None, max_nbytes=1e6, mmap_mode='r', verbose=0,
+        prewarm=False, unlink_on_gc_collect=True, **kwargs):
+    """Construct a pair of memmapping reducer linked to a tmpdir.
+
+    This function manage the creation and the clean up of the temporary folders
+    underlying the memory maps and should be use to get the reducers necessary
+    to construct joblib pool or executor.
+    """
+    if forward_reducers is None:
+        forward_reducers = dict()
+    if backward_reducers is None:
+        backward_reducers = dict()
+
+    if np is not None:
+        # Register smart numpy.ndarray reducers that detects memmap backed
+        # arrays and that is also able to dump to memmap large in-memory
+        # arrays over the max_nbytes threshold
+        forward_reduce_ndarray = ArrayMemmapForwardReducer(
+            max_nbytes, temp_folder_resolver, mmap_mode, unlink_on_gc_collect,
+            verbose, prewarm=prewarm)
+        forward_reducers[np.ndarray] = forward_reduce_ndarray
+        forward_reducers[np.memmap] = forward_reduce_ndarray
+
+        # Communication from child process to the parent process always
+        # pickles in-memory numpy.ndarray without dumping them as memmap
+        # to avoid confusing the caller and make it tricky to collect the
+        # temporary folder
+        backward_reducers[np.ndarray] = reduce_array_memmap_backward
+        backward_reducers[np.memmap] = reduce_array_memmap_backward
+
+    return forward_reducers, backward_reducers
+
+
+class TemporaryResourcesManager(object):
+    """Stateful object able to manage temporary folder and pickles
+
+    It exposes:
+    - a per-context folder name resolving API that memmap-based reducers will
+      rely on to know where to pickle the temporary memmaps
+    - a temporary file/folder management API that internally uses the
+      resource_tracker.
+    """
+
+    def __init__(self, temp_folder_root=None, context_id=None):
+        self._current_temp_folder = None
+        self._temp_folder_root = temp_folder_root
+        self._use_shared_mem = None
+        self._cached_temp_folders = dict()
+        self._id = uuid4().hex
+        self._finalizers = {}
+        if context_id is None:
+            # It would be safer to not assign a default context id (less silent
+            # bugs), but doing this while maintaining backward compatibility
+            # with the previous, context-unaware version get_memmaping_executor
+            # exposes too many low-level details.
+            context_id = uuid4().hex
+        self.set_current_context(context_id)
+
+    def set_current_context(self, context_id):
+        self._current_context_id = context_id
+        self.register_new_context(context_id)
+
+    def register_new_context(self, context_id):
+        # Prepare a sub-folder name specific to a context (usually a unique id
+        # generated by each instance of the Parallel class). Do not create in
+        # advance to spare FS write access if no array is to be dumped).
+        if context_id in self._cached_temp_folders:
+            return
+        else:
+            # During its lifecycle, one Parallel object can have several
+            # executors associated to it (for instance, if a loky worker raises
+            # an exception, joblib shutdowns the executor and instantly
+            # recreates a new one before raising the error - see
+            # ``ensure_ready``.  Because we don't want two executors tied to
+            # the same Parallel object (and thus the same context id) to
+            # register/use/delete the same folder, we also add an id specific
+            # to the current Manager (and thus specific to its associated
+            # executor) to the folder name.
+            new_folder_name = (
+                "joblib_memmapping_folder_{}_{}_{}".format(
+                    os.getpid(), self._id, context_id)
+            )
+            new_folder_path, _ = _get_temp_dir(
+                new_folder_name, self._temp_folder_root
+            )
+            self.register_folder_finalizer(new_folder_path, context_id)
+            self._cached_temp_folders[context_id] = new_folder_path
+
+    def resolve_temp_folder_name(self):
+        """Return a folder name specific to the currently activated context"""
+        return self._cached_temp_folders[self._current_context_id]
+
+    # resource management API
+
+    def register_folder_finalizer(self, pool_subfolder, context_id):
+        # Register the garbage collector at program exit in case caller forgets
+        # to call terminate explicitly: note we do not pass any reference to
+        # ensure that this callback won't prevent garbage collection of
+        # parallel instance and related file handler resources such as POSIX
+        # semaphores and pipes
+        pool_module_name = whichmodule(delete_folder, 'delete_folder')
+        resource_tracker.register(pool_subfolder, "folder")
+
+        def _cleanup():
+            # In some cases the Python runtime seems to set delete_folder to
+            # None just before exiting when accessing the delete_folder
+            # function from the closure namespace. So instead we reimport
+            # the delete_folder function explicitly.
+            # https://github.com/joblib/joblib/issues/328
+            # We cannot just use from 'joblib.pool import delete_folder'
+            # because joblib should only use relative imports to allow
+            # easy vendoring.
+            delete_folder = __import__(
+                pool_module_name, fromlist=['delete_folder']
+            ).delete_folder
+            try:
+                delete_folder(pool_subfolder, allow_non_empty=True)
+                resource_tracker.unregister(pool_subfolder, "folder")
+            except OSError:
+                warnings.warn("Failed to delete temporary folder: {}"
+                              .format(pool_subfolder))
+
+        self._finalizers[context_id] = atexit.register(_cleanup)
+
+    def _clean_temporary_resources(self, context_id=None, force=False,
+                                   allow_non_empty=False):
+        """Clean temporary resources created by a process-based pool"""
+        if context_id is None:
+            # Iterates over a copy of the cache keys to avoid Error due to
+            # iterating over a changing size dictionary.
+            for context_id in list(self._cached_temp_folders):
+                self._clean_temporary_resources(
+                    context_id, force=force, allow_non_empty=allow_non_empty
+                )
+        else:
+            temp_folder = self._cached_temp_folders.get(context_id)
+            if temp_folder and os.path.exists(temp_folder):
+                for filename in os.listdir(temp_folder):
+                    if force:
+                        # Some workers have failed and the ref counted might
+                        # be off. The workers should have shut down by this
+                        # time so forcefully clean up the files.
+                        resource_tracker.unregister(
+                            os.path.join(temp_folder, filename), "file"
+                        )
+                    else:
+                        resource_tracker.maybe_unlink(
+                            os.path.join(temp_folder, filename), "file"
+                        )
+
+                # When forcing clean-up, try to delete the folder even if some
+                # files are still in it. Otherwise, try to delete the folder
+                allow_non_empty |= force
+
+                # Clean up the folder if possible, either if it is empty or
+                # if none of the files in it are in used and allow_non_empty.
+                try:
+                    delete_folder(
+                        temp_folder, allow_non_empty=allow_non_empty
+                    )
+                    # Forget the folder once it has been deleted
+                    self._cached_temp_folders.pop(context_id, None)
+                    resource_tracker.unregister(temp_folder, "folder")
+
+                    # Also cancel the finalizers  that gets triggered at gc.
+                    finalizer = self._finalizers.pop(context_id, None)
+                    if finalizer is not None:
+                        atexit.unregister(finalizer)
+
+                except OSError:
+                    # Temporary folder cannot be deleted right now.
+                    # This folder will be cleaned up by an atexit
+                    # finalizer registered by the memmapping_reducer.
+                    pass

env-llmeval/lib/python3.10/site-packages/joblib/_multiprocessing_helpers.py

@@ -0,0 +1,53 @@
+"""Helper module to factorize the conditional multiprocessing import logic
+
+We use a distinct module to simplify import statements and avoid introducing
+circular dependencies (for instance for the assert_spawning name).
+"""
+import os
+import warnings
+
+
+# Obtain possible configuration from the environment, assuming 1 (on)
+# by default, upon 0 set to None. Should instructively fail if some non
+# 0/1 value is set.
+mp = int(os.environ.get('JOBLIB_MULTIPROCESSING', 1)) or None
+if mp:
+    try:
+        import multiprocessing as mp
+        import _multiprocessing  # noqa
+    except ImportError:
+        mp = None
+
+# 2nd stage: validate that locking is available on the system and
+#            issue a warning if not
+if mp is not None:
+    try:
+        # try to create a named semaphore using SemLock to make sure they are
+        # available on this platform. We use the low level object
+        # _multiprocessing.SemLock to avoid spawning a resource tracker on
+        # Unix system or changing the default backend.
+        import tempfile
+        from _multiprocessing import SemLock
+
+        _rand = tempfile._RandomNameSequence()
+        for i in range(100):
+            try:
+                name = '/joblib-{}-{}' .format(
+                    os.getpid(), next(_rand))
+                _sem = SemLock(0, 0, 1, name=name, unlink=True)
+                del _sem  # cleanup
+                break
+            except FileExistsError as e:  # pragma: no cover
+                if i >= 99:
+                    raise FileExistsError(
+                        'cannot find name for semaphore') from e
+    except (FileExistsError, AttributeError, ImportError, OSError) as e:
+        mp = None
+        warnings.warn('%s.  joblib will operate in serial mode' % (e,))
+
+
+# 3rd stage: backward compat for the assert_spawning helper
+if mp is not None:
+    from multiprocessing.context import assert_spawning
+else:
+    assert_spawning = None

env-llmeval/lib/python3.10/site-packages/joblib/_parallel_backends.py

@@ -0,0 +1,649 @@
+"""
+Backends for embarrassingly parallel code.
+"""
+
+import gc
+import os
+import warnings
+import threading
+import contextlib
+from abc import ABCMeta, abstractmethod
+
+from ._utils import (
+    _TracebackCapturingWrapper,
+    _retrieve_traceback_capturing_wrapped_call
+)
+
+from ._multiprocessing_helpers import mp
+
+if mp is not None:
+    from .pool import MemmappingPool
+    from multiprocessing.pool import ThreadPool
+    from .executor import get_memmapping_executor
+
+    # Import loky only if multiprocessing is present
+    from .externals.loky import process_executor, cpu_count
+    from .externals.loky.process_executor import ShutdownExecutorError
+
+
+class ParallelBackendBase(metaclass=ABCMeta):
+    """Helper abc which defines all methods a ParallelBackend must implement"""
+
+    supports_inner_max_num_threads = False
+    supports_retrieve_callback = False
+    default_n_jobs = 1
+
+    @property
+    def supports_return_generator(self):
+        return self.supports_retrieve_callback
+
+    @property
+    def supports_timeout(self):
+        return self.supports_retrieve_callback
+
+    nesting_level = None
+
+    def __init__(self, nesting_level=None, inner_max_num_threads=None,
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.nesting_level = nesting_level
+        self.inner_max_num_threads = inner_max_num_threads
+
+    MAX_NUM_THREADS_VARS = [
+        'OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS',
+        'BLIS_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS', 'NUMBA_NUM_THREADS',
+        'NUMEXPR_NUM_THREADS',
+    ]
+
+    TBB_ENABLE_IPC_VAR = "ENABLE_IPC"
+
+    @abstractmethod
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs that can actually run in parallel
+
+        n_jobs is the number of workers requested by the callers. Passing
+        n_jobs=-1 means requesting all available workers for instance matching
+        the number of CPU cores on the worker host(s).
+
+        This method should return a guesstimate of the number of workers that
+        can actually perform work concurrently. The primary use case is to make
+        it possible for the caller to know in how many chunks to slice the
+        work.
+
+        In general working on larger data chunks is more efficient (less
+        scheduling overhead and better use of CPU cache prefetching heuristics)
+        as long as all the workers have enough work to do.
+        """
+
+    @abstractmethod
+    def apply_async(self, func, callback=None):
+        """Schedule a func to be run"""
+
+    def retrieve_result_callback(self, out):
+        """Called within the callback function passed in apply_async.
+
+        The argument of this function is the argument given to a callback in
+        the considered backend. It is supposed to return the outcome of a task
+        if it succeeded or raise the exception if it failed.
+        """
+
+    def configure(self, n_jobs=1, parallel=None, prefer=None, require=None,
+                  **backend_args):
+        """Reconfigure the backend and return the number of workers.
+
+        This makes it possible to reuse an existing backend instance for
+        successive independent calls to Parallel with different parameters.
+        """
+        self.parallel = parallel
+        return self.effective_n_jobs(n_jobs)
+
+    def start_call(self):
+        """Call-back method called at the beginning of a Parallel call"""
+
+    def stop_call(self):
+        """Call-back method called at the end of a Parallel call"""
+
+    def terminate(self):
+        """Shutdown the workers and free the shared memory."""
+
+    def compute_batch_size(self):
+        """Determine the optimal batch size"""
+        return 1
+
+    def batch_completed(self, batch_size, duration):
+        """Callback indicate how long it took to run a batch"""
+
+    def get_exceptions(self):
+        """List of exception types to be captured."""
+        return []
+
+    def abort_everything(self, ensure_ready=True):
+        """Abort any running tasks
+
+        This is called when an exception has been raised when executing a task
+        and all the remaining tasks will be ignored and can therefore be
+        aborted to spare computation resources.
+
+        If ensure_ready is True, the backend should be left in an operating
+        state as future tasks might be re-submitted via that same backend
+        instance.
+
+        If ensure_ready is False, the implementer of this method can decide
+        to leave the backend in a closed / terminated state as no new task
+        are expected to be submitted to this backend.
+
+        Setting ensure_ready to False is an optimization that can be leveraged
+        when aborting tasks via killing processes from a local process pool
+        managed by the backend it-self: if we expect no new tasks, there is no
+        point in re-creating new workers.
+        """
+        # Does nothing by default: to be overridden in subclasses when
+        # canceling tasks is possible.
+        pass
+
+    def get_nested_backend(self):
+        """Backend instance to be used by nested Parallel calls.
+
+        By default a thread-based backend is used for the first level of
+        nesting. Beyond, switch to sequential backend to avoid spawning too
+        many threads on the host.
+        """
+        nesting_level = getattr(self, 'nesting_level', 0) + 1
+        if nesting_level > 1:
+            return SequentialBackend(nesting_level=nesting_level), None
+        else:
+            return ThreadingBackend(nesting_level=nesting_level), None
+
+    @contextlib.contextmanager
+    def retrieval_context(self):
+        """Context manager to manage an execution context.
+
+        Calls to Parallel.retrieve will be made inside this context.
+
+        By default, this does nothing. It may be useful for subclasses to
+        handle nested parallelism. In particular, it may be required to avoid
+        deadlocks if a backend manages a fixed number of workers, when those
+        workers may be asked to do nested Parallel calls. Without
+        'retrieval_context' this could lead to deadlock, as all the workers
+        managed by the backend may be "busy" waiting for the nested parallel
+        calls to finish, but the backend has no free workers to execute those
+        tasks.
+        """
+        yield
+
+    def _prepare_worker_env(self, n_jobs):
+        """Return environment variables limiting threadpools in external libs.
+
+        This function return a dict containing environment variables to pass
+        when creating a pool of process. These environment variables limit the
+        number of threads to `n_threads` for OpenMP, MKL, Accelerated and
+        OpenBLAS libraries in the child processes.
+        """
+        explicit_n_threads = self.inner_max_num_threads
+        default_n_threads = max(cpu_count() // n_jobs, 1)
+
+        # Set the inner environment variables to self.inner_max_num_threads if
+        # it is given. Else, default to cpu_count // n_jobs unless the variable
+        # is already present in the parent process environment.
+        env = {}
+        for var in self.MAX_NUM_THREADS_VARS:
+            if explicit_n_threads is None:
+                var_value = os.environ.get(var, default_n_threads)
+            else:
+                var_value = explicit_n_threads
+
+            env[var] = str(var_value)
+
+        if self.TBB_ENABLE_IPC_VAR not in os.environ:
+            # To avoid over-subscription when using TBB, let the TBB schedulers
+            # use Inter Process Communication to coordinate:
+            env[self.TBB_ENABLE_IPC_VAR] = "1"
+        return env
+
+    @staticmethod
+    def in_main_thread():
+        return isinstance(threading.current_thread(), threading._MainThread)
+
+
+class SequentialBackend(ParallelBackendBase):
+    """A ParallelBackend which will execute all batches sequentially.
+
+    Does not use/create any threading objects, and hence has minimal
+    overhead. Used when n_jobs == 1.
+    """
+
+    uses_threads = True
+    supports_timeout = False
+    supports_retrieve_callback = False
+    supports_sharedmem = True
+
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs which are going to run in parallel"""
+        if n_jobs == 0:
+            raise ValueError('n_jobs == 0 in Parallel has no meaning')
+        return 1
+
+    def apply_async(self, func, callback=None):
+        """Schedule a func to be run"""
+        raise RuntimeError("Should never be called for SequentialBackend.")
+
+    def retrieve_result_callback(self, out):
+        raise RuntimeError("Should never be called for SequentialBackend.")
+
+    def get_nested_backend(self):
+        # import is not top level to avoid cyclic import errors.
+        from .parallel import get_active_backend
+
+        # SequentialBackend should neither change the nesting level, the
+        # default backend or the number of jobs. Just return the current one.
+        return get_active_backend()
+
+
+class PoolManagerMixin(object):
+    """A helper class for managing pool of workers."""
+
+    _pool = None
+
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs which are going to run in parallel"""
+        if n_jobs == 0:
+            raise ValueError('n_jobs == 0 in Parallel has no meaning')
+        elif mp is None or n_jobs is None:
+            # multiprocessing is not available or disabled, fallback
+            # to sequential mode
+            return 1
+        elif n_jobs < 0:
+            n_jobs = max(cpu_count() + 1 + n_jobs, 1)
+        return n_jobs
+
+    def terminate(self):
+        """Shutdown the process or thread pool"""
+        if self._pool is not None:
+            self._pool.close()
+            self._pool.terminate()  # terminate does a join()
+            self._pool = None
+
+    def _get_pool(self):
+        """Used by apply_async to make it possible to implement lazy init"""
+        return self._pool
+
+    def apply_async(self, func, callback=None):
+        """Schedule a func to be run"""
+        # Here, we need a wrapper to avoid crashes on KeyboardInterruptErrors.
+        # We also call the callback on error, to make sure the pool does not
+        # wait on crashed jobs.
+        return self._get_pool().apply_async(
+            _TracebackCapturingWrapper(func), (),
+            callback=callback, error_callback=callback
+        )
+
+    def retrieve_result_callback(self, out):
+        """Mimic concurrent.futures results, raising an error if needed."""
+        return _retrieve_traceback_capturing_wrapped_call(out)
+
+    def abort_everything(self, ensure_ready=True):
+        """Shutdown the pool and restart a new one with the same parameters"""
+        self.terminate()
+        if ensure_ready:
+            self.configure(n_jobs=self.parallel.n_jobs, parallel=self.parallel,
+                           **self.parallel._backend_args)
+
+
+class AutoBatchingMixin(object):
+    """A helper class for automagically batching jobs."""
+
+    # In seconds, should be big enough to hide multiprocessing dispatching
+    # overhead.
+    # This settings was found by running benchmarks/bench_auto_batching.py
+    # with various parameters on various platforms.
+    MIN_IDEAL_BATCH_DURATION = .2
+
+    # Should not be too high to avoid stragglers: long jobs running alone
+    # on a single worker while other workers have no work to process any more.
+    MAX_IDEAL_BATCH_DURATION = 2
+
+    # Batching counters default values
+    _DEFAULT_EFFECTIVE_BATCH_SIZE = 1
+    _DEFAULT_SMOOTHED_BATCH_DURATION = 0.0
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._effective_batch_size = self._DEFAULT_EFFECTIVE_BATCH_SIZE
+        self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_DURATION
+
+    def compute_batch_size(self):
+        """Determine the optimal batch size"""
+        old_batch_size = self._effective_batch_size
+        batch_duration = self._smoothed_batch_duration
+        if (batch_duration > 0 and
+                batch_duration < self.MIN_IDEAL_BATCH_DURATION):
+            # The current batch size is too small: the duration of the
+            # processing of a batch of task is not large enough to hide
+            # the scheduling overhead.
+            ideal_batch_size = int(old_batch_size *
+                                   self.MIN_IDEAL_BATCH_DURATION /
+                                   batch_duration)
+            # Multiply by two to limit oscilations between min and max.
+            ideal_batch_size *= 2
+
+            # dont increase the batch size too fast to limit huge batch sizes
+            # potentially leading to starving worker
+            batch_size = min(2 * old_batch_size, ideal_batch_size)
+
+            batch_size = max(batch_size, 1)
+
+            self._effective_batch_size = batch_size
+            if self.parallel.verbose >= 10:
+                self.parallel._print(
+                    f"Batch computation too fast ({batch_duration}s.) "
+                    f"Setting batch_size={batch_size}."
+                )
+        elif (batch_duration > self.MAX_IDEAL_BATCH_DURATION and
+              old_batch_size >= 2):
+            # The current batch size is too big. If we schedule overly long
+            # running batches some CPUs might wait with nothing left to do
+            # while a couple of CPUs a left processing a few long running
+            # batches. Better reduce the batch size a bit to limit the
+            # likelihood of scheduling such stragglers.
+
+            # decrease the batch size quickly to limit potential starving
+            ideal_batch_size = int(
+                old_batch_size * self.MIN_IDEAL_BATCH_DURATION / batch_duration
+            )
+            # Multiply by two to limit oscilations between min and max.
+            batch_size = max(2 * ideal_batch_size, 1)
+            self._effective_batch_size = batch_size
+            if self.parallel.verbose >= 10:
+                self.parallel._print(
+                    f"Batch computation too slow ({batch_duration}s.) "
+                    f"Setting batch_size={batch_size}."
+                )
+        else:
+            # No batch size adjustment
+            batch_size = old_batch_size
+
+        if batch_size != old_batch_size:
+            # Reset estimation of the smoothed mean batch duration: this
+            # estimate is updated in the multiprocessing apply_async
+            # CallBack as long as the batch_size is constant. Therefore
+            # we need to reset the estimate whenever we re-tune the batch
+            # size.
+            self._smoothed_batch_duration = \
+                self._DEFAULT_SMOOTHED_BATCH_DURATION
+
+        return batch_size
+
+    def batch_completed(self, batch_size, duration):
+        """Callback indicate how long it took to run a batch"""
+        if batch_size == self._effective_batch_size:
+            # Update the smoothed streaming estimate of the duration of a batch
+            # from dispatch to completion
+            old_duration = self._smoothed_batch_duration
+            if old_duration == self._DEFAULT_SMOOTHED_BATCH_DURATION:
+                # First record of duration for this batch size after the last
+                # reset.
+                new_duration = duration
+            else:
+                # Update the exponentially weighted average of the duration of
+                # batch for the current effective size.
+                new_duration = 0.8 * old_duration + 0.2 * duration
+            self._smoothed_batch_duration = new_duration
+
+    def reset_batch_stats(self):
+        """Reset batch statistics to default values.
+
+        This avoids interferences with future jobs.
+        """
+        self._effective_batch_size = self._DEFAULT_EFFECTIVE_BATCH_SIZE
+        self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_DURATION
+
+
+class ThreadingBackend(PoolManagerMixin, ParallelBackendBase):
+    """A ParallelBackend which will use a thread pool to execute batches in.
+
+    This is a low-overhead backend but it suffers from the Python Global
+    Interpreter Lock if the called function relies a lot on Python objects.
+    Mostly useful when the execution bottleneck is a compiled extension that
+    explicitly releases the GIL (for instance a Cython loop wrapped in a "with
+    nogil" block or an expensive call to a library such as NumPy).
+
+    The actual thread pool is lazily initialized: the actual thread pool
+    construction is delayed to the first call to apply_async.
+
+    ThreadingBackend is used as the default backend for nested calls.
+    """
+
+    supports_retrieve_callback = True
+    uses_threads = True
+    supports_sharedmem = True
+
+    def configure(self, n_jobs=1, parallel=None, **backend_args):
+        """Build a process or thread pool and return the number of workers"""
+        n_jobs = self.effective_n_jobs(n_jobs)
+        if n_jobs == 1:
+            # Avoid unnecessary overhead and use sequential backend instead.
+            raise FallbackToBackend(
+                SequentialBackend(nesting_level=self.nesting_level))
+        self.parallel = parallel
+        self._n_jobs = n_jobs
+        return n_jobs
+
+    def _get_pool(self):
+        """Lazily initialize the thread pool
+
+        The actual pool of worker threads is only initialized at the first
+        call to apply_async.
+        """
+        if self._pool is None:
+            self._pool = ThreadPool(self._n_jobs)
+        return self._pool
+
+
+class MultiprocessingBackend(PoolManagerMixin, AutoBatchingMixin,
+                             ParallelBackendBase):
+    """A ParallelBackend which will use a multiprocessing.Pool.
+
+    Will introduce some communication and memory overhead when exchanging
+    input and output data with the with the worker Python processes.
+    However, does not suffer from the Python Global Interpreter Lock.
+    """
+
+    supports_retrieve_callback = True
+    supports_return_generator = False
+
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs which are going to run in parallel.
+
+        This also checks if we are attempting to create a nested parallel
+        loop.
+        """
+        if mp is None:
+            return 1
+
+        if mp.current_process().daemon:
+            # Daemonic processes cannot have children
+            if n_jobs != 1:
+                if inside_dask_worker():
+                    msg = (
+                        "Inside a Dask worker with daemon=True, "
+                        "setting n_jobs=1.\nPossible work-arounds:\n"
+                        "- dask.config.set("
+                        "{'distributed.worker.daemon': False})"
+                        "- set the environment variable "
+                        "DASK_DISTRIBUTED__WORKER__DAEMON=False\n"
+                        "before creating your Dask cluster."
+                    )
+                else:
+                    msg = (
+                        'Multiprocessing-backed parallel loops '
+                        'cannot be nested, setting n_jobs=1'
+                    )
+                warnings.warn(msg, stacklevel=3)
+            return 1
+
+        if process_executor._CURRENT_DEPTH > 0:
+            # Mixing loky and multiprocessing in nested loop is not supported
+            if n_jobs != 1:
+                warnings.warn(
+                    'Multiprocessing-backed parallel loops cannot be nested,'
+                    ' below loky, setting n_jobs=1',
+                    stacklevel=3)
+            return 1
+
+        elif not (self.in_main_thread() or self.nesting_level == 0):
+            # Prevent posix fork inside in non-main posix threads
+            if n_jobs != 1:
+                warnings.warn(
+                    'Multiprocessing-backed parallel loops cannot be nested'
+                    ' below threads, setting n_jobs=1',
+                    stacklevel=3)
+            return 1
+
+        return super(MultiprocessingBackend, self).effective_n_jobs(n_jobs)
+
+    def configure(self, n_jobs=1, parallel=None, prefer=None, require=None,
+                  **memmappingpool_args):
+        """Build a process or thread pool and return the number of workers"""
+        n_jobs = self.effective_n_jobs(n_jobs)
+        if n_jobs == 1:
+            raise FallbackToBackend(
+                SequentialBackend(nesting_level=self.nesting_level))
+
+        # Make sure to free as much memory as possible before forking
+        gc.collect()
+        self._pool = MemmappingPool(n_jobs, **memmappingpool_args)
+        self.parallel = parallel
+        return n_jobs
+
+    def terminate(self):
+        """Shutdown the process or thread pool"""
+        super(MultiprocessingBackend, self).terminate()
+        self.reset_batch_stats()
+
+
+class LokyBackend(AutoBatchingMixin, ParallelBackendBase):
+    """Managing pool of workers with loky instead of multiprocessing."""
+
+    supports_retrieve_callback = True
+    supports_inner_max_num_threads = True
+
+    def configure(self, n_jobs=1, parallel=None, prefer=None, require=None,
+                  idle_worker_timeout=300, **memmappingexecutor_args):
+        """Build a process executor and return the number of workers"""
+        n_jobs = self.effective_n_jobs(n_jobs)
+        if n_jobs == 1:
+            raise FallbackToBackend(
+                SequentialBackend(nesting_level=self.nesting_level))
+
+        self._workers = get_memmapping_executor(
+            n_jobs, timeout=idle_worker_timeout,
+            env=self._prepare_worker_env(n_jobs=n_jobs),
+            context_id=parallel._id, **memmappingexecutor_args)
+        self.parallel = parallel
+        return n_jobs
+
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs which are going to run in parallel"""
+        if n_jobs == 0:
+            raise ValueError('n_jobs == 0 in Parallel has no meaning')
+        elif mp is None or n_jobs is None:
+            # multiprocessing is not available or disabled, fallback
+            # to sequential mode
+            return 1
+        elif mp.current_process().daemon:
+            # Daemonic processes cannot have children
+            if n_jobs != 1:
+                if inside_dask_worker():
+                    msg = (
+                        "Inside a Dask worker with daemon=True, "
+                        "setting n_jobs=1.\nPossible work-arounds:\n"
+                        "- dask.config.set("
+                        "{'distributed.worker.daemon': False})\n"
+                        "- set the environment variable "
+                        "DASK_DISTRIBUTED__WORKER__DAEMON=False\n"
+                        "before creating your Dask cluster."
+                    )
+                else:
+                    msg = (
+                        'Loky-backed parallel loops cannot be called in a'
+                        ' multiprocessing, setting n_jobs=1'
+                    )
+                warnings.warn(msg, stacklevel=3)
+
+            return 1
+        elif not (self.in_main_thread() or self.nesting_level == 0):
+            # Prevent posix fork inside in non-main posix threads
+            if n_jobs != 1:
+                warnings.warn(
+                    'Loky-backed parallel loops cannot be nested below '
+                    'threads, setting n_jobs=1',
+                    stacklevel=3)
+            return 1
+        elif n_jobs < 0:
+            n_jobs = max(cpu_count() + 1 + n_jobs, 1)
+        return n_jobs
+
+    def apply_async(self, func, callback=None):
+        """Schedule a func to be run"""
+        future = self._workers.submit(func)
+        if callback is not None:
+            future.add_done_callback(callback)
+        return future
+
+    def retrieve_result_callback(self, out):
+        try:
+            return out.result()
+        except ShutdownExecutorError:
+            raise RuntimeError(
+                "The executor underlying Parallel has been shutdown. "
+                "This is likely due to the garbage collection of a previous "
+                "generator from a call to Parallel with return_as='generator'."
+                " Make sure the generator is not garbage collected when "
+                "submitting a new job or that it is first properly exhausted."
+            )
+
+    def terminate(self):
+        if self._workers is not None:
+            # Don't terminate the workers as we want to reuse them in later
+            # calls, but cleanup the temporary resources that the Parallel call
+            # created. This 'hack' requires a private, low-level operation.
+            self._workers._temp_folder_manager._clean_temporary_resources(
+                context_id=self.parallel._id, force=False
+            )
+            self._workers = None
+
+        self.reset_batch_stats()
+
+    def abort_everything(self, ensure_ready=True):
+        """Shutdown the workers and restart a new one with the same parameters
+        """
+        self._workers.terminate(kill_workers=True)
+        self._workers = None
+
+        if ensure_ready:
+            self.configure(n_jobs=self.parallel.n_jobs, parallel=self.parallel)
+
+
+class FallbackToBackend(Exception):
+    """Raised when configuration should fallback to another backend"""
+
+    def __init__(self, backend):
+        self.backend = backend
+
+
+def inside_dask_worker():
+    """Check whether the current function is executed inside a Dask worker.
+    """
+    # This function can not be in joblib._dask because there would be a
+    # circular import:
+    # _dask imports _parallel_backend that imports _dask ...
+    try:
+        from distributed import get_worker
+    except ImportError:
+        return False
+
+    try:
+        get_worker()
+        return True
+    except ValueError:
+        return False

env-llmeval/lib/python3.10/site-packages/joblib/_store_backends.py

@@ -0,0 +1,474 @@
+"""Storage providers backends for Memory caching."""
+
+from pickle import PicklingError
+import re
+import os
+import os.path
+import datetime
+import json
+import shutil
+import time
+import warnings
+import collections
+import operator
+import threading
+from abc import ABCMeta, abstractmethod
+
+from .backports import concurrency_safe_rename
+from .disk import mkdirp, memstr_to_bytes, rm_subdirs
+from .logger import format_time
+from . import numpy_pickle
+
+CacheItemInfo = collections.namedtuple('CacheItemInfo',
+                                       'path size last_access')
+
+
+class CacheWarning(Warning):
+    """Warning to capture dump failures except for PicklingError."""
+    pass
+
+
+def concurrency_safe_write(object_to_write, filename, write_func):
+    """Writes an object into a unique file in a concurrency-safe way."""
+    thread_id = id(threading.current_thread())
+    temporary_filename = '{}.thread-{}-pid-{}'.format(
+        filename, thread_id, os.getpid())
+    write_func(object_to_write, temporary_filename)
+
+    return temporary_filename
+
+
+class StoreBackendBase(metaclass=ABCMeta):
+    """Helper Abstract Base Class which defines all methods that
+       a StorageBackend must implement."""
+
+    location = None
+
+    @abstractmethod
+    def _open_item(self, f, mode):
+        """Opens an item on the store and return a file-like object.
+
+        This method is private and only used by the StoreBackendMixin object.
+
+        Parameters
+        ----------
+        f: a file-like object
+            The file-like object where an item is stored and retrieved
+        mode: string, optional
+            the mode in which the file-like object is opened allowed valued are
+            'rb', 'wb'
+
+        Returns
+        -------
+        a file-like object
+        """
+
+    @abstractmethod
+    def _item_exists(self, location):
+        """Checks if an item location exists in the store.
+
+        This method is private and only used by the StoreBackendMixin object.
+
+        Parameters
+        ----------
+        location: string
+            The location of an item. On a filesystem, this corresponds to the
+            absolute path, including the filename, of a file.
+
+        Returns
+        -------
+        True if the item exists, False otherwise
+        """
+
+    @abstractmethod
+    def _move_item(self, src, dst):
+        """Moves an item from src to dst in the store.
+
+        This method is private and only used by the StoreBackendMixin object.
+
+        Parameters
+        ----------
+        src: string
+            The source location of an item
+        dst: string
+            The destination location of an item
+        """
+
+    @abstractmethod
+    def create_location(self, location):
+        """Creates a location on the store.
+
+        Parameters
+        ----------
+        location: string
+            The location in the store. On a filesystem, this corresponds to a
+            directory.
+        """
+
+    @abstractmethod
+    def clear_location(self, location):
+        """Clears a location on the store.
+
+        Parameters
+        ----------
+        location: string
+            The location in the store. On a filesystem, this corresponds to a
+            directory or a filename absolute path
+        """
+
+    @abstractmethod
+    def get_items(self):
+        """Returns the whole list of items available in the store.
+
+        Returns
+        -------
+        The list of items identified by their ids (e.g filename in a
+        filesystem).
+        """
+
+    @abstractmethod
+    def configure(self, location, verbose=0, backend_options=dict()):
+        """Configures the store.
+
+        Parameters
+        ----------
+        location: string
+            The base location used by the store. On a filesystem, this
+            corresponds to a directory.
+        verbose: int
+            The level of verbosity of the store
+        backend_options: dict
+            Contains a dictionary of named parameters used to configure the
+            store backend.
+        """
+
+
+class StoreBackendMixin(object):
+    """Class providing all logic for managing the store in a generic way.
+
+    The StoreBackend subclass has to implement 3 methods: create_location,
+    clear_location and configure. The StoreBackend also has to provide
+    a private _open_item, _item_exists and _move_item methods. The _open_item
+    method has to have the same signature as the builtin open and return a
+    file-like object.
+    """
+
+    def load_item(self, call_id, verbose=1, timestamp=None, metadata=None):
+        """Load an item from the store given its id as a list of str."""
+        full_path = os.path.join(self.location, *call_id)
+
+        if verbose > 1:
+            ts_string = ('{: <16}'.format(format_time(time.time() - timestamp))
+                         if timestamp is not None else '')
+            signature = os.path.basename(call_id[0])
+            if metadata is not None and 'input_args' in metadata:
+                kwargs = ', '.join('{}={}'.format(*item)
+                                   for item in metadata['input_args'].items())
+                signature += '({})'.format(kwargs)
+            msg = '[Memory]{}: Loading {}'.format(ts_string, signature)
+            if verbose < 10:
+                print('{0}...'.format(msg))
+            else:
+                print('{0} from {1}'.format(msg, full_path))
+
+        mmap_mode = (None if not hasattr(self, 'mmap_mode')
+                     else self.mmap_mode)
+
+        filename = os.path.join(full_path, 'output.pkl')
+        if not self._item_exists(filename):
+            raise KeyError("Non-existing item (may have been "
+                           "cleared).\nFile %s does not exist" % filename)
+
+        # file-like object cannot be used when mmap_mode is set
+        if mmap_mode is None:
+            with self._open_item(filename, "rb") as f:
+                item = numpy_pickle.load(f)
+        else:
+            item = numpy_pickle.load(filename, mmap_mode=mmap_mode)
+        return item
+
+    def dump_item(self, call_id, item, verbose=1):
+        """Dump an item in the store at the id given as a list of str."""
+        try:
+            item_path = os.path.join(self.location, *call_id)
+            if not self._item_exists(item_path):
+                self.create_location(item_path)
+            filename = os.path.join(item_path, 'output.pkl')
+            if verbose > 10:
+                print('Persisting in %s' % item_path)
+
+            def write_func(to_write, dest_filename):
+                with self._open_item(dest_filename, "wb") as f:
+                    try:
+                        numpy_pickle.dump(to_write, f, compress=self.compress)
+                    except PicklingError as e:
+                        # TODO(1.5) turn into error
+                        warnings.warn(
+                            "Unable to cache to disk: failed to pickle "
+                            "output. In version 1.5 this will raise an "
+                            f"exception. Exception: {e}.",
+                            FutureWarning
+                        )
+
+            self._concurrency_safe_write(item, filename, write_func)
+        except Exception as e:  # noqa: E722
+            warnings.warn(
+                "Unable to cache to disk. Possibly a race condition in the "
+                f"creation of the directory. Exception: {e}.",
+                CacheWarning
+            )
+
+    def clear_item(self, call_id):
+        """Clear the item at the id, given as a list of str."""
+        item_path = os.path.join(self.location, *call_id)
+        if self._item_exists(item_path):
+            self.clear_location(item_path)
+
+    def contains_item(self, call_id):
+        """Check if there is an item at the id, given as a list of str."""
+        item_path = os.path.join(self.location, *call_id)
+        filename = os.path.join(item_path, 'output.pkl')
+
+        return self._item_exists(filename)
+
+    def get_item_info(self, call_id):
+        """Return information about item."""
+        return {'location': os.path.join(self.location, *call_id)}
+
+    def get_metadata(self, call_id):
+        """Return actual metadata of an item."""
+        try:
+            item_path = os.path.join(self.location, *call_id)
+            filename = os.path.join(item_path, 'metadata.json')
+            with self._open_item(filename, 'rb') as f:
+                return json.loads(f.read().decode('utf-8'))
+        except:  # noqa: E722
+            return {}
+
+    def store_metadata(self, call_id, metadata):
+        """Store metadata of a computation."""
+        try:
+            item_path = os.path.join(self.location, *call_id)
+            self.create_location(item_path)
+            filename = os.path.join(item_path, 'metadata.json')
+
+            def write_func(to_write, dest_filename):
+                with self._open_item(dest_filename, "wb") as f:
+                    f.write(json.dumps(to_write).encode('utf-8'))
+
+            self._concurrency_safe_write(metadata, filename, write_func)
+        except:  # noqa: E722
+            pass
+
+    def contains_path(self, call_id):
+        """Check cached function is available in store."""
+        func_path = os.path.join(self.location, *call_id)
+        return self.object_exists(func_path)
+
+    def clear_path(self, call_id):
+        """Clear all items with a common path in the store."""
+        func_path = os.path.join(self.location, *call_id)
+        if self._item_exists(func_path):
+            self.clear_location(func_path)
+
+    def store_cached_func_code(self, call_id, func_code=None):
+        """Store the code of the cached function."""
+        func_path = os.path.join(self.location, *call_id)
+        if not self._item_exists(func_path):
+            self.create_location(func_path)
+
+        if func_code is not None:
+            filename = os.path.join(func_path, "func_code.py")
+            with self._open_item(filename, 'wb') as f:
+                f.write(func_code.encode('utf-8'))
+
+    def get_cached_func_code(self, call_id):
+        """Store the code of the cached function."""
+        filename = os.path.join(self.location, *call_id, 'func_code.py')
+        try:
+            with self._open_item(filename, 'rb') as f:
+                return f.read().decode('utf-8')
+        except:  # noqa: E722
+            raise
+
+    def get_cached_func_info(self, call_id):
+        """Return information related to the cached function if it exists."""
+        return {'location': os.path.join(self.location, *call_id)}
+
+    def clear(self):
+        """Clear the whole store content."""
+        self.clear_location(self.location)
+
+    def enforce_store_limits(
+            self, bytes_limit, items_limit=None, age_limit=None
+    ):
+        """
+        Remove the store's oldest files to enforce item, byte, and age limits.
+        """
+        items_to_delete = self._get_items_to_delete(
+            bytes_limit, items_limit, age_limit
+        )
+
+        for item in items_to_delete:
+            if self.verbose > 10:
+                print('Deleting item {0}'.format(item))
+            try:
+                self.clear_location(item.path)
+            except OSError:
+                # Even with ignore_errors=True shutil.rmtree can raise OSError
+                # with:
+                # [Errno 116] Stale file handle if another process has deleted
+                # the folder already.
+                pass
+
+    def _get_items_to_delete(
+            self, bytes_limit, items_limit=None, age_limit=None
+    ):
+        """
+        Get items to delete to keep the store under size, file, & age limits.
+        """
+        if isinstance(bytes_limit, str):
+            bytes_limit = memstr_to_bytes(bytes_limit)
+
+        items = self.get_items()
+        if not items:
+            return []
+
+        size = sum(item.size for item in items)
+
+        if bytes_limit is not None:
+            to_delete_size = size - bytes_limit
+        else:
+            to_delete_size = 0
+
+        if items_limit is not None:
+            to_delete_items = len(items) - items_limit
+        else:
+            to_delete_items = 0
+
+        if age_limit is not None:
+            older_item = min(item.last_access for item in items)
+            deadline = datetime.datetime.now() - age_limit
+        else:
+            deadline = None
+
+        if (
+            to_delete_size <= 0 and to_delete_items <= 0
+            and (deadline is None or older_item > deadline)
+        ):
+            return []
+
+        # We want to delete first the cache items that were accessed a
+        # long time ago
+        items.sort(key=operator.attrgetter('last_access'))
+
+        items_to_delete = []
+        size_so_far = 0
+        items_so_far = 0
+
+        for item in items:
+            if (
+                (size_so_far >= to_delete_size)
+                and items_so_far >= to_delete_items
+                and (deadline is None or deadline < item.last_access)
+            ):
+                break
+
+            items_to_delete.append(item)
+            size_so_far += item.size
+            items_so_far += 1
+
+        return items_to_delete
+
+    def _concurrency_safe_write(self, to_write, filename, write_func):
+        """Writes an object into a file in a concurrency-safe way."""
+        temporary_filename = concurrency_safe_write(to_write,
+                                                    filename, write_func)
+        self._move_item(temporary_filename, filename)
+
+    def __repr__(self):
+        """Printable representation of the store location."""
+        return '{class_name}(location="{location}")'.format(
+            class_name=self.__class__.__name__, location=self.location)
+
+
+class FileSystemStoreBackend(StoreBackendBase, StoreBackendMixin):
+    """A StoreBackend used with local or network file systems."""
+
+    _open_item = staticmethod(open)
+    _item_exists = staticmethod(os.path.exists)
+    _move_item = staticmethod(concurrency_safe_rename)
+
+    def clear_location(self, location):
+        """Delete location on store."""
+        if (location == self.location):
+            rm_subdirs(location)
+        else:
+            shutil.rmtree(location, ignore_errors=True)
+
+    def create_location(self, location):
+        """Create object location on store"""
+        mkdirp(location)
+
+    def get_items(self):
+        """Returns the whole list of items available in the store."""
+        items = []
+
+        for dirpath, _, filenames in os.walk(self.location):
+            is_cache_hash_dir = re.match('[a-f0-9]{32}',
+                                         os.path.basename(dirpath))
+
+            if is_cache_hash_dir:
+                output_filename = os.path.join(dirpath, 'output.pkl')
+                try:
+                    last_access = os.path.getatime(output_filename)
+                except OSError:
+                    try:
+                        last_access = os.path.getatime(dirpath)
+                    except OSError:
+                        # The directory has already been deleted
+                        continue
+
+                last_access = datetime.datetime.fromtimestamp(last_access)
+                try:
+                    full_filenames = [os.path.join(dirpath, fn)
+                                      for fn in filenames]
+                    dirsize = sum(os.path.getsize(fn)
+                                  for fn in full_filenames)
+                except OSError:
+                    # Either output_filename or one of the files in
+                    # dirpath does not exist any more. We assume this
+                    # directory is being cleaned by another process already
+                    continue
+
+                items.append(CacheItemInfo(dirpath, dirsize,
+                                           last_access))
+
+        return items
+
+    def configure(self, location, verbose=1, backend_options=None):
+        """Configure the store backend.
+
+        For this backend, valid store options are 'compress' and 'mmap_mode'
+        """
+        if backend_options is None:
+            backend_options = {}
+
+        # setup location directory
+        self.location = location
+        if not os.path.exists(self.location):
+            mkdirp(self.location)
+
+        # item can be stored compressed for faster I/O
+        self.compress = backend_options.get('compress', False)
+
+        # FileSystemStoreBackend can be used with mmap_mode options under
+        # certain conditions.
+        mmap_mode = backend_options.get('mmap_mode')
+        if self.compress and mmap_mode is not None:
+            warnings.warn('Compressed items cannot be memmapped in a '
+                          'filesystem store. Option will be ignored.',
+                          stacklevel=2)
+
+        self.mmap_mode = mmap_mode
+        self.verbose = verbose

env-llmeval/lib/python3.10/site-packages/joblib/_utils.py

@@ -0,0 +1,83 @@
+# Adapted from https://stackoverflow.com/a/9558001/2536294
+
+import ast
+from dataclasses import dataclass
+import operator as op
+
+
+from ._multiprocessing_helpers import mp
+
+if mp is not None:
+    from .externals.loky.process_executor import _ExceptionWithTraceback
+
+
+# supported operators
+operators = {
+    ast.Add: op.add,
+    ast.Sub: op.sub,
+    ast.Mult: op.mul,
+    ast.Div: op.truediv,
+    ast.FloorDiv: op.floordiv,
+    ast.Mod: op.mod,
+    ast.Pow: op.pow,
+    ast.USub: op.neg,
+}
+
+
+def eval_expr(expr):
+    """
+    >>> eval_expr('2*6')
+    12
+    >>> eval_expr('2**6')
+    64
+    >>> eval_expr('1 + 2*3**(4) / (6 + -7)')
+    -161.0
+    """
+    try:
+        return eval_(ast.parse(expr, mode="eval").body)
+    except (TypeError, SyntaxError, KeyError) as e:
+        raise ValueError(
+            f"{expr!r} is not a valid or supported arithmetic expression."
+        ) from e
+
+
+def eval_(node):
+    if isinstance(node, ast.Constant):  # <constant>
+        return node.value
+    elif isinstance(node, ast.BinOp):  # <left> <operator> <right>
+        return operators[type(node.op)](eval_(node.left), eval_(node.right))
+    elif isinstance(node, ast.UnaryOp):  # <operator> <operand> e.g., -1
+        return operators[type(node.op)](eval_(node.operand))
+    else:
+        raise TypeError(node)
+
+
+@dataclass(frozen=True)
+class _Sentinel:
+    """A sentinel to mark a parameter as not explicitly set"""
+    default_value: object
+
+    def __repr__(self):
+        return f"default({self.default_value!r})"
+
+
+class _TracebackCapturingWrapper:
+    """Protect function call and return error with traceback."""
+
+    def __init__(self, func):
+        self.func = func
+
+    def __call__(self, **kwargs):
+        try:
+            return self.func(**kwargs)
+        except BaseException as e:
+            return _ExceptionWithTraceback(e)
+
+
+def _retrieve_traceback_capturing_wrapped_call(out):
+    if isinstance(out, _ExceptionWithTraceback):
+        rebuild, args = out.__reduce__()
+        out = rebuild(*args)
+    if isinstance(out, BaseException):
+        raise out
+    return out

env-llmeval/lib/python3.10/site-packages/joblib/compressor.py

@@ -0,0 +1,570 @@
+"""Classes and functions for managing compressors."""
+
+import io
+import zlib
+from joblib.backports import LooseVersion
+
+try:
+    from threading import RLock
+except ImportError:
+    from dummy_threading import RLock
+
+try:
+    import bz2
+except ImportError:
+    bz2 = None
+
+try:
+    import lz4
+    from lz4.frame import LZ4FrameFile
+except ImportError:
+    lz4 = None
+
+try:
+    import lzma
+except ImportError:
+    lzma = None
+
+
+LZ4_NOT_INSTALLED_ERROR = ('LZ4 is not installed. Install it with pip: '
+                           'https://python-lz4.readthedocs.io/')
+
+# Registered compressors
+_COMPRESSORS = {}
+
+# Magic numbers of supported compression file formats.
+_ZFILE_PREFIX = b'ZF'  # used with pickle files created before 0.9.3.
+_ZLIB_PREFIX = b'\x78'
+_GZIP_PREFIX = b'\x1f\x8b'
+_BZ2_PREFIX = b'BZ'
+_XZ_PREFIX = b'\xfd\x37\x7a\x58\x5a'
+_LZMA_PREFIX = b'\x5d\x00'
+_LZ4_PREFIX = b'\x04\x22\x4D\x18'
+
+
+def register_compressor(compressor_name, compressor,
+                        force=False):
+    """Register a new compressor.
+
+    Parameters
+    ----------
+    compressor_name: str.
+        The name of the compressor.
+    compressor: CompressorWrapper
+        An instance of a 'CompressorWrapper'.
+    """
+    global _COMPRESSORS
+    if not isinstance(compressor_name, str):
+        raise ValueError("Compressor name should be a string, "
+                         "'{}' given.".format(compressor_name))
+
+    if not isinstance(compressor, CompressorWrapper):
+        raise ValueError("Compressor should implement the CompressorWrapper "
+                         "interface, '{}' given.".format(compressor))
+
+    if (compressor.fileobj_factory is not None and
+            (not hasattr(compressor.fileobj_factory, 'read') or
+             not hasattr(compressor.fileobj_factory, 'write') or
+             not hasattr(compressor.fileobj_factory, 'seek') or
+             not hasattr(compressor.fileobj_factory, 'tell'))):
+        raise ValueError("Compressor 'fileobj_factory' attribute should "
+                         "implement the file object interface, '{}' given."
+                         .format(compressor.fileobj_factory))
+
+    if compressor_name in _COMPRESSORS and not force:
+        raise ValueError("Compressor '{}' already registered."
+                         .format(compressor_name))
+
+    _COMPRESSORS[compressor_name] = compressor
+
+
+class CompressorWrapper():
+    """A wrapper around a compressor file object.
+
+    Attributes
+    ----------
+    obj: a file-like object
+        The object must implement the buffer interface and will be used
+        internally to compress/decompress the data.
+    prefix: bytestring
+        A bytestring corresponding to the magic number that identifies the
+        file format associated to the compressor.
+    extension: str
+        The file extension used to automatically select this compressor during
+        a dump to a file.
+    """
+
+    def __init__(self, obj, prefix=b'', extension=''):
+        self.fileobj_factory = obj
+        self.prefix = prefix
+        self.extension = extension
+
+    def compressor_file(self, fileobj, compresslevel=None):
+        """Returns an instance of a compressor file object."""
+        if compresslevel is None:
+            return self.fileobj_factory(fileobj, 'wb')
+        else:
+            return self.fileobj_factory(fileobj, 'wb',
+                                        compresslevel=compresslevel)
+
+    def decompressor_file(self, fileobj):
+        """Returns an instance of a decompressor file object."""
+        return self.fileobj_factory(fileobj, 'rb')
+
+
+class BZ2CompressorWrapper(CompressorWrapper):
+
+    prefix = _BZ2_PREFIX
+    extension = '.bz2'
+
+    def __init__(self):
+        if bz2 is not None:
+            self.fileobj_factory = bz2.BZ2File
+        else:
+            self.fileobj_factory = None
+
+    def _check_versions(self):
+        if bz2 is None:
+            raise ValueError('bz2 module is not compiled on your python '
+                             'standard library.')
+
+    def compressor_file(self, fileobj, compresslevel=None):
+        """Returns an instance of a compressor file object."""
+        self._check_versions()
+        if compresslevel is None:
+            return self.fileobj_factory(fileobj, 'wb')
+        else:
+            return self.fileobj_factory(fileobj, 'wb',
+                                        compresslevel=compresslevel)
+
+    def decompressor_file(self, fileobj):
+        """Returns an instance of a decompressor file object."""
+        self._check_versions()
+        fileobj = self.fileobj_factory(fileobj, 'rb')
+        return fileobj
+
+
+class LZMACompressorWrapper(CompressorWrapper):
+
+    prefix = _LZMA_PREFIX
+    extension = '.lzma'
+    _lzma_format_name = 'FORMAT_ALONE'
+
+    def __init__(self):
+        if lzma is not None:
+            self.fileobj_factory = lzma.LZMAFile
+            self._lzma_format = getattr(lzma, self._lzma_format_name)
+        else:
+            self.fileobj_factory = None
+
+    def _check_versions(self):
+        if lzma is None:
+            raise ValueError('lzma module is not compiled on your python '
+                             'standard library.')
+
+    def compressor_file(self, fileobj, compresslevel=None):
+        """Returns an instance of a compressor file object."""
+        if compresslevel is None:
+            return self.fileobj_factory(fileobj, 'wb',
+                                        format=self._lzma_format)
+        else:
+            return self.fileobj_factory(fileobj, 'wb',
+                                        format=self._lzma_format,
+                                        preset=compresslevel)
+
+    def decompressor_file(self, fileobj):
+        """Returns an instance of a decompressor file object."""
+        return lzma.LZMAFile(fileobj, 'rb')
+
+
+class XZCompressorWrapper(LZMACompressorWrapper):
+
+    prefix = _XZ_PREFIX
+    extension = '.xz'
+    _lzma_format_name = 'FORMAT_XZ'
+
+
+class LZ4CompressorWrapper(CompressorWrapper):
+
+    prefix = _LZ4_PREFIX
+    extension = '.lz4'
+
+    def __init__(self):
+        if lz4 is not None:
+            self.fileobj_factory = LZ4FrameFile
+        else:
+            self.fileobj_factory = None
+
+    def _check_versions(self):
+        if lz4 is None:
+            raise ValueError(LZ4_NOT_INSTALLED_ERROR)
+        lz4_version = lz4.__version__
+        if lz4_version.startswith("v"):
+            lz4_version = lz4_version[1:]
+        if LooseVersion(lz4_version) < LooseVersion('0.19'):
+            raise ValueError(LZ4_NOT_INSTALLED_ERROR)
+
+    def compressor_file(self, fileobj, compresslevel=None):
+        """Returns an instance of a compressor file object."""
+        self._check_versions()
+        if compresslevel is None:
+            return self.fileobj_factory(fileobj, 'wb')
+        else:
+            return self.fileobj_factory(fileobj, 'wb',
+                                        compression_level=compresslevel)
+
+    def decompressor_file(self, fileobj):
+        """Returns an instance of a decompressor file object."""
+        self._check_versions()
+        return self.fileobj_factory(fileobj, 'rb')
+
+
+###############################################################################
+#  base file compression/decompression object definition
+_MODE_CLOSED = 0
+_MODE_READ = 1
+_MODE_READ_EOF = 2
+_MODE_WRITE = 3
+_BUFFER_SIZE = 8192
+
+
+class BinaryZlibFile(io.BufferedIOBase):
+    """A file object providing transparent zlib (de)compression.
+
+    TODO python2_drop: is it still needed since we dropped Python 2 support A
+    BinaryZlibFile can act as a wrapper for an existing file object, or refer
+    directly to a named file on disk.
+
+    Note that BinaryZlibFile provides only a *binary* file interface: data read
+    is returned as bytes, and data to be written should be given as bytes.
+
+    This object is an adaptation of the BZ2File object and is compatible with
+    versions of python >= 2.7.
+
+    If filename is a str or bytes object, it gives the name
+    of the file to be opened. Otherwise, it should be a file object,
+    which will be used to read or write the compressed data.
+
+    mode can be 'rb' for reading (default) or 'wb' for (over)writing
+
+    If mode is 'wb', compresslevel can be a number between 1
+    and 9 specifying the level of compression: 1 produces the least
+    compression, and 9 produces the most compression. 3 is the default.
+    """
+
+    wbits = zlib.MAX_WBITS
+
+    def __init__(self, filename, mode="rb", compresslevel=3):
+        # This lock must be recursive, so that BufferedIOBase's
+        # readline(), readlines() and writelines() don't deadlock.
+        self._lock = RLock()
+        self._fp = None
+        self._closefp = False
+        self._mode = _MODE_CLOSED
+        self._pos = 0
+        self._size = -1
+        self.compresslevel = compresslevel
+
+        if not isinstance(compresslevel, int) or not (1 <= compresslevel <= 9):
+            raise ValueError("'compresslevel' must be an integer "
+                             "between 1 and 9. You provided 'compresslevel={}'"
+                             .format(compresslevel))
+
+        if mode == "rb":
+            self._mode = _MODE_READ
+            self._decompressor = zlib.decompressobj(self.wbits)
+            self._buffer = b""
+            self._buffer_offset = 0
+        elif mode == "wb":
+            self._mode = _MODE_WRITE
+            self._compressor = zlib.compressobj(self.compresslevel,
+                                                zlib.DEFLATED, self.wbits,
+                                                zlib.DEF_MEM_LEVEL, 0)
+        else:
+            raise ValueError("Invalid mode: %r" % (mode,))
+
+        if isinstance(filename, str):
+            self._fp = io.open(filename, mode)
+            self._closefp = True
+        elif hasattr(filename, "read") or hasattr(filename, "write"):
+            self._fp = filename
+        else:
+            raise TypeError("filename must be a str or bytes object, "
+                            "or a file")
+
+    def close(self):
+        """Flush and close the file.
+
+        May be called more than once without error. Once the file is
+        closed, any other operation on it will raise a ValueError.
+        """
+        with self._lock:
+            if self._mode == _MODE_CLOSED:
+                return
+            try:
+                if self._mode in (_MODE_READ, _MODE_READ_EOF):
+                    self._decompressor = None
+                elif self._mode == _MODE_WRITE:
+                    self._fp.write(self._compressor.flush())
+                    self._compressor = None
+            finally:
+                try:
+                    if self._closefp:
+                        self._fp.close()
+                finally:
+                    self._fp = None
+                    self._closefp = False
+                    self._mode = _MODE_CLOSED
+                    self._buffer = b""
+                    self._buffer_offset = 0
+
+    @property
+    def closed(self):
+        """True if this file is closed."""
+        return self._mode == _MODE_CLOSED
+
+    def fileno(self):
+        """Return the file descriptor for the underlying file."""
+        self._check_not_closed()
+        return self._fp.fileno()
+
+    def seekable(self):
+        """Return whether the file supports seeking."""
+        return self.readable() and self._fp.seekable()
+
+    def readable(self):
+        """Return whether the file was opened for reading."""
+        self._check_not_closed()
+        return self._mode in (_MODE_READ, _MODE_READ_EOF)
+
+    def writable(self):
+        """Return whether the file was opened for writing."""
+        self._check_not_closed()
+        return self._mode == _MODE_WRITE
+
+    # Mode-checking helper functions.
+
+    def _check_not_closed(self):
+        if self.closed:
+            fname = getattr(self._fp, 'name', None)
+            msg = "I/O operation on closed file"
+            if fname is not None:
+                msg += " {}".format(fname)
+            msg += "."
+            raise ValueError(msg)
+
+    def _check_can_read(self):
+        if self._mode not in (_MODE_READ, _MODE_READ_EOF):
+            self._check_not_closed()
+            raise io.UnsupportedOperation("File not open for reading")
+
+    def _check_can_write(self):
+        if self._mode != _MODE_WRITE:
+            self._check_not_closed()
+            raise io.UnsupportedOperation("File not open for writing")
+
+    def _check_can_seek(self):
+        if self._mode not in (_MODE_READ, _MODE_READ_EOF):
+            self._check_not_closed()
+            raise io.UnsupportedOperation("Seeking is only supported "
+                                          "on files open for reading")
+        if not self._fp.seekable():
+            raise io.UnsupportedOperation("The underlying file object "
+                                          "does not support seeking")
+
+    # Fill the readahead buffer if it is empty. Returns False on EOF.
+    def _fill_buffer(self):
+        if self._mode == _MODE_READ_EOF:
+            return False
+        # Depending on the input data, our call to the decompressor may not
+        # return any data. In this case, try again after reading another block.
+        while self._buffer_offset == len(self._buffer):
+            try:
+                rawblock = (self._decompressor.unused_data or
+                            self._fp.read(_BUFFER_SIZE))
+                if not rawblock:
+                    raise EOFError
+            except EOFError:
+                # End-of-stream marker and end of file. We're good.
+                self._mode = _MODE_READ_EOF
+                self._size = self._pos
+                return False
+            else:
+                self._buffer = self._decompressor.decompress(rawblock)
+            self._buffer_offset = 0
+        return True
+
+    # Read data until EOF.
+    # If return_data is false, consume the data without returning it.
+    def _read_all(self, return_data=True):
+        # The loop assumes that _buffer_offset is 0. Ensure that this is true.
+        self._buffer = self._buffer[self._buffer_offset:]
+        self._buffer_offset = 0
+
+        blocks = []
+        while self._fill_buffer():
+            if return_data:
+                blocks.append(self._buffer)
+            self._pos += len(self._buffer)
+            self._buffer = b""
+        if return_data:
+            return b"".join(blocks)
+
+    # Read a block of up to n bytes.
+    # If return_data is false, consume the data without returning it.
+    def _read_block(self, n_bytes, return_data=True):
+        # If we have enough data buffered, return immediately.
+        end = self._buffer_offset + n_bytes
+        if end <= len(self._buffer):
+            data = self._buffer[self._buffer_offset: end]
+            self._buffer_offset = end
+            self._pos += len(data)
+            return data if return_data else None
+
+        # The loop assumes that _buffer_offset is 0. Ensure that this is true.
+        self._buffer = self._buffer[self._buffer_offset:]
+        self._buffer_offset = 0
+
+        blocks = []
+        while n_bytes > 0 and self._fill_buffer():
+            if n_bytes < len(self._buffer):
+                data = self._buffer[:n_bytes]
+                self._buffer_offset = n_bytes
+            else:
+                data = self._buffer
+                self._buffer = b""
+            if return_data:
+                blocks.append(data)
+            self._pos += len(data)
+            n_bytes -= len(data)
+        if return_data:
+            return b"".join(blocks)
+
+    def read(self, size=-1):
+        """Read up to size uncompressed bytes from the file.
+
+        If size is negative or omitted, read until EOF is reached.
+        Returns b'' if the file is already at EOF.
+        """
+        with self._lock:
+            self._check_can_read()
+            if size == 0:
+                return b""
+            elif size < 0:
+                return self._read_all()
+            else:
+                return self._read_block(size)
+
+    def readinto(self, b):
+        """Read up to len(b) bytes into b.
+
+        Returns the number of bytes read (0 for EOF).
+        """
+        with self._lock:
+            return io.BufferedIOBase.readinto(self, b)
+
+    def write(self, data):
+        """Write a byte string to the file.
+
+        Returns the number of uncompressed bytes written, which is
+        always len(data). Note that due to buffering, the file on disk
+        may not reflect the data written until close() is called.
+        """
+        with self._lock:
+            self._check_can_write()
+            # Convert data type if called by io.BufferedWriter.
+            if isinstance(data, memoryview):
+                data = data.tobytes()
+
+            compressed = self._compressor.compress(data)
+            self._fp.write(compressed)
+            self._pos += len(data)
+            return len(data)
+
+    # Rewind the file to the beginning of the data stream.
+    def _rewind(self):
+        self._fp.seek(0, 0)
+        self._mode = _MODE_READ
+        self._pos = 0
+        self._decompressor = zlib.decompressobj(self.wbits)
+        self._buffer = b""
+        self._buffer_offset = 0
+
+    def seek(self, offset, whence=0):
+        """Change the file position.
+
+        The new position is specified by offset, relative to the
+        position indicated by whence. Values for whence are:
+
+            0: start of stream (default); offset must not be negative
+            1: current stream position
+            2: end of stream; offset must not be positive
+
+        Returns the new file position.
+
+        Note that seeking is emulated, so depending on the parameters,
+        this operation may be extremely slow.
+        """
+        with self._lock:
+            self._check_can_seek()
+
+            # Recalculate offset as an absolute file position.
+            if whence == 0:
+                pass
+            elif whence == 1:
+                offset = self._pos + offset
+            elif whence == 2:
+                # Seeking relative to EOF - we need to know the file's size.
+                if self._size < 0:
+                    self._read_all(return_data=False)
+                offset = self._size + offset
+            else:
+                raise ValueError("Invalid value for whence: %s" % (whence,))
+
+            # Make it so that offset is the number of bytes to skip forward.
+            if offset < self._pos:
+                self._rewind()
+            else:
+                offset -= self._pos
+
+            # Read and discard data until we reach the desired position.
+            self._read_block(offset, return_data=False)
+
+            return self._pos
+
+    def tell(self):
+        """Return the current file position."""
+        with self._lock:
+            self._check_not_closed()
+            return self._pos
+
+
+class ZlibCompressorWrapper(CompressorWrapper):
+
+    def __init__(self):
+        CompressorWrapper.__init__(self, obj=BinaryZlibFile,
+                                   prefix=_ZLIB_PREFIX, extension='.z')
+
+
+class BinaryGzipFile(BinaryZlibFile):
+    """A file object providing transparent gzip (de)compression.
+
+    If filename is a str or bytes object, it gives the name
+    of the file to be opened. Otherwise, it should be a file object,
+    which will be used to read or write the compressed data.
+
+    mode can be 'rb' for reading (default) or 'wb' for (over)writing
+
+    If mode is 'wb', compresslevel can be a number between 1
+    and 9 specifying the level of compression: 1 produces the least
+    compression, and 9 produces the most compression. 3 is the default.
+    """
+
+    wbits = 31  # zlib compressor/decompressor wbits value for gzip format.
+
+
+class GzipCompressorWrapper(CompressorWrapper):
+
+    def __init__(self):
+        CompressorWrapper.__init__(self, obj=BinaryGzipFile,
+                                   prefix=_GZIP_PREFIX, extension='.gz')

env-llmeval/lib/python3.10/site-packages/joblib/disk.py

@@ -0,0 +1,136 @@
+"""
+Disk management utilities.
+"""
+
+# Authors: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+#          Lars Buitinck
+# Copyright (c) 2010 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+
+import os
+import sys
+import time
+import errno
+import shutil
+
+from multiprocessing import util
+
+
+try:
+    WindowsError
+except NameError:
+    WindowsError = OSError
+
+
+def disk_used(path):
+    """ Return the disk usage in a directory."""
+    size = 0
+    for file in os.listdir(path) + ['.']:
+        stat = os.stat(os.path.join(path, file))
+        if hasattr(stat, 'st_blocks'):
+            size += stat.st_blocks * 512
+        else:
+            # on some platform st_blocks is not available (e.g., Windows)
+            # approximate by rounding to next multiple of 512
+            size += (stat.st_size // 512 + 1) * 512
+    # We need to convert to int to avoid having longs on some systems (we
+    # don't want longs to avoid problems we SQLite)
+    return int(size / 1024.)
+
+
+def memstr_to_bytes(text):
+    """ Convert a memory text to its value in bytes.
+    """
+    kilo = 1024
+    units = dict(K=kilo, M=kilo ** 2, G=kilo ** 3)
+    try:
+        size = int(units[text[-1]] * float(text[:-1]))
+    except (KeyError, ValueError) as e:
+        raise ValueError(
+            "Invalid literal for size give: %s (type %s) should be "
+            "alike '10G', '500M', '50K'." % (text, type(text))) from e
+    return size
+
+
+def mkdirp(d):
+    """Ensure directory d exists (like mkdir -p on Unix)
+    No guarantee that the directory is writable.
+    """
+    try:
+        os.makedirs(d)
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+
+
+# if a rmtree operation fails in rm_subdirs, wait for this much time (in secs),
+# then retry up to RM_SUBDIRS_N_RETRY times. If it still fails, raise the
+# exception. this mechanism ensures that the sub-process gc have the time to
+# collect and close the memmaps before we fail.
+RM_SUBDIRS_RETRY_TIME = 0.1
+RM_SUBDIRS_N_RETRY = 10
+
+
+def rm_subdirs(path, onerror=None):
+    """Remove all subdirectories in this path.
+
+    The directory indicated by `path` is left in place, and its subdirectories
+    are erased.
+
+    If onerror is set, it is called to handle the error with arguments (func,
+    path, exc_info) where func is os.listdir, os.remove, or os.rmdir;
+    path is the argument to that function that caused it to fail; and
+    exc_info is a tuple returned by sys.exc_info().  If onerror is None,
+    an exception is raised.
+    """
+
+    # NOTE this code is adapted from the one in shutil.rmtree, and is
+    # just as fast
+
+    names = []
+    try:
+        names = os.listdir(path)
+    except os.error:
+        if onerror is not None:
+            onerror(os.listdir, path, sys.exc_info())
+        else:
+            raise
+
+    for name in names:
+        fullname = os.path.join(path, name)
+        delete_folder(fullname, onerror=onerror)
+
+
+def delete_folder(folder_path, onerror=None, allow_non_empty=True):
+    """Utility function to cleanup a temporary folder if it still exists."""
+    if os.path.isdir(folder_path):
+        if onerror is not None:
+            shutil.rmtree(folder_path, False, onerror)
+        else:
+            # allow the rmtree to fail once, wait and re-try.
+            # if the error is raised again, fail
+            err_count = 0
+            while True:
+                files = os.listdir(folder_path)
+                try:
+                    if len(files) == 0 or allow_non_empty:
+                        shutil.rmtree(
+                            folder_path, ignore_errors=False, onerror=None
+                        )
+                        util.debug(
+                            "Successfully deleted {}".format(folder_path))
+                        break
+                    else:
+                        raise OSError(
+                            "Expected empty folder {} but got {} "
+                            "files.".format(folder_path, len(files))
+                        )
+                except (OSError, WindowsError):
+                    err_count += 1
+                    if err_count > RM_SUBDIRS_N_RETRY:
+                        # the folder cannot be deleted right now. It maybe
+                        # because some temporary files have not been deleted
+                        # yet.
+                        raise
+                time.sleep(RM_SUBDIRS_RETRY_TIME)

env-llmeval/lib/python3.10/site-packages/joblib/executor.py

@@ -0,0 +1,117 @@
+"""Utility function to construct a loky.ReusableExecutor with custom pickler.
+
+This module provides efficient ways of working with data stored in
+shared memory with numpy.memmap arrays without inducing any memory
+copy between the parent and child processes.
+"""
+# Author: Thomas Moreau <[email protected]>
+# Copyright: 2017, Thomas Moreau
+# License: BSD 3 clause
+
+from ._memmapping_reducer import get_memmapping_reducers
+from ._memmapping_reducer import TemporaryResourcesManager
+from .externals.loky.reusable_executor import _ReusablePoolExecutor
+
+
+_executor_args = None
+
+
+def get_memmapping_executor(n_jobs, **kwargs):
+    return MemmappingExecutor.get_memmapping_executor(n_jobs, **kwargs)
+
+
+class MemmappingExecutor(_ReusablePoolExecutor):
+
+    @classmethod
+    def get_memmapping_executor(cls, n_jobs, timeout=300, initializer=None,
+                                initargs=(), env=None, temp_folder=None,
+                                context_id=None, **backend_args):
+        """Factory for ReusableExecutor with automatic memmapping for large
+        numpy arrays.
+        """
+        global _executor_args
+        # Check if we can reuse the executor here instead of deferring the test
+        # to loky as the reducers are objects that changes at each call.
+        executor_args = backend_args.copy()
+        executor_args.update(env if env else {})
+        executor_args.update(dict(
+            timeout=timeout, initializer=initializer, initargs=initargs))
+        reuse = _executor_args is None or _executor_args == executor_args
+        _executor_args = executor_args
+
+        manager = TemporaryResourcesManager(temp_folder)
+
+        # reducers access the temporary folder in which to store temporary
+        # pickles through a call to manager.resolve_temp_folder_name. resolving
+        # the folder name dynamically is useful to use different folders across
+        # calls of a same reusable executor
+        job_reducers, result_reducers = get_memmapping_reducers(
+            unlink_on_gc_collect=True,
+            temp_folder_resolver=manager.resolve_temp_folder_name,
+            **backend_args)
+        _executor, executor_is_reused = super().get_reusable_executor(
+            n_jobs, job_reducers=job_reducers, result_reducers=result_reducers,
+            reuse=reuse, timeout=timeout, initializer=initializer,
+            initargs=initargs, env=env
+        )
+
+        if not executor_is_reused:
+            # Only set a _temp_folder_manager for new executors. Reused
+            # executors already have a _temporary_folder_manager that must not
+            # be re-assigned like that because it is referenced in various
+            # places in the reducing machinery of the executor.
+            _executor._temp_folder_manager = manager
+
+        if context_id is not None:
+            # Only register the specified context once we know which manager
+            # the current executor is using, in order to not register an atexit
+            # finalizer twice for the same folder.
+            _executor._temp_folder_manager.register_new_context(context_id)
+
+        return _executor
+
+    def terminate(self, kill_workers=False):
+
+        self.shutdown(kill_workers=kill_workers)
+
+        # When workers are killed in a brutal manner, they cannot execute the
+        # finalizer of their shared memmaps. The refcount of those memmaps may
+        # be off by an unknown number, so instead of decref'ing them, we force
+        # delete the whole temporary folder, and unregister them. There is no
+        # risk of PermissionError at folder deletion because at this
+        # point, all child processes are dead, so all references to temporary
+        # memmaps are closed. Otherwise, just try to delete as much as possible
+        # with allow_non_empty=True but if we can't, it will be clean up later
+        # on by the resource_tracker.
+        with self._submit_resize_lock:
+            self._temp_folder_manager._clean_temporary_resources(
+                force=kill_workers, allow_non_empty=True
+            )
+
+    @property
+    def _temp_folder(self):
+        # Legacy property in tests. could be removed if we refactored the
+        # memmapping tests. SHOULD ONLY BE USED IN TESTS!
+        # We cache this property because it is called late in the tests - at
+        # this point, all context have been unregistered, and
+        # resolve_temp_folder_name raises an error.
+        if getattr(self, '_cached_temp_folder', None) is not None:
+            return self._cached_temp_folder
+        else:
+            self._cached_temp_folder = self._temp_folder_manager.resolve_temp_folder_name()  # noqa
+            return self._cached_temp_folder
+
+
+class _TestingMemmappingExecutor(MemmappingExecutor):
+    """Wrapper around ReusableExecutor to ease memmapping testing with Pool
+    and Executor. This is only for testing purposes.
+
+    """
+    def apply_async(self, func, args):
+        """Schedule a func to be run"""
+        future = self.submit(func, *args)
+        future.get = future.result
+        return future
+
+    def map(self, f, *args):
+        return list(super().map(f, *args))

env-llmeval/lib/python3.10/site-packages/joblib/externals/cloudpickle/__init__.py

@@ -0,0 +1,18 @@
+from . import cloudpickle
+from .cloudpickle import *  # noqa
+
+__doc__ = cloudpickle.__doc__
+
+__version__ = "3.0.0"
+
+__all__ = [  # noqa
+    "__version__",
+    "Pickler",
+    "CloudPickler",
+    "dumps",
+    "loads",
+    "dump",
+    "load",
+    "register_pickle_by_value",
+    "unregister_pickle_by_value",
+]

env-llmeval/lib/python3.10/site-packages/joblib/externals/cloudpickle/cloudpickle.py

@@ -0,0 +1,1487 @@
+"""Pickler class to extend the standard pickle.Pickler functionality
+
+The main objective is to make it natural to perform distributed computing on
+clusters (such as PySpark, Dask, Ray...) with interactively defined code
+(functions, classes, ...) written in notebooks or console.
+
+In particular this pickler adds the following features:
+- serialize interactively-defined or locally-defined functions, classes,
+  enums, typevars, lambdas and nested functions to compiled byte code;
+- deal with some other non-serializable objects in an ad-hoc manner where
+  applicable.
+
+This pickler is therefore meant to be used for the communication between short
+lived Python processes running the same version of Python and libraries. In
+particular, it is not meant to be used for long term storage of Python objects.
+
+It does not include an unpickler, as standard Python unpickling suffices.
+
+This module was extracted from the `cloud` package, developed by `PiCloud, Inc.
+<https://web.archive.org/web/20140626004012/http://www.picloud.com/>`_.
+
+Copyright (c) 2012-now, CloudPickle developers and contributors.
+Copyright (c) 2012, Regents of the University of California.
+Copyright (c) 2009 `PiCloud, Inc. <https://web.archive.org/web/20140626004012/http://www.picloud.com/>`_.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the University of California, Berkeley nor the
+      names of its contributors may be used to endorse or promote
+      products derived from this software without specific prior written
+      permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+import _collections_abc
+from collections import ChainMap, OrderedDict
+import abc
+import builtins
+import copyreg
+import dataclasses
+import dis
+from enum import Enum
+import io
+import itertools
+import logging
+import opcode
+import pickle
+from pickle import _getattribute
+import platform
+import struct
+import sys
+import threading
+import types
+import typing
+import uuid
+import warnings
+import weakref
+
+# The following import is required to be imported in the cloudpickle
+# namespace to be able to load pickle files generated with older versions of
+# cloudpickle. See: tests/test_backward_compat.py
+from types import CellType  # noqa: F401
+
+
+# cloudpickle is meant for inter process communication: we expect all
+# communicating processes to run the same Python version hence we favor
+# communication speed over compatibility:
+DEFAULT_PROTOCOL = pickle.HIGHEST_PROTOCOL
+
+# Names of modules whose resources should be treated as dynamic.
+_PICKLE_BY_VALUE_MODULES = set()
+
+# Track the provenance of reconstructed dynamic classes to make it possible to
+# reconstruct instances from the matching singleton class definition when
+# appropriate and preserve the usual "isinstance" semantics of Python objects.
+_DYNAMIC_CLASS_TRACKER_BY_CLASS = weakref.WeakKeyDictionary()
+_DYNAMIC_CLASS_TRACKER_BY_ID = weakref.WeakValueDictionary()
+_DYNAMIC_CLASS_TRACKER_LOCK = threading.Lock()
+
+PYPY = platform.python_implementation() == "PyPy"
+
+builtin_code_type = None
+if PYPY:
+    # builtin-code objects only exist in pypy
+    builtin_code_type = type(float.__new__.__code__)
+
+_extract_code_globals_cache = weakref.WeakKeyDictionary()
+
+
+def _get_or_create_tracker_id(class_def):
+    with _DYNAMIC_CLASS_TRACKER_LOCK:
+        class_tracker_id = _DYNAMIC_CLASS_TRACKER_BY_CLASS.get(class_def)
+        if class_tracker_id is None:
+            class_tracker_id = uuid.uuid4().hex
+            _DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
+            _DYNAMIC_CLASS_TRACKER_BY_ID[class_tracker_id] = class_def
+    return class_tracker_id
+
+
+def _lookup_class_or_track(class_tracker_id, class_def):
+    if class_tracker_id is not None:
+        with _DYNAMIC_CLASS_TRACKER_LOCK:
+            class_def = _DYNAMIC_CLASS_TRACKER_BY_ID.setdefault(
+                class_tracker_id, class_def
+            )
+            _DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
+    return class_def
+
+
+def register_pickle_by_value(module):
+    """Register a module to make it functions and classes picklable by value.
+
+    By default, functions and classes that are attributes of an importable
+    module are to be pickled by reference, that is relying on re-importing
+    the attribute from the module at load time.
+
+    If `register_pickle_by_value(module)` is called, all its functions and
+    classes are subsequently to be pickled by value, meaning that they can
+    be loaded in Python processes where the module is not importable.
+
+    This is especially useful when developing a module in a distributed
+    execution environment: restarting the client Python process with the new
+    source code is enough: there is no need to re-install the new version
+    of the module on all the worker nodes nor to restart the workers.
+
+    Note: this feature is considered experimental. See the cloudpickle
+    README.md file for more details and limitations.
+    """
+    if not isinstance(module, types.ModuleType):
+        raise ValueError(f"Input should be a module object, got {str(module)} instead")
+    # In the future, cloudpickle may need a way to access any module registered
+    # for pickling by value in order to introspect relative imports inside
+    # functions pickled by value. (see
+    # https://github.com/cloudpipe/cloudpickle/pull/417#issuecomment-873684633).
+    # This access can be ensured by checking that module is present in
+    # sys.modules at registering time and assuming that it will still be in
+    # there when accessed during pickling. Another alternative would be to
+    # store a weakref to the module. Even though cloudpickle does not implement
+    # this introspection yet, in order to avoid a possible breaking change
+    # later, we still enforce the presence of module inside sys.modules.
+    if module.__name__ not in sys.modules:
+        raise ValueError(
+            f"{module} was not imported correctly, have you used an "
+            "`import` statement to access it?"
+        )
+    _PICKLE_BY_VALUE_MODULES.add(module.__name__)
+
+
+def unregister_pickle_by_value(module):
+    """Unregister that the input module should be pickled by value."""
+    if not isinstance(module, types.ModuleType):
+        raise ValueError(f"Input should be a module object, got {str(module)} instead")
+    if module.__name__ not in _PICKLE_BY_VALUE_MODULES:
+        raise ValueError(f"{module} is not registered for pickle by value")
+    else:
+        _PICKLE_BY_VALUE_MODULES.remove(module.__name__)
+
+
+def list_registry_pickle_by_value():
+    return _PICKLE_BY_VALUE_MODULES.copy()
+
+
+def _is_registered_pickle_by_value(module):
+    module_name = module.__name__
+    if module_name in _PICKLE_BY_VALUE_MODULES:
+        return True
+    while True:
+        parent_name = module_name.rsplit(".", 1)[0]
+        if parent_name == module_name:
+            break
+        if parent_name in _PICKLE_BY_VALUE_MODULES:
+            return True
+        module_name = parent_name
+    return False
+
+
+def _whichmodule(obj, name):
+    """Find the module an object belongs to.
+
+    This function differs from ``pickle.whichmodule`` in two ways:
+    - it does not mangle the cases where obj's module is __main__ and obj was
+      not found in any module.
+    - Errors arising during module introspection are ignored, as those errors
+      are considered unwanted side effects.
+    """
+    module_name = getattr(obj, "__module__", None)
+
+    if module_name is not None:
+        return module_name
+    # Protect the iteration by using a copy of sys.modules against dynamic
+    # modules that trigger imports of other modules upon calls to getattr or
+    # other threads importing at the same time.
+    for module_name, module in sys.modules.copy().items():
+        # Some modules such as coverage can inject non-module objects inside
+        # sys.modules
+        if (
+            module_name == "__main__"
+            or module is None
+            or not isinstance(module, types.ModuleType)
+        ):
+            continue
+        try:
+            if _getattribute(module, name)[0] is obj:
+                return module_name
+        except Exception:
+            pass
+    return None
+
+
+def _should_pickle_by_reference(obj, name=None):
+    """Test whether an function or a class should be pickled by reference
+
+    Pickling by reference means by that the object (typically a function or a
+    class) is an attribute of a module that is assumed to be importable in the
+    target Python environment. Loading will therefore rely on importing the
+    module and then calling `getattr` on it to access the function or class.
+
+    Pickling by reference is the only option to pickle functions and classes
+    in the standard library. In cloudpickle the alternative option is to
+    pickle by value (for instance for interactively or locally defined
+    functions and classes or for attributes of modules that have been
+    explicitly registered to be pickled by value.
+    """
+    if isinstance(obj, types.FunctionType) or issubclass(type(obj), type):
+        module_and_name = _lookup_module_and_qualname(obj, name=name)
+        if module_and_name is None:
+            return False
+        module, name = module_and_name
+        return not _is_registered_pickle_by_value(module)
+
+    elif isinstance(obj, types.ModuleType):
+        # We assume that sys.modules is primarily used as a cache mechanism for
+        # the Python import machinery. Checking if a module has been added in
+        # is sys.modules therefore a cheap and simple heuristic to tell us
+        # whether we can assume that a given module could be imported by name
+        # in another Python process.
+        if _is_registered_pickle_by_value(obj):
+            return False
+        return obj.__name__ in sys.modules
+    else:
+        raise TypeError(
+            "cannot check importability of {} instances".format(type(obj).__name__)
+        )
+
+
+def _lookup_module_and_qualname(obj, name=None):
+    if name is None:
+        name = getattr(obj, "__qualname__", None)
+    if name is None:  # pragma: no cover
+        # This used to be needed for Python 2.7 support but is probably not
+        # needed anymore. However we keep the __name__ introspection in case
+        # users of cloudpickle rely on this old behavior for unknown reasons.
+        name = getattr(obj, "__name__", None)
+
+    module_name = _whichmodule(obj, name)
+
+    if module_name is None:
+        # In this case, obj.__module__ is None AND obj was not found in any
+        # imported module. obj is thus treated as dynamic.
+        return None
+
+    if module_name == "__main__":
+        return None
+
+    # Note: if module_name is in sys.modules, the corresponding module is
+    # assumed importable at unpickling time. See #357
+    module = sys.modules.get(module_name, None)
+    if module is None:
+        # The main reason why obj's module would not be imported is that this
+        # module has been dynamically created, using for example
+        # types.ModuleType. The other possibility is that module was removed
+        # from sys.modules after obj was created/imported. But this case is not
+        # supported, as the standard pickle does not support it either.
+        return None
+
+    try:
+        obj2, parent = _getattribute(module, name)
+    except AttributeError:
+        # obj was not found inside the module it points to
+        return None
+    if obj2 is not obj:
+        return None
+    return module, name
+
+
+def _extract_code_globals(co):
+    """Find all globals names read or written to by codeblock co."""
+    out_names = _extract_code_globals_cache.get(co)
+    if out_names is None:
+        # We use a dict with None values instead of a set to get a
+        # deterministic order and avoid introducing non-deterministic pickle
+        # bytes as a results.
+        out_names = {name: None for name in _walk_global_ops(co)}
+
+        # Declaring a function inside another one using the "def ..." syntax
+        # generates a constant code object corresponding to the one of the
+        # nested function's As the nested function may itself need global
+        # variables, we need to introspect its code, extract its globals, (look
+        # for code object in it's co_consts attribute..) and add the result to
+        # code_globals
+        if co.co_consts:
+            for const in co.co_consts:
+                if isinstance(const, types.CodeType):
+                    out_names.update(_extract_code_globals(const))
+
+        _extract_code_globals_cache[co] = out_names
+
+    return out_names
+
+
+def _find_imported_submodules(code, top_level_dependencies):
+    """Find currently imported submodules used by a function.
+
+    Submodules used by a function need to be detected and referenced for the
+    function to work correctly at depickling time. Because submodules can be
+    referenced as attribute of their parent package (``package.submodule``), we
+    need a special introspection technique that does not rely on GLOBAL-related
+    opcodes to find references of them in a code object.
+
+    Example:
+    ```
+    import concurrent.futures
+    import cloudpickle
+    def func():
+        x = concurrent.futures.ThreadPoolExecutor
+    if __name__ == '__main__':
+        cloudpickle.dumps(func)
+    ```
+    The globals extracted by cloudpickle in the function's state include the
+    concurrent package, but not its submodule (here, concurrent.futures), which
+    is the module used by func. Find_imported_submodules will detect the usage
+    of concurrent.futures. Saving this module alongside with func will ensure
+    that calling func once depickled does not fail due to concurrent.futures
+    not being imported
+    """
+
+    subimports = []
+    # check if any known dependency is an imported package
+    for x in top_level_dependencies:
+        if (
+            isinstance(x, types.ModuleType)
+            and hasattr(x, "__package__")
+            and x.__package__
+        ):
+            # check if the package has any currently loaded sub-imports
+            prefix = x.__name__ + "."
+            # A concurrent thread could mutate sys.modules,
+            # make sure we iterate over a copy to avoid exceptions
+            for name in list(sys.modules):
+                # Older versions of pytest will add a "None" module to
+                # sys.modules.
+                if name is not None and name.startswith(prefix):
+                    # check whether the function can address the sub-module
+                    tokens = set(name[len(prefix) :].split("."))
+                    if not tokens - set(code.co_names):
+                        subimports.append(sys.modules[name])
+    return subimports
+
+
+# relevant opcodes
+STORE_GLOBAL = opcode.opmap["STORE_GLOBAL"]
+DELETE_GLOBAL = opcode.opmap["DELETE_GLOBAL"]
+LOAD_GLOBAL = opcode.opmap["LOAD_GLOBAL"]
+GLOBAL_OPS = (STORE_GLOBAL, DELETE_GLOBAL, LOAD_GLOBAL)
+HAVE_ARGUMENT = dis.HAVE_ARGUMENT
+EXTENDED_ARG = dis.EXTENDED_ARG
+
+
+_BUILTIN_TYPE_NAMES = {}
+for k, v in types.__dict__.items():
+    if type(v) is type:
+        _BUILTIN_TYPE_NAMES[v] = k
+
+
+def _builtin_type(name):
+    if name == "ClassType":  # pragma: no cover
+        # Backward compat to load pickle files generated with cloudpickle
+        # < 1.3 even if loading pickle files from older versions is not
+        # officially supported.
+        return type
+    return getattr(types, name)
+
+
+def _walk_global_ops(code):
+    """Yield referenced name for global-referencing instructions in code."""
+    for instr in dis.get_instructions(code):
+        op = instr.opcode
+        if op in GLOBAL_OPS:
+            yield instr.argval
+
+
+def _extract_class_dict(cls):
+    """Retrieve a copy of the dict of a class without the inherited method."""
+    clsdict = dict(cls.__dict__)  # copy dict proxy to a dict
+    if len(cls.__bases__) == 1:
+        inherited_dict = cls.__bases__[0].__dict__
+    else:
+        inherited_dict = {}
+        for base in reversed(cls.__bases__):
+            inherited_dict.update(base.__dict__)
+    to_remove = []
+    for name, value in clsdict.items():
+        try:
+            base_value = inherited_dict[name]
+            if value is base_value:
+                to_remove.append(name)
+        except KeyError:
+            pass
+    for name in to_remove:
+        clsdict.pop(name)
+    return clsdict
+
+
+def is_tornado_coroutine(func):
+    """Return whether `func` is a Tornado coroutine function.
+
+    Running coroutines are not supported.
+    """
+    warnings.warn(
+        "is_tornado_coroutine is deprecated in cloudpickle 3.0 and will be "
+        "removed in cloudpickle 4.0. Use tornado.gen.is_coroutine_function "
+        "directly instead.",
+        category=DeprecationWarning,
+    )
+    if "tornado.gen" not in sys.modules:
+        return False
+    gen = sys.modules["tornado.gen"]
+    if not hasattr(gen, "is_coroutine_function"):
+        # Tornado version is too old
+        return False
+    return gen.is_coroutine_function(func)
+
+
+def subimport(name):
+    # We cannot do simply: `return __import__(name)`: Indeed, if ``name`` is
+    # the name of a submodule, __import__ will return the top-level root module
+    # of this submodule. For instance, __import__('os.path') returns the `os`
+    # module.
+    __import__(name)
+    return sys.modules[name]
+
+
+def dynamic_subimport(name, vars):
+    mod = types.ModuleType(name)
+    mod.__dict__.update(vars)
+    mod.__dict__["__builtins__"] = builtins.__dict__
+    return mod
+
+
+def _get_cell_contents(cell):
+    try:
+        return cell.cell_contents
+    except ValueError:
+        # Handle empty cells explicitly with a sentinel value.
+        return _empty_cell_value
+
+
+def instance(cls):
+    """Create a new instance of a class.
+
+    Parameters
+    ----------
+    cls : type
+        The class to create an instance of.
+
+    Returns
+    -------
+    instance : cls
+        A new instance of ``cls``.
+    """
+    return cls()
+
+
+@instance
+class _empty_cell_value:
+    """Sentinel for empty closures."""
+
+    @classmethod
+    def __reduce__(cls):
+        return cls.__name__
+
+
+def _make_function(code, globals, name, argdefs, closure):
+    # Setting __builtins__ in globals is needed for nogil CPython.
+    globals["__builtins__"] = __builtins__
+    return types.FunctionType(code, globals, name, argdefs, closure)
+
+
+def _make_empty_cell():
+    if False:
+        # trick the compiler into creating an empty cell in our lambda
+        cell = None
+        raise AssertionError("this route should not be executed")
+
+    return (lambda: cell).__closure__[0]
+
+
+def _make_cell(value=_empty_cell_value):
+    cell = _make_empty_cell()
+    if value is not _empty_cell_value:
+        cell.cell_contents = value
+    return cell
+
+
+def _make_skeleton_class(
+    type_constructor, name, bases, type_kwargs, class_tracker_id, extra
+):
+    """Build dynamic class with an empty __dict__ to be filled once memoized
+
+    If class_tracker_id is not None, try to lookup an existing class definition
+    matching that id. If none is found, track a newly reconstructed class
+    definition under that id so that other instances stemming from the same
+    class id will also reuse this class definition.
+
+    The "extra" variable is meant to be a dict (or None) that can be used for
+    forward compatibility shall the need arise.
+    """
+    skeleton_class = types.new_class(
+        name, bases, {"metaclass": type_constructor}, lambda ns: ns.update(type_kwargs)
+    )
+    return _lookup_class_or_track(class_tracker_id, skeleton_class)
+
+
+def _make_skeleton_enum(
+    bases, name, qualname, members, module, class_tracker_id, extra
+):
+    """Build dynamic enum with an empty __dict__ to be filled once memoized
+
+    The creation of the enum class is inspired by the code of
+    EnumMeta._create_.
+
+    If class_tracker_id is not None, try to lookup an existing enum definition
+    matching that id. If none is found, track a newly reconstructed enum
+    definition under that id so that other instances stemming from the same
+    class id will also reuse this enum definition.
+
+    The "extra" variable is meant to be a dict (or None) that can be used for
+    forward compatibility shall the need arise.
+    """
+    # enums always inherit from their base Enum class at the last position in
+    # the list of base classes:
+    enum_base = bases[-1]
+    metacls = enum_base.__class__
+    classdict = metacls.__prepare__(name, bases)
+
+    for member_name, member_value in members.items():
+        classdict[member_name] = member_value
+    enum_class = metacls.__new__(metacls, name, bases, classdict)
+    enum_class.__module__ = module
+    enum_class.__qualname__ = qualname
+
+    return _lookup_class_or_track(class_tracker_id, enum_class)
+
+
+def _make_typevar(name, bound, constraints, covariant, contravariant, class_tracker_id):
+    tv = typing.TypeVar(
+        name,
+        *constraints,
+        bound=bound,
+        covariant=covariant,
+        contravariant=contravariant,
+    )
+    return _lookup_class_or_track(class_tracker_id, tv)
+
+
+def _decompose_typevar(obj):
+    return (
+        obj.__name__,
+        obj.__bound__,
+        obj.__constraints__,
+        obj.__covariant__,
+        obj.__contravariant__,
+        _get_or_create_tracker_id(obj),
+    )
+
+
+def _typevar_reduce(obj):
+    # TypeVar instances require the module information hence why we
+    # are not using the _should_pickle_by_reference directly
+    module_and_name = _lookup_module_and_qualname(obj, name=obj.__name__)
+
+    if module_and_name is None:
+        return (_make_typevar, _decompose_typevar(obj))
+    elif _is_registered_pickle_by_value(module_and_name[0]):
+        return (_make_typevar, _decompose_typevar(obj))
+
+    return (getattr, module_and_name)
+
+
+def _get_bases(typ):
+    if "__orig_bases__" in getattr(typ, "__dict__", {}):
+        # For generic types (see PEP 560)
+        # Note that simply checking `hasattr(typ, '__orig_bases__')` is not
+        # correct.  Subclasses of a fully-parameterized generic class does not
+        # have `__orig_bases__` defined, but `hasattr(typ, '__orig_bases__')`
+        # will return True because it's defined in the base class.
+        bases_attr = "__orig_bases__"
+    else:
+        # For regular class objects
+        bases_attr = "__bases__"
+    return getattr(typ, bases_attr)
+
+
+def _make_dict_keys(obj, is_ordered=False):
+    if is_ordered:
+        return OrderedDict.fromkeys(obj).keys()
+    else:
+        return dict.fromkeys(obj).keys()
+
+
+def _make_dict_values(obj, is_ordered=False):
+    if is_ordered:
+        return OrderedDict((i, _) for i, _ in enumerate(obj)).values()
+    else:
+        return {i: _ for i, _ in enumerate(obj)}.values()
+
+
+def _make_dict_items(obj, is_ordered=False):
+    if is_ordered:
+        return OrderedDict(obj).items()
+    else:
+        return obj.items()
+
+
+# COLLECTION OF OBJECTS __getnewargs__-LIKE METHODS
+# -------------------------------------------------
+
+
+def _class_getnewargs(obj):
+    type_kwargs = {}
+    if "__module__" in obj.__dict__:
+        type_kwargs["__module__"] = obj.__module__
+
+    __dict__ = obj.__dict__.get("__dict__", None)
+    if isinstance(__dict__, property):
+        type_kwargs["__dict__"] = __dict__
+
+    return (
+        type(obj),
+        obj.__name__,
+        _get_bases(obj),
+        type_kwargs,
+        _get_or_create_tracker_id(obj),
+        None,
+    )
+
+
+def _enum_getnewargs(obj):
+    members = {e.name: e.value for e in obj}
+    return (
+        obj.__bases__,
+        obj.__name__,
+        obj.__qualname__,
+        members,
+        obj.__module__,
+        _get_or_create_tracker_id(obj),
+        None,
+    )
+
+
+# COLLECTION OF OBJECTS RECONSTRUCTORS
+# ------------------------------------
+def _file_reconstructor(retval):
+    return retval
+
+
+# COLLECTION OF OBJECTS STATE GETTERS
+# -----------------------------------
+
+
+def _function_getstate(func):
+    # - Put func's dynamic attributes (stored in func.__dict__) in state. These
+    #   attributes will be restored at unpickling time using
+    #   f.__dict__.update(state)
+    # - Put func's members into slotstate. Such attributes will be restored at
+    #   unpickling time by iterating over slotstate and calling setattr(func,
+    #   slotname, slotvalue)
+    slotstate = {
+        "__name__": func.__name__,
+        "__qualname__": func.__qualname__,
+        "__annotations__": func.__annotations__,
+        "__kwdefaults__": func.__kwdefaults__,
+        "__defaults__": func.__defaults__,
+        "__module__": func.__module__,
+        "__doc__": func.__doc__,
+        "__closure__": func.__closure__,
+    }
+
+    f_globals_ref = _extract_code_globals(func.__code__)
+    f_globals = {k: func.__globals__[k] for k in f_globals_ref if k in func.__globals__}
+
+    if func.__closure__ is not None:
+        closure_values = list(map(_get_cell_contents, func.__closure__))
+    else:
+        closure_values = ()
+
+    # Extract currently-imported submodules used by func. Storing these modules
+    # in a smoke _cloudpickle_subimports attribute of the object's state will
+    # trigger the side effect of importing these modules at unpickling time
+    # (which is necessary for func to work correctly once depickled)
+    slotstate["_cloudpickle_submodules"] = _find_imported_submodules(
+        func.__code__, itertools.chain(f_globals.values(), closure_values)
+    )
+    slotstate["__globals__"] = f_globals
+
+    state = func.__dict__
+    return state, slotstate
+
+
+def _class_getstate(obj):
+    clsdict = _extract_class_dict(obj)
+    clsdict.pop("__weakref__", None)
+
+    if issubclass(type(obj), abc.ABCMeta):
+        # If obj is an instance of an ABCMeta subclass, don't pickle the
+        # cache/negative caches populated during isinstance/issubclass
+        # checks, but pickle the list of registered subclasses of obj.
+        clsdict.pop("_abc_cache", None)
+        clsdict.pop("_abc_negative_cache", None)
+        clsdict.pop("_abc_negative_cache_version", None)
+        registry = clsdict.pop("_abc_registry", None)
+        if registry is None:
+            # The abc caches and registered subclasses of a
+            # class are bundled into the single _abc_impl attribute
+            clsdict.pop("_abc_impl", None)
+            (registry, _, _, _) = abc._get_dump(obj)
+
+            clsdict["_abc_impl"] = [subclass_weakref() for subclass_weakref in registry]
+        else:
+            # In the above if clause, registry is a set of weakrefs -- in
+            # this case, registry is a WeakSet
+            clsdict["_abc_impl"] = [type_ for type_ in registry]
+
+    if "__slots__" in clsdict:
+        # pickle string length optimization: member descriptors of obj are
+        # created automatically from obj's __slots__ attribute, no need to
+        # save them in obj's state
+        if isinstance(obj.__slots__, str):
+            clsdict.pop(obj.__slots__)
+        else:
+            for k in obj.__slots__:
+                clsdict.pop(k, None)
+
+    clsdict.pop("__dict__", None)  # unpicklable property object
+
+    return (clsdict, {})
+
+
+def _enum_getstate(obj):
+    clsdict, slotstate = _class_getstate(obj)
+
+    members = {e.name: e.value for e in obj}
+    # Cleanup the clsdict that will be passed to _make_skeleton_enum:
+    # Those attributes are already handled by the metaclass.
+    for attrname in [
+        "_generate_next_value_",
+        "_member_names_",
+        "_member_map_",
+        "_member_type_",
+        "_value2member_map_",
+    ]:
+        clsdict.pop(attrname, None)
+    for member in members:
+        clsdict.pop(member)
+        # Special handling of Enum subclasses
+    return clsdict, slotstate
+
+
+# COLLECTIONS OF OBJECTS REDUCERS
+# -------------------------------
+# A reducer is a function taking a single argument (obj), and that returns a
+# tuple with all the necessary data to re-construct obj. Apart from a few
+# exceptions (list, dict, bytes, int, etc.), a reducer is necessary to
+# correctly pickle an object.
+# While many built-in objects (Exceptions objects, instances of the "object"
+# class, etc), are shipped with their own built-in reducer (invoked using
+# obj.__reduce__), some do not. The following methods were created to "fill
+# these holes".
+
+
+def _code_reduce(obj):
+    """code object reducer."""
+    # If you are not sure about the order of arguments, take a look at help
+    # of the specific type from types, for example:
+    # >>> from types import CodeType
+    # >>> help(CodeType)
+    if hasattr(obj, "co_exceptiontable"):
+        # Python 3.11 and later: there are some new attributes
+        # related to the enhanced exceptions.
+        args = (
+            obj.co_argcount,
+            obj.co_posonlyargcount,
+            obj.co_kwonlyargcount,
+            obj.co_nlocals,
+            obj.co_stacksize,
+            obj.co_flags,
+            obj.co_code,
+            obj.co_consts,
+            obj.co_names,
+            obj.co_varnames,
+            obj.co_filename,
+            obj.co_name,
+            obj.co_qualname,
+            obj.co_firstlineno,
+            obj.co_linetable,
+            obj.co_exceptiontable,
+            obj.co_freevars,
+            obj.co_cellvars,
+        )
+    elif hasattr(obj, "co_linetable"):
+        # Python 3.10 and later: obj.co_lnotab is deprecated and constructor
+        # expects obj.co_linetable instead.
+        args = (
+            obj.co_argcount,
+            obj.co_posonlyargcount,
+            obj.co_kwonlyargcount,
+            obj.co_nlocals,
+            obj.co_stacksize,
+            obj.co_flags,
+            obj.co_code,
+            obj.co_consts,
+            obj.co_names,
+            obj.co_varnames,
+            obj.co_filename,
+            obj.co_name,
+            obj.co_firstlineno,
+            obj.co_linetable,
+            obj.co_freevars,
+            obj.co_cellvars,
+        )
+    elif hasattr(obj, "co_nmeta"):  # pragma: no cover
+        # "nogil" Python: modified attributes from 3.9
+        args = (
+            obj.co_argcount,
+            obj.co_posonlyargcount,
+            obj.co_kwonlyargcount,
+            obj.co_nlocals,
+            obj.co_framesize,
+            obj.co_ndefaultargs,
+            obj.co_nmeta,
+            obj.co_flags,
+            obj.co_code,
+            obj.co_consts,
+            obj.co_varnames,
+            obj.co_filename,
+            obj.co_name,
+            obj.co_firstlineno,
+            obj.co_lnotab,
+            obj.co_exc_handlers,
+            obj.co_jump_table,
+            obj.co_freevars,
+            obj.co_cellvars,
+            obj.co_free2reg,
+            obj.co_cell2reg,
+        )
+    else:
+        # Backward compat for 3.8 and 3.9
+        args = (
+            obj.co_argcount,
+            obj.co_posonlyargcount,
+            obj.co_kwonlyargcount,
+            obj.co_nlocals,
+            obj.co_stacksize,
+            obj.co_flags,
+            obj.co_code,
+            obj.co_consts,
+            obj.co_names,
+            obj.co_varnames,
+            obj.co_filename,
+            obj.co_name,
+            obj.co_firstlineno,
+            obj.co_lnotab,
+            obj.co_freevars,
+            obj.co_cellvars,
+        )
+    return types.CodeType, args
+
+
+def _cell_reduce(obj):
+    """Cell (containing values of a function's free variables) reducer."""
+    try:
+        obj.cell_contents
+    except ValueError:  # cell is empty
+        return _make_empty_cell, ()
+    else:
+        return _make_cell, (obj.cell_contents,)
+
+
+def _classmethod_reduce(obj):
+    orig_func = obj.__func__
+    return type(obj), (orig_func,)
+
+
+def _file_reduce(obj):
+    """Save a file."""
+    import io
+
+    if not hasattr(obj, "name") or not hasattr(obj, "mode"):
+        raise pickle.PicklingError(
+            "Cannot pickle files that do not map to an actual file"
+        )
+    if obj is sys.stdout:
+        return getattr, (sys, "stdout")
+    if obj is sys.stderr:
+        return getattr, (sys, "stderr")
+    if obj is sys.stdin:
+        raise pickle.PicklingError("Cannot pickle standard input")
+    if obj.closed:
+        raise pickle.PicklingError("Cannot pickle closed files")
+    if hasattr(obj, "isatty") and obj.isatty():
+        raise pickle.PicklingError("Cannot pickle files that map to tty objects")
+    if "r" not in obj.mode and "+" not in obj.mode:
+        raise pickle.PicklingError(
+            "Cannot pickle files that are not opened for reading: %s" % obj.mode
+        )
+
+    name = obj.name
+
+    retval = io.StringIO()
+
+    try:
+        # Read the whole file
+        curloc = obj.tell()
+        obj.seek(0)
+        contents = obj.read()
+        obj.seek(curloc)
+    except OSError as e:
+        raise pickle.PicklingError(
+            "Cannot pickle file %s as it cannot be read" % name
+        ) from e
+    retval.write(contents)
+    retval.seek(curloc)
+
+    retval.name = name
+    return _file_reconstructor, (retval,)
+
+
+def _getset_descriptor_reduce(obj):
+    return getattr, (obj.__objclass__, obj.__name__)
+
+
+def _mappingproxy_reduce(obj):
+    return types.MappingProxyType, (dict(obj),)
+
+
+def _memoryview_reduce(obj):
+    return bytes, (obj.tobytes(),)
+
+
+def _module_reduce(obj):
+    if _should_pickle_by_reference(obj):
+        return subimport, (obj.__name__,)
+    else:
+        # Some external libraries can populate the "__builtins__" entry of a
+        # module's `__dict__` with unpicklable objects (see #316). For that
+        # reason, we do not attempt to pickle the "__builtins__" entry, and
+        # restore a default value for it at unpickling time.
+        state = obj.__dict__.copy()
+        state.pop("__builtins__", None)
+        return dynamic_subimport, (obj.__name__, state)
+
+
+def _method_reduce(obj):
+    return (types.MethodType, (obj.__func__, obj.__self__))
+
+
+def _logger_reduce(obj):
+    return logging.getLogger, (obj.name,)
+
+
+def _root_logger_reduce(obj):
+    return logging.getLogger, ()
+
+
+def _property_reduce(obj):
+    return property, (obj.fget, obj.fset, obj.fdel, obj.__doc__)
+
+
+def _weakset_reduce(obj):
+    return weakref.WeakSet, (list(obj),)
+
+
+def _dynamic_class_reduce(obj):
+    """Save a class that can't be referenced as a module attribute.
+
+    This method is used to serialize classes that are defined inside
+    functions, or that otherwise can't be serialized as attribute lookups
+    from importable modules.
+    """
+    if Enum is not None and issubclass(obj, Enum):
+        return (
+            _make_skeleton_enum,
+            _enum_getnewargs(obj),
+            _enum_getstate(obj),
+            None,
+            None,
+            _class_setstate,
+        )
+    else:
+        return (
+            _make_skeleton_class,
+            _class_getnewargs(obj),
+            _class_getstate(obj),
+            None,
+            None,
+            _class_setstate,
+        )
+
+
+def _class_reduce(obj):
+    """Select the reducer depending on the dynamic nature of the class obj."""
+    if obj is type(None):  # noqa
+        return type, (None,)
+    elif obj is type(Ellipsis):
+        return type, (Ellipsis,)
+    elif obj is type(NotImplemented):
+        return type, (NotImplemented,)
+    elif obj in _BUILTIN_TYPE_NAMES:
+        return _builtin_type, (_BUILTIN_TYPE_NAMES[obj],)
+    elif not _should_pickle_by_reference(obj):
+        return _dynamic_class_reduce(obj)
+    return NotImplemented
+
+
+def _dict_keys_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_keys, (list(obj),)
+
+
+def _dict_values_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_values, (list(obj),)
+
+
+def _dict_items_reduce(obj):
+    return _make_dict_items, (dict(obj),)
+
+
+def _odict_keys_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_keys, (list(obj), True)
+
+
+def _odict_values_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_values, (list(obj), True)
+
+
+def _odict_items_reduce(obj):
+    return _make_dict_items, (dict(obj), True)
+
+
+def _dataclass_field_base_reduce(obj):
+    return _get_dataclass_field_type_sentinel, (obj.name,)
+
+
+# COLLECTIONS OF OBJECTS STATE SETTERS
+# ------------------------------------
+# state setters are called at unpickling time, once the object is created and
+# it has to be updated to how it was at unpickling time.
+
+
+def _function_setstate(obj, state):
+    """Update the state of a dynamic function.
+
+    As __closure__ and __globals__ are readonly attributes of a function, we
+    cannot rely on the native setstate routine of pickle.load_build, that calls
+    setattr on items of the slotstate. Instead, we have to modify them inplace.
+    """
+    state, slotstate = state
+    obj.__dict__.update(state)
+
+    obj_globals = slotstate.pop("__globals__")
+    obj_closure = slotstate.pop("__closure__")
+    # _cloudpickle_subimports is a set of submodules that must be loaded for
+    # the pickled function to work correctly at unpickling time. Now that these
+    # submodules are depickled (hence imported), they can be removed from the
+    # object's state (the object state only served as a reference holder to
+    # these submodules)
+    slotstate.pop("_cloudpickle_submodules")
+
+    obj.__globals__.update(obj_globals)
+    obj.__globals__["__builtins__"] = __builtins__
+
+    if obj_closure is not None:
+        for i, cell in enumerate(obj_closure):
+            try:
+                value = cell.cell_contents
+            except ValueError:  # cell is empty
+                continue
+            obj.__closure__[i].cell_contents = value
+
+    for k, v in slotstate.items():
+        setattr(obj, k, v)
+
+
+def _class_setstate(obj, state):
+    state, slotstate = state
+    registry = None
+    for attrname, attr in state.items():
+        if attrname == "_abc_impl":
+            registry = attr
+        else:
+            setattr(obj, attrname, attr)
+    if registry is not None:
+        for subclass in registry:
+            obj.register(subclass)
+
+    return obj
+
+
+# COLLECTION OF DATACLASS UTILITIES
+# ---------------------------------
+# There are some internal sentinel values whose identity must be preserved when
+# unpickling dataclass fields. Each sentinel value has a unique name that we can
+# use to retrieve its identity at unpickling time.
+
+
+_DATACLASSE_FIELD_TYPE_SENTINELS = {
+    dataclasses._FIELD.name: dataclasses._FIELD,
+    dataclasses._FIELD_CLASSVAR.name: dataclasses._FIELD_CLASSVAR,
+    dataclasses._FIELD_INITVAR.name: dataclasses._FIELD_INITVAR,
+}
+
+
+def _get_dataclass_field_type_sentinel(name):
+    return _DATACLASSE_FIELD_TYPE_SENTINELS[name]
+
+
+class Pickler(pickle.Pickler):
+    # set of reducers defined and used by cloudpickle (private)
+    _dispatch_table = {}
+    _dispatch_table[classmethod] = _classmethod_reduce
+    _dispatch_table[io.TextIOWrapper] = _file_reduce
+    _dispatch_table[logging.Logger] = _logger_reduce
+    _dispatch_table[logging.RootLogger] = _root_logger_reduce
+    _dispatch_table[memoryview] = _memoryview_reduce
+    _dispatch_table[property] = _property_reduce
+    _dispatch_table[staticmethod] = _classmethod_reduce
+    _dispatch_table[CellType] = _cell_reduce
+    _dispatch_table[types.CodeType] = _code_reduce
+    _dispatch_table[types.GetSetDescriptorType] = _getset_descriptor_reduce
+    _dispatch_table[types.ModuleType] = _module_reduce
+    _dispatch_table[types.MethodType] = _method_reduce
+    _dispatch_table[types.MappingProxyType] = _mappingproxy_reduce
+    _dispatch_table[weakref.WeakSet] = _weakset_reduce
+    _dispatch_table[typing.TypeVar] = _typevar_reduce
+    _dispatch_table[_collections_abc.dict_keys] = _dict_keys_reduce
+    _dispatch_table[_collections_abc.dict_values] = _dict_values_reduce
+    _dispatch_table[_collections_abc.dict_items] = _dict_items_reduce
+    _dispatch_table[type(OrderedDict().keys())] = _odict_keys_reduce
+    _dispatch_table[type(OrderedDict().values())] = _odict_values_reduce
+    _dispatch_table[type(OrderedDict().items())] = _odict_items_reduce
+    _dispatch_table[abc.abstractmethod] = _classmethod_reduce
+    _dispatch_table[abc.abstractclassmethod] = _classmethod_reduce
+    _dispatch_table[abc.abstractstaticmethod] = _classmethod_reduce
+    _dispatch_table[abc.abstractproperty] = _property_reduce
+    _dispatch_table[dataclasses._FIELD_BASE] = _dataclass_field_base_reduce
+
+    dispatch_table = ChainMap(_dispatch_table, copyreg.dispatch_table)
+
+    # function reducers are defined as instance methods of cloudpickle.Pickler
+    # objects, as they rely on a cloudpickle.Pickler attribute (globals_ref)
+    def _dynamic_function_reduce(self, func):
+        """Reduce a function that is not pickleable via attribute lookup."""
+        newargs = self._function_getnewargs(func)
+        state = _function_getstate(func)
+        return (_make_function, newargs, state, None, None, _function_setstate)
+
+    def _function_reduce(self, obj):
+        """Reducer for function objects.
+
+        If obj is a top-level attribute of a file-backed module, this reducer
+        returns NotImplemented, making the cloudpickle.Pickler fall back to
+        traditional pickle.Pickler routines to save obj. Otherwise, it reduces
+        obj using a custom cloudpickle reducer designed specifically to handle
+        dynamic functions.
+        """
+        if _should_pickle_by_reference(obj):
+            return NotImplemented
+        else:
+            return self._dynamic_function_reduce(obj)
+
+    def _function_getnewargs(self, func):
+        code = func.__code__
+
+        # base_globals represents the future global namespace of func at
+        # unpickling time. Looking it up and storing it in
+        # cloudpickle.Pickler.globals_ref allow functions sharing the same
+        # globals at pickling time to also share them once unpickled, at one
+        # condition: since globals_ref is an attribute of a cloudpickle.Pickler
+        # instance, and that a new cloudpickle.Pickler is created each time
+        # cloudpickle.dump or cloudpickle.dumps is called, functions also need
+        # to be saved within the same invocation of
+        # cloudpickle.dump/cloudpickle.dumps (for example:
+        # cloudpickle.dumps([f1, f2])). There is no such limitation when using
+        # cloudpickle.Pickler.dump, as long as the multiple invocations are
+        # bound to the same cloudpickle.Pickler instance.
+        base_globals = self.globals_ref.setdefault(id(func.__globals__), {})
+
+        if base_globals == {}:
+            # Add module attributes used to resolve relative imports
+            # instructions inside func.
+            for k in ["__package__", "__name__", "__path__", "__file__"]:
+                if k in func.__globals__:
+                    base_globals[k] = func.__globals__[k]
+
+        # Do not bind the free variables before the function is created to
+        # avoid infinite recursion.
+        if func.__closure__ is None:
+            closure = None
+        else:
+            closure = tuple(_make_empty_cell() for _ in range(len(code.co_freevars)))
+
+        return code, base_globals, None, None, closure
+
+    def dump(self, obj):
+        try:
+            return super().dump(obj)
+        except RuntimeError as e:
+            if len(e.args) > 0 and "recursion" in e.args[0]:
+                msg = "Could not pickle object as excessively deep recursion required."
+                raise pickle.PicklingError(msg) from e
+            else:
+                raise
+
+    def __init__(self, file, protocol=None, buffer_callback=None):
+        if protocol is None:
+            protocol = DEFAULT_PROTOCOL
+        super().__init__(file, protocol=protocol, buffer_callback=buffer_callback)
+        # map functions __globals__ attribute ids, to ensure that functions
+        # sharing the same global namespace at pickling time also share
+        # their global namespace at unpickling time.
+        self.globals_ref = {}
+        self.proto = int(protocol)
+
+    if not PYPY:
+        # pickle.Pickler is the C implementation of the CPython pickler and
+        # therefore we rely on reduce_override method to customize the pickler
+        # behavior.
+
+        # `cloudpickle.Pickler.dispatch` is only left for backward
+        # compatibility - note that when using protocol 5,
+        # `cloudpickle.Pickler.dispatch` is not an extension of
+        # `pickle._Pickler.dispatch` dictionary, because `cloudpickle.Pickler`
+        # subclasses the C-implemented `pickle.Pickler`, which does not expose
+        # a `dispatch` attribute.  Earlier versions of `cloudpickle.Pickler`
+        # used `cloudpickle.Pickler.dispatch` as a class-level attribute
+        # storing all reducers implemented by cloudpickle, but the attribute
+        # name was not a great choice given because it would collide with a
+        # similarly named attribute in the pure-Python `pickle._Pickler`
+        # implementation in the standard library.
+        dispatch = dispatch_table
+
+        # Implementation of the reducer_override callback, in order to
+        # efficiently serialize dynamic functions and classes by subclassing
+        # the C-implemented `pickle.Pickler`.
+        # TODO: decorrelate reducer_override (which is tied to CPython's
+        # implementation - would it make sense to backport it to pypy? - and
+        # pickle's protocol 5 which is implementation agnostic. Currently, the
+        # availability of both notions coincide on CPython's pickle, but it may
+        # not be the case anymore when pypy implements protocol 5.
+
+        def reducer_override(self, obj):
+            """Type-agnostic reducing callback for function and classes.
+
+            For performance reasons, subclasses of the C `pickle.Pickler` class
+            cannot register custom reducers for functions and classes in the
+            dispatch_table attribute. Reducers for such types must instead
+            implemented via the special `reducer_override` method.
+
+            Note that this method will be called for any object except a few
+            builtin-types (int, lists, dicts etc.), which differs from reducers
+            in the Pickler's dispatch_table, each of them being invoked for
+            objects of a specific type only.
+
+            This property comes in handy for classes: although most classes are
+            instances of the ``type`` metaclass, some of them can be instances
+            of other custom metaclasses (such as enum.EnumMeta for example). In
+            particular, the metaclass will likely not be known in advance, and
+            thus cannot be special-cased using an entry in the dispatch_table.
+            reducer_override, among other things, allows us to register a
+            reducer that will be called for any class, independently of its
+            type.
+
+            Notes:
+
+            * reducer_override has the priority over dispatch_table-registered
+            reducers.
+            * reducer_override can be used to fix other limitations of
+              cloudpickle for other types that suffered from type-specific
+              reducers, such as Exceptions. See
+              https://github.com/cloudpipe/cloudpickle/issues/248
+            """
+            t = type(obj)
+            try:
+                is_anyclass = issubclass(t, type)
+            except TypeError:  # t is not a class (old Boost; see SF #502085)
+                is_anyclass = False
+
+            if is_anyclass:
+                return _class_reduce(obj)
+            elif isinstance(obj, types.FunctionType):
+                return self._function_reduce(obj)
+            else:
+                # fallback to save_global, including the Pickler's
+                # dispatch_table
+                return NotImplemented
+
+    else:
+        # When reducer_override is not available, hack the pure-Python
+        # Pickler's types.FunctionType and type savers. Note: the type saver
+        # must override Pickler.save_global, because pickle.py contains a
+        # hard-coded call to save_global when pickling meta-classes.
+        dispatch = pickle.Pickler.dispatch.copy()
+
+        def _save_reduce_pickle5(
+            self,
+            func,
+            args,
+            state=None,
+            listitems=None,
+            dictitems=None,
+            state_setter=None,
+            obj=None,
+        ):
+            save = self.save
+            write = self.write
+            self.save_reduce(
+                func,
+                args,
+                state=None,
+                listitems=listitems,
+                dictitems=dictitems,
+                obj=obj,
+            )
+            # backport of the Python 3.8 state_setter pickle operations
+            save(state_setter)
+            save(obj)  # simple BINGET opcode as obj is already memoized.
+            save(state)
+            write(pickle.TUPLE2)
+            # Trigger a state_setter(obj, state) function call.
+            write(pickle.REDUCE)
+            # The purpose of state_setter is to carry-out an
+            # inplace modification of obj. We do not care about what the
+            # method might return, so its output is eventually removed from
+            # the stack.
+            write(pickle.POP)
+
+        def save_global(self, obj, name=None, pack=struct.pack):
+            """Main dispatch method.
+
+            The name of this method is somewhat misleading: all types get
+            dispatched here.
+            """
+            if obj is type(None):  # noqa
+                return self.save_reduce(type, (None,), obj=obj)
+            elif obj is type(Ellipsis):
+                return self.save_reduce(type, (Ellipsis,), obj=obj)
+            elif obj is type(NotImplemented):
+                return self.save_reduce(type, (NotImplemented,), obj=obj)
+            elif obj in _BUILTIN_TYPE_NAMES:
+                return self.save_reduce(
+                    _builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj
+                )
+
+            if name is not None:
+                super().save_global(obj, name=name)
+            elif not _should_pickle_by_reference(obj, name=name):
+                self._save_reduce_pickle5(*_dynamic_class_reduce(obj), obj=obj)
+            else:
+                super().save_global(obj, name=name)
+
+        dispatch[type] = save_global
+
+        def save_function(self, obj, name=None):
+            """Registered with the dispatch to handle all function types.
+
+            Determines what kind of function obj is (e.g. lambda, defined at
+            interactive prompt, etc) and handles the pickling appropriately.
+            """
+            if _should_pickle_by_reference(obj, name=name):
+                return super().save_global(obj, name=name)
+            elif PYPY and isinstance(obj.__code__, builtin_code_type):
+                return self.save_pypy_builtin_func(obj)
+            else:
+                return self._save_reduce_pickle5(
+                    *self._dynamic_function_reduce(obj), obj=obj
+                )
+
+        def save_pypy_builtin_func(self, obj):
+            """Save pypy equivalent of builtin functions.
+
+            PyPy does not have the concept of builtin-functions. Instead,
+            builtin-functions are simple function instances, but with a
+            builtin-code attribute.
+            Most of the time, builtin functions should be pickled by attribute.
+            But PyPy has flaky support for __qualname__, so some builtin
+            functions such as float.__new__ will be classified as dynamic. For
+            this reason only, we created this special routine. Because
+            builtin-functions are not expected to have closure or globals,
+            there is no additional hack (compared the one already implemented
+            in pickle) to protect ourselves from reference cycles. A simple
+            (reconstructor, newargs, obj.__dict__) tuple is save_reduced.  Note
+            also that PyPy improved their support for __qualname__ in v3.6, so
+            this routing should be removed when cloudpickle supports only PyPy
+            3.6 and later.
+            """
+            rv = (
+                types.FunctionType,
+                (obj.__code__, {}, obj.__name__, obj.__defaults__, obj.__closure__),
+                obj.__dict__,
+            )
+            self.save_reduce(*rv, obj=obj)
+
+        dispatch[types.FunctionType] = save_function
+
+
+# Shorthands similar to pickle.dump/pickle.dumps
+
+
+def dump(obj, file, protocol=None, buffer_callback=None):
+    """Serialize obj as bytes streamed into file
+
+    protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
+    pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
+    speed between processes running the same Python version.
+
+    Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
+    compatibility with older versions of Python (although this is not always
+    guaranteed to work because cloudpickle relies on some internal
+    implementation details that can change from one Python version to the
+    next).
+    """
+    Pickler(file, protocol=protocol, buffer_callback=buffer_callback).dump(obj)
+
+
+def dumps(obj, protocol=None, buffer_callback=None):
+    """Serialize obj as a string of bytes allocated in memory
+
+    protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
+    pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
+    speed between processes running the same Python version.
+
+    Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
+    compatibility with older versions of Python (although this is not always
+    guaranteed to work because cloudpickle relies on some internal
+    implementation details that can change from one Python version to the
+    next).
+    """
+    with io.BytesIO() as file:
+        cp = Pickler(file, protocol=protocol, buffer_callback=buffer_callback)
+        cp.dump(obj)
+        return file.getvalue()
+
+
+# Include pickles unloading functions in this namespace for convenience.
+load, loads = pickle.load, pickle.loads
+
+# Backward compat alias.
+CloudPickler = Pickler

env-llmeval/lib/python3.10/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py

@@ -0,0 +1,13 @@
+"""Compatibility module.
+
+It can be necessary to load files generated by previous versions of cloudpickle
+that rely on symbols being defined under the `cloudpickle.cloudpickle_fast`
+namespace.
+
+See: tests/test_backward_compat.py
+"""
+from . import cloudpickle
+
+
+def __getattr__(name):
+    return getattr(cloudpickle, name)

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/__init__.py

@@ -0,0 +1,14 @@
+import os
+from multiprocessing import synchronize
+
+from .context import get_context
+
+
+def _make_name():
+    return f"/loky-{os.getpid()}-{next(synchronize.SemLock._rand)}"
+
+
+# monkey patch the name creation for multiprocessing
+synchronize.SemLock._make_name = staticmethod(_make_name)
+
+__all__ = ["get_context"]

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/_posix_reduction.py

@@ -0,0 +1,67 @@
+###############################################################################
+# Extra reducers for Unix based system and connections objects
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+# adapted from multiprocessing/reduction.py (17/02/2017)
+#  * Add adapted reduction for LokyProcesses and socket/Connection
+#
+import os
+import socket
+import _socket
+from multiprocessing.connection import Connection
+from multiprocessing.context import get_spawning_popen
+
+from .reduction import register
+
+HAVE_SEND_HANDLE = (
+    hasattr(socket, "CMSG_LEN")
+    and hasattr(socket, "SCM_RIGHTS")
+    and hasattr(socket.socket, "sendmsg")
+)
+
+
+def _mk_inheritable(fd):
+    os.set_inheritable(fd, True)
+    return fd
+
+
+def DupFd(fd):
+    """Return a wrapper for an fd."""
+    popen_obj = get_spawning_popen()
+    if popen_obj is not None:
+        return popen_obj.DupFd(popen_obj.duplicate_for_child(fd))
+    elif HAVE_SEND_HANDLE:
+        from multiprocessing import resource_sharer
+
+        return resource_sharer.DupFd(fd)
+    else:
+        raise TypeError(
+            "Cannot pickle connection object. This object can only be "
+            "passed when spawning a new process"
+        )
+
+
+def _reduce_socket(s):
+    df = DupFd(s.fileno())
+    return _rebuild_socket, (df, s.family, s.type, s.proto)
+
+
+def _rebuild_socket(df, family, type, proto):
+    fd = df.detach()
+    return socket.fromfd(fd, family, type, proto)
+
+
+def rebuild_connection(df, readable, writable):
+    fd = df.detach()
+    return Connection(fd, readable, writable)
+
+
+def reduce_connection(conn):
+    df = DupFd(conn.fileno())
+    return rebuild_connection, (df, conn.readable, conn.writable)
+
+
+register(socket.socket, _reduce_socket)
+register(_socket.socket, _reduce_socket)
+register(Connection, reduce_connection)

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/_win_reduction.py

@@ -0,0 +1,18 @@
+###############################################################################
+# Extra reducers for Windows system and connections objects
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+# adapted from multiprocessing/reduction.py (17/02/2017)
+#  * Add adapted reduction for LokyProcesses and socket/PipeConnection
+#
+import socket
+from multiprocessing import connection
+from multiprocessing.reduction import _reduce_socket
+
+from .reduction import register
+
+# register reduction for win32 communication objects
+register(socket.socket, _reduce_socket)
+register(connection.Connection, connection.reduce_connection)
+register(connection.PipeConnection, connection.reduce_pipe_connection)

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/context.py

@@ -0,0 +1,378 @@
+###############################################################################
+# Basic context management with LokyContext
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+# adapted from multiprocessing/context.py
+#  * Create a context ensuring loky uses only objects that are compatible
+#  * Add LokyContext to the list of context of multiprocessing so loky can be
+#    used with multiprocessing.set_start_method
+#  * Implement a CFS-aware amd physical-core aware cpu_count function.
+#
+import os
+import sys
+import math
+import subprocess
+import traceback
+import warnings
+import multiprocessing as mp
+from multiprocessing import get_context as mp_get_context
+from multiprocessing.context import BaseContext
+
+
+from .process import LokyProcess, LokyInitMainProcess
+
+# Apparently, on older Python versions, loky cannot work 61 workers on Windows
+# but instead 60: ¯\_(ツ)_/¯
+if sys.version_info >= (3, 8):
+    from concurrent.futures.process import _MAX_WINDOWS_WORKERS
+
+    if sys.version_info < (3, 10):
+        _MAX_WINDOWS_WORKERS = _MAX_WINDOWS_WORKERS - 1
+else:
+    # compat for versions before 3.8 which do not define this.
+    _MAX_WINDOWS_WORKERS = 60
+
+START_METHODS = ["loky", "loky_init_main", "spawn"]
+if sys.platform != "win32":
+    START_METHODS += ["fork", "forkserver"]
+
+_DEFAULT_START_METHOD = None
+
+# Cache for the number of physical cores to avoid repeating subprocess calls.
+# It should not change during the lifetime of the program.
+physical_cores_cache = None
+
+
+def get_context(method=None):
+    # Try to overload the default context
+    method = method or _DEFAULT_START_METHOD or "loky"
+    if method == "fork":
+        # If 'fork' is explicitly requested, warn user about potential issues.
+        warnings.warn(
+            "`fork` start method should not be used with "
+            "`loky` as it does not respect POSIX. Try using "
+            "`spawn` or `loky` instead.",
+            UserWarning,
+        )
+    try:
+        return mp_get_context(method)
+    except ValueError:
+        raise ValueError(
+            f"Unknown context '{method}'. Value should be in "
+            f"{START_METHODS}."
+        )
+
+
+def set_start_method(method, force=False):
+    global _DEFAULT_START_METHOD
+    if _DEFAULT_START_METHOD is not None and not force:
+        raise RuntimeError("context has already been set")
+    assert method is None or method in START_METHODS, (
+        f"'{method}' is not a valid start_method. It should be in "
+        f"{START_METHODS}"
+    )
+
+    _DEFAULT_START_METHOD = method
+
+
+def get_start_method():
+    return _DEFAULT_START_METHOD
+
+
+def cpu_count(only_physical_cores=False):
+    """Return the number of CPUs the current process can use.
+
+    The returned number of CPUs accounts for:
+     * the number of CPUs in the system, as given by
+       ``multiprocessing.cpu_count``;
+     * the CPU affinity settings of the current process
+       (available on some Unix systems);
+     * Cgroup CPU bandwidth limit (available on Linux only, typically
+       set by docker and similar container orchestration systems);
+     * the value of the LOKY_MAX_CPU_COUNT environment variable if defined.
+    and is given as the minimum of these constraints.
+
+    If ``only_physical_cores`` is True, return the number of physical cores
+    instead of the number of logical cores (hyperthreading / SMT). Note that
+    this option is not enforced if the number of usable cores is controlled in
+    any other way such as: process affinity, Cgroup restricted CPU bandwidth
+    or the LOKY_MAX_CPU_COUNT environment variable. If the number of physical
+    cores is not found, return the number of logical cores.
+
+    Note that on Windows, the returned number of CPUs cannot exceed 61 (or 60 for
+    Python < 3.10), see:
+    https://bugs.python.org/issue26903.
+
+    It is also always larger or equal to 1.
+    """
+    # Note: os.cpu_count() is allowed to return None in its docstring
+    os_cpu_count = os.cpu_count() or 1
+    if sys.platform == "win32":
+        # On Windows, attempting to use more than 61 CPUs would result in a
+        # OS-level error. See https://bugs.python.org/issue26903. According to
+        # https://learn.microsoft.com/en-us/windows/win32/procthread/processor-groups
+        # it might be possible to go beyond with a lot of extra work but this
+        # does not look easy.
+        os_cpu_count = min(os_cpu_count, _MAX_WINDOWS_WORKERS)
+
+    cpu_count_user = _cpu_count_user(os_cpu_count)
+    aggregate_cpu_count = max(min(os_cpu_count, cpu_count_user), 1)
+
+    if not only_physical_cores:
+        return aggregate_cpu_count
+
+    if cpu_count_user < os_cpu_count:
+        # Respect user setting
+        return max(cpu_count_user, 1)
+
+    cpu_count_physical, exception = _count_physical_cores()
+    if cpu_count_physical != "not found":
+        return cpu_count_physical
+
+    # Fallback to default behavior
+    if exception is not None:
+        # warns only the first time
+        warnings.warn(
+            "Could not find the number of physical cores for the "
+            f"following reason:\n{exception}\n"
+            "Returning the number of logical cores instead. You can "
+            "silence this warning by setting LOKY_MAX_CPU_COUNT to "
+            "the number of cores you want to use."
+        )
+        traceback.print_tb(exception.__traceback__)
+
+    return aggregate_cpu_count
+
+
+def _cpu_count_cgroup(os_cpu_count):
+    # Cgroup CPU bandwidth limit available in Linux since 2.6 kernel
+    cpu_max_fname = "/sys/fs/cgroup/cpu.max"
+    cfs_quota_fname = "/sys/fs/cgroup/cpu/cpu.cfs_quota_us"
+    cfs_period_fname = "/sys/fs/cgroup/cpu/cpu.cfs_period_us"
+    if os.path.exists(cpu_max_fname):
+        # cgroup v2
+        # https://www.kernel.org/doc/html/latest/admin-guide/cgroup-v2.html
+        with open(cpu_max_fname) as fh:
+            cpu_quota_us, cpu_period_us = fh.read().strip().split()
+    elif os.path.exists(cfs_quota_fname) and os.path.exists(cfs_period_fname):
+        # cgroup v1
+        # https://www.kernel.org/doc/html/latest/scheduler/sched-bwc.html#management
+        with open(cfs_quota_fname) as fh:
+            cpu_quota_us = fh.read().strip()
+        with open(cfs_period_fname) as fh:
+            cpu_period_us = fh.read().strip()
+    else:
+        # No Cgroup CPU bandwidth limit (e.g. non-Linux platform)
+        cpu_quota_us = "max"
+        cpu_period_us = 100_000  # unused, for consistency with default values
+
+    if cpu_quota_us == "max":
+        # No active Cgroup quota on a Cgroup-capable platform
+        return os_cpu_count
+    else:
+        cpu_quota_us = int(cpu_quota_us)
+        cpu_period_us = int(cpu_period_us)
+        if cpu_quota_us > 0 and cpu_period_us > 0:
+            return math.ceil(cpu_quota_us / cpu_period_us)
+        else:  # pragma: no cover
+            # Setting a negative cpu_quota_us value is a valid way to disable
+            # cgroup CPU bandwith limits
+            return os_cpu_count
+
+
+def _cpu_count_affinity(os_cpu_count):
+    # Number of available CPUs given affinity settings
+    if hasattr(os, "sched_getaffinity"):
+        try:
+            return len(os.sched_getaffinity(0))
+        except NotImplementedError:
+            pass
+
+    # On PyPy and possibly other platforms, os.sched_getaffinity does not exist
+    # or raises NotImplementedError, let's try with the psutil if installed.
+    try:
+        import psutil
+
+        p = psutil.Process()
+        if hasattr(p, "cpu_affinity"):
+            return len(p.cpu_affinity())
+
+    except ImportError:  # pragma: no cover
+        if (
+            sys.platform == "linux"
+            and os.environ.get("LOKY_MAX_CPU_COUNT") is None
+        ):
+            # PyPy does not implement os.sched_getaffinity on Linux which
+            # can cause severe oversubscription problems. Better warn the
+            # user in this particularly pathological case which can wreck
+            # havoc, typically on CI workers.
+            warnings.warn(
+                "Failed to inspect CPU affinity constraints on this system. "
+                "Please install psutil or explictly set LOKY_MAX_CPU_COUNT."
+            )
+
+    # This can happen for platforms that do not implement any kind of CPU
+    # infinity such as macOS-based platforms.
+    return os_cpu_count
+
+
+def _cpu_count_user(os_cpu_count):
+    """Number of user defined available CPUs"""
+    cpu_count_affinity = _cpu_count_affinity(os_cpu_count)
+
+    cpu_count_cgroup = _cpu_count_cgroup(os_cpu_count)
+
+    # User defined soft-limit passed as a loky specific environment variable.
+    cpu_count_loky = int(os.environ.get("LOKY_MAX_CPU_COUNT", os_cpu_count))
+
+    return min(cpu_count_affinity, cpu_count_cgroup, cpu_count_loky)
+
+
+def _count_physical_cores():
+    """Return a tuple (number of physical cores, exception)
+
+    If the number of physical cores is found, exception is set to None.
+    If it has not been found, return ("not found", exception).
+
+    The number of physical cores is cached to avoid repeating subprocess calls.
+    """
+    exception = None
+
+    # First check if the value is cached
+    global physical_cores_cache
+    if physical_cores_cache is not None:
+        return physical_cores_cache, exception
+
+    # Not cached yet, find it
+    try:
+        if sys.platform == "linux":
+            cpu_info = subprocess.run(
+                "lscpu --parse=core".split(), capture_output=True, text=True
+            )
+            cpu_info = cpu_info.stdout.splitlines()
+            cpu_info = {line for line in cpu_info if not line.startswith("#")}
+            cpu_count_physical = len(cpu_info)
+        elif sys.platform == "win32":
+            cpu_info = subprocess.run(
+                "wmic CPU Get NumberOfCores /Format:csv".split(),
+                capture_output=True,
+                text=True,
+            )
+            cpu_info = cpu_info.stdout.splitlines()
+            cpu_info = [
+                l.split(",")[1]
+                for l in cpu_info
+                if (l and l != "Node,NumberOfCores")
+            ]
+            cpu_count_physical = sum(map(int, cpu_info))
+        elif sys.platform == "darwin":
+            cpu_info = subprocess.run(
+                "sysctl -n hw.physicalcpu".split(),
+                capture_output=True,
+                text=True,
+            )
+            cpu_info = cpu_info.stdout
+            cpu_count_physical = int(cpu_info)
+        else:
+            raise NotImplementedError(f"unsupported platform: {sys.platform}")
+
+        # if cpu_count_physical < 1, we did not find a valid value
+        if cpu_count_physical < 1:
+            raise ValueError(f"found {cpu_count_physical} physical cores < 1")
+
+    except Exception as e:
+        exception = e
+        cpu_count_physical = "not found"
+
+    # Put the result in cache
+    physical_cores_cache = cpu_count_physical
+
+    return cpu_count_physical, exception
+
+
+class LokyContext(BaseContext):
+    """Context relying on the LokyProcess."""
+
+    _name = "loky"
+    Process = LokyProcess
+    cpu_count = staticmethod(cpu_count)
+
+    def Queue(self, maxsize=0, reducers=None):
+        """Returns a queue object"""
+        from .queues import Queue
+
+        return Queue(maxsize, reducers=reducers, ctx=self.get_context())
+
+    def SimpleQueue(self, reducers=None):
+        """Returns a queue object"""
+        from .queues import SimpleQueue
+
+        return SimpleQueue(reducers=reducers, ctx=self.get_context())
+
+    if sys.platform != "win32":
+        """For Unix platform, use our custom implementation of synchronize
+        ensuring that we use the loky.backend.resource_tracker to clean-up
+        the semaphores in case of a worker crash.
+        """
+
+        def Semaphore(self, value=1):
+            """Returns a semaphore object"""
+            from .synchronize import Semaphore
+
+            return Semaphore(value=value)
+
+        def BoundedSemaphore(self, value):
+            """Returns a bounded semaphore object"""
+            from .synchronize import BoundedSemaphore
+
+            return BoundedSemaphore(value)
+
+        def Lock(self):
+            """Returns a lock object"""
+            from .synchronize import Lock
+
+            return Lock()
+
+        def RLock(self):
+            """Returns a recurrent lock object"""
+            from .synchronize import RLock
+
+            return RLock()
+
+        def Condition(self, lock=None):
+            """Returns a condition object"""
+            from .synchronize import Condition
+
+            return Condition(lock)
+
+        def Event(self):
+            """Returns an event object"""
+            from .synchronize import Event
+
+            return Event()
+
+
+class LokyInitMainContext(LokyContext):
+    """Extra context with LokyProcess, which does load the main module
+
+    This context is used for compatibility in the case ``cloudpickle`` is not
+    present on the running system. This permits to load functions defined in
+    the ``main`` module, using proper safeguards. The declaration of the
+    ``executor`` should be protected by ``if __name__ == "__main__":`` and the
+    functions and variable used from main should be out of this block.
+
+    This mimics the default behavior of multiprocessing under Windows and the
+    behavior of the ``spawn`` start method on a posix system.
+    For more details, see the end of the following section of python doc
+    https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming
+    """
+
+    _name = "loky_init_main"
+    Process = LokyInitMainProcess
+
+
+# Register loky context so it works with multiprocessing.get_context
+ctx_loky = LokyContext()
+mp.context._concrete_contexts["loky"] = ctx_loky
+mp.context._concrete_contexts["loky_init_main"] = LokyInitMainContext()

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/fork_exec.py

@@ -0,0 +1,43 @@
+###############################################################################
+# Launch a subprocess using forkexec and make sure only the needed fd are
+# shared in the two process.
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+import os
+import sys
+
+
+def close_fds(keep_fds):  # pragma: no cover
+    """Close all the file descriptors except those in keep_fds."""
+
+    # Make sure to keep stdout and stderr open for logging purpose
+    keep_fds = {*keep_fds, 1, 2}
+
+    # We try to retrieve all the open fds
+    try:
+        open_fds = {int(fd) for fd in os.listdir("/proc/self/fd")}
+    except FileNotFoundError:
+        import resource
+
+        max_nfds = resource.getrlimit(resource.RLIMIT_NOFILE)[0]
+        open_fds = {*range(max_nfds)}
+
+    for i in open_fds - keep_fds:
+        try:
+            os.close(i)
+        except OSError:
+            pass
+
+
+def fork_exec(cmd, keep_fds, env=None):
+    # copy the environment variables to set in the child process
+    env = env or {}
+    child_env = {**os.environ, **env}
+
+    pid = os.fork()
+    if pid == 0:  # pragma: no cover
+        close_fds(keep_fds)
+        os.execve(sys.executable, cmd, child_env)
+    else:
+        return pid

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/popen_loky_posix.py

@@ -0,0 +1,193 @@
+###############################################################################
+# Popen for LokyProcess.
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+import os
+import sys
+import signal
+import pickle
+from io import BytesIO
+from multiprocessing import util, process
+from multiprocessing.connection import wait
+from multiprocessing.context import set_spawning_popen
+
+from . import reduction, resource_tracker, spawn
+
+
+__all__ = ["Popen"]
+
+
+#
+# Wrapper for an fd used while launching a process
+#
+
+
+class _DupFd:
+    def __init__(self, fd):
+        self.fd = reduction._mk_inheritable(fd)
+
+    def detach(self):
+        return self.fd
+
+
+#
+# Start child process using subprocess.Popen
+#
+
+
+class Popen:
+    method = "loky"
+    DupFd = _DupFd
+
+    def __init__(self, process_obj):
+        sys.stdout.flush()
+        sys.stderr.flush()
+        self.returncode = None
+        self._fds = []
+        self._launch(process_obj)
+
+    def duplicate_for_child(self, fd):
+        self._fds.append(fd)
+        return reduction._mk_inheritable(fd)
+
+    def poll(self, flag=os.WNOHANG):
+        if self.returncode is None:
+            while True:
+                try:
+                    pid, sts = os.waitpid(self.pid, flag)
+                except OSError:
+                    # Child process not yet created. See #1731717
+                    # e.errno == errno.ECHILD == 10
+                    return None
+                else:
+                    break
+            if pid == self.pid:
+                if os.WIFSIGNALED(sts):
+                    self.returncode = -os.WTERMSIG(sts)
+                else:
+                    assert os.WIFEXITED(sts)
+                    self.returncode = os.WEXITSTATUS(sts)
+        return self.returncode
+
+    def wait(self, timeout=None):
+        if self.returncode is None:
+            if timeout is not None:
+                if not wait([self.sentinel], timeout):
+                    return None
+            # This shouldn't block if wait() returned successfully.
+            return self.poll(os.WNOHANG if timeout == 0.0 else 0)
+        return self.returncode
+
+    def terminate(self):
+        if self.returncode is None:
+            try:
+                os.kill(self.pid, signal.SIGTERM)
+            except ProcessLookupError:
+                pass
+            except OSError:
+                if self.wait(timeout=0.1) is None:
+                    raise
+
+    def _launch(self, process_obj):
+
+        tracker_fd = resource_tracker._resource_tracker.getfd()
+
+        fp = BytesIO()
+        set_spawning_popen(self)
+        try:
+            prep_data = spawn.get_preparation_data(
+                process_obj._name,
+                getattr(process_obj, "init_main_module", True),
+            )
+            reduction.dump(prep_data, fp)
+            reduction.dump(process_obj, fp)
+
+        finally:
+            set_spawning_popen(None)
+
+        try:
+            parent_r, child_w = os.pipe()
+            child_r, parent_w = os.pipe()
+            # for fd in self._fds:
+            #     _mk_inheritable(fd)
+
+            cmd_python = [sys.executable]
+            cmd_python += ["-m", self.__module__]
+            cmd_python += ["--process-name", str(process_obj.name)]
+            cmd_python += ["--pipe", str(reduction._mk_inheritable(child_r))]
+            reduction._mk_inheritable(child_w)
+            reduction._mk_inheritable(tracker_fd)
+            self._fds += [child_r, child_w, tracker_fd]
+            if sys.version_info >= (3, 8) and os.name == "posix":
+                mp_tracker_fd = prep_data["mp_tracker_args"]["fd"]
+                self.duplicate_for_child(mp_tracker_fd)
+
+            from .fork_exec import fork_exec
+
+            pid = fork_exec(cmd_python, self._fds, env=process_obj.env)
+            util.debug(
+                f"launched python with pid {pid} and cmd:\n{cmd_python}"
+            )
+            self.sentinel = parent_r
+
+            method = "getbuffer"
+            if not hasattr(fp, method):
+                method = "getvalue"
+            with os.fdopen(parent_w, "wb") as f:
+                f.write(getattr(fp, method)())
+            self.pid = pid
+        finally:
+            if parent_r is not None:
+                util.Finalize(self, os.close, (parent_r,))
+            for fd in (child_r, child_w):
+                if fd is not None:
+                    os.close(fd)
+
+    @staticmethod
+    def thread_is_spawning():
+        return True
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser("Command line parser")
+    parser.add_argument(
+        "--pipe", type=int, required=True, help="File handle for the pipe"
+    )
+    parser.add_argument(
+        "--process-name",
+        type=str,
+        default=None,
+        help="Identifier for debugging purpose",
+    )
+
+    args = parser.parse_args()
+
+    info = {}
+    exitcode = 1
+    try:
+        with os.fdopen(args.pipe, "rb") as from_parent:
+            process.current_process()._inheriting = True
+            try:
+                prep_data = pickle.load(from_parent)
+                spawn.prepare(prep_data)
+                process_obj = pickle.load(from_parent)
+            finally:
+                del process.current_process()._inheriting
+
+        exitcode = process_obj._bootstrap()
+    except Exception:
+        print("\n\n" + "-" * 80)
+        print(f"{args.process_name} failed with traceback: ")
+        print("-" * 80)
+        import traceback
+
+        print(traceback.format_exc())
+        print("\n" + "-" * 80)
+    finally:
+        if from_parent is not None:
+            from_parent.close()
+
+        sys.exit(exitcode)

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/popen_loky_win32.py

@@ -0,0 +1,173 @@
+import os
+import sys
+import msvcrt
+import _winapi
+from pickle import load
+from multiprocessing import process, util
+from multiprocessing.context import set_spawning_popen
+from multiprocessing.popen_spawn_win32 import Popen as _Popen
+
+from . import reduction, spawn
+
+
+__all__ = ["Popen"]
+
+#
+#
+#
+
+
+def _path_eq(p1, p2):
+    return p1 == p2 or os.path.normcase(p1) == os.path.normcase(p2)
+
+
+WINENV = hasattr(sys, "_base_executable") and not _path_eq(
+    sys.executable, sys._base_executable
+)
+
+
+def _close_handles(*handles):
+    for handle in handles:
+        _winapi.CloseHandle(handle)
+
+
+#
+# We define a Popen class similar to the one from subprocess, but
+# whose constructor takes a process object as its argument.
+#
+
+
+class Popen(_Popen):
+    """
+    Start a subprocess to run the code of a process object.
+
+    We differ from cpython implementation with the way we handle environment
+    variables, in order to be able to modify then in the child processes before
+    importing any library, in order to control the number of threads in C-level
+    threadpools.
+
+    We also use the loky preparation data, in particular to handle main_module
+    inits and the loky resource tracker.
+    """
+
+    method = "loky"
+
+    def __init__(self, process_obj):
+        prep_data = spawn.get_preparation_data(
+            process_obj._name, getattr(process_obj, "init_main_module", True)
+        )
+
+        # read end of pipe will be duplicated by the child process
+        # -- see spawn_main() in spawn.py.
+        #
+        # bpo-33929: Previously, the read end of pipe was "stolen" by the child
+        # process, but it leaked a handle if the child process had been
+        # terminated before it could steal the handle from the parent process.
+        rhandle, whandle = _winapi.CreatePipe(None, 0)
+        wfd = msvcrt.open_osfhandle(whandle, 0)
+        cmd = get_command_line(parent_pid=os.getpid(), pipe_handle=rhandle)
+
+        python_exe = spawn.get_executable()
+
+        # copy the environment variables to set in the child process
+        child_env = {**os.environ, **process_obj.env}
+
+        # bpo-35797: When running in a venv, we bypass the redirect
+        # executor and launch our base Python.
+        if WINENV and _path_eq(python_exe, sys.executable):
+            cmd[0] = python_exe = sys._base_executable
+            child_env["__PYVENV_LAUNCHER__"] = sys.executable
+
+        cmd = " ".join(f'"{x}"' for x in cmd)
+
+        with open(wfd, "wb") as to_child:
+            # start process
+            try:
+                hp, ht, pid, _ = _winapi.CreateProcess(
+                    python_exe,
+                    cmd,
+                    None,
+                    None,
+                    False,
+                    0,
+                    child_env,
+                    None,
+                    None,
+                )
+                _winapi.CloseHandle(ht)
+            except BaseException:
+                _winapi.CloseHandle(rhandle)
+                raise
+
+            # set attributes of self
+            self.pid = pid
+            self.returncode = None
+            self._handle = hp
+            self.sentinel = int(hp)
+            self.finalizer = util.Finalize(
+                self, _close_handles, (self.sentinel, int(rhandle))
+            )
+
+            # send information to child
+            set_spawning_popen(self)
+            try:
+                reduction.dump(prep_data, to_child)
+                reduction.dump(process_obj, to_child)
+            finally:
+                set_spawning_popen(None)
+
+
+def get_command_line(pipe_handle, parent_pid, **kwds):
+    """Returns prefix of command line used for spawning a child process."""
+    if getattr(sys, "frozen", False):
+        return [sys.executable, "--multiprocessing-fork", pipe_handle]
+    else:
+        prog = (
+            "from joblib.externals.loky.backend.popen_loky_win32 import main; "
+            f"main(pipe_handle={pipe_handle}, parent_pid={parent_pid})"
+        )
+        opts = util._args_from_interpreter_flags()
+        return [
+            spawn.get_executable(),
+            *opts,
+            "-c",
+            prog,
+            "--multiprocessing-fork",
+        ]
+
+
+def is_forking(argv):
+    """Return whether commandline indicates we are forking."""
+    if len(argv) >= 2 and argv[1] == "--multiprocessing-fork":
+        return True
+    else:
+        return False
+
+
+def main(pipe_handle, parent_pid=None):
+    """Run code specified by data received over pipe."""
+    assert is_forking(sys.argv), "Not forking"
+
+    if parent_pid is not None:
+        source_process = _winapi.OpenProcess(
+            _winapi.SYNCHRONIZE | _winapi.PROCESS_DUP_HANDLE, False, parent_pid
+        )
+    else:
+        source_process = None
+    new_handle = reduction.duplicate(
+        pipe_handle, source_process=source_process
+    )
+    fd = msvcrt.open_osfhandle(new_handle, os.O_RDONLY)
+    parent_sentinel = source_process
+
+    with os.fdopen(fd, "rb", closefd=True) as from_parent:
+        process.current_process()._inheriting = True
+        try:
+            preparation_data = load(from_parent)
+            spawn.prepare(preparation_data, parent_sentinel)
+            self = load(from_parent)
+        finally:
+            del process.current_process()._inheriting
+
+    exitcode = self._bootstrap(parent_sentinel)
+    sys.exit(exitcode)

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/process.py

@@ -0,0 +1,85 @@
+###############################################################################
+# LokyProcess implementation
+#
+# authors: Thomas Moreau and Olivier Grisel
+#
+# based on multiprocessing/process.py  (17/02/2017)
+#
+import sys
+from multiprocessing.context import assert_spawning
+from multiprocessing.process import BaseProcess
+
+
+class LokyProcess(BaseProcess):
+    _start_method = "loky"
+
+    def __init__(
+        self,
+        group=None,
+        target=None,
+        name=None,
+        args=(),
+        kwargs={},
+        daemon=None,
+        init_main_module=False,
+        env=None,
+    ):
+        super().__init__(
+            group=group,
+            target=target,
+            name=name,
+            args=args,
+            kwargs=kwargs,
+            daemon=daemon,
+        )
+        self.env = {} if env is None else env
+        self.authkey = self.authkey
+        self.init_main_module = init_main_module
+
+    @staticmethod
+    def _Popen(process_obj):
+        if sys.platform == "win32":
+            from .popen_loky_win32 import Popen
+        else:
+            from .popen_loky_posix import Popen
+        return Popen(process_obj)
+
+
+class LokyInitMainProcess(LokyProcess):
+    _start_method = "loky_init_main"
+
+    def __init__(
+        self,
+        group=None,
+        target=None,
+        name=None,
+        args=(),
+        kwargs={},
+        daemon=None,
+    ):
+        super().__init__(
+            group=group,
+            target=target,
+            name=name,
+            args=args,
+            kwargs=kwargs,
+            daemon=daemon,
+            init_main_module=True,
+        )
+
+
+#
+# We subclass bytes to avoid accidental transmission of auth keys over network
+#
+
+
+class AuthenticationKey(bytes):
+    def __reduce__(self):
+        try:
+            assert_spawning(self)
+        except RuntimeError:
+            raise TypeError(
+                "Pickling an AuthenticationKey object is "
+                "disallowed for security reasons"
+            )
+        return AuthenticationKey, (bytes(self),)

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/queues.py

@@ -0,0 +1,236 @@
+###############################################################################
+# Queue and SimpleQueue implementation for loky
+#
+# authors: Thomas Moreau, Olivier Grisel
+#
+# based on multiprocessing/queues.py (16/02/2017)
+# * Add some custom reducers for the Queues/SimpleQueue to tweak the
+#   pickling process. (overload Queue._feed/SimpleQueue.put)
+#
+import os
+import sys
+import errno
+import weakref
+import threading
+from multiprocessing import util
+from multiprocessing.queues import (
+    Full,
+    Queue as mp_Queue,
+    SimpleQueue as mp_SimpleQueue,
+    _sentinel,
+)
+from multiprocessing.context import assert_spawning
+
+from .reduction import dumps
+
+
+__all__ = ["Queue", "SimpleQueue", "Full"]
+
+
+class Queue(mp_Queue):
+    def __init__(self, maxsize=0, reducers=None, ctx=None):
+        super().__init__(maxsize=maxsize, ctx=ctx)
+        self._reducers = reducers
+
+    # Use custom queue set/get state to be able to reduce the custom reducers
+    def __getstate__(self):
+        assert_spawning(self)
+        return (
+            self._ignore_epipe,
+            self._maxsize,
+            self._reader,
+            self._writer,
+            self._reducers,
+            self._rlock,
+            self._wlock,
+            self._sem,
+            self._opid,
+        )
+
+    def __setstate__(self, state):
+        (
+            self._ignore_epipe,
+            self._maxsize,
+            self._reader,
+            self._writer,
+            self._reducers,
+            self._rlock,
+            self._wlock,
+            self._sem,
+            self._opid,
+        ) = state
+        if sys.version_info >= (3, 9):
+            self._reset()
+        else:
+            self._after_fork()
+
+    # Overload _start_thread to correctly call our custom _feed
+    def _start_thread(self):
+        util.debug("Queue._start_thread()")
+
+        # Start thread which transfers data from buffer to pipe
+        self._buffer.clear()
+        self._thread = threading.Thread(
+            target=Queue._feed,
+            args=(
+                self._buffer,
+                self._notempty,
+                self._send_bytes,
+                self._wlock,
+                self._writer.close,
+                self._reducers,
+                self._ignore_epipe,
+                self._on_queue_feeder_error,
+                self._sem,
+            ),
+            name="QueueFeederThread",
+        )
+        self._thread.daemon = True
+
+        util.debug("doing self._thread.start()")
+        self._thread.start()
+        util.debug("... done self._thread.start()")
+
+        # On process exit we will wait for data to be flushed to pipe.
+        #
+        # However, if this process created the queue then all
+        # processes which use the queue will be descendants of this
+        # process.  Therefore waiting for the queue to be flushed
+        # is pointless once all the child processes have been joined.
+        created_by_this_process = self._opid == os.getpid()
+        if not self._joincancelled and not created_by_this_process:
+            self._jointhread = util.Finalize(
+                self._thread,
+                Queue._finalize_join,
+                [weakref.ref(self._thread)],
+                exitpriority=-5,
+            )
+
+        # Send sentinel to the thread queue object when garbage collected
+        self._close = util.Finalize(
+            self,
+            Queue._finalize_close,
+            [self._buffer, self._notempty],
+            exitpriority=10,
+        )
+
+    # Overload the _feed methods to use our custom pickling strategy.
+    @staticmethod
+    def _feed(
+        buffer,
+        notempty,
+        send_bytes,
+        writelock,
+        close,
+        reducers,
+        ignore_epipe,
+        onerror,
+        queue_sem,
+    ):
+        util.debug("starting thread to feed data to pipe")
+        nacquire = notempty.acquire
+        nrelease = notempty.release
+        nwait = notempty.wait
+        bpopleft = buffer.popleft
+        sentinel = _sentinel
+        if sys.platform != "win32":
+            wacquire = writelock.acquire
+            wrelease = writelock.release
+        else:
+            wacquire = None
+
+        while True:
+            try:
+                nacquire()
+                try:
+                    if not buffer:
+                        nwait()
+                finally:
+                    nrelease()
+                try:
+                    while True:
+                        obj = bpopleft()
+                        if obj is sentinel:
+                            util.debug("feeder thread got sentinel -- exiting")
+                            close()
+                            return
+
+                        # serialize the data before acquiring the lock
+                        obj_ = dumps(obj, reducers=reducers)
+                        if wacquire is None:
+                            send_bytes(obj_)
+                        else:
+                            wacquire()
+                            try:
+                                send_bytes(obj_)
+                            finally:
+                                wrelease()
+                        # Remove references early to avoid leaking memory
+                        del obj, obj_
+                except IndexError:
+                    pass
+            except BaseException as e:
+                if ignore_epipe and getattr(e, "errno", 0) == errno.EPIPE:
+                    return
+                # Since this runs in a daemon thread the resources it uses
+                # may be become unusable while the process is cleaning up.
+                # We ignore errors which happen after the process has
+                # started to cleanup.
+                if util.is_exiting():
+                    util.info(f"error in queue thread: {e}")
+                    return
+                else:
+                    queue_sem.release()
+                    onerror(e, obj)
+
+    def _on_queue_feeder_error(self, e, obj):
+        """
+        Private API hook called when feeding data in the background thread
+        raises an exception.  For overriding by concurrent.futures.
+        """
+        import traceback
+
+        traceback.print_exc()
+
+
+class SimpleQueue(mp_SimpleQueue):
+    def __init__(self, reducers=None, ctx=None):
+        super().__init__(ctx=ctx)
+
+        # Add possiblity to use custom reducers
+        self._reducers = reducers
+
+    def close(self):
+        self._reader.close()
+        self._writer.close()
+
+    # Use custom queue set/get state to be able to reduce the custom reducers
+    def __getstate__(self):
+        assert_spawning(self)
+        return (
+            self._reader,
+            self._writer,
+            self._reducers,
+            self._rlock,
+            self._wlock,
+        )
+
+    def __setstate__(self, state):
+        (
+            self._reader,
+            self._writer,
+            self._reducers,
+            self._rlock,
+            self._wlock,
+        ) = state
+
+    # Overload put to use our customizable reducer
+    def put(self, obj):
+        # serialize the data before acquiring the lock
+        obj = dumps(obj, reducers=self._reducers)
+        if self._wlock is None:
+            # writes to a message oriented win32 pipe are atomic
+            self._writer.send_bytes(obj)
+        else:
+            with self._wlock:
+                self._writer.send_bytes(obj)

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/reduction.py

@@ -0,0 +1,224 @@
+###############################################################################
+# Customizable Pickler with some basic reducers
+#
+# author: Thomas Moreau
+#
+# adapted from multiprocessing/reduction.py (17/02/2017)
+#  * Replace the ForkingPickler with a similar _LokyPickler,
+#  * Add CustomizableLokyPickler to allow customizing pickling process
+#    on the fly.
+#
+import copyreg
+import io
+import functools
+import types
+import sys
+import os
+
+from multiprocessing import util
+from pickle import loads, HIGHEST_PROTOCOL
+
+###############################################################################
+# Enable custom pickling in Loky.
+
+_dispatch_table = {}
+
+
+def register(type_, reduce_function):
+    _dispatch_table[type_] = reduce_function
+
+
+###############################################################################
+# Registers extra pickling routines to improve picklization  for loky
+
+
+# make methods picklable
+def _reduce_method(m):
+    if m.__self__ is None:
+        return getattr, (m.__class__, m.__func__.__name__)
+    else:
+        return getattr, (m.__self__, m.__func__.__name__)
+
+
+class _C:
+    def f(self):
+        pass
+
+    @classmethod
+    def h(cls):
+        pass
+
+
+register(type(_C().f), _reduce_method)
+register(type(_C.h), _reduce_method)
+
+
+if not hasattr(sys, "pypy_version_info"):
+    # PyPy uses functions instead of method_descriptors and wrapper_descriptors
+    def _reduce_method_descriptor(m):
+        return getattr, (m.__objclass__, m.__name__)
+
+    register(type(list.append), _reduce_method_descriptor)
+    register(type(int.__add__), _reduce_method_descriptor)
+
+
+# Make partial func pickable
+def _reduce_partial(p):
+    return _rebuild_partial, (p.func, p.args, p.keywords or {})
+
+
+def _rebuild_partial(func, args, keywords):
+    return functools.partial(func, *args, **keywords)
+
+
+register(functools.partial, _reduce_partial)
+
+if sys.platform != "win32":
+    from ._posix_reduction import _mk_inheritable  # noqa: F401
+else:
+    from . import _win_reduction  # noqa: F401
+
+# global variable to change the pickler behavior
+try:
+    from joblib.externals import cloudpickle  # noqa: F401
+
+    DEFAULT_ENV = "cloudpickle"
+except ImportError:
+    # If cloudpickle is not present, fallback to pickle
+    DEFAULT_ENV = "pickle"
+
+ENV_LOKY_PICKLER = os.environ.get("LOKY_PICKLER", DEFAULT_ENV)
+_LokyPickler = None
+_loky_pickler_name = None
+
+
+def set_loky_pickler(loky_pickler=None):
+    global _LokyPickler, _loky_pickler_name
+
+    if loky_pickler is None:
+        loky_pickler = ENV_LOKY_PICKLER
+
+    loky_pickler_cls = None
+
+    # The default loky_pickler is cloudpickle
+    if loky_pickler in ["", None]:
+        loky_pickler = "cloudpickle"
+
+    if loky_pickler == _loky_pickler_name:
+        return
+
+    if loky_pickler == "cloudpickle":
+        from joblib.externals.cloudpickle import CloudPickler as loky_pickler_cls
+    else:
+        try:
+            from importlib import import_module
+
+            module_pickle = import_module(loky_pickler)
+            loky_pickler_cls = module_pickle.Pickler
+        except (ImportError, AttributeError) as e:
+            extra_info = (
+                "\nThis error occurred while setting loky_pickler to"
+                f" '{loky_pickler}', as required by the env variable "
+                "LOKY_PICKLER or the function set_loky_pickler."
+            )
+            e.args = (e.args[0] + extra_info,) + e.args[1:]
+            e.msg = e.args[0]
+            raise e
+
+    util.debug(
+        f"Using '{loky_pickler if loky_pickler else 'cloudpickle'}' for "
+        "serialization."
+    )
+
+    class CustomizablePickler(loky_pickler_cls):
+        _loky_pickler_cls = loky_pickler_cls
+
+        def _set_dispatch_table(self, dispatch_table):
+            for ancestor_class in self._loky_pickler_cls.mro():
+                dt_attribute = getattr(ancestor_class, "dispatch_table", None)
+                if isinstance(dt_attribute, types.MemberDescriptorType):
+                    # Ancestor class (typically _pickle.Pickler) has a
+                    # member_descriptor for its "dispatch_table" attribute. Use
+                    # it to set the dispatch_table as a member instead of a
+                    # dynamic attribute in the __dict__ of the instance,
+                    # otherwise it will not be taken into account by the C
+                    # implementation of the dump method if a subclass defines a
+                    # class-level dispatch_table attribute as was done in
+                    # cloudpickle 1.6.0:
+                    # https://github.com/joblib/loky/pull/260
+                    dt_attribute.__set__(self, dispatch_table)
+                    break
+
+            # On top of member descriptor set, also use setattr such that code
+            # that directly access self.dispatch_table gets a consistent view
+            # of the same table.
+            self.dispatch_table = dispatch_table
+
+        def __init__(self, writer, reducers=None, protocol=HIGHEST_PROTOCOL):
+            loky_pickler_cls.__init__(self, writer, protocol=protocol)
+            if reducers is None:
+                reducers = {}
+
+            if hasattr(self, "dispatch_table"):
+                # Force a copy that we will update without mutating the
+                # any class level defined dispatch_table.
+                loky_dt = dict(self.dispatch_table)
+            else:
+                # Use standard reducers as bases
+                loky_dt = copyreg.dispatch_table.copy()
+
+            # Register loky specific reducers
+            loky_dt.update(_dispatch_table)
+
+            # Set the new dispatch table, taking care of the fact that we
+            # need to use the member_descriptor when we inherit from a
+            # subclass of the C implementation of the Pickler base class
+            # with an class level dispatch_table attribute.
+            self._set_dispatch_table(loky_dt)
+
+            # Register the reducers
+            for type, reduce_func in reducers.items():
+                self.register(type, reduce_func)
+
+        def register(self, type, reduce_func):
+            """Attach a reducer function to a given type in the dispatch table."""
+            self.dispatch_table[type] = reduce_func
+
+    _LokyPickler = CustomizablePickler
+    _loky_pickler_name = loky_pickler
+
+
+def get_loky_pickler_name():
+    global _loky_pickler_name
+    return _loky_pickler_name
+
+
+def get_loky_pickler():
+    global _LokyPickler
+    return _LokyPickler
+
+
+# Set it to its default value
+set_loky_pickler()
+
+
+def dump(obj, file, reducers=None, protocol=None):
+    """Replacement for pickle.dump() using _LokyPickler."""
+    global _LokyPickler
+    _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj)
+
+
+def dumps(obj, reducers=None, protocol=None):
+    global _LokyPickler
+
+    buf = io.BytesIO()
+    dump(obj, buf, reducers=reducers, protocol=protocol)
+    return buf.getbuffer()
+
+
+__all__ = ["dump", "dumps", "loads", "register", "set_loky_pickler"]
+
+if sys.platform == "win32":
+    from multiprocessing.reduction import duplicate
+
+    __all__ += ["duplicate"]

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/resource_tracker.py

@@ -0,0 +1,378 @@
+###############################################################################
+# Server process to keep track of unlinked resources, like folders and
+# semaphores and clean them.
+#
+# author: Thomas Moreau
+#
+# adapted from multiprocessing/semaphore_tracker.py  (17/02/2017)
+#  * include custom spawnv_passfds to start the process
+#  * add some VERBOSE logging
+#
+# TODO: multiprocessing.resource_tracker was contributed to Python 3.8 so
+# once loky drops support for Python 3.7 it might be possible to stop
+# maintaining this loky-specific fork. As a consequence, it might also be
+# possible to stop maintaining the loky.backend.synchronize fork of
+# multiprocessing.synchronize.
+
+#
+# On Unix we run a server process which keeps track of unlinked
+# resources. The server ignores SIGINT and SIGTERM and reads from a
+# pipe. The resource_tracker implements a reference counting scheme: each time
+# a Python process anticipates the shared usage of a resource by another
+# process, it signals the resource_tracker of this shared usage, and in return,
+# the resource_tracker increments the resource's reference count by 1.
+# Similarly, when access to a resource is closed by a Python process, the
+# process notifies the resource_tracker by asking it to decrement the
+# resource's reference count by 1.  When the reference count drops to 0, the
+# resource_tracker attempts to clean up the underlying resource.
+
+# Finally, every other process connected to the resource tracker has a copy of
+# the writable end of the pipe used to communicate with it, so the resource
+# tracker gets EOF when all other processes have exited. Then the
+# resource_tracker process unlinks any remaining leaked resources (with
+# reference count above 0)
+
+# For semaphores, this is important because the system only supports a limited
+# number of named semaphores, and they will not be automatically removed till
+# the next reboot.  Without this resource tracker process, "killall python"
+# would probably leave unlinked semaphores.
+
+# Note that this behavior differs from CPython's resource_tracker, which only
+# implements list of shared resources, and not a proper refcounting scheme.
+# Also, CPython's resource tracker will only attempt to cleanup those shared
+# resources once all procsses connected to the resouce tracker have exited.
+
+
+import os
+import shutil
+import sys
+import signal
+import warnings
+import threading
+from _multiprocessing import sem_unlink
+from multiprocessing import util
+
+from . import spawn
+
+if sys.platform == "win32":
+    import _winapi
+    import msvcrt
+    from multiprocessing.reduction import duplicate
+
+
+__all__ = ["ensure_running", "register", "unregister"]
+
+_HAVE_SIGMASK = hasattr(signal, "pthread_sigmask")
+_IGNORED_SIGNALS = (signal.SIGINT, signal.SIGTERM)
+
+_CLEANUP_FUNCS = {"folder": shutil.rmtree, "file": os.unlink}
+
+if os.name == "posix":
+    _CLEANUP_FUNCS["semlock"] = sem_unlink
+
+
+VERBOSE = False
+
+
+class ResourceTracker:
+    def __init__(self):
+        self._lock = threading.Lock()
+        self._fd = None
+        self._pid = None
+
+    def getfd(self):
+        self.ensure_running()
+        return self._fd
+
+    def ensure_running(self):
+        """Make sure that resource tracker process is running.
+
+        This can be run from any process.  Usually a child process will use
+        the resource created by its parent."""
+        with self._lock:
+            if self._fd is not None:
+                # resource tracker was launched before, is it still running?
+                if self._check_alive():
+                    # => still alive
+                    return
+                # => dead, launch it again
+                os.close(self._fd)
+                if os.name == "posix":
+                    try:
+                        # At this point, the resource_tracker process has been
+                        # killed or crashed. Let's remove the process entry
+                        # from the process table to avoid zombie processes.
+                        os.waitpid(self._pid, 0)
+                    except OSError:
+                        # The process was terminated or is a child from an
+                        # ancestor of the current process.
+                        pass
+                self._fd = None
+                self._pid = None
+
+                warnings.warn(
+                    "resource_tracker: process died unexpectedly, "
+                    "relaunching.  Some folders/sempahores might "
+                    "leak."
+                )
+
+            fds_to_pass = []
+            try:
+                fds_to_pass.append(sys.stderr.fileno())
+            except Exception:
+                pass
+
+            r, w = os.pipe()
+            if sys.platform == "win32":
+                _r = duplicate(msvcrt.get_osfhandle(r), inheritable=True)
+                os.close(r)
+                r = _r
+
+            cmd = f"from {main.__module__} import main; main({r}, {VERBOSE})"
+            try:
+                fds_to_pass.append(r)
+                # process will out live us, so no need to wait on pid
+                exe = spawn.get_executable()
+                args = [exe, *util._args_from_interpreter_flags(), "-c", cmd]
+                util.debug(f"launching resource tracker: {args}")
+                # bpo-33613: Register a signal mask that will block the
+                # signals.  This signal mask will be inherited by the child
+                # that is going to be spawned and will protect the child from a
+                # race condition that can make the child die before it
+                # registers signal handlers for SIGINT and SIGTERM. The mask is
+                # unregistered after spawning the child.
+                try:
+                    if _HAVE_SIGMASK:
+                        signal.pthread_sigmask(
+                            signal.SIG_BLOCK, _IGNORED_SIGNALS
+                        )
+                    pid = spawnv_passfds(exe, args, fds_to_pass)
+                finally:
+                    if _HAVE_SIGMASK:
+                        signal.pthread_sigmask(
+                            signal.SIG_UNBLOCK, _IGNORED_SIGNALS
+                        )
+            except BaseException:
+                os.close(w)
+                raise
+            else:
+                self._fd = w
+                self._pid = pid
+            finally:
+                if sys.platform == "win32":
+                    _winapi.CloseHandle(r)
+                else:
+                    os.close(r)
+
+    def _check_alive(self):
+        """Check for the existence of the resource tracker process."""
+        try:
+            self._send("PROBE", "", "")
+        except BrokenPipeError:
+            return False
+        else:
+            return True
+
+    def register(self, name, rtype):
+        """Register a named resource, and increment its refcount."""
+        self.ensure_running()
+        self._send("REGISTER", name, rtype)
+
+    def unregister(self, name, rtype):
+        """Unregister a named resource with resource tracker."""
+        self.ensure_running()
+        self._send("UNREGISTER", name, rtype)
+
+    def maybe_unlink(self, name, rtype):
+        """Decrement the refcount of a resource, and delete it if it hits 0"""
+        self.ensure_running()
+        self._send("MAYBE_UNLINK", name, rtype)
+
+    def _send(self, cmd, name, rtype):
+        if len(name) > 512:
+            # posix guarantees that writes to a pipe of less than PIPE_BUF
+            # bytes are atomic, and that PIPE_BUF >= 512
+            raise ValueError("name too long")
+        msg = f"{cmd}:{name}:{rtype}\n".encode("ascii")
+        nbytes = os.write(self._fd, msg)
+        assert nbytes == len(msg)
+
+
+_resource_tracker = ResourceTracker()
+ensure_running = _resource_tracker.ensure_running
+register = _resource_tracker.register
+maybe_unlink = _resource_tracker.maybe_unlink
+unregister = _resource_tracker.unregister
+getfd = _resource_tracker.getfd
+
+
+def main(fd, verbose=0):
+    """Run resource tracker."""
+    # protect the process from ^C and "killall python" etc
+    if verbose:
+        util.log_to_stderr(level=util.DEBUG)
+
+    signal.signal(signal.SIGINT, signal.SIG_IGN)
+    signal.signal(signal.SIGTERM, signal.SIG_IGN)
+
+    if _HAVE_SIGMASK:
+        signal.pthread_sigmask(signal.SIG_UNBLOCK, _IGNORED_SIGNALS)
+
+    for f in (sys.stdin, sys.stdout):
+        try:
+            f.close()
+        except Exception:
+            pass
+
+    if verbose:
+        util.debug("Main resource tracker is running")
+
+    registry = {rtype: {} for rtype in _CLEANUP_FUNCS.keys()}
+    try:
+        # keep track of registered/unregistered resources
+        if sys.platform == "win32":
+            fd = msvcrt.open_osfhandle(fd, os.O_RDONLY)
+        with open(fd, "rb") as f:
+            while True:
+                line = f.readline()
+                if line == b"":  # EOF
+                    break
+                try:
+                    splitted = line.strip().decode("ascii").split(":")
+                    # name can potentially contain separator symbols (for
+                    # instance folders on Windows)
+                    cmd, name, rtype = (
+                        splitted[0],
+                        ":".join(splitted[1:-1]),
+                        splitted[-1],
+                    )
+
+                    if cmd == "PROBE":
+                        continue
+
+                    if rtype not in _CLEANUP_FUNCS:
+                        raise ValueError(
+                            f"Cannot register {name} for automatic cleanup: "
+                            f"unknown resource type ({rtype}). Resource type "
+                            "should be one of the following: "
+                            f"{list(_CLEANUP_FUNCS.keys())}"
+                        )
+
+                    if cmd == "REGISTER":
+                        if name not in registry[rtype]:
+                            registry[rtype][name] = 1
+                        else:
+                            registry[rtype][name] += 1
+
+                        if verbose:
+                            util.debug(
+                                "[ResourceTracker] incremented refcount of "
+                                f"{rtype} {name} "
+                                f"(current {registry[rtype][name]})"
+                            )
+                    elif cmd == "UNREGISTER":
+                        del registry[rtype][name]
+                        if verbose:
+                            util.debug(
+                                f"[ResourceTracker] unregister {name} {rtype}: "
+                                f"registry({len(registry)})"
+                            )
+                    elif cmd == "MAYBE_UNLINK":
+                        registry[rtype][name] -= 1
+                        if verbose:
+                            util.debug(
+                                "[ResourceTracker] decremented refcount of "
+                                f"{rtype} {name} "
+                                f"(current {registry[rtype][name]})"
+                            )
+
+                        if registry[rtype][name] == 0:
+                            del registry[rtype][name]
+                            try:
+                                if verbose:
+                                    util.debug(
+                                        f"[ResourceTracker] unlink {name}"
+                                    )
+                                _CLEANUP_FUNCS[rtype](name)
+                            except Exception as e:
+                                warnings.warn(
+                                    f"resource_tracker: {name}: {e!r}"
+                                )
+
+                    else:
+                        raise RuntimeError(f"unrecognized command {cmd!r}")
+                except BaseException:
+                    try:
+                        sys.excepthook(*sys.exc_info())
+                    except BaseException:
+                        pass
+    finally:
+        # all processes have terminated; cleanup any remaining resources
+        def _unlink_resources(rtype_registry, rtype):
+            if rtype_registry:
+                try:
+                    warnings.warn(
+                        "resource_tracker: There appear to be "
+                        f"{len(rtype_registry)} leaked {rtype} objects to "
+                        "clean up at shutdown"
+                    )
+                except Exception:
+                    pass
+            for name in rtype_registry:
+                # For some reason the process which created and registered this
+                # resource has failed to unregister it. Presumably it has
+                # died.  We therefore clean it up.
+                try:
+                    _CLEANUP_FUNCS[rtype](name)
+                    if verbose:
+                        util.debug(f"[ResourceTracker] unlink {name}")
+                except Exception as e:
+                    warnings.warn(f"resource_tracker: {name}: {e!r}")
+
+        for rtype, rtype_registry in registry.items():
+            if rtype == "folder":
+                continue
+            else:
+                _unlink_resources(rtype_registry, rtype)
+
+        # The default cleanup routine for folders deletes everything inside
+        # those folders recursively, which can include other resources tracked
+        # by the resource tracker). To limit the risk of the resource tracker
+        # attempting to delete twice a resource (once as part of a tracked
+        # folder, and once as a resource), we delete the folders after all
+        # other resource types.
+        if "folder" in registry:
+            _unlink_resources(registry["folder"], "folder")
+
+    if verbose:
+        util.debug("resource tracker shut down")
+
+
+#
+# Start a program with only specified fds kept open
+#
+
+
+def spawnv_passfds(path, args, passfds):
+    passfds = sorted(passfds)
+    if sys.platform != "win32":
+        errpipe_read, errpipe_write = os.pipe()
+        try:
+            from .reduction import _mk_inheritable
+            from .fork_exec import fork_exec
+
+            _pass = [_mk_inheritable(fd) for fd in passfds]
+            return fork_exec(args, _pass)
+        finally:
+            os.close(errpipe_read)
+            os.close(errpipe_write)
+    else:
+        cmd = " ".join(f'"{x}"' for x in args)
+        try:
+            _, ht, pid, _ = _winapi.CreateProcess(
+                path, cmd, None, None, True, 0, None, None, None
+            )
+            _winapi.CloseHandle(ht)
+        except BaseException:
+            pass
+        return pid

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/spawn.py

@@ -0,0 +1,250 @@
+###############################################################################
+# Prepares and processes the data to setup the new process environment
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+# adapted from multiprocessing/spawn.py (17/02/2017)
+#  * Improve logging data
+#
+import os
+import sys
+import runpy
+import textwrap
+import types
+from multiprocessing import process, util
+
+
+if sys.platform != "win32":
+    WINEXE = False
+    WINSERVICE = False
+else:
+    import msvcrt
+    from multiprocessing.reduction import duplicate
+
+    WINEXE = sys.platform == "win32" and getattr(sys, "frozen", False)
+    WINSERVICE = sys.executable.lower().endswith("pythonservice.exe")
+
+if WINSERVICE:
+    _python_exe = os.path.join(sys.exec_prefix, "python.exe")
+else:
+    _python_exe = sys.executable
+
+
+def get_executable():
+    return _python_exe
+
+
+def _check_not_importing_main():
+    if getattr(process.current_process(), "_inheriting", False):
+        raise RuntimeError(
+            textwrap.dedent(
+                """\
+            An attempt has been made to start a new process before the
+            current process has finished its bootstrapping phase.
+
+            This probably means that you are not using fork to start your
+            child processes and you have forgotten to use the proper idiom
+            in the main module:
+
+                if __name__ == '__main__':
+                    freeze_support()
+                    ...
+
+            The "freeze_support()" line can be omitted if the program
+            is not going to be frozen to produce an executable."""
+            )
+        )
+
+
+def get_preparation_data(name, init_main_module=True):
+    """Return info about parent needed by child to unpickle process object."""
+    _check_not_importing_main()
+    d = dict(
+        log_to_stderr=util._log_to_stderr,
+        authkey=bytes(process.current_process().authkey),
+        name=name,
+        sys_argv=sys.argv,
+        orig_dir=process.ORIGINAL_DIR,
+        dir=os.getcwd(),
+    )
+
+    # Send sys_path and make sure the current directory will not be changed
+    d["sys_path"] = [p if p != "" else process.ORIGINAL_DIR for p in sys.path]
+
+    # Make sure to pass the information if the multiprocessing logger is active
+    if util._logger is not None:
+        d["log_level"] = util._logger.getEffectiveLevel()
+        if util._logger.handlers:
+            h = util._logger.handlers[0]
+            d["log_fmt"] = h.formatter._fmt
+
+    # Tell the child how to communicate with the resource_tracker
+    from .resource_tracker import _resource_tracker
+
+    _resource_tracker.ensure_running()
+    d["tracker_args"] = {"pid": _resource_tracker._pid}
+    if sys.platform == "win32":
+        d["tracker_args"]["fh"] = msvcrt.get_osfhandle(_resource_tracker._fd)
+    else:
+        d["tracker_args"]["fd"] = _resource_tracker._fd
+
+    if sys.version_info >= (3, 8) and os.name == "posix":
+        # joblib/loky#242: allow loky processes to retrieve the resource
+        # tracker of their parent in case the child processes depickles
+        # shared_memory objects, that are still tracked by multiprocessing's
+        # resource_tracker by default.
+        # XXX: this is a workaround that may be error prone: in the future, it
+        # would be better to have loky subclass multiprocessing's shared_memory
+        # to force registration of shared_memory segments via loky's
+        # resource_tracker.
+        from multiprocessing.resource_tracker import (
+            _resource_tracker as mp_resource_tracker,
+        )
+
+        # multiprocessing's resource_tracker must be running before loky
+        # process is created (othewise the child won't be able to use it if it
+        # is created later on)
+        mp_resource_tracker.ensure_running()
+        d["mp_tracker_args"] = {
+            "fd": mp_resource_tracker._fd,
+            "pid": mp_resource_tracker._pid,
+        }
+
+    # Figure out whether to initialise main in the subprocess as a module
+    # or through direct execution (or to leave it alone entirely)
+    if init_main_module:
+        main_module = sys.modules["__main__"]
+        try:
+            main_mod_name = getattr(main_module.__spec__, "name", None)
+        except BaseException:
+            main_mod_name = None
+        if main_mod_name is not None:
+            d["init_main_from_name"] = main_mod_name
+        elif sys.platform != "win32" or (not WINEXE and not WINSERVICE):
+            main_path = getattr(main_module, "__file__", None)
+            if main_path is not None:
+                if (
+                    not os.path.isabs(main_path)
+                    and process.ORIGINAL_DIR is not None
+                ):
+                    main_path = os.path.join(process.ORIGINAL_DIR, main_path)
+                d["init_main_from_path"] = os.path.normpath(main_path)
+
+    return d
+
+
+#
+# Prepare current process
+#
+old_main_modules = []
+
+
+def prepare(data, parent_sentinel=None):
+    """Try to get current process ready to unpickle process object."""
+    if "name" in data:
+        process.current_process().name = data["name"]
+
+    if "authkey" in data:
+        process.current_process().authkey = data["authkey"]
+
+    if "log_to_stderr" in data and data["log_to_stderr"]:
+        util.log_to_stderr()
+
+    if "log_level" in data:
+        util.get_logger().setLevel(data["log_level"])
+
+    if "log_fmt" in data:
+        import logging
+
+        util.get_logger().handlers[0].setFormatter(
+            logging.Formatter(data["log_fmt"])
+        )
+
+    if "sys_path" in data:
+        sys.path = data["sys_path"]
+
+    if "sys_argv" in data:
+        sys.argv = data["sys_argv"]
+
+    if "dir" in data:
+        os.chdir(data["dir"])
+
+    if "orig_dir" in data:
+        process.ORIGINAL_DIR = data["orig_dir"]
+
+    if "mp_tracker_args" in data:
+        from multiprocessing.resource_tracker import (
+            _resource_tracker as mp_resource_tracker,
+        )
+
+        mp_resource_tracker._fd = data["mp_tracker_args"]["fd"]
+        mp_resource_tracker._pid = data["mp_tracker_args"]["pid"]
+    if "tracker_args" in data:
+        from .resource_tracker import _resource_tracker
+
+        _resource_tracker._pid = data["tracker_args"]["pid"]
+        if sys.platform == "win32":
+            handle = data["tracker_args"]["fh"]
+            handle = duplicate(handle, source_process=parent_sentinel)
+            _resource_tracker._fd = msvcrt.open_osfhandle(handle, os.O_RDONLY)
+        else:
+            _resource_tracker._fd = data["tracker_args"]["fd"]
+
+    if "init_main_from_name" in data:
+        _fixup_main_from_name(data["init_main_from_name"])
+    elif "init_main_from_path" in data:
+        _fixup_main_from_path(data["init_main_from_path"])
+
+
+# Multiprocessing module helpers to fix up the main module in
+# spawned subprocesses
+def _fixup_main_from_name(mod_name):
+    # __main__.py files for packages, directories, zip archives, etc, run
+    # their "main only" code unconditionally, so we don't even try to
+    # populate anything in __main__, nor do we make any changes to
+    # __main__ attributes
+    current_main = sys.modules["__main__"]
+    if mod_name == "__main__" or mod_name.endswith(".__main__"):
+        return
+
+    # If this process was forked, __main__ may already be populated
+    if getattr(current_main.__spec__, "name", None) == mod_name:
+        return
+
+    # Otherwise, __main__ may contain some non-main code where we need to
+    # support unpickling it properly. We rerun it as __mp_main__ and make
+    # the normal __main__ an alias to that
+    old_main_modules.append(current_main)
+    main_module = types.ModuleType("__mp_main__")
+    main_content = runpy.run_module(
+        mod_name, run_name="__mp_main__", alter_sys=True
+    )
+    main_module.__dict__.update(main_content)
+    sys.modules["__main__"] = sys.modules["__mp_main__"] = main_module
+
+
+def _fixup_main_from_path(main_path):
+    # If this process was forked, __main__ may already be populated
+    current_main = sys.modules["__main__"]
+
+    # Unfortunately, the main ipython launch script historically had no
+    # "if __name__ == '__main__'" guard, so we work around that
+    # by treating it like a __main__.py file
+    # See https://github.com/ipython/ipython/issues/4698
+    main_name = os.path.splitext(os.path.basename(main_path))[0]
+    if main_name == "ipython":
+        return
+
+    # Otherwise, if __file__ already has the setting we expect,
+    # there's nothing more to do
+    if getattr(current_main, "__file__", None) == main_path:
+        return
+
+    # If the parent process has sent a path through rather than a module
+    # name we assume it is an executable script that may contain
+    # non-main code that needs to be executed
+    old_main_modules.append(current_main)
+    main_module = types.ModuleType("__mp_main__")
+    main_content = runpy.run_path(main_path, run_name="__mp_main__")
+    main_module.__dict__.update(main_content)
+    sys.modules["__main__"] = sys.modules["__mp_main__"] = main_module

env-llmeval/lib/python3.10/site-packages/joblib/externals/loky/backend/synchronize.py

@@ -0,0 +1,409 @@
+###############################################################################
+# Synchronization primitives based on our SemLock implementation
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+# adapted from multiprocessing/synchronize.py (17/02/2017)
+#  * Remove ctx argument for compatibility reason
+#  * Registers a cleanup function with the loky resource_tracker to remove the
+#    semaphore when the process dies instead.
+#
+# TODO: investigate which Python version is required to be able to use
+# multiprocessing.resource_tracker and therefore multiprocessing.synchronize
+# instead of a loky-specific fork.
+
+import os
+import sys
+import tempfile
+import threading
+import _multiprocessing
+from time import time as _time
+from multiprocessing import process, util
+from multiprocessing.context import assert_spawning
+
+from . import resource_tracker
+
+__all__ = [
+    "Lock",
+    "RLock",
+    "Semaphore",
+    "BoundedSemaphore",
+    "Condition",
+    "Event",
+]
+# Try to import the mp.synchronize module cleanly, if it fails
+# raise ImportError for platforms lacking a working sem_open implementation.
+# See issue 3770
+try:
+    from _multiprocessing import SemLock as _SemLock
+    from _multiprocessing import sem_unlink
+except ImportError:
+    raise ImportError(
+        "This platform lacks a functioning sem_open"
+        " implementation, therefore, the required"
+        " synchronization primitives needed will not"
+        " function, see issue 3770."
+    )
+
+#
+# Constants
+#
+
+RECURSIVE_MUTEX, SEMAPHORE = range(2)
+SEM_VALUE_MAX = _multiprocessing.SemLock.SEM_VALUE_MAX
+
+
+#
+# Base class for semaphores and mutexes; wraps `_multiprocessing.SemLock`
+#
+
+
+class SemLock:
+
+    _rand = tempfile._RandomNameSequence()
+
+    def __init__(self, kind, value, maxvalue, name=None):
+        # unlink_now is only used on win32 or when we are using fork.
+        unlink_now = False
+        if name is None:
+            # Try to find an unused name for the SemLock instance.
+            for _ in range(100):
+                try:
+                    self._semlock = _SemLock(
+                        kind, value, maxvalue, SemLock._make_name(), unlink_now
+                    )
+                except FileExistsError:  # pragma: no cover
+                    pass
+                else:
+                    break
+            else:  # pragma: no cover
+                raise FileExistsError("cannot find name for semaphore")
+        else:
+            self._semlock = _SemLock(kind, value, maxvalue, name, unlink_now)
+        self.name = name
+        util.debug(
+            f"created semlock with handle {self._semlock.handle} and name "
+            f'"{self.name}"'
+        )
+
+        self._make_methods()
+
+        def _after_fork(obj):
+            obj._semlock._after_fork()
+
+        util.register_after_fork(self, _after_fork)
+
+        # When the object is garbage collected or the
+        # process shuts down we unlink the semaphore name
+        resource_tracker.register(self._semlock.name, "semlock")
+        util.Finalize(
+            self, SemLock._cleanup, (self._semlock.name,), exitpriority=0
+        )
+
+    @staticmethod
+    def _cleanup(name):
+        try:
+            sem_unlink(name)
+        except FileNotFoundError:
+            # Already unlinked, possibly by user code: ignore and make sure to
+            # unregister the semaphore from the resource tracker.
+            pass
+        finally:
+            resource_tracker.unregister(name, "semlock")
+
+    def _make_methods(self):
+        self.acquire = self._semlock.acquire
+        self.release = self._semlock.release
+
+    def __enter__(self):
+        return self._semlock.acquire()
+
+    def __exit__(self, *args):
+        return self._semlock.release()
+
+    def __getstate__(self):
+        assert_spawning(self)
+        sl = self._semlock
+        h = sl.handle
+        return (h, sl.kind, sl.maxvalue, sl.name)
+
+    def __setstate__(self, state):
+        self._semlock = _SemLock._rebuild(*state)
+        util.debug(
+            f'recreated blocker with handle {state[0]!r} and name "{state[3]}"'
+        )
+        self._make_methods()
+
+    @staticmethod
+    def _make_name():
+        # OSX does not support long names for semaphores
+        return f"/loky-{os.getpid()}-{next(SemLock._rand)}"
+
+
+#
+# Semaphore
+#
+
+
+class Semaphore(SemLock):
+    def __init__(self, value=1):
+        SemLock.__init__(self, SEMAPHORE, value, SEM_VALUE_MAX)
+
+    def get_value(self):
+        if sys.platform == "darwin":
+            raise NotImplementedError("OSX does not implement sem_getvalue")
+        return self._semlock._get_value()
+
+    def __repr__(self):
+        try:
+            value = self._semlock._get_value()
+        except Exception:
+            value = "unknown"
+        return f"<{self.__class__.__name__}(value={value})>"
+
+
+#
+# Bounded semaphore
+#
+
+
+class BoundedSemaphore(Semaphore):
+    def __init__(self, value=1):
+        SemLock.__init__(self, SEMAPHORE, value, value)
+
+    def __repr__(self):
+        try:
+            value = self._semlock._get_value()
+        except Exception:
+            value = "unknown"
+        return (
+            f"<{self.__class__.__name__}(value={value}, "
+            f"maxvalue={self._semlock.maxvalue})>"
+        )
+
+
+#
+# Non-recursive lock
+#
+
+
+class Lock(SemLock):
+    def __init__(self):
+        super().__init__(SEMAPHORE, 1, 1)
+
+    def __repr__(self):
+        try:
+            if self._semlock._is_mine():
+                name = process.current_process().name
+                if threading.current_thread().name != "MainThread":
+                    name = f"{name}|{threading.current_thread().name}"
+            elif self._semlock._get_value() == 1:
+                name = "None"
+            elif self._semlock._count() > 0:
+                name = "SomeOtherThread"
+            else:
+                name = "SomeOtherProcess"
+        except Exception:
+            name = "unknown"
+        return f"<{self.__class__.__name__}(owner={name})>"
+
+
+#
+# Recursive lock
+#
+
+
+class RLock(SemLock):
+    def __init__(self):
+        super().__init__(RECURSIVE_MUTEX, 1, 1)
+
+    def __repr__(self):
+        try:
+            if self._semlock._is_mine():
+                name = process.current_process().name
+                if threading.current_thread().name != "MainThread":
+                    name = f"{name}|{threading.current_thread().name}"
+                count = self._semlock._count()
+            elif self._semlock._get_value() == 1:
+                name, count = "None", 0
+            elif self._semlock._count() > 0:
+                name, count = "SomeOtherThread", "nonzero"
+            else:
+                name, count = "SomeOtherProcess", "nonzero"
+        except Exception:
+            name, count = "unknown", "unknown"
+        return f"<{self.__class__.__name__}({name}, {count})>"
+
+
+#
+# Condition variable
+#
+
+
+class Condition:
+    def __init__(self, lock=None):
+        self._lock = lock or RLock()
+        self._sleeping_count = Semaphore(0)
+        self._woken_count = Semaphore(0)
+        self._wait_semaphore = Semaphore(0)
+        self._make_methods()
+
+    def __getstate__(self):
+        assert_spawning(self)
+        return (
+            self._lock,
+            self._sleeping_count,
+            self._woken_count,
+            self._wait_semaphore,
+        )
+
+    def __setstate__(self, state):
+        (
+            self._lock,
+            self._sleeping_count,
+            self._woken_count,
+            self._wait_semaphore,
+        ) = state
+        self._make_methods()
+
+    def __enter__(self):
+        return self._lock.__enter__()
+
+    def __exit__(self, *args):
+        return self._lock.__exit__(*args)
+
+    def _make_methods(self):
+        self.acquire = self._lock.acquire
+        self.release = self._lock.release
+
+    def __repr__(self):
+        try:
+            num_waiters = (
+                self._sleeping_count._semlock._get_value()
+                - self._woken_count._semlock._get_value()
+            )
+        except Exception:
+            num_waiters = "unknown"
+        return f"<{self.__class__.__name__}({self._lock}, {num_waiters})>"
+
+    def wait(self, timeout=None):
+        assert (
+            self._lock._semlock._is_mine()
+        ), "must acquire() condition before using wait()"
+
+        # indicate that this thread is going to sleep
+        self._sleeping_count.release()
+
+        # release lock
+        count = self._lock._semlock._count()
+        for _ in range(count):
+            self._lock.release()
+
+        try:
+            # wait for notification or timeout
+            return self._wait_semaphore.acquire(True, timeout)
+        finally:
+            # indicate that this thread has woken
+            self._woken_count.release()
+
+            # reacquire lock
+            for _ in range(count):
+                self._lock.acquire()
+
+    def notify(self):
+        assert self._lock._semlock._is_mine(), "lock is not owned"
+        assert not self._wait_semaphore.acquire(False)
+
+        # to take account of timeouts since last notify() we subtract
+        # woken_count from sleeping_count and rezero woken_count
+        while self._woken_count.acquire(False):
+            res = self._sleeping_count.acquire(False)
+            assert res
+
+        if self._sleeping_count.acquire(False):  # try grabbing a sleeper
+            self._wait_semaphore.release()  # wake up one sleeper
+            self._woken_count.acquire()  # wait for the sleeper to wake
+
+            # rezero _wait_semaphore in case a timeout just happened
+            self._wait_semaphore.acquire(False)
+
+    def notify_all(self):
+        assert self._lock._semlock._is_mine(), "lock is not owned"
+        assert not self._wait_semaphore.acquire(False)
+
+        # to take account of timeouts since last notify*() we subtract
+        # woken_count from sleeping_count and rezero woken_count
+        while self._woken_count.acquire(False):
+            res = self._sleeping_count.acquire(False)
+            assert res
+
+        sleepers = 0
+        while self._sleeping_count.acquire(False):
+            self._wait_semaphore.release()  # wake up one sleeper
+            sleepers += 1
+
+        if sleepers:
+            for _ in range(sleepers):
+                self._woken_count.acquire()  # wait for a sleeper to wake
+
+            # rezero wait_semaphore in case some timeouts just happened
+            while self._wait_semaphore.acquire(False):
+                pass
+
+    def wait_for(self, predicate, timeout=None):
+        result = predicate()
+        if result:
+            return result
+        if timeout is not None:
+            endtime = _time() + timeout
+        else:
+            endtime = None
+            waittime = None
+        while not result:
+            if endtime is not None:
+                waittime = endtime - _time()
+                if waittime <= 0:
+                    break
+            self.wait(waittime)
+            result = predicate()
+        return result
+
+
+#
+# Event
+#
+
+
+class Event:
+    def __init__(self):
+        self._cond = Condition(Lock())
+        self._flag = Semaphore(0)
+
+    def is_set(self):
+        with self._cond:
+            if self._flag.acquire(False):
+                self._flag.release()
+                return True
+            return False
+
+    def set(self):
+        with self._cond:
+            self._flag.acquire(False)
+            self._flag.release()
+            self._cond.notify_all()
+
+    def clear(self):
+        with self._cond:
+            self._flag.acquire(False)
+
+    def wait(self, timeout=None):
+        with self._cond:
+            if self._flag.acquire(False):
+                self._flag.release()
+            else:
+                self._cond.wait(timeout)
+
+            if self._flag.acquire(False):
+                self._flag.release()
+                return True
+            return False