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llmeval-env/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__ = '1.4.2'
+
+
+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")

llmeval-env/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()

llmeval-env/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

llmeval-env/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')

llmeval-env/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)

llmeval-env/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))

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

@@ -0,0 +1,265 @@
+"""
+Fast cryptographic hash of Python objects, with a special case for fast
+hashing of numpy arrays.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2009 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+import pickle
+import hashlib
+import sys
+import types
+import struct
+import io
+import decimal
+
+
+Pickler = pickle._Pickler
+
+
+class _ConsistentSet(object):
+    """ Class used to ensure the hash of Sets is preserved
+        whatever the order of its items.
+    """
+    def __init__(self, set_sequence):
+        # Forces order of elements in set to ensure consistent hash.
+        try:
+            # Trying first to order the set assuming the type of elements is
+            # consistent and orderable.
+            # This fails on python 3 when elements are unorderable
+            # but we keep it in a try as it's faster.
+            self._sequence = sorted(set_sequence)
+        except (TypeError, decimal.InvalidOperation):
+            # If elements are unorderable, sorting them using their hash.
+            # This is slower but works in any case.
+            self._sequence = sorted((hash(e) for e in set_sequence))
+
+
+class _MyHash(object):
+    """ Class used to hash objects that won't normally pickle """
+
+    def __init__(self, *args):
+        self.args = args
+
+
+class Hasher(Pickler):
+    """ A subclass of pickler, to do cryptographic hashing, rather than
+        pickling.
+    """
+
+    def __init__(self, hash_name='md5'):
+        self.stream = io.BytesIO()
+        # By default we want a pickle protocol that only changes with
+        # the major python version and not the minor one
+        protocol = 3
+        Pickler.__init__(self, self.stream, protocol=protocol)
+        # Initialise the hash obj
+        self._hash = hashlib.new(hash_name)
+
+    def hash(self, obj, return_digest=True):
+        try:
+            self.dump(obj)
+        except pickle.PicklingError as e:
+            e.args += ('PicklingError while hashing %r: %r' % (obj, e),)
+            raise
+        dumps = self.stream.getvalue()
+        self._hash.update(dumps)
+        if return_digest:
+            return self._hash.hexdigest()
+
+    def save(self, obj):
+        if isinstance(obj, (types.MethodType, type({}.pop))):
+            # the Pickler cannot pickle instance methods; here we decompose
+            # them into components that make them uniquely identifiable
+            if hasattr(obj, '__func__'):
+                func_name = obj.__func__.__name__
+            else:
+                func_name = obj.__name__
+            inst = obj.__self__
+            if type(inst) is type(pickle):
+                obj = _MyHash(func_name, inst.__name__)
+            elif inst is None:
+                # type(None) or type(module) do not pickle
+                obj = _MyHash(func_name, inst)
+            else:
+                cls = obj.__self__.__class__
+                obj = _MyHash(func_name, inst, cls)
+        Pickler.save(self, obj)
+
+    def memoize(self, obj):
+        # We want hashing to be sensitive to value instead of reference.
+        # For example we want ['aa', 'aa'] and ['aa', 'aaZ'[:2]]
+        # to hash to the same value and that's why we disable memoization
+        # for strings
+        if isinstance(obj, (bytes, str)):
+            return
+        Pickler.memoize(self, obj)
+
+    # The dispatch table of the pickler is not accessible in Python
+    # 3, as these lines are only bugware for IPython, we skip them.
+    def save_global(self, obj, name=None, pack=struct.pack):
+        # We have to override this method in order to deal with objects
+        # defined interactively in IPython that are not injected in
+        # __main__
+        kwargs = dict(name=name, pack=pack)
+        del kwargs['pack']
+        try:
+            Pickler.save_global(self, obj, **kwargs)
+        except pickle.PicklingError:
+            Pickler.save_global(self, obj, **kwargs)
+            module = getattr(obj, "__module__", None)
+            if module == '__main__':
+                my_name = name
+                if my_name is None:
+                    my_name = obj.__name__
+                mod = sys.modules[module]
+                if not hasattr(mod, my_name):
+                    # IPython doesn't inject the variables define
+                    # interactively in __main__
+                    setattr(mod, my_name, obj)
+
+    dispatch = Pickler.dispatch.copy()
+    # builtin
+    dispatch[type(len)] = save_global
+    # type
+    dispatch[type(object)] = save_global
+    # classobj
+    dispatch[type(Pickler)] = save_global
+    # function
+    dispatch[type(pickle.dump)] = save_global
+
+    def _batch_setitems(self, items):
+        # forces order of keys in dict to ensure consistent hash.
+        try:
+            # Trying first to compare dict assuming the type of keys is
+            # consistent and orderable.
+            # This fails on python 3 when keys are unorderable
+            # but we keep it in a try as it's faster.
+            Pickler._batch_setitems(self, iter(sorted(items)))
+        except TypeError:
+            # If keys are unorderable, sorting them using their hash. This is
+            # slower but works in any case.
+            Pickler._batch_setitems(self, iter(sorted((hash(k), v)
+                                                      for k, v in items)))
+
+    def save_set(self, set_items):
+        # forces order of items in Set to ensure consistent hash
+        Pickler.save(self, _ConsistentSet(set_items))
+
+    dispatch[type(set())] = save_set
+
+
+class NumpyHasher(Hasher):
+    """ Special case the hasher for when numpy is loaded.
+    """
+
+    def __init__(self, hash_name='md5', coerce_mmap=False):
+        """
+            Parameters
+            ----------
+            hash_name: string
+                The hash algorithm to be used
+            coerce_mmap: boolean
+                Make no difference between np.memmap and np.ndarray
+                objects.
+        """
+        self.coerce_mmap = coerce_mmap
+        Hasher.__init__(self, hash_name=hash_name)
+        # delayed import of numpy, to avoid tight coupling
+        import numpy as np
+        self.np = np
+        if hasattr(np, 'getbuffer'):
+            self._getbuffer = np.getbuffer
+        else:
+            self._getbuffer = memoryview
+
+    def save(self, obj):
+        """ Subclass the save method, to hash ndarray subclass, rather
+            than pickling them. Off course, this is a total abuse of
+            the Pickler class.
+        """
+        if isinstance(obj, self.np.ndarray) and not obj.dtype.hasobject:
+            # Compute a hash of the object
+            # The update function of the hash requires a c_contiguous buffer.
+            if obj.shape == ():
+                # 0d arrays need to be flattened because viewing them as bytes
+                # raises a ValueError exception.
+                obj_c_contiguous = obj.flatten()
+            elif obj.flags.c_contiguous:
+                obj_c_contiguous = obj
+            elif obj.flags.f_contiguous:
+                obj_c_contiguous = obj.T
+            else:
+                # Cater for non-single-segment arrays: this creates a
+                # copy, and thus alleviates this issue.
+                # XXX: There might be a more efficient way of doing this
+                obj_c_contiguous = obj.flatten()
+
+            # memoryview is not supported for some dtypes, e.g. datetime64, see
+            # https://github.com/numpy/numpy/issues/4983. The
+            # workaround is to view the array as bytes before
+            # taking the memoryview.
+            self._hash.update(
+                self._getbuffer(obj_c_contiguous.view(self.np.uint8)))
+
+            # We store the class, to be able to distinguish between
+            # Objects with the same binary content, but different
+            # classes.
+            if self.coerce_mmap and isinstance(obj, self.np.memmap):
+                # We don't make the difference between memmap and
+                # normal ndarrays, to be able to reload previously
+                # computed results with memmap.
+                klass = self.np.ndarray
+            else:
+                klass = obj.__class__
+            # We also return the dtype and the shape, to distinguish
+            # different views on the same data with different dtypes.
+
+            # The object will be pickled by the pickler hashed at the end.
+            obj = (klass, ('HASHED', obj.dtype, obj.shape, obj.strides))
+        elif isinstance(obj, self.np.dtype):
+            # numpy.dtype consistent hashing is tricky to get right. This comes
+            # from the fact that atomic np.dtype objects are interned:
+            # ``np.dtype('f4') is np.dtype('f4')``. The situation is
+            # complicated by the fact that this interning does not resist a
+            # simple pickle.load/dump roundtrip:
+            # ``pickle.loads(pickle.dumps(np.dtype('f4'))) is not
+            # np.dtype('f4') Because pickle relies on memoization during
+            # pickling, it is easy to
+            # produce different hashes for seemingly identical objects, such as
+            # ``[np.dtype('f4'), np.dtype('f4')]``
+            # and ``[np.dtype('f4'), pickle.loads(pickle.dumps('f4'))]``.
+            # To prevent memoization from interfering with hashing, we isolate
+            # the serialization (and thus the pickle memoization) of each dtype
+            # using each time a different ``pickle.dumps`` call unrelated to
+            # the current Hasher instance.
+            self._hash.update("_HASHED_DTYPE".encode('utf-8'))
+            self._hash.update(pickle.dumps(obj))
+            return
+        Hasher.save(self, obj)
+
+
+def hash(obj, hash_name='md5', coerce_mmap=False):
+    """ Quick calculation of a hash to identify uniquely Python objects
+        containing numpy arrays.
+
+        Parameters
+        ----------
+        hash_name: 'md5' or 'sha1'
+            Hashing algorithm used. sha1 is supposedly safer, but md5 is
+            faster.
+        coerce_mmap: boolean
+            Make no difference between np.memmap and np.ndarray
+    """
+    valid_hash_names = ('md5', 'sha1')
+    if hash_name not in valid_hash_names:
+        raise ValueError("Valid options for 'hash_name' are {}. "
+                         "Got hash_name={!r} instead."
+                         .format(valid_hash_names, hash_name))
+    if 'numpy' in sys.modules:
+        hasher = NumpyHasher(hash_name=hash_name, coerce_mmap=coerce_mmap)
+    else:
+        hasher = Hasher(hash_name=hash_name)
+    return hasher.hash(obj)

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

@@ -0,0 +1,244 @@
+"""Numpy pickle compatibility functions."""
+
+import pickle
+import os
+import zlib
+import inspect
+
+from io import BytesIO
+
+from .numpy_pickle_utils import _ZFILE_PREFIX
+from .numpy_pickle_utils import Unpickler
+from .numpy_pickle_utils import _ensure_native_byte_order
+
+
+def hex_str(an_int):
+    """Convert an int to an hexadecimal string."""
+    return '{:#x}'.format(an_int)
+
+
+def asbytes(s):
+    if isinstance(s, bytes):
+        return s
+    return s.encode('latin1')
+
+
+_MAX_LEN = len(hex_str(2 ** 64))
+_CHUNK_SIZE = 64 * 1024
+
+
+def read_zfile(file_handle):
+    """Read the z-file and return the content as a string.
+
+    Z-files are raw data compressed with zlib used internally by joblib
+    for persistence. Backward compatibility is not guaranteed. Do not
+    use for external purposes.
+    """
+    file_handle.seek(0)
+    header_length = len(_ZFILE_PREFIX) + _MAX_LEN
+    length = file_handle.read(header_length)
+    length = length[len(_ZFILE_PREFIX):]
+    length = int(length, 16)
+
+    # With python2 and joblib version <= 0.8.4 compressed pickle header is one
+    # character wider so we need to ignore an additional space if present.
+    # Note: the first byte of the zlib data is guaranteed not to be a
+    # space according to
+    # https://tools.ietf.org/html/rfc6713#section-2.1
+    next_byte = file_handle.read(1)
+    if next_byte != b' ':
+        # The zlib compressed data has started and we need to go back
+        # one byte
+        file_handle.seek(header_length)
+
+    # We use the known length of the data to tell Zlib the size of the
+    # buffer to allocate.
+    data = zlib.decompress(file_handle.read(), 15, length)
+    assert len(data) == length, (
+        "Incorrect data length while decompressing %s."
+        "The file could be corrupted." % file_handle)
+    return data
+
+
+def write_zfile(file_handle, data, compress=1):
+    """Write the data in the given file as a Z-file.
+
+    Z-files are raw data compressed with zlib used internally by joblib
+    for persistence. Backward compatibility is not guaranteed. Do not
+    use for external purposes.
+    """
+    file_handle.write(_ZFILE_PREFIX)
+    length = hex_str(len(data))
+    # Store the length of the data
+    file_handle.write(asbytes(length.ljust(_MAX_LEN)))
+    file_handle.write(zlib.compress(asbytes(data), compress))
+
+###############################################################################
+# Utility objects for persistence.
+
+
+class NDArrayWrapper(object):
+    """An object to be persisted instead of numpy arrays.
+
+    The only thing this object does, is to carry the filename in which
+    the array has been persisted, and the array subclass.
+    """
+
+    def __init__(self, filename, subclass, allow_mmap=True):
+        """Constructor. Store the useful information for later."""
+        self.filename = filename
+        self.subclass = subclass
+        self.allow_mmap = allow_mmap
+
+    def read(self, unpickler):
+        """Reconstruct the array."""
+        filename = os.path.join(unpickler._dirname, self.filename)
+        # Load the array from the disk
+        # use getattr instead of self.allow_mmap to ensure backward compat
+        # with NDArrayWrapper instances pickled with joblib < 0.9.0
+        allow_mmap = getattr(self, 'allow_mmap', True)
+        kwargs = {}
+        if allow_mmap:
+            kwargs['mmap_mode'] = unpickler.mmap_mode
+        if "allow_pickle" in inspect.signature(unpickler.np.load).parameters:
+            # Required in numpy 1.16.3 and later to aknowledge the security
+            # risk.
+            kwargs["allow_pickle"] = True
+        array = unpickler.np.load(filename, **kwargs)
+
+        # Detect byte order mismatch and swap as needed.
+        array = _ensure_native_byte_order(array)
+
+        # Reconstruct subclasses. This does not work with old
+        # versions of numpy
+        if (hasattr(array, '__array_prepare__') and
+            self.subclass not in (unpickler.np.ndarray,
+                                  unpickler.np.memmap)):
+            # We need to reconstruct another subclass
+            new_array = unpickler.np.core.multiarray._reconstruct(
+                self.subclass, (0,), 'b')
+            return new_array.__array_prepare__(array)
+        else:
+            return array
+
+
+class ZNDArrayWrapper(NDArrayWrapper):
+    """An object to be persisted instead of numpy arrays.
+
+    This object store the Zfile filename in which
+    the data array has been persisted, and the meta information to
+    retrieve it.
+    The reason that we store the raw buffer data of the array and
+    the meta information, rather than array representation routine
+    (tobytes) is that it enables us to use completely the strided
+    model to avoid memory copies (a and a.T store as fast). In
+    addition saving the heavy information separately can avoid
+    creating large temporary buffers when unpickling data with
+    large arrays.
+    """
+
+    def __init__(self, filename, init_args, state):
+        """Constructor. Store the useful information for later."""
+        self.filename = filename
+        self.state = state
+        self.init_args = init_args
+
+    def read(self, unpickler):
+        """Reconstruct the array from the meta-information and the z-file."""
+        # Here we a simply reproducing the unpickling mechanism for numpy
+        # arrays
+        filename = os.path.join(unpickler._dirname, self.filename)
+        array = unpickler.np.core.multiarray._reconstruct(*self.init_args)
+        with open(filename, 'rb') as f:
+            data = read_zfile(f)
+        state = self.state + (data,)
+        array.__setstate__(state)
+        return array
+
+
+class ZipNumpyUnpickler(Unpickler):
+    """A subclass of the Unpickler to unpickle our numpy pickles."""
+
+    dispatch = Unpickler.dispatch.copy()
+
+    def __init__(self, filename, file_handle, mmap_mode=None):
+        """Constructor."""
+        self._filename = os.path.basename(filename)
+        self._dirname = os.path.dirname(filename)
+        self.mmap_mode = mmap_mode
+        self.file_handle = self._open_pickle(file_handle)
+        Unpickler.__init__(self, self.file_handle)
+        try:
+            import numpy as np
+        except ImportError:
+            np = None
+        self.np = np
+
+    def _open_pickle(self, file_handle):
+        return BytesIO(read_zfile(file_handle))
+
+    def load_build(self):
+        """Set the state of a newly created object.
+
+        We capture it to replace our place-holder objects,
+        NDArrayWrapper, by the array we are interested in. We
+        replace them directly in the stack of pickler.
+        """
+        Unpickler.load_build(self)
+        if isinstance(self.stack[-1], NDArrayWrapper):
+            if self.np is None:
+                raise ImportError("Trying to unpickle an ndarray, "
+                                  "but numpy didn't import correctly")
+            nd_array_wrapper = self.stack.pop()
+            array = nd_array_wrapper.read(self)
+            self.stack.append(array)
+
+    dispatch[pickle.BUILD[0]] = load_build
+
+
+def load_compatibility(filename):
+    """Reconstruct a Python object from a file persisted with joblib.dump.
+
+    This function ensures the compatibility with joblib old persistence format
+    (<= 0.9.3).
+
+    Parameters
+    ----------
+    filename: string
+        The name of the file from which to load the object
+
+    Returns
+    -------
+    result: any Python object
+        The object stored in the file.
+
+    See Also
+    --------
+    joblib.dump : function to save an object
+
+    Notes
+    -----
+
+    This function can load numpy array files saved separately during the
+    dump.
+    """
+    with open(filename, 'rb') as file_handle:
+        # We are careful to open the file handle early and keep it open to
+        # avoid race-conditions on renames. That said, if data is stored in
+        # companion files, moving the directory will create a race when
+        # joblib tries to access the companion files.
+        unpickler = ZipNumpyUnpickler(filename, file_handle=file_handle)
+        try:
+            obj = unpickler.load()
+        except UnicodeDecodeError as exc:
+            # More user-friendly error message
+            new_exc = ValueError(
+                'You may be trying to read with '
+                'python 3 a joblib pickle generated with python 2. '
+                'This feature is not supported by joblib.')
+            new_exc.__cause__ = exc
+            raise new_exc
+        finally:
+            if hasattr(unpickler, 'file_handle'):
+                unpickler.file_handle.close()
+        return obj

llmeval-env/lib/python3.10/site-packages/joblib/test/data/create_numpy_pickle.py

@@ -0,0 +1,95 @@
+"""
+This script is used to generate test data for joblib/test/test_numpy_pickle.py
+"""
+
+import sys
+import re
+
+# pytest needs to be able to import this module even when numpy is
+# not installed
+try:
+    import numpy as np
+except ImportError:
+    np = None
+
+import joblib
+
+
+def get_joblib_version(joblib_version=joblib.__version__):
+    """Normalize joblib version by removing suffix.
+
+    >>> get_joblib_version('0.8.4')
+    '0.8.4'
+    >>> get_joblib_version('0.8.4b1')
+    '0.8.4'
+    >>> get_joblib_version('0.9.dev0')
+    '0.9'
+    """
+    matches = [re.match(r'(\d+).*', each)
+               for each in joblib_version.split('.')]
+    return '.'.join([m.group(1) for m in matches if m is not None])
+
+
+def write_test_pickle(to_pickle, args):
+    kwargs = {}
+    compress = args.compress
+    method = args.method
+    joblib_version = get_joblib_version()
+    py_version = '{0[0]}{0[1]}'.format(sys.version_info)
+    numpy_version = ''.join(np.__version__.split('.')[:2])
+
+    # The game here is to generate the right filename according to the options.
+    body = '_compressed' if (compress and method == 'zlib') else ''
+    if compress:
+        if method == 'zlib':
+            kwargs['compress'] = True
+            extension = '.gz'
+        else:
+            kwargs['compress'] = (method, 3)
+            extension = '.pkl.{}'.format(method)
+        if args.cache_size:
+            kwargs['cache_size'] = 0
+            body += '_cache_size'
+    else:
+        extension = '.pkl'
+
+    pickle_filename = 'joblib_{}{}_pickle_py{}_np{}{}'.format(
+        joblib_version, body, py_version, numpy_version, extension)
+
+    try:
+        joblib.dump(to_pickle, pickle_filename, **kwargs)
+    except Exception as e:
+        # With old python version (=< 3.3.), we can arrive there when
+        # dumping compressed pickle with LzmaFile.
+        print("Error: cannot generate file '{}' with arguments '{}'. "
+              "Error was: {}".format(pickle_filename, kwargs, e))
+    else:
+        print("File '{}' generated successfully.".format(pickle_filename))
+
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser(description="Joblib pickle data "
+                                                 "generator.")
+    parser.add_argument('--cache_size', action="store_true",
+                        help="Force creation of companion numpy "
+                             "files for pickled arrays.")
+    parser.add_argument('--compress', action="store_true",
+                        help="Generate compress pickles.")
+    parser.add_argument('--method', type=str, default='zlib',
+                        choices=['zlib', 'gzip', 'bz2', 'xz', 'lzma', 'lz4'],
+                        help="Set compression method.")
+    # We need to be specific about dtypes in particular endianness
+    # because the pickles can be generated on one architecture and
+    # the tests run on another one. See
+    # https://github.com/joblib/joblib/issues/279.
+    to_pickle = [np.arange(5, dtype=np.dtype('<i8')),
+                 np.arange(5, dtype=np.dtype('<f8')),
+                 np.array([1, 'abc', {'a': 1, 'b': 2}], dtype='O'),
+                 # all possible bytes as a byte string
+                 np.arange(256, dtype=np.uint8).tobytes(),
+                 np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
+                 # unicode string with non-ascii chars
+                 u"C'est l'\xe9t\xe9 !"]
+
+    write_test_pickle(to_pickle, parser.parse_args())

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

@@ -0,0 +1,35 @@
+import mmap
+
+from joblib.backports import make_memmap, concurrency_safe_rename
+from joblib.test.common import with_numpy
+from joblib.testing import parametrize
+from joblib import Parallel, delayed
+
+
+@with_numpy
+def test_memmap(tmpdir):
+    fname = tmpdir.join('test.mmap').strpath
+    size = 5 * mmap.ALLOCATIONGRANULARITY
+    offset = mmap.ALLOCATIONGRANULARITY + 1
+    memmap_obj = make_memmap(fname, shape=size, mode='w+', offset=offset)
+    assert memmap_obj.offset == offset
+
+
+@parametrize('dst_content', [None, 'dst content'])
+@parametrize('backend', [None, 'threading'])
+def test_concurrency_safe_rename(tmpdir, dst_content, backend):
+    src_paths = [tmpdir.join('src_%d' % i) for i in range(4)]
+    for src_path in src_paths:
+        src_path.write('src content')
+    dst_path = tmpdir.join('dst')
+    if dst_content is not None:
+        dst_path.write(dst_content)
+
+    Parallel(n_jobs=4, backend=backend)(
+        delayed(concurrency_safe_rename)(src_path.strpath, dst_path.strpath)
+        for src_path in src_paths
+    )
+    assert dst_path.exists()
+    assert dst_path.read() == 'src content'
+    for src_path in src_paths:
+        assert not src_path.exists()

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

@@ -0,0 +1,27 @@
+"""
+Test that our implementation of wrap_non_picklable_objects mimics
+properly the loky implementation.
+"""
+
+from .._cloudpickle_wrapper import wrap_non_picklable_objects
+from .._cloudpickle_wrapper import _my_wrap_non_picklable_objects
+
+
+def a_function(x):
+    return x
+
+
+class AClass(object):
+
+    def __call__(self, x):
+        return x
+
+
+def test_wrap_non_picklable_objects():
+    # Mostly a smoke test: test that we can use callable in the same way
+    # with both our implementation of wrap_non_picklable_objects and the
+    # upstream one
+    for obj in (a_function, AClass()):
+        wrapped_obj = wrap_non_picklable_objects(obj)
+        my_wrapped_obj = _my_wrap_non_picklable_objects(obj)
+        assert wrapped_obj(1) == my_wrapped_obj(1)

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

@@ -0,0 +1,151 @@
+import os
+
+from joblib.parallel import parallel_config
+from joblib.parallel import parallel_backend
+from joblib.parallel import Parallel, delayed
+
+from joblib.parallel import BACKENDS
+from joblib.parallel import DEFAULT_BACKEND
+from joblib.parallel import EXTERNAL_BACKENDS
+
+from joblib._parallel_backends import LokyBackend
+from joblib._parallel_backends import ThreadingBackend
+from joblib._parallel_backends import MultiprocessingBackend
+
+from joblib.testing import parametrize, raises
+from joblib.test.common import np, with_numpy
+from joblib.test.common import with_multiprocessing
+from joblib.test.test_parallel import check_memmap
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_global_parallel_backend(context):
+    default = Parallel()._backend
+
+    pb = context('threading')
+    try:
+        assert isinstance(Parallel()._backend, ThreadingBackend)
+    finally:
+        pb.unregister()
+    assert type(Parallel()._backend) is type(default)
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_external_backends(context):
+    def register_foo():
+        BACKENDS['foo'] = ThreadingBackend
+
+    EXTERNAL_BACKENDS['foo'] = register_foo
+    try:
+        with context('foo'):
+            assert isinstance(Parallel()._backend, ThreadingBackend)
+    finally:
+        del EXTERNAL_BACKENDS['foo']
+
+
+@with_numpy
+@with_multiprocessing
+def test_parallel_config_no_backend(tmpdir):
+    # Check that parallel_config allows to change the config
+    # even if no backend is set.
+    with parallel_config(n_jobs=2, max_nbytes=1, temp_folder=tmpdir):
+        with Parallel(prefer="processes") as p:
+            assert isinstance(p._backend, LokyBackend)
+            assert p.n_jobs == 2
+
+            # Checks that memmapping is enabled
+            p(delayed(check_memmap)(a) for a in [np.random.random(10)] * 2)
+            assert len(os.listdir(tmpdir)) > 0
+
+
+@with_numpy
+@with_multiprocessing
+def test_parallel_config_params_explicit_set(tmpdir):
+    with parallel_config(n_jobs=3, max_nbytes=1, temp_folder=tmpdir):
+        with Parallel(n_jobs=2, prefer="processes", max_nbytes='1M') as p:
+            assert isinstance(p._backend, LokyBackend)
+            assert p.n_jobs == 2
+
+            # Checks that memmapping is disabled
+            with raises(TypeError, match="Expected np.memmap instance"):
+                p(delayed(check_memmap)(a) for a in [np.random.random(10)] * 2)
+
+
+@parametrize("param", ["prefer", "require"])
+def test_parallel_config_bad_params(param):
+    # Check that an error is raised when setting a wrong backend
+    # hint or constraint
+    with raises(ValueError, match=f"{param}=wrong is not a valid"):
+        with parallel_config(**{param: "wrong"}):
+            Parallel()
+
+
+def test_parallel_config_constructor_params():
+    # Check that an error is raised when backend is None
+    # but backend constructor params are given
+    with raises(ValueError, match="only supported when backend is not None"):
+        with parallel_config(inner_max_num_threads=1):
+            pass
+
+    with raises(ValueError, match="only supported when backend is not None"):
+        with parallel_config(backend_param=1):
+            pass
+
+
+def test_parallel_config_nested():
+    # Check that nested configuration retrieves the info from the
+    # parent config and do not reset them.
+
+    with parallel_config(n_jobs=2):
+        p = Parallel()
+        assert isinstance(p._backend, BACKENDS[DEFAULT_BACKEND])
+        assert p.n_jobs == 2
+
+    with parallel_config(backend='threading'):
+        with parallel_config(n_jobs=2):
+            p = Parallel()
+            assert isinstance(p._backend, ThreadingBackend)
+            assert p.n_jobs == 2
+
+    with parallel_config(verbose=100):
+        with parallel_config(n_jobs=2):
+            p = Parallel()
+            assert p.verbose == 100
+            assert p.n_jobs == 2
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('backend', ['multiprocessing', 'threading',
+                         MultiprocessingBackend(), ThreadingBackend()])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_threadpool_limitation_in_child_context_error(context, backend):
+
+    with raises(AssertionError, match=r"does not acc.*inner_max_num_threads"):
+        context(backend, inner_max_num_threads=1)
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_parallel_n_jobs_none(context):
+    # Check that n_jobs=None is interpreted as "unset" in Parallel
+    # non regression test for #1473
+    with context(backend="threading", n_jobs=2):
+        with Parallel(n_jobs=None) as p:
+            assert p.n_jobs == 2
+
+    with context(backend="threading"):
+        default_n_jobs = Parallel().n_jobs
+        with Parallel(n_jobs=None) as p:
+            assert p.n_jobs == default_n_jobs
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_parallel_config_n_jobs_none(context):
+    # Check that n_jobs=None is interpreted as "explicitly set" in
+    # parallel_(config/backend)
+    # non regression test for #1473
+    with context(backend="threading", n_jobs=2):
+        with context(backend="threading", n_jobs=None):
+            # n_jobs=None resets n_jobs to backend's default
+            with Parallel() as p:
+                assert p.n_jobs == 1

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

@@ -0,0 +1,499 @@
+from __future__ import print_function, division, absolute_import
+import os
+import warnings
+
+import pytest
+from random import random
+from uuid import uuid4
+from time import sleep
+
+from .. import Parallel, delayed, parallel_config
+from ..parallel import ThreadingBackend, AutoBatchingMixin
+from .._dask import DaskDistributedBackend
+
+distributed = pytest.importorskip('distributed')
+dask = pytest.importorskip('dask')
+
+# These imports need to be after the pytest.importorskip hence the noqa: E402
+from distributed import Client, LocalCluster, get_client  # noqa: E402
+from distributed.metrics import time  # noqa: E402
+# Note: pytest requires to manually import all fixtures used in the test
+# and their dependencies.
+from distributed.utils_test import cluster, inc, cleanup  # noqa: E402, F401
+
+
+def noop(*args, **kwargs):
+    pass
+
+
+def slow_raise_value_error(condition, duration=0.05):
+    sleep(duration)
+    if condition:
+        raise ValueError("condition evaluated to True")
+
+
+def count_events(event_name, client):
+    worker_events = client.run(lambda dask_worker: dask_worker.log)
+    event_counts = {}
+    for w, events in worker_events.items():
+        event_counts[w] = len([event for event in list(events)
+                               if event[1] == event_name])
+    return event_counts
+
+
+def test_simple(loop):
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                seq = Parallel()(delayed(inc)(i) for i in range(10))
+                assert seq == [inc(i) for i in range(10)]
+
+                with pytest.raises(ValueError):
+                    Parallel()(delayed(slow_raise_value_error)(i == 3)
+                               for i in range(10))
+
+                seq = Parallel()(delayed(inc)(i) for i in range(10))
+                assert seq == [inc(i) for i in range(10)]
+
+
+def test_dask_backend_uses_autobatching(loop):
+    assert (DaskDistributedBackend.compute_batch_size
+            is AutoBatchingMixin.compute_batch_size)
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                with Parallel() as parallel:
+                    # The backend should be initialized with a default
+                    # batch size of 1:
+                    backend = parallel._backend
+                    assert isinstance(backend, DaskDistributedBackend)
+                    assert backend.parallel is parallel
+                    assert backend._effective_batch_size == 1
+
+                    # Launch many short tasks that should trigger
+                    # auto-batching:
+                    parallel(
+                        delayed(lambda: None)()
+                        for _ in range(int(1e4))
+                    )
+                    assert backend._effective_batch_size > 10
+
+
+def random2():
+    return random()
+
+
+def test_dont_assume_function_purity(loop):
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                x, y = Parallel()(delayed(random2)() for i in range(2))
+                assert x != y
+
+
+@pytest.mark.parametrize("mixed", [True, False])
+def test_dask_funcname(loop, mixed):
+    from joblib._dask import Batch
+    if not mixed:
+        tasks = [delayed(inc)(i) for i in range(4)]
+        batch_repr = 'batch_of_inc_4_calls'
+    else:
+        tasks = [
+            delayed(abs)(i) if i % 2 else delayed(inc)(i) for i in range(4)
+        ]
+        batch_repr = 'mixed_batch_of_inc_4_calls'
+
+    assert repr(Batch(tasks)) == batch_repr
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:
+            with parallel_config(backend='dask'):
+                _ = Parallel(batch_size=2, pre_dispatch='all')(tasks)
+
+            def f(dask_scheduler):
+                return list(dask_scheduler.transition_log)
+            batch_repr = batch_repr.replace('4', '2')
+            log = client.run_on_scheduler(f)
+            assert all('batch_of_inc' in tup[0] for tup in log)
+
+
+def test_no_undesired_distributed_cache_hit():
+    # Dask has a pickle cache for callables that are called many times. Because
+    # the dask backends used to wrap both the functions and the arguments
+    # under instances of the Batch callable class this caching mechanism could
+    # lead to bugs as described in: https://github.com/joblib/joblib/pull/1055
+    # The joblib-dask backend has been refactored to avoid bundling the
+    # arguments as an attribute of the Batch instance to avoid this problem.
+    # This test serves as non-regression problem.
+
+    # Use a large number of input arguments to give the AutoBatchingMixin
+    # enough tasks to kick-in.
+    lists = [[] for _ in range(100)]
+    np = pytest.importorskip('numpy')
+    X = np.arange(int(1e6))
+
+    def isolated_operation(list_, data=None):
+        if data is not None:
+            np.testing.assert_array_equal(data, X)
+        list_.append(uuid4().hex)
+        return list_
+
+    cluster = LocalCluster(n_workers=1, threads_per_worker=2)
+    client = Client(cluster)
+    try:
+        with parallel_config(backend='dask'):
+            # dispatches joblib.parallel.BatchedCalls
+            res = Parallel()(
+                delayed(isolated_operation)(list_) for list_ in lists
+            )
+
+        # The original arguments should not have been mutated as the mutation
+        # happens in the dask worker process.
+        assert lists == [[] for _ in range(100)]
+
+        # Here we did not pass any large numpy array as argument to
+        # isolated_operation so no scattering event should happen under the
+        # hood.
+        counts = count_events('receive-from-scatter', client)
+        assert sum(counts.values()) == 0
+        assert all([len(r) == 1 for r in res])
+
+        with parallel_config(backend='dask'):
+            # Append a large array which will be scattered by dask, and
+            # dispatch joblib._dask.Batch
+            res = Parallel()(
+                delayed(isolated_operation)(list_, data=X) for list_ in lists
+            )
+
+        # This time, auto-scattering should have kicked it.
+        counts = count_events('receive-from-scatter', client)
+        assert sum(counts.values()) > 0
+        assert all([len(r) == 1 for r in res])
+    finally:
+        client.close(timeout=30)
+        cluster.close(timeout=30)
+
+
+class CountSerialized(object):
+    def __init__(self, x):
+        self.x = x
+        self.count = 0
+
+    def __add__(self, other):
+        return self.x + getattr(other, 'x', other)
+
+    __radd__ = __add__
+
+    def __reduce__(self):
+        self.count += 1
+        return (CountSerialized, (self.x,))
+
+
+def add5(a, b, c, d=0, e=0):
+    return a + b + c + d + e
+
+
+def test_manual_scatter(loop):
+    x = CountSerialized(1)
+    y = CountSerialized(2)
+    z = CountSerialized(3)
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask', scatter=[x, y]):
+                f = delayed(add5)
+                tasks = [f(x, y, z, d=4, e=5),
+                         f(x, z, y, d=5, e=4),
+                         f(y, x, z, d=x, e=5),
+                         f(z, z, x, d=z, e=y)]
+                expected = [func(*args, **kwargs)
+                            for func, args, kwargs in tasks]
+                results = Parallel()(tasks)
+
+            # Scatter must take a list/tuple
+            with pytest.raises(TypeError):
+                with parallel_config(backend='dask', loop=loop, scatter=1):
+                    pass
+
+    assert results == expected
+
+    # Scattered variables only serialized once
+    assert x.count == 1
+    assert y.count == 1
+    # Depending on the version of distributed, the unscattered z variable
+    # is either pickled 4 or 6 times, possibly because of the memoization
+    # of objects that appear several times in the arguments of a delayed
+    # task.
+    assert z.count in (4, 6)
+
+
+# When the same IOLoop is used for multiple clients in a row, use
+# loop_in_thread instead of loop to prevent the Client from closing it.  See
+# dask/distributed #4112
+def test_auto_scatter(loop_in_thread):
+    np = pytest.importorskip('numpy')
+    data1 = np.ones(int(1e4), dtype=np.uint8)
+    data2 = np.ones(int(1e4), dtype=np.uint8)
+    data_to_process = ([data1] * 3) + ([data2] * 3)
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop_in_thread) as client:
+            with parallel_config(backend='dask'):
+                # Passing the same data as arg and kwarg triggers a single
+                # scatter operation whose result is reused.
+                Parallel()(delayed(noop)(data, data, i, opt=data)
+                           for i, data in enumerate(data_to_process))
+            # By default large array are automatically scattered with
+            # broadcast=1 which means that one worker must directly receive
+            # the data from the scatter operation once.
+            counts = count_events('receive-from-scatter', client)
+            assert counts[a['address']] + counts[b['address']] == 2
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop_in_thread) as client:
+            with parallel_config(backend='dask'):
+                Parallel()(delayed(noop)(data1[:3], i) for i in range(5))
+            # Small arrays are passed within the task definition without going
+            # through a scatter operation.
+            counts = count_events('receive-from-scatter', client)
+            assert counts[a['address']] == 0
+            assert counts[b['address']] == 0
+
+
+@pytest.mark.parametrize("retry_no", list(range(2)))
+def test_nested_scatter(loop, retry_no):
+
+    np = pytest.importorskip('numpy')
+
+    NUM_INNER_TASKS = 10
+    NUM_OUTER_TASKS = 10
+
+    def my_sum(x, i, j):
+        return np.sum(x)
+
+    def outer_function_joblib(array, i):
+        client = get_client()  # noqa
+        with parallel_config(backend="dask"):
+            results = Parallel()(
+                delayed(my_sum)(array[j:], i, j) for j in range(
+                    NUM_INNER_TASKS)
+            )
+        return sum(results)
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as _:
+            with parallel_config(backend="dask"):
+                my_array = np.ones(10000)
+                _ = Parallel()(
+                    delayed(outer_function_joblib)(
+                        my_array[i:], i) for i in range(NUM_OUTER_TASKS)
+                )
+
+
+def test_nested_backend_context_manager(loop_in_thread):
+    def get_nested_pids():
+        pids = set(Parallel(n_jobs=2)(delayed(os.getpid)() for _ in range(2)))
+        pids |= set(Parallel(n_jobs=2)(delayed(os.getpid)() for _ in range(2)))
+        return pids
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop_in_thread) as client:
+            with parallel_config(backend='dask'):
+                pid_groups = Parallel(n_jobs=2)(
+                    delayed(get_nested_pids)()
+                    for _ in range(10)
+                )
+                for pid_group in pid_groups:
+                    assert len(set(pid_group)) <= 2
+
+        # No deadlocks
+        with Client(s['address'], loop=loop_in_thread) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                pid_groups = Parallel(n_jobs=2)(
+                    delayed(get_nested_pids)()
+                    for _ in range(10)
+                )
+                for pid_group in pid_groups:
+                    assert len(set(pid_group)) <= 2
+
+
+def test_nested_backend_context_manager_implicit_n_jobs(loop):
+    # Check that Parallel with no explicit n_jobs value automatically selects
+    # all the dask workers, including in nested calls.
+
+    def _backend_type(p):
+        return p._backend.__class__.__name__
+
+    def get_nested_implicit_n_jobs():
+        with Parallel() as p:
+            return _backend_type(p), p.n_jobs
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                with Parallel() as p:
+                    assert _backend_type(p) == "DaskDistributedBackend"
+                    assert p.n_jobs == -1
+                    all_nested_n_jobs = p(
+                        delayed(get_nested_implicit_n_jobs)()
+                        for _ in range(2)
+                    )
+                for backend_type, nested_n_jobs in all_nested_n_jobs:
+                    assert backend_type == "DaskDistributedBackend"
+                    assert nested_n_jobs == -1
+
+
+def test_errors(loop):
+    with pytest.raises(ValueError) as info:
+        with parallel_config(backend='dask'):
+            pass
+
+    assert "create a dask client" in str(info.value).lower()
+
+
+def test_correct_nested_backend(loop):
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            # No requirement, should be us
+            with parallel_config(backend='dask'):
+                result = Parallel(n_jobs=2)(
+                    delayed(outer)(nested_require=None) for _ in range(1))
+                assert isinstance(result[0][0][0], DaskDistributedBackend)
+
+            # Require threads, should be threading
+            with parallel_config(backend='dask'):
+                result = Parallel(n_jobs=2)(
+                    delayed(outer)(nested_require='sharedmem')
+                    for _ in range(1))
+                assert isinstance(result[0][0][0], ThreadingBackend)
+
+
+def outer(nested_require):
+    return Parallel(n_jobs=2, prefer='threads')(
+        delayed(middle)(nested_require) for _ in range(1)
+    )
+
+
+def middle(require):
+    return Parallel(n_jobs=2, require=require)(
+        delayed(inner)() for _ in range(1)
+    )
+
+
+def inner():
+    return Parallel()._backend
+
+
+def test_secede_with_no_processes(loop):
+    # https://github.com/dask/distributed/issues/1775
+    with Client(loop=loop, processes=False, set_as_default=True):
+        with parallel_config(backend='dask'):
+            Parallel(n_jobs=4)(delayed(id)(i) for i in range(2))
+
+
+def _worker_address(_):
+    from distributed import get_worker
+    return get_worker().address
+
+
+def test_dask_backend_keywords(loop):
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask', workers=a['address']):
+                seq = Parallel()(
+                    delayed(_worker_address)(i) for i in range(10))
+                assert seq == [a['address']] * 10
+
+            with parallel_config(backend='dask', workers=b['address']):
+                seq = Parallel()(
+                    delayed(_worker_address)(i) for i in range(10))
+                assert seq == [b['address']] * 10
+
+
+def test_scheduler_tasks_cleanup(loop):
+    with Client(processes=False, loop=loop) as client:
+        with parallel_config(backend='dask'):
+            Parallel()(delayed(inc)(i) for i in range(10))
+
+        start = time()
+        while client.cluster.scheduler.tasks:
+            sleep(0.01)
+            assert time() < start + 5
+
+        assert not client.futures
+
+
+@pytest.mark.parametrize("cluster_strategy", ["adaptive", "late_scaling"])
+@pytest.mark.skipif(
+    distributed.__version__ <= '2.1.1' and distributed.__version__ >= '1.28.0',
+    reason="distributed bug - https://github.com/dask/distributed/pull/2841")
+def test_wait_for_workers(cluster_strategy):
+    cluster = LocalCluster(n_workers=0, processes=False, threads_per_worker=2)
+    client = Client(cluster)
+    if cluster_strategy == "adaptive":
+        cluster.adapt(minimum=0, maximum=2)
+    elif cluster_strategy == "late_scaling":
+        # Tell the cluster to start workers but this is a non-blocking call
+        # and new workers might take time to connect. In this case the Parallel
+        # call should wait for at least one worker to come up before starting
+        # to schedule work.
+        cluster.scale(2)
+    try:
+        with parallel_config(backend='dask'):
+            # The following should wait a bit for at least one worker to
+            # become available.
+            Parallel()(delayed(inc)(i) for i in range(10))
+    finally:
+        client.close()
+        cluster.close()
+
+
+def test_wait_for_workers_timeout():
+    # Start a cluster with 0 worker:
+    cluster = LocalCluster(n_workers=0, processes=False, threads_per_worker=2)
+    client = Client(cluster)
+    try:
+        with parallel_config(backend='dask', wait_for_workers_timeout=0.1):
+            # Short timeout: DaskDistributedBackend
+            msg = "DaskDistributedBackend has no worker after 0.1 seconds."
+            with pytest.raises(TimeoutError, match=msg):
+                Parallel()(delayed(inc)(i) for i in range(10))
+
+        with parallel_config(backend='dask', wait_for_workers_timeout=0):
+            # No timeout: fallback to generic joblib failure:
+            msg = "DaskDistributedBackend has no active worker"
+            with pytest.raises(RuntimeError, match=msg):
+                Parallel()(delayed(inc)(i) for i in range(10))
+    finally:
+        client.close()
+        cluster.close()
+
+
+@pytest.mark.parametrize("backend", ["loky", "multiprocessing"])
+def test_joblib_warning_inside_dask_daemonic_worker(backend):
+    cluster = LocalCluster(n_workers=2)
+    client = Client(cluster)
+    try:
+
+        def func_using_joblib_parallel():
+            # Somehow trying to check the warning type here (e.g. with
+            # pytest.warns(UserWarning)) make the test hang. Work-around:
+            # return the warning record to the client and the warning check is
+            # done client-side.
+            with warnings.catch_warnings(record=True) as record:
+                Parallel(n_jobs=2, backend=backend)(
+                    delayed(inc)(i) for i in range(10))
+
+            return record
+
+        fut = client.submit(func_using_joblib_parallel)
+        record = fut.result()
+
+        assert len(record) == 1
+        warning = record[0].message
+        assert isinstance(warning, UserWarning)
+        assert "distributed.worker.daemon" in str(warning)
+    finally:
+        client.close(timeout=30)
+        cluster.close(timeout=30)

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

@@ -0,0 +1,71 @@
+"""
+Unit tests for the disk utilities.
+"""
+
+# Authors: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+#          Lars Buitinck
+# Copyright (c) 2010 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+from __future__ import with_statement
+import array
+import os
+
+from joblib.disk import disk_used, memstr_to_bytes, mkdirp, rm_subdirs
+from joblib.testing import parametrize, raises
+
+###############################################################################
+
+
+def test_disk_used(tmpdir):
+    cachedir = tmpdir.strpath
+    # Not write a file that is 1M big in this directory, and check the
+    # size. The reason we use such a big file is that it makes us robust
+    # to errors due to block allocation.
+    a = array.array('i')
+    sizeof_i = a.itemsize
+    target_size = 1024
+    n = int(target_size * 1024 / sizeof_i)
+    a = array.array('i', n * (1,))
+    with open(os.path.join(cachedir, 'test'), 'wb') as output:
+        a.tofile(output)
+    assert disk_used(cachedir) >= target_size
+    assert disk_used(cachedir) < target_size + 12
+
+
+@parametrize('text,value',
+             [('80G', 80 * 1024 ** 3),
+              ('1.4M', int(1.4 * 1024 ** 2)),
+              ('120M', 120 * 1024 ** 2),
+              ('53K', 53 * 1024)])
+def test_memstr_to_bytes(text, value):
+    assert memstr_to_bytes(text) == value
+
+
+@parametrize('text,exception,regex',
+             [('fooG', ValueError, r'Invalid literal for size.*fooG.*'),
+              ('1.4N', ValueError, r'Invalid literal for size.*1.4N.*')])
+def test_memstr_to_bytes_exception(text, exception, regex):
+    with raises(exception) as excinfo:
+        memstr_to_bytes(text)
+    assert excinfo.match(regex)
+
+
+def test_mkdirp(tmpdir):
+    mkdirp(os.path.join(tmpdir.strpath, 'ham'))
+    mkdirp(os.path.join(tmpdir.strpath, 'ham'))
+    mkdirp(os.path.join(tmpdir.strpath, 'spam', 'spam'))
+
+    # Not all OSErrors are ignored
+    with raises(OSError):
+        mkdirp('')
+
+
+def test_rm_subdirs(tmpdir):
+    sub_path = os.path.join(tmpdir.strpath, "am", "stram")
+    full_path = os.path.join(sub_path, "gram")
+    mkdirp(os.path.join(full_path))
+
+    rm_subdirs(sub_path)
+    assert os.path.exists(sub_path)
+    assert not os.path.exists(full_path)

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

@@ -0,0 +1,310 @@
+"""
+Test the func_inspect module.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2009 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+import functools
+
+from joblib.func_inspect import filter_args, get_func_name, get_func_code
+from joblib.func_inspect import _clean_win_chars, format_signature
+from joblib.memory import Memory
+from joblib.test.common import with_numpy
+from joblib.testing import fixture, parametrize, raises
+
+
+###############################################################################
+# Module-level functions and fixture, for tests
+def f(x, y=0):
+    pass
+
+
+def g(x):
+    pass
+
+
+def h(x, y=0, *args, **kwargs):
+    pass
+
+
+def i(x=1):
+    pass
+
+
+def j(x, y, **kwargs):
+    pass
+
+
+def k(*args, **kwargs):
+    pass
+
+
+def m1(x, *, y):
+    pass
+
+
+def m2(x, *, y, z=3):
+    pass
+
+
+@fixture(scope='module')
+def cached_func(tmpdir_factory):
+    # Create a Memory object to test decorated functions.
+    # We should be careful not to call the decorated functions, so that
+    # cache directories are not created in the temp dir.
+    cachedir = tmpdir_factory.mktemp("joblib_test_func_inspect")
+    mem = Memory(cachedir.strpath)
+
+    @mem.cache
+    def cached_func_inner(x):
+        return x
+
+    return cached_func_inner
+
+
+class Klass(object):
+
+    def f(self, x):
+        return x
+
+
+###############################################################################
+# Tests
+
+@parametrize('func,args,filtered_args',
+             [(f, [[], (1, )], {'x': 1, 'y': 0}),
+              (f, [['x'], (1, )], {'y': 0}),
+              (f, [['y'], (0, )], {'x': 0}),
+              (f, [['y'], (0, ), {'y': 1}], {'x': 0}),
+              (f, [['x', 'y'], (0, )], {}),
+              (f, [[], (0,), {'y': 1}], {'x': 0, 'y': 1}),
+              (f, [['y'], (), {'x': 2, 'y': 1}], {'x': 2}),
+              (g, [[], (), {'x': 1}], {'x': 1}),
+              (i, [[], (2, )], {'x': 2})])
+def test_filter_args(func, args, filtered_args):
+    assert filter_args(func, *args) == filtered_args
+
+
+def test_filter_args_method():
+    obj = Klass()
+    assert filter_args(obj.f, [], (1, )) == {'x': 1, 'self': obj}
+
+
+@parametrize('func,args,filtered_args',
+             [(h, [[], (1, )],
+               {'x': 1, 'y': 0, '*': [], '**': {}}),
+              (h, [[], (1, 2, 3, 4)],
+               {'x': 1, 'y': 2, '*': [3, 4], '**': {}}),
+              (h, [[], (1, 25), {'ee': 2}],
+               {'x': 1, 'y': 25, '*': [], '**': {'ee': 2}}),
+              (h, [['*'], (1, 2, 25), {'ee': 2}],
+               {'x': 1, 'y': 2, '**': {'ee': 2}})])
+def test_filter_varargs(func, args, filtered_args):
+    assert filter_args(func, *args) == filtered_args
+
+
+test_filter_kwargs_extra_params = [
+    (m1, [[], (1,), {'y': 2}], {'x': 1, 'y': 2}),
+    (m2, [[], (1,), {'y': 2}], {'x': 1, 'y': 2, 'z': 3})
+]
+
+
+@parametrize('func,args,filtered_args',
+             [(k, [[], (1, 2), {'ee': 2}],
+               {'*': [1, 2], '**': {'ee': 2}}),
+              (k, [[], (3, 4)],
+               {'*': [3, 4], '**': {}})] +
+             test_filter_kwargs_extra_params)
+def test_filter_kwargs(func, args, filtered_args):
+    assert filter_args(func, *args) == filtered_args
+
+
+def test_filter_args_2():
+    assert (filter_args(j, [], (1, 2), {'ee': 2}) ==
+            {'x': 1, 'y': 2, '**': {'ee': 2}})
+
+    ff = functools.partial(f, 1)
+    # filter_args has to special-case partial
+    assert filter_args(ff, [], (1, )) == {'*': [1], '**': {}}
+    assert filter_args(ff, ['y'], (1, )) == {'*': [1], '**': {}}
+
+
+@parametrize('func,funcname', [(f, 'f'), (g, 'g'),
+                               (cached_func, 'cached_func')])
+def test_func_name(func, funcname):
+    # Check that we are not confused by decoration
+    # here testcase 'cached_func' is the function itself
+    assert get_func_name(func)[1] == funcname
+
+
+def test_func_name_on_inner_func(cached_func):
+    # Check that we are not confused by decoration
+    # here testcase 'cached_func' is the 'cached_func_inner' function
+    # returned by 'cached_func' fixture
+    assert get_func_name(cached_func)[1] == 'cached_func_inner'
+
+
+def test_func_name_collision_on_inner_func():
+    # Check that two functions defining and caching an inner function
+    # with the same do not cause (module, name) collision
+    def f():
+        def inner_func():
+            return  # pragma: no cover
+        return get_func_name(inner_func)
+
+    def g():
+        def inner_func():
+            return  # pragma: no cover
+        return get_func_name(inner_func)
+
+    module, name = f()
+    other_module, other_name = g()
+
+    assert name == other_name
+    assert module != other_module
+
+
+def test_func_inspect_errors():
+    # Check that func_inspect is robust and will work on weird objects
+    assert get_func_name('a'.lower)[-1] == 'lower'
+    assert get_func_code('a'.lower)[1:] == (None, -1)
+    ff = lambda x: x  # noqa: E731
+    assert get_func_name(ff, win_characters=False)[-1] == '<lambda>'
+    assert get_func_code(ff)[1] == __file__.replace('.pyc', '.py')
+    # Simulate a function defined in __main__
+    ff.__module__ = '__main__'
+    assert get_func_name(ff, win_characters=False)[-1] == '<lambda>'
+    assert get_func_code(ff)[1] == __file__.replace('.pyc', '.py')
+
+
+def func_with_kwonly_args(a, b, *, kw1='kw1', kw2='kw2'):
+    pass
+
+
+def func_with_signature(a: int, b: int) -> None:
+    pass
+
+
+def test_filter_args_edge_cases():
+    assert (
+        filter_args(func_with_kwonly_args, [], (1, 2),
+                    {'kw1': 3, 'kw2': 4}) ==
+        {'a': 1, 'b': 2, 'kw1': 3, 'kw2': 4})
+
+    # filter_args doesn't care about keyword-only arguments so you
+    # can pass 'kw1' into *args without any problem
+    with raises(ValueError) as excinfo:
+        filter_args(func_with_kwonly_args, [], (1, 2, 3), {'kw2': 2})
+    excinfo.match("Keyword-only parameter 'kw1' was passed as positional "
+                  "parameter")
+
+    assert (
+        filter_args(func_with_kwonly_args, ['b', 'kw2'], (1, 2),
+                    {'kw1': 3, 'kw2': 4}) ==
+        {'a': 1, 'kw1': 3})
+
+    assert (filter_args(func_with_signature, ['b'], (1, 2)) == {'a': 1})
+
+
+def test_bound_methods():
+    """ Make sure that calling the same method on two different instances
+        of the same class does resolv to different signatures.
+    """
+    a = Klass()
+    b = Klass()
+    assert filter_args(a.f, [], (1, )) != filter_args(b.f, [], (1, ))
+
+
+@parametrize('exception,regex,func,args',
+             [(ValueError, 'ignore_lst must be a list of parameters to ignore',
+               f, ['bar', (None, )]),
+              (ValueError, r'Ignore list: argument \'(.*)\' is not defined',
+               g, [['bar'], (None, )]),
+              (ValueError, 'Wrong number of arguments',
+               h, [[]])])
+def test_filter_args_error_msg(exception, regex, func, args):
+    """ Make sure that filter_args returns decent error messages, for the
+        sake of the user.
+    """
+    with raises(exception) as excinfo:
+        filter_args(func, *args)
+    excinfo.match(regex)
+
+
+def test_filter_args_no_kwargs_mutation():
+    """None-regression test against 0.12.0 changes.
+
+    https://github.com/joblib/joblib/pull/75
+
+    Make sure filter args doesn't mutate the kwargs dict that gets passed in.
+    """
+    kwargs = {'x': 0}
+    filter_args(g, [], [], kwargs)
+    assert kwargs == {'x': 0}
+
+
+def test_clean_win_chars():
+    string = r'C:\foo\bar\main.py'
+    mangled_string = _clean_win_chars(string)
+    for char in ('\\', ':', '<', '>', '!'):
+        assert char not in mangled_string
+
+
+@parametrize('func,args,kwargs,sgn_expected',
+             [(g, [list(range(5))], {}, 'g([0, 1, 2, 3, 4])'),
+              (k, [1, 2, (3, 4)], {'y': True}, 'k(1, 2, (3, 4), y=True)')])
+def test_format_signature(func, args, kwargs, sgn_expected):
+    # Test signature formatting.
+    path, sgn_result = format_signature(func, *args, **kwargs)
+    assert sgn_result == sgn_expected
+
+
+def test_format_signature_long_arguments():
+    shortening_threshold = 1500
+    # shortening gets it down to 700 characters but there is the name
+    # of the function in the signature and a few additional things
+    # like dots for the ellipsis
+    shortening_target = 700 + 10
+
+    arg = 'a' * shortening_threshold
+    _, signature = format_signature(h, arg)
+    assert len(signature) < shortening_target
+
+    nb_args = 5
+    args = [arg for _ in range(nb_args)]
+    _, signature = format_signature(h, *args)
+    assert len(signature) < shortening_target * nb_args
+
+    kwargs = {str(i): arg for i, arg in enumerate(args)}
+    _, signature = format_signature(h, **kwargs)
+    assert len(signature) < shortening_target * nb_args
+
+    _, signature = format_signature(h, *args, **kwargs)
+    assert len(signature) < shortening_target * 2 * nb_args
+
+
+@with_numpy
+def test_format_signature_numpy():
+    """ Test the format signature formatting with numpy.
+    """
+
+
+def test_special_source_encoding():
+    from joblib.test.test_func_inspect_special_encoding import big5_f
+    func_code, source_file, first_line = get_func_code(big5_f)
+    assert first_line == 5
+    assert "def big5_f():" in func_code
+    assert "test_func_inspect_special_encoding" in source_file
+
+
+def _get_code():
+    from joblib.test.test_func_inspect_special_encoding import big5_f
+    return get_func_code(big5_f)[0]
+
+
+def test_func_code_consistency():
+    from joblib.parallel import Parallel, delayed
+    codes = Parallel(n_jobs=2)(delayed(_get_code)() for _ in range(5))
+    assert len(set(codes)) == 1

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

@@ -0,0 +1,495 @@
+"""
+Test the hashing module.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2009 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+import time
+import hashlib
+import sys
+import gc
+import io
+import collections
+import itertools
+import pickle
+import random
+from concurrent.futures import ProcessPoolExecutor
+from decimal import Decimal
+
+from joblib.hashing import hash
+from joblib.func_inspect import filter_args
+from joblib.memory import Memory
+from joblib.testing import raises, skipif, fixture, parametrize
+from joblib.test.common import np, with_numpy
+
+
+def unicode(s):
+    return s
+
+
+###############################################################################
+# Helper functions for the tests
+def time_func(func, *args):
+    """ Time function func on *args.
+    """
+    times = list()
+    for _ in range(3):
+        t1 = time.time()
+        func(*args)
+        times.append(time.time() - t1)
+    return min(times)
+
+
+def relative_time(func1, func2, *args):
+    """ Return the relative time between func1 and func2 applied on
+        *args.
+    """
+    time_func1 = time_func(func1, *args)
+    time_func2 = time_func(func2, *args)
+    relative_diff = 0.5 * (abs(time_func1 - time_func2)
+                           / (time_func1 + time_func2))
+    return relative_diff
+
+
+class Klass(object):
+
+    def f(self, x):
+        return x
+
+
+class KlassWithCachedMethod(object):
+
+    def __init__(self, cachedir):
+        mem = Memory(location=cachedir)
+        self.f = mem.cache(self.f)
+
+    def f(self, x):
+        return x
+
+
+###############################################################################
+# Tests
+
+input_list = [1, 2, 1., 2., 1 + 1j, 2. + 1j,
+              'a', 'b',
+              (1,), (1, 1,), [1, ], [1, 1, ],
+              {1: 1}, {1: 2}, {2: 1},
+              None,
+              gc.collect,
+              [1, ].append,
+              # Next 2 sets have unorderable elements in python 3.
+              set(('a', 1)),
+              set(('a', 1, ('a', 1))),
+              # Next 2 dicts have unorderable type of keys in python 3.
+              {'a': 1, 1: 2},
+              {'a': 1, 1: 2, 'd': {'a': 1}}]
+
+
+@parametrize('obj1', input_list)
+@parametrize('obj2', input_list)
+def test_trivial_hash(obj1, obj2):
+    """Smoke test hash on various types."""
+    # Check that 2 objects have the same hash only if they are the same.
+    are_hashes_equal = hash(obj1) == hash(obj2)
+    are_objs_identical = obj1 is obj2
+    assert are_hashes_equal == are_objs_identical
+
+
+def test_hash_methods():
+    # Check that hashing instance methods works
+    a = io.StringIO(unicode('a'))
+    assert hash(a.flush) == hash(a.flush)
+    a1 = collections.deque(range(10))
+    a2 = collections.deque(range(9))
+    assert hash(a1.extend) != hash(a2.extend)
+
+
+@fixture(scope='function')
+@with_numpy
+def three_np_arrays():
+    rnd = np.random.RandomState(0)
+    arr1 = rnd.random_sample((10, 10))
+    arr2 = arr1.copy()
+    arr3 = arr2.copy()
+    arr3[0] += 1
+    return arr1, arr2, arr3
+
+
+def test_hash_numpy_arrays(three_np_arrays):
+    arr1, arr2, arr3 = three_np_arrays
+
+    for obj1, obj2 in itertools.product(three_np_arrays, repeat=2):
+        are_hashes_equal = hash(obj1) == hash(obj2)
+        are_arrays_equal = np.all(obj1 == obj2)
+        assert are_hashes_equal == are_arrays_equal
+
+    assert hash(arr1) != hash(arr1.T)
+
+
+def test_hash_numpy_dict_of_arrays(three_np_arrays):
+    arr1, arr2, arr3 = three_np_arrays
+
+    d1 = {1: arr1, 2: arr2}
+    d2 = {1: arr2, 2: arr1}
+    d3 = {1: arr2, 2: arr3}
+
+    assert hash(d1) == hash(d2)
+    assert hash(d1) != hash(d3)
+
+
+@with_numpy
+@parametrize('dtype', ['datetime64[s]', 'timedelta64[D]'])
+def test_numpy_datetime_array(dtype):
+    # memoryview is not supported for some dtypes e.g. datetime64
+    # see https://github.com/joblib/joblib/issues/188 for more details
+    a_hash = hash(np.arange(10))
+    array = np.arange(0, 10, dtype=dtype)
+    assert hash(array) != a_hash
+
+
+@with_numpy
+def test_hash_numpy_noncontiguous():
+    a = np.asarray(np.arange(6000).reshape((1000, 2, 3)),
+                   order='F')[:, :1, :]
+    b = np.ascontiguousarray(a)
+    assert hash(a) != hash(b)
+
+    c = np.asfortranarray(a)
+    assert hash(a) != hash(c)
+
+
+@with_numpy
+@parametrize('coerce_mmap', [True, False])
+def test_hash_memmap(tmpdir, coerce_mmap):
+    """Check that memmap and arrays hash identically if coerce_mmap is True."""
+    filename = tmpdir.join('memmap_temp').strpath
+    try:
+        m = np.memmap(filename, shape=(10, 10), mode='w+')
+        a = np.asarray(m)
+        are_hashes_equal = (hash(a, coerce_mmap=coerce_mmap) ==
+                            hash(m, coerce_mmap=coerce_mmap))
+        assert are_hashes_equal == coerce_mmap
+    finally:
+        if 'm' in locals():
+            del m
+            # Force a garbage-collection cycle, to be certain that the
+            # object is delete, and we don't run in a problem under
+            # Windows with a file handle still open.
+            gc.collect()
+
+
+@with_numpy
+@skipif(sys.platform == 'win32', reason='This test is not stable under windows'
+                                        ' for some reason')
+def test_hash_numpy_performance():
+    """ Check the performance of hashing numpy arrays:
+
+        In [22]: a = np.random.random(1000000)
+
+        In [23]: %timeit hashlib.md5(a).hexdigest()
+        100 loops, best of 3: 20.7 ms per loop
+
+        In [24]: %timeit hashlib.md5(pickle.dumps(a, protocol=2)).hexdigest()
+        1 loops, best of 3: 73.1 ms per loop
+
+        In [25]: %timeit hashlib.md5(cPickle.dumps(a, protocol=2)).hexdigest()
+        10 loops, best of 3: 53.9 ms per loop
+
+        In [26]: %timeit hash(a)
+        100 loops, best of 3: 20.8 ms per loop
+    """
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(1000000)
+
+    def md5_hash(x):
+        return hashlib.md5(memoryview(x)).hexdigest()
+
+    relative_diff = relative_time(md5_hash, hash, a)
+    assert relative_diff < 0.3
+
+    # Check that hashing an tuple of 3 arrays takes approximately
+    # 3 times as much as hashing one array
+    time_hashlib = 3 * time_func(md5_hash, a)
+    time_hash = time_func(hash, (a, a, a))
+    relative_diff = 0.5 * (abs(time_hash - time_hashlib)
+                           / (time_hash + time_hashlib))
+    assert relative_diff < 0.3
+
+
+def test_bound_methods_hash():
+    """ Make sure that calling the same method on two different instances
+    of the same class does resolve to the same hashes.
+    """
+    a = Klass()
+    b = Klass()
+    assert (hash(filter_args(a.f, [], (1, ))) ==
+            hash(filter_args(b.f, [], (1, ))))
+
+
+def test_bound_cached_methods_hash(tmpdir):
+    """ Make sure that calling the same _cached_ method on two different
+    instances of the same class does resolve to the same hashes.
+    """
+    a = KlassWithCachedMethod(tmpdir.strpath)
+    b = KlassWithCachedMethod(tmpdir.strpath)
+    assert (hash(filter_args(a.f.func, [], (1, ))) ==
+            hash(filter_args(b.f.func, [], (1, ))))
+
+
+@with_numpy
+def test_hash_object_dtype():
+    """ Make sure that ndarrays with dtype `object' hash correctly."""
+
+    a = np.array([np.arange(i) for i in range(6)], dtype=object)
+    b = np.array([np.arange(i) for i in range(6)], dtype=object)
+
+    assert hash(a) == hash(b)
+
+
+@with_numpy
+def test_numpy_scalar():
+    # Numpy scalars are built from compiled functions, and lead to
+    # strange pickling paths explored, that can give hash collisions
+    a = np.float64(2.0)
+    b = np.float64(3.0)
+    assert hash(a) != hash(b)
+
+
+def test_dict_hash(tmpdir):
+    # Check that dictionaries hash consistently, even though the ordering
+    # of the keys is not guaranteed
+    k = KlassWithCachedMethod(tmpdir.strpath)
+
+    d = {'#s12069__c_maps.nii.gz': [33],
+         '#s12158__c_maps.nii.gz': [33],
+         '#s12258__c_maps.nii.gz': [33],
+         '#s12277__c_maps.nii.gz': [33],
+         '#s12300__c_maps.nii.gz': [33],
+         '#s12401__c_maps.nii.gz': [33],
+         '#s12430__c_maps.nii.gz': [33],
+         '#s13817__c_maps.nii.gz': [33],
+         '#s13903__c_maps.nii.gz': [33],
+         '#s13916__c_maps.nii.gz': [33],
+         '#s13981__c_maps.nii.gz': [33],
+         '#s13982__c_maps.nii.gz': [33],
+         '#s13983__c_maps.nii.gz': [33]}
+
+    a = k.f(d)
+    b = k.f(a)
+
+    assert hash(a) == hash(b)
+
+
+def test_set_hash(tmpdir):
+    # Check that sets hash consistently, even though their ordering
+    # is not guaranteed
+    k = KlassWithCachedMethod(tmpdir.strpath)
+
+    s = set(['#s12069__c_maps.nii.gz',
+             '#s12158__c_maps.nii.gz',
+             '#s12258__c_maps.nii.gz',
+             '#s12277__c_maps.nii.gz',
+             '#s12300__c_maps.nii.gz',
+             '#s12401__c_maps.nii.gz',
+             '#s12430__c_maps.nii.gz',
+             '#s13817__c_maps.nii.gz',
+             '#s13903__c_maps.nii.gz',
+             '#s13916__c_maps.nii.gz',
+             '#s13981__c_maps.nii.gz',
+             '#s13982__c_maps.nii.gz',
+             '#s13983__c_maps.nii.gz'])
+
+    a = k.f(s)
+    b = k.f(a)
+
+    assert hash(a) == hash(b)
+
+
+def test_set_decimal_hash():
+    # Check that sets containing decimals hash consistently, even though
+    # ordering is not guaranteed
+    assert (hash(set([Decimal(0), Decimal('NaN')])) ==
+            hash(set([Decimal('NaN'), Decimal(0)])))
+
+
+def test_string():
+    # Test that we obtain the same hash for object owning several strings,
+    # whatever the past of these strings (which are immutable in Python)
+    string = 'foo'
+    a = {string: 'bar'}
+    b = {string: 'bar'}
+    c = pickle.loads(pickle.dumps(b))
+    assert hash([a, b]) == hash([a, c])
+
+
+@with_numpy
+def test_numpy_dtype_pickling():
+    # numpy dtype hashing is tricky to get right: see #231, #239, #251 #1080,
+    # #1082, and explanatory comments inside
+    # ``joblib.hashing.NumpyHasher.save``.
+
+    # In this test, we make sure that the pickling of numpy dtypes is robust to
+    # object identity and object copy.
+
+    dt1 = np.dtype('f4')
+    dt2 = np.dtype('f4')
+
+    # simple dtypes objects are interned
+    assert dt1 is dt2
+    assert hash(dt1) == hash(dt2)
+
+    dt1_roundtripped = pickle.loads(pickle.dumps(dt1))
+    assert dt1 is not dt1_roundtripped
+    assert hash(dt1) == hash(dt1_roundtripped)
+
+    assert hash([dt1, dt1]) == hash([dt1_roundtripped, dt1_roundtripped])
+    assert hash([dt1, dt1]) == hash([dt1, dt1_roundtripped])
+
+    complex_dt1 = np.dtype(
+        [('name', np.str_, 16), ('grades', np.float64, (2,))]
+    )
+    complex_dt2 = np.dtype(
+        [('name', np.str_, 16), ('grades', np.float64, (2,))]
+    )
+
+    # complex dtypes objects are not interned
+    assert hash(complex_dt1) == hash(complex_dt2)
+
+    complex_dt1_roundtripped = pickle.loads(pickle.dumps(complex_dt1))
+    assert complex_dt1_roundtripped is not complex_dt1
+    assert hash(complex_dt1) == hash(complex_dt1_roundtripped)
+
+    assert hash([complex_dt1, complex_dt1]) == hash(
+        [complex_dt1_roundtripped, complex_dt1_roundtripped]
+    )
+    assert hash([complex_dt1, complex_dt1]) == hash(
+        [complex_dt1_roundtripped, complex_dt1]
+    )
+
+
+@parametrize('to_hash,expected',
+             [('This is a string to hash',
+               '71b3f47df22cb19431d85d92d0b230b2'),
+              (u"C'est l\xe9t\xe9",
+               '2d8d189e9b2b0b2e384d93c868c0e576'),
+              ((123456, 54321, -98765),
+               'e205227dd82250871fa25aa0ec690aa3'),
+              ([random.Random(42).random() for _ in range(5)],
+               'a11ffad81f9682a7d901e6edc3d16c84'),
+              ({'abcde': 123, 'sadfas': [-9999, 2, 3]},
+                  'aeda150553d4bb5c69f0e69d51b0e2ef')])
+def test_hashes_stay_the_same(to_hash, expected):
+    # We want to make sure that hashes don't change with joblib
+    # version. For end users, that would mean that they have to
+    # regenerate their cache from scratch, which potentially means
+    # lengthy recomputations.
+    # Expected results have been generated with joblib 0.9.2
+    assert hash(to_hash) == expected
+
+
+@with_numpy
+def test_hashes_are_different_between_c_and_fortran_contiguous_arrays():
+    # We want to be sure that the c-contiguous and f-contiguous versions of the
+    # same array produce 2 different hashes.
+    rng = np.random.RandomState(0)
+    arr_c = rng.random_sample((10, 10))
+    arr_f = np.asfortranarray(arr_c)
+    assert hash(arr_c) != hash(arr_f)
+
+
+@with_numpy
+def test_0d_array():
+    hash(np.array(0))
+
+
+@with_numpy
+def test_0d_and_1d_array_hashing_is_different():
+    assert hash(np.array(0)) != hash(np.array([0]))
+
+
+@with_numpy
+def test_hashes_stay_the_same_with_numpy_objects():
+    # Note: joblib used to test numpy objects hashing by comparing the produced
+    # hash of an object with some hard-coded target value to guarantee that
+    # hashing remains the same across joblib versions. However, since numpy
+    # 1.20 and joblib 1.0, joblib relies on potentially unstable implementation
+    # details of numpy to hash np.dtype objects, which makes the stability of
+    # hash values across different environments hard to guarantee and to test.
+    # As a result, hashing stability across joblib versions becomes best-effort
+    # only, and we only test the consistency within a single environment by
+    # making sure:
+    # - the hash of two copies of the same objects is the same
+    # - hashing some object in two different python processes produces the same
+    #   value. This should be viewed as a proxy for testing hash consistency
+    #   through time between Python sessions (provided no change in the
+    #   environment was done between sessions).
+
+    def create_objects_to_hash():
+        rng = np.random.RandomState(42)
+        # Being explicit about dtypes in order to avoid
+        # architecture-related differences. Also using 'f4' rather than
+        # 'f8' for float arrays because 'f8' arrays generated by
+        # rng.random.randn don't seem to be bit-identical on 32bit and
+        # 64bit machines.
+        to_hash_list = [
+            rng.randint(-1000, high=1000, size=50).astype('<i8'),
+            tuple(rng.randn(3).astype('<f4') for _ in range(5)),
+            [rng.randn(3).astype('<f4') for _ in range(5)],
+            {
+                -3333: rng.randn(3, 5).astype('<f4'),
+                0: [
+                    rng.randint(10, size=20).astype('<i8'),
+                    rng.randn(10).astype('<f4')
+                ]
+            },
+            # Non regression cases for
+            # https://github.com/joblib/joblib/issues/308
+            np.arange(100, dtype='<i8').reshape((10, 10)),
+            # Fortran contiguous array
+            np.asfortranarray(np.arange(100, dtype='<i8').reshape((10, 10))),
+            # Non contiguous array
+            np.arange(100, dtype='<i8').reshape((10, 10))[:, :2],
+        ]
+        return to_hash_list
+
+    # Create two lists containing copies of the same objects.  joblib.hash
+    # should return the same hash for to_hash_list_one[i] and
+    # to_hash_list_two[i]
+    to_hash_list_one = create_objects_to_hash()
+    to_hash_list_two = create_objects_to_hash()
+
+    e1 = ProcessPoolExecutor(max_workers=1)
+    e2 = ProcessPoolExecutor(max_workers=1)
+
+    try:
+        for obj_1, obj_2 in zip(to_hash_list_one, to_hash_list_two):
+            # testing consistency of hashes across python processes
+            hash_1 = e1.submit(hash, obj_1).result()
+            hash_2 = e2.submit(hash, obj_1).result()
+            assert hash_1 == hash_2
+
+            # testing consistency when hashing two copies of the same objects.
+            hash_3 = e1.submit(hash, obj_2).result()
+            assert hash_1 == hash_3
+
+    finally:
+        e1.shutdown()
+        e2.shutdown()
+
+
+def test_hashing_pickling_error():
+    def non_picklable():
+        return 42
+
+    with raises(pickle.PicklingError) as excinfo:
+        hash(non_picklable)
+    excinfo.match('PicklingError while hashing')
+
+
+def test_wrong_hash_name():
+    msg = "Valid options for 'hash_name' are"
+    with raises(ValueError, match=msg):
+        data = {'foo': 'bar'}
+        hash(data, hash_name='invalid')

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

@@ -0,0 +1,31 @@
+"""
+Test the logger module.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2009 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+import re
+
+from joblib.logger import PrintTime
+
+
+def test_print_time(tmpdir, capsys):
+    # A simple smoke test for PrintTime.
+    logfile = tmpdir.join('test.log').strpath
+    print_time = PrintTime(logfile=logfile)
+    print_time('Foo')
+    # Create a second time, to smoke test log rotation.
+    print_time = PrintTime(logfile=logfile)
+    print_time('Foo')
+    # And a third time
+    print_time = PrintTime(logfile=logfile)
+    print_time('Foo')
+
+    out_printed_text, err_printed_text = capsys.readouterr()
+    # Use regexps to be robust to time variations
+    match = r"Foo: 0\..s, 0\..min\nFoo: 0\..s, 0..min\nFoo: " + \
+            r".\..s, 0..min\n"
+    if not re.match(match, err_printed_text):
+        raise AssertionError('Excepted %s, got %s' %
+                             (match, err_printed_text))

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

@@ -0,0 +1,1191 @@
+import os
+import mmap
+import sys
+import platform
+import gc
+import pickle
+import itertools
+from time import sleep
+import subprocess
+import threading
+import faulthandler
+
+import pytest
+
+from joblib.test.common import with_numpy, np
+from joblib.test.common import with_multiprocessing
+from joblib.test.common import with_dev_shm
+from joblib.testing import raises, parametrize, skipif
+from joblib.backports import make_memmap
+from joblib.parallel import Parallel, delayed
+
+from joblib.pool import MemmappingPool
+from joblib.executor import _TestingMemmappingExecutor as TestExecutor
+from joblib._memmapping_reducer import has_shareable_memory
+from joblib._memmapping_reducer import ArrayMemmapForwardReducer
+from joblib._memmapping_reducer import _strided_from_memmap
+from joblib._memmapping_reducer import _get_temp_dir
+from joblib._memmapping_reducer import _WeakArrayKeyMap
+from joblib._memmapping_reducer import _get_backing_memmap
+import joblib._memmapping_reducer as jmr
+
+
+def setup_module():
+    faulthandler.dump_traceback_later(timeout=300, exit=True)
+
+
+def teardown_module():
+    faulthandler.cancel_dump_traceback_later()
+
+
+def check_memmap_and_send_back(array):
+    assert _get_backing_memmap(array) is not None
+    return array
+
+
+def check_array(args):
+    """Dummy helper function to be executed in subprocesses
+
+    Check that the provided array has the expected values in the provided
+    range.
+
+    """
+    data, position, expected = args
+    np.testing.assert_array_equal(data[position], expected)
+
+
+def inplace_double(args):
+    """Dummy helper function to be executed in subprocesses
+
+
+    Check that the input array has the right values in the provided range
+    and perform an inplace modification to double the values in the range by
+    two.
+
+    """
+    data, position, expected = args
+    assert data[position] == expected
+    data[position] *= 2
+    np.testing.assert_array_equal(data[position], 2 * expected)
+
+
+@with_numpy
+@with_multiprocessing
+def test_memmap_based_array_reducing(tmpdir):
+    """Check that it is possible to reduce a memmap backed array"""
+    assert_array_equal = np.testing.assert_array_equal
+    filename = tmpdir.join('test.mmap').strpath
+
+    # Create a file larger than what will be used by a
+    buffer = np.memmap(filename, dtype=np.float64, shape=500, mode='w+')
+
+    # Fill the original buffer with negative markers to detect over of
+    # underflow in case of test failures
+    buffer[:] = - 1.0 * np.arange(buffer.shape[0], dtype=buffer.dtype)
+    buffer.flush()
+
+    # Memmap a 2D fortran array on a offsetted subsection of the previous
+    # buffer
+    a = np.memmap(filename, dtype=np.float64, shape=(3, 5, 4),
+                  mode='r+', order='F', offset=4)
+    a[:] = np.arange(60).reshape(a.shape)
+
+    # Build various views that share the buffer with the original memmap
+
+    # b is an memmap sliced view on an memmap instance
+    b = a[1:-1, 2:-1, 2:4]
+
+    # c and d are array views
+    c = np.asarray(b)
+    d = c.T
+
+    # Array reducer with auto dumping disabled
+    reducer = ArrayMemmapForwardReducer(None, tmpdir.strpath, 'c', True)
+
+    def reconstruct_array_or_memmap(x):
+        cons, args = reducer(x)
+        return cons(*args)
+
+    # Reconstruct original memmap
+    a_reconstructed = reconstruct_array_or_memmap(a)
+    assert has_shareable_memory(a_reconstructed)
+    assert isinstance(a_reconstructed, np.memmap)
+    assert_array_equal(a_reconstructed, a)
+
+    # Reconstruct strided memmap view
+    b_reconstructed = reconstruct_array_or_memmap(b)
+    assert has_shareable_memory(b_reconstructed)
+    assert_array_equal(b_reconstructed, b)
+
+    # Reconstruct arrays views on memmap base
+    c_reconstructed = reconstruct_array_or_memmap(c)
+    assert not isinstance(c_reconstructed, np.memmap)
+    assert has_shareable_memory(c_reconstructed)
+    assert_array_equal(c_reconstructed, c)
+
+    d_reconstructed = reconstruct_array_or_memmap(d)
+    assert not isinstance(d_reconstructed, np.memmap)
+    assert has_shareable_memory(d_reconstructed)
+    assert_array_equal(d_reconstructed, d)
+
+    # Test graceful degradation on fake memmap instances with in-memory
+    # buffers
+    a3 = a * 3
+    assert not has_shareable_memory(a3)
+    a3_reconstructed = reconstruct_array_or_memmap(a3)
+    assert not has_shareable_memory(a3_reconstructed)
+    assert not isinstance(a3_reconstructed, np.memmap)
+    assert_array_equal(a3_reconstructed, a * 3)
+
+    # Test graceful degradation on arrays derived from fake memmap instances
+    b3 = np.asarray(a3)
+    assert not has_shareable_memory(b3)
+
+    b3_reconstructed = reconstruct_array_or_memmap(b3)
+    assert isinstance(b3_reconstructed, np.ndarray)
+    assert not has_shareable_memory(b3_reconstructed)
+    assert_array_equal(b3_reconstructed, b3)
+
+
+@with_multiprocessing
+@skipif((sys.platform != "win32") or (),
+        reason="PermissionError only easily triggerable on Windows")
+def test_resource_tracker_retries_when_permissionerror(tmpdir):
+    # Test resource_tracker retry mechanism when unlinking memmaps.  See more
+    # thorough information in the ``unlink_file`` documentation of joblib.
+    filename = tmpdir.join('test.mmap').strpath
+    cmd = """if 1:
+    import os
+    import numpy as np
+    import time
+    from joblib.externals.loky.backend import resource_tracker
+    resource_tracker.VERBOSE = 1
+
+    # Start the resource tracker
+    resource_tracker.ensure_running()
+    time.sleep(1)
+
+    # Create a file containing numpy data
+    memmap = np.memmap(r"{filename}", dtype=np.float64, shape=10, mode='w+')
+    memmap[:] = np.arange(10).astype(np.int8).data
+    memmap.flush()
+    assert os.path.exists(r"{filename}")
+    del memmap
+
+    # Create a np.memmap backed by this file
+    memmap = np.memmap(r"{filename}", dtype=np.float64, shape=10, mode='w+')
+    resource_tracker.register(r"{filename}", "file")
+
+    # Ask the resource_tracker to delete the file backing the np.memmap , this
+    # should raise PermissionError that the resource_tracker will log.
+    resource_tracker.maybe_unlink(r"{filename}", "file")
+
+    # Wait for the resource_tracker to process the maybe_unlink before cleaning
+    # up the memmap
+    time.sleep(2)
+    """.format(filename=filename)
+    p = subprocess.Popen([sys.executable, '-c', cmd], stderr=subprocess.PIPE,
+                         stdout=subprocess.PIPE)
+    p.wait()
+    out, err = p.communicate()
+    assert p.returncode == 0
+    assert out == b''
+    msg = 'tried to unlink {}, got PermissionError'.format(filename)
+    assert msg in err.decode()
+
+
+@with_numpy
+@with_multiprocessing
+def test_high_dimension_memmap_array_reducing(tmpdir):
+    assert_array_equal = np.testing.assert_array_equal
+
+    filename = tmpdir.join('test.mmap').strpath
+
+    # Create a high dimensional memmap
+    a = np.memmap(filename, dtype=np.float64, shape=(100, 15, 15, 3),
+                  mode='w+')
+    a[:] = np.arange(100 * 15 * 15 * 3).reshape(a.shape)
+
+    # Create some slices/indices at various dimensions
+    b = a[0:10]
+    c = a[:, 5:10]
+    d = a[:, :, :, 0]
+    e = a[1:3:4]
+
+    # Array reducer with auto dumping disabled
+    reducer = ArrayMemmapForwardReducer(None, tmpdir.strpath, 'c', True)
+
+    def reconstruct_array_or_memmap(x):
+        cons, args = reducer(x)
+        return cons(*args)
+
+    a_reconstructed = reconstruct_array_or_memmap(a)
+    assert has_shareable_memory(a_reconstructed)
+    assert isinstance(a_reconstructed, np.memmap)
+    assert_array_equal(a_reconstructed, a)
+
+    b_reconstructed = reconstruct_array_or_memmap(b)
+    assert has_shareable_memory(b_reconstructed)
+    assert_array_equal(b_reconstructed, b)
+
+    c_reconstructed = reconstruct_array_or_memmap(c)
+    assert has_shareable_memory(c_reconstructed)
+    assert_array_equal(c_reconstructed, c)
+
+    d_reconstructed = reconstruct_array_or_memmap(d)
+    assert has_shareable_memory(d_reconstructed)
+    assert_array_equal(d_reconstructed, d)
+
+    e_reconstructed = reconstruct_array_or_memmap(e)
+    assert has_shareable_memory(e_reconstructed)
+    assert_array_equal(e_reconstructed, e)
+
+
+@with_numpy
+def test__strided_from_memmap(tmpdir):
+    fname = tmpdir.join('test.mmap').strpath
+    size = 5 * mmap.ALLOCATIONGRANULARITY
+    offset = mmap.ALLOCATIONGRANULARITY + 1
+    # This line creates the mmap file that is reused later
+    memmap_obj = np.memmap(fname, mode='w+', shape=size + offset)
+    # filename, dtype, mode, offset, order, shape, strides, total_buffer_len
+    memmap_obj = _strided_from_memmap(fname, dtype='uint8', mode='r',
+                                      offset=offset, order='C', shape=size,
+                                      strides=None, total_buffer_len=None,
+                                      unlink_on_gc_collect=False)
+    assert isinstance(memmap_obj, np.memmap)
+    assert memmap_obj.offset == offset
+    memmap_backed_obj = _strided_from_memmap(
+        fname, dtype='uint8', mode='r', offset=offset, order='C',
+        shape=(size // 2,), strides=(2,), total_buffer_len=size,
+        unlink_on_gc_collect=False
+    )
+    assert _get_backing_memmap(memmap_backed_obj).offset == offset
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_pool_with_memmap(factory, tmpdir):
+    """Check that subprocess can access and update shared memory memmap"""
+    assert_array_equal = np.testing.assert_array_equal
+
+    # Fork the subprocess before allocating the objects to be passed
+    pool_temp_folder = tmpdir.mkdir('pool').strpath
+    p = factory(10, max_nbytes=2, temp_folder=pool_temp_folder)
+    try:
+        filename = tmpdir.join('test.mmap').strpath
+        a = np.memmap(filename, dtype=np.float32, shape=(3, 5), mode='w+')
+        a.fill(1.0)
+
+        p.map(inplace_double, [(a, (i, j), 1.0)
+                               for i in range(a.shape[0])
+                               for j in range(a.shape[1])])
+
+        assert_array_equal(a, 2 * np.ones(a.shape))
+
+        # Open a copy-on-write view on the previous data
+        b = np.memmap(filename, dtype=np.float32, shape=(5, 3), mode='c')
+
+        p.map(inplace_double, [(b, (i, j), 2.0)
+                               for i in range(b.shape[0])
+                               for j in range(b.shape[1])])
+
+        # Passing memmap instances to the pool should not trigger the creation
+        # of new files on the FS
+        assert os.listdir(pool_temp_folder) == []
+
+        # the original data is untouched
+        assert_array_equal(a, 2 * np.ones(a.shape))
+        assert_array_equal(b, 2 * np.ones(b.shape))
+
+        # readonly maps can be read but not updated
+        c = np.memmap(filename, dtype=np.float32, shape=(10,), mode='r',
+                      offset=5 * 4)
+
+        with raises(AssertionError):
+            p.map(check_array, [(c, i, 3.0) for i in range(c.shape[0])])
+
+        # depending on the version of numpy one can either get a RuntimeError
+        # or a ValueError
+        with raises((RuntimeError, ValueError)):
+            p.map(inplace_double, [(c, i, 2.0) for i in range(c.shape[0])])
+    finally:
+        # Clean all filehandlers held by the pool
+        p.terminate()
+        del p
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_pool_with_memmap_array_view(factory, tmpdir):
+    """Check that subprocess can access and update shared memory array"""
+    assert_array_equal = np.testing.assert_array_equal
+
+    # Fork the subprocess before allocating the objects to be passed
+    pool_temp_folder = tmpdir.mkdir('pool').strpath
+    p = factory(10, max_nbytes=2, temp_folder=pool_temp_folder)
+    try:
+
+        filename = tmpdir.join('test.mmap').strpath
+        a = np.memmap(filename, dtype=np.float32, shape=(3, 5), mode='w+')
+        a.fill(1.0)
+
+        # Create an ndarray view on the memmap instance
+        a_view = np.asarray(a)
+        assert not isinstance(a_view, np.memmap)
+        assert has_shareable_memory(a_view)
+
+        p.map(inplace_double, [(a_view, (i, j), 1.0)
+                               for i in range(a.shape[0])
+                               for j in range(a.shape[1])])
+
+        # Both a and the a_view have been updated
+        assert_array_equal(a, 2 * np.ones(a.shape))
+        assert_array_equal(a_view, 2 * np.ones(a.shape))
+
+        # Passing memmap array view to the pool should not trigger the
+        # creation of new files on the FS
+        assert os.listdir(pool_temp_folder) == []
+
+    finally:
+        p.terminate()
+        del p
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_permission_error_windows_reference_cycle(backend):
+    # Non regression test for:
+    # https://github.com/joblib/joblib/issues/806
+    #
+    # The issue happens when trying to delete a memory mapped file that has
+    # not yet been closed by one of the worker processes.
+    cmd = """if 1:
+        import numpy as np
+        from joblib import Parallel, delayed
+
+
+        data = np.random.rand(int(2e6)).reshape((int(1e6), 2))
+
+        # Build a complex cyclic reference that is likely to delay garbage
+        # collection of the memmapped array in the worker processes.
+        first_list = current_list = [data]
+        for i in range(10):
+            current_list = [current_list]
+        first_list.append(current_list)
+
+        if __name__ == "__main__":
+            results = Parallel(n_jobs=2, backend="{b}")(
+                delayed(len)(current_list) for i in range(10))
+            assert results == [1] * 10
+    """.format(b=backend)
+    p = subprocess.Popen([sys.executable, '-c', cmd], stderr=subprocess.PIPE,
+                         stdout=subprocess.PIPE)
+    p.wait()
+    out, err = p.communicate()
+    assert p.returncode == 0, out.decode() + "\n\n" + err.decode()
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_permission_error_windows_memmap_sent_to_parent(backend):
+    # Second non-regression test for:
+    # https://github.com/joblib/joblib/issues/806
+    # previously, child process would not convert temporary memmaps to numpy
+    # arrays when sending the data back to the parent process. This would lead
+    # to permission errors on windows when deleting joblib's temporary folder,
+    # as the memmaped files handles would still opened in the parent process.
+    cmd = '''if 1:
+        import os
+        import time
+
+        import numpy as np
+
+        from joblib import Parallel, delayed
+        from testutils import return_slice_of_data
+
+        data = np.ones(int(2e6))
+
+        if __name__ == '__main__':
+            # warm-up call to launch the workers and start the resource_tracker
+            _ = Parallel(n_jobs=2, verbose=5, backend='{b}')(
+                delayed(id)(i) for i in range(20))
+
+            time.sleep(0.5)
+
+            slice_of_data = Parallel(n_jobs=2, verbose=5, backend='{b}')(
+                delayed(return_slice_of_data)(data, 0, 20) for _ in range(10))
+    '''.format(b=backend)
+
+    for _ in range(3):
+        env = os.environ.copy()
+        env['PYTHONPATH'] = os.path.dirname(__file__)
+        p = subprocess.Popen([sys.executable, '-c', cmd],
+                             stderr=subprocess.PIPE,
+                             stdout=subprocess.PIPE, env=env)
+        p.wait()
+        out, err = p.communicate()
+        assert p.returncode == 0, err
+        assert out == b''
+        if sys.version_info[:3] not in [(3, 8, 0), (3, 8, 1)]:
+            # In early versions of Python 3.8, a reference leak
+            # https://github.com/cloudpipe/cloudpickle/issues/327, holds
+            # references to pickled objects, generating race condition during
+            # cleanup finalizers of joblib and noisy resource_tracker outputs.
+            assert b'resource_tracker' not in err
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_parallel_isolated_temp_folders(backend):
+    # Test that consecutive Parallel call use isolated subfolders, even
+    # for the loky backend that reuses its executor instance across calls.
+    array = np.arange(int(1e2))
+    [filename_1] = Parallel(n_jobs=2, backend=backend, max_nbytes=10)(
+        delayed(getattr)(array, 'filename') for _ in range(1)
+    )
+    [filename_2] = Parallel(n_jobs=2, backend=backend, max_nbytes=10)(
+        delayed(getattr)(array, 'filename') for _ in range(1)
+    )
+    assert os.path.dirname(filename_2) != os.path.dirname(filename_1)
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_managed_backend_reuse_temp_folder(backend):
+    # Test that calls to a managed parallel object reuse the same memmaps.
+    array = np.arange(int(1e2))
+    with Parallel(n_jobs=2, backend=backend, max_nbytes=10) as p:
+        [filename_1] = p(
+            delayed(getattr)(array, 'filename') for _ in range(1)
+        )
+        [filename_2] = p(
+            delayed(getattr)(array, 'filename') for _ in range(1)
+        )
+    assert os.path.dirname(filename_2) == os.path.dirname(filename_1)
+
+
+@with_numpy
+@with_multiprocessing
+def test_memmapping_temp_folder_thread_safety():
+    # Concurrent calls to Parallel with the loky backend will use the same
+    # executor, and thus the same reducers. Make sure that those reducers use
+    # different temporary folders depending on which Parallel objects called
+    # them, which is necessary to limit potential race conditions during the
+    # garbage collection of temporary memmaps.
+    array = np.arange(int(1e2))
+
+    temp_dirs_thread_1 = set()
+    temp_dirs_thread_2 = set()
+
+    def concurrent_get_filename(array, temp_dirs):
+        with Parallel(backend='loky', n_jobs=2, max_nbytes=10) as p:
+            for i in range(10):
+                [filename] = p(
+                    delayed(getattr)(array, 'filename') for _ in range(1)
+                )
+                temp_dirs.add(os.path.dirname(filename))
+
+    t1 = threading.Thread(
+        target=concurrent_get_filename, args=(array, temp_dirs_thread_1)
+    )
+    t2 = threading.Thread(
+        target=concurrent_get_filename, args=(array, temp_dirs_thread_2)
+    )
+
+    t1.start()
+    t2.start()
+
+    t1.join()
+    t2.join()
+
+    assert len(temp_dirs_thread_1) == 1
+    assert len(temp_dirs_thread_2) == 1
+
+    assert temp_dirs_thread_1 != temp_dirs_thread_2
+
+
+@with_numpy
+@with_multiprocessing
+def test_multithreaded_parallel_termination_resource_tracker_silent():
+    # test that concurrent termination attempts of a same executor does not
+    # emit any spurious error from the resource_tracker. We test various
+    # situations making 0, 1 or both parallel call sending a task that will
+    # make the worker (and thus the whole Parallel call) error out.
+    cmd = '''if 1:
+        import os
+        import numpy as np
+        from joblib import Parallel, delayed
+        from joblib.externals.loky.backend import resource_tracker
+        from concurrent.futures import ThreadPoolExecutor, wait
+
+        resource_tracker.VERBOSE = 0
+
+        array = np.arange(int(1e2))
+
+        temp_dirs_thread_1 = set()
+        temp_dirs_thread_2 = set()
+
+
+        def raise_error(array):
+            raise ValueError
+
+
+        def parallel_get_filename(array, temp_dirs):
+            with Parallel(backend="loky", n_jobs=2, max_nbytes=10) as p:
+                for i in range(10):
+                    [filename] = p(
+                        delayed(getattr)(array, "filename") for _ in range(1)
+                    )
+                    temp_dirs.add(os.path.dirname(filename))
+
+
+        def parallel_raise(array, temp_dirs):
+            with Parallel(backend="loky", n_jobs=2, max_nbytes=10) as p:
+                for i in range(10):
+                    [filename] = p(
+                        delayed(raise_error)(array) for _ in range(1)
+                    )
+                    temp_dirs.add(os.path.dirname(filename))
+
+
+        executor = ThreadPoolExecutor(max_workers=2)
+
+        # both function calls will use the same loky executor, but with a
+        # different Parallel object.
+        future_1 = executor.submit({f1}, array, temp_dirs_thread_1)
+        future_2 = executor.submit({f2}, array, temp_dirs_thread_2)
+
+        # Wait for both threads to terminate their backend
+        wait([future_1, future_2])
+
+        future_1.result()
+        future_2.result()
+    '''
+    functions_and_returncodes = [
+        ("parallel_get_filename", "parallel_get_filename", 0),
+        ("parallel_get_filename", "parallel_raise", 1),
+        ("parallel_raise", "parallel_raise", 1)
+    ]
+
+    for f1, f2, returncode in functions_and_returncodes:
+        p = subprocess.Popen([sys.executable, '-c', cmd.format(f1=f1, f2=f2)],
+                             stderr=subprocess.PIPE, stdout=subprocess.PIPE)
+        p.wait()
+        out, err = p.communicate()
+        assert p.returncode == returncode, out.decode()
+        assert b"resource_tracker" not in err, err.decode()
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_many_parallel_calls_on_same_object(backend):
+    # After #966 got merged, consecutive Parallel objects were sharing temp
+    # folder, which would lead to race conditions happening during the
+    # temporary resources management with the resource_tracker. This is a
+    # non-regression test that makes sure that consecutive Parallel operations
+    # on the same object do not error out.
+    cmd = '''if 1:
+        import os
+        import time
+
+        import numpy as np
+
+        from joblib import Parallel, delayed
+        from testutils import return_slice_of_data
+
+        data = np.ones(100)
+
+        if __name__ == '__main__':
+            for i in range(5):
+                slice_of_data = Parallel(
+                    n_jobs=2, max_nbytes=1, backend='{b}')(
+                        delayed(return_slice_of_data)(data, 0, 20)
+                        for _ in range(10)
+                    )
+    '''.format(b=backend)
+    env = os.environ.copy()
+    env['PYTHONPATH'] = os.path.dirname(__file__)
+    p = subprocess.Popen(
+        [sys.executable, '-c', cmd],
+        stderr=subprocess.PIPE,
+        stdout=subprocess.PIPE,
+        env=env,
+    )
+    p.wait()
+    out, err = p.communicate()
+    assert p.returncode == 0, err
+    assert out == b''
+    if sys.version_info[:3] not in [(3, 8, 0), (3, 8, 1)]:
+        # In early versions of Python 3.8, a reference leak
+        # https://github.com/cloudpipe/cloudpickle/issues/327, holds
+        # references to pickled objects, generating race condition during
+        # cleanup finalizers of joblib and noisy resource_tracker outputs.
+        assert b'resource_tracker' not in err
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_memmap_returned_as_regular_array(backend):
+    data = np.ones(int(1e3))
+    # Check that child processes send temporary memmaps back as numpy arrays.
+    [result] = Parallel(n_jobs=2, backend=backend, max_nbytes=100)(
+        delayed(check_memmap_and_send_back)(data) for _ in range(1))
+    assert _get_backing_memmap(result) is None
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_resource_tracker_silent_when_reference_cycles(backend):
+    # There is a variety of reasons that can make joblib with loky backend
+    # output noisy warnings when a reference cycle is preventing a memmap from
+    # being garbage collected. Especially, joblib's main process finalizer
+    # deletes the temporary folder if it was not done before, which can
+    # interact badly with the resource_tracker. We don't risk leaking any
+    # resources, but this will likely make joblib output a lot of low-level
+    # confusing messages.
+    #
+    # This test makes sure that the resource_tracker is silent when a reference
+    # has been collected concurrently on non-Windows platforms.
+    #
+    # Note that the script in ``cmd`` is the exact same script as in
+    # test_permission_error_windows_reference_cycle.
+    if backend == "loky" and sys.platform.startswith('win'):
+        # XXX: on Windows, reference cycles can delay timely garbage collection
+        # and make it impossible to properly delete the temporary folder in the
+        # main process because of permission errors.
+        pytest.xfail(
+            "The temporary folder cannot be deleted on Windows in the "
+            "presence of a reference cycle"
+        )
+
+    cmd = """if 1:
+        import numpy as np
+        from joblib import Parallel, delayed
+
+
+        data = np.random.rand(int(2e6)).reshape((int(1e6), 2))
+
+        # Build a complex cyclic reference that is likely to delay garbage
+        # collection of the memmapped array in the worker processes.
+        first_list = current_list = [data]
+        for i in range(10):
+            current_list = [current_list]
+        first_list.append(current_list)
+
+        if __name__ == "__main__":
+            results = Parallel(n_jobs=2, backend="{b}")(
+                delayed(len)(current_list) for i in range(10))
+            assert results == [1] * 10
+    """.format(b=backend)
+    p = subprocess.Popen([sys.executable, '-c', cmd], stderr=subprocess.PIPE,
+                         stdout=subprocess.PIPE)
+    p.wait()
+    out, err = p.communicate()
+    out = out.decode()
+    err = err.decode()
+    assert p.returncode == 0, out + "\n\n" + err
+    assert "resource_tracker" not in err, err
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_pool_for_large_arrays(factory, tmpdir):
+    """Check that large arrays are not copied in memory"""
+
+    # Check that the tempfolder is empty
+    assert os.listdir(tmpdir.strpath) == []
+
+    # Build an array reducers that automatically dump large array content
+    # to filesystem backed memmap instances to avoid memory explosion
+    p = factory(3, max_nbytes=40, temp_folder=tmpdir.strpath, verbose=2)
+    try:
+        # The temporary folder for the pool is not provisioned in advance
+        assert os.listdir(tmpdir.strpath) == []
+        assert not os.path.exists(p._temp_folder)
+
+        small = np.ones(5, dtype=np.float32)
+        assert small.nbytes == 20
+        p.map(check_array, [(small, i, 1.0) for i in range(small.shape[0])])
+
+        # Memory has been copied, the pool filesystem folder is unused
+        assert os.listdir(tmpdir.strpath) == []
+
+        # Try with a file larger than the memmap threshold of 40 bytes
+        large = np.ones(100, dtype=np.float64)
+        assert large.nbytes == 800
+        p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
+
+        # The data has been dumped in a temp folder for subprocess to share it
+        # without per-child memory copies
+        assert os.path.isdir(p._temp_folder)
+        dumped_filenames = os.listdir(p._temp_folder)
+        assert len(dumped_filenames) == 1
+
+        # Check that memory mapping is not triggered for arrays with
+        # dtype='object'
+        objects = np.array(['abc'] * 100, dtype='object')
+        results = p.map(has_shareable_memory, [objects])
+        assert not results[0]
+
+    finally:
+        # check FS garbage upon pool termination
+        p.terminate()
+        for i in range(10):
+            sleep(.1)
+            if not os.path.exists(p._temp_folder):
+                break
+        else:  # pragma: no cover
+            raise AssertionError(
+                'temporary folder {} was not deleted'.format(p._temp_folder)
+            )
+        del p
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize(
+    "backend",
+    [
+        pytest.param(
+            "multiprocessing",
+            marks=pytest.mark.xfail(
+                reason='https://github.com/joblib/joblib/issues/1086'
+            ),
+        ),
+        "loky",
+    ]
+)
+def test_child_raises_parent_exits_cleanly(backend):
+    # When a task executed by a child process raises an error, the parent
+    # process's backend is notified, and calls abort_everything.
+    # In loky, abort_everything itself calls shutdown(kill_workers=True) which
+    # sends SIGKILL to the worker, preventing it from running the finalizers
+    # supposed to signal the resource_tracker when the worker is done using
+    # objects relying on a shared resource (e.g np.memmaps). Because this
+    # behavior is prone to :
+    # - cause a resource leak
+    # - make the resource tracker emit noisy resource warnings
+    # we explicitly test that, when the said situation occurs:
+    # - no resources are actually leaked
+    # - the temporary resources are deleted as soon as possible (typically, at
+    #   the end of the failing Parallel call)
+    # - the resource_tracker does not emit any warnings.
+    cmd = """if 1:
+        import os
+        from pathlib import Path
+        from time import sleep
+
+        import numpy as np
+        from joblib import Parallel, delayed
+        from testutils import print_filename_and_raise
+
+        data = np.random.rand(1000)
+
+        def get_temp_folder(parallel_obj, backend):
+            if "{b}" == "loky":
+                return Path(parallel_obj._backend._workers._temp_folder)
+            else:
+                return Path(parallel_obj._backend._pool._temp_folder)
+
+
+        if __name__ == "__main__":
+            try:
+                with Parallel(n_jobs=2, backend="{b}", max_nbytes=100) as p:
+                    temp_folder = get_temp_folder(p, "{b}")
+                    p(delayed(print_filename_and_raise)(data)
+                              for i in range(1))
+            except ValueError as e:
+                # the temporary folder should be deleted by the end of this
+                # call but apparently on some file systems, this takes
+                # some time to be visible.
+                #
+                # We attempt to write into the temporary folder to test for
+                # its existence and we wait for a maximum of 10 seconds.
+                for i in range(100):
+                    try:
+                        with open(temp_folder / "some_file.txt", "w") as f:
+                            f.write("some content")
+                    except FileNotFoundError:
+                        # temp_folder has been deleted, all is fine
+                        break
+
+                    # ... else, wait a bit and try again
+                    sleep(.1)
+                else:
+                    raise AssertionError(
+                        str(temp_folder) + " was not deleted"
+                    ) from e
+    """.format(b=backend)
+    env = os.environ.copy()
+    env['PYTHONPATH'] = os.path.dirname(__file__)
+    p = subprocess.Popen([sys.executable, '-c', cmd], stderr=subprocess.PIPE,
+                         stdout=subprocess.PIPE, env=env)
+    p.wait()
+    out, err = p.communicate()
+    out, err = out.decode(), err.decode()
+    filename = out.split('\n')[0]
+    assert p.returncode == 0, err or out
+    assert err == ''  # no resource_tracker warnings.
+    assert not os.path.exists(filename)
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_pool_for_large_arrays_disabled(factory, tmpdir):
+    """Check that large arrays memmapping can be disabled"""
+    # Set max_nbytes to None to disable the auto memmapping feature
+    p = factory(3, max_nbytes=None, temp_folder=tmpdir.strpath)
+    try:
+
+        # Check that the tempfolder is empty
+        assert os.listdir(tmpdir.strpath) == []
+
+        # Try with a file largish than the memmap threshold of 40 bytes
+        large = np.ones(100, dtype=np.float64)
+        assert large.nbytes == 800
+        p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
+
+        # Check that the tempfolder is still empty
+        assert os.listdir(tmpdir.strpath) == []
+
+    finally:
+        # Cleanup open file descriptors
+        p.terminate()
+        del p
+
+
+@with_numpy
+@with_multiprocessing
+@with_dev_shm
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_on_large_enough_dev_shm(factory):
+    """Check that memmapping uses /dev/shm when possible"""
+    orig_size = jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE
+    try:
+        # Make joblib believe that it can use /dev/shm even when running on a
+        # CI container where the size of the /dev/shm is not very large (that
+        # is at least 32 MB instead of 2 GB by default).
+        jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = int(32e6)
+        p = factory(3, max_nbytes=10)
+        try:
+            # Check that the pool has correctly detected the presence of the
+            # shared memory filesystem.
+            pool_temp_folder = p._temp_folder
+            folder_prefix = '/dev/shm/joblib_memmapping_folder_'
+            assert pool_temp_folder.startswith(folder_prefix)
+            assert os.path.exists(pool_temp_folder)
+
+            # Try with a file larger than the memmap threshold of 10 bytes
+            a = np.ones(100, dtype=np.float64)
+            assert a.nbytes == 800
+            p.map(id, [a] * 10)
+            # a should have been memmapped to the pool temp folder: the joblib
+            # pickling procedure generate one .pkl file:
+            assert len(os.listdir(pool_temp_folder)) == 1
+
+            # create a new array with content that is different from 'a' so
+            # that it is mapped to a different file in the temporary folder of
+            # the pool.
+            b = np.ones(100, dtype=np.float64) * 2
+            assert b.nbytes == 800
+            p.map(id, [b] * 10)
+            # A copy of both a and b are now stored in the shared memory folder
+            assert len(os.listdir(pool_temp_folder)) == 2
+        finally:
+            # Cleanup open file descriptors
+            p.terminate()
+            del p
+
+        for i in range(100):
+            # The temp folder is cleaned up upon pool termination
+            if not os.path.exists(pool_temp_folder):
+                break
+            sleep(.1)
+        else:  # pragma: no cover
+            raise AssertionError('temporary folder of pool was not deleted')
+    finally:
+        jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = orig_size
+
+
+@with_numpy
+@with_multiprocessing
+@with_dev_shm
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_on_too_small_dev_shm(factory):
+    orig_size = jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE
+    try:
+        # Make joblib believe that it cannot use /dev/shm unless there is
+        # 42 exabytes of available shared memory in /dev/shm
+        jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = int(42e18)
+
+        p = factory(3, max_nbytes=10)
+        try:
+            # Check that the pool has correctly detected the presence of the
+            # shared memory filesystem.
+            pool_temp_folder = p._temp_folder
+            assert not pool_temp_folder.startswith('/dev/shm')
+        finally:
+            # Cleanup open file descriptors
+            p.terminate()
+            del p
+
+        # The temp folder is cleaned up upon pool termination
+        assert not os.path.exists(pool_temp_folder)
+    finally:
+        jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = orig_size
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_pool_for_large_arrays_in_return(factory, tmpdir):
+    """Check that large arrays are not copied in memory in return"""
+    assert_array_equal = np.testing.assert_array_equal
+
+    # Build an array reducers that automatically dump large array content
+    # but check that the returned datastructure are regular arrays to avoid
+    # passing a memmap array pointing to a pool controlled temp folder that
+    # might be confusing to the user
+
+    # The MemmappingPool user can always return numpy.memmap object explicitly
+    # to avoid memory copy
+    p = factory(3, max_nbytes=10, temp_folder=tmpdir.strpath)
+    try:
+        res = p.apply_async(np.ones, args=(1000,))
+        large = res.get()
+        assert not has_shareable_memory(large)
+        assert_array_equal(large, np.ones(1000))
+    finally:
+        p.terminate()
+        del p
+
+
+def _worker_multiply(a, n_times):
+    """Multiplication function to be executed by subprocess"""
+    assert has_shareable_memory(a)
+    return a * n_times
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_workaround_against_bad_memmap_with_copied_buffers(factory, tmpdir):
+    """Check that memmaps with a bad buffer are returned as regular arrays
+
+    Unary operations and ufuncs on memmap instances return a new memmap
+    instance with an in-memory buffer (probably a numpy bug).
+    """
+    assert_array_equal = np.testing.assert_array_equal
+
+    p = factory(3, max_nbytes=10, temp_folder=tmpdir.strpath)
+    try:
+        # Send a complex, large-ish view on a array that will be converted to
+        # a memmap in the worker process
+        a = np.asarray(np.arange(6000).reshape((1000, 2, 3)),
+                       order='F')[:, :1, :]
+
+        # Call a non-inplace multiply operation on the worker and memmap and
+        # send it back to the parent.
+        b = p.apply_async(_worker_multiply, args=(a, 3)).get()
+        assert not has_shareable_memory(b)
+        assert_array_equal(b, 3 * a)
+    finally:
+        p.terminate()
+        del p
+
+
+def identity(arg):
+    return arg
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize(
+    "factory,retry_no",
+    list(itertools.product(
+        [MemmappingPool, TestExecutor.get_memmapping_executor], range(3))),
+    ids=['{}, {}'.format(x, y) for x, y in itertools.product(
+        ["multiprocessing", "loky"], map(str, range(3)))])
+def test_pool_memmap_with_big_offset(factory, retry_no, tmpdir):
+    # Test that numpy memmap offset is set correctly if greater than
+    # mmap.ALLOCATIONGRANULARITY, see
+    # https://github.com/joblib/joblib/issues/451 and
+    # https://github.com/numpy/numpy/pull/8443 for more details.
+    fname = tmpdir.join('test.mmap').strpath
+    size = 5 * mmap.ALLOCATIONGRANULARITY
+    offset = mmap.ALLOCATIONGRANULARITY + 1
+    obj = make_memmap(fname, mode='w+', shape=size, dtype='uint8',
+                      offset=offset)
+
+    p = factory(2, temp_folder=tmpdir.strpath)
+    result = p.apply_async(identity, args=(obj,)).get()
+    assert isinstance(result, np.memmap)
+    assert result.offset == offset
+    np.testing.assert_array_equal(obj, result)
+    p.terminate()
+
+
+def test_pool_get_temp_dir(tmpdir):
+    pool_folder_name = 'test.tmpdir'
+    pool_folder, shared_mem = _get_temp_dir(pool_folder_name, tmpdir.strpath)
+    assert shared_mem is False
+    assert pool_folder == tmpdir.join('test.tmpdir').strpath
+
+    pool_folder, shared_mem = _get_temp_dir(pool_folder_name, temp_folder=None)
+    if sys.platform.startswith('win'):
+        assert shared_mem is False
+    assert pool_folder.endswith(pool_folder_name)
+
+
+def test_pool_get_temp_dir_no_statvfs(tmpdir, monkeypatch):
+    """Check that _get_temp_dir works when os.statvfs is not defined
+
+    Regression test for #902
+    """
+    pool_folder_name = 'test.tmpdir'
+    import joblib._memmapping_reducer
+    if hasattr(joblib._memmapping_reducer.os, 'statvfs'):
+        # We are on Unix, since Windows doesn't have this function
+        monkeypatch.delattr(joblib._memmapping_reducer.os, 'statvfs')
+
+    pool_folder, shared_mem = _get_temp_dir(pool_folder_name, temp_folder=None)
+    if sys.platform.startswith('win'):
+        assert shared_mem is False
+    assert pool_folder.endswith(pool_folder_name)
+
+
+@with_numpy
+@skipif(sys.platform == 'win32', reason='This test fails with a '
+        'PermissionError on Windows')
+@parametrize("mmap_mode", ["r+", "w+"])
+def test_numpy_arrays_use_different_memory(mmap_mode):
+    def func(arr, value):
+        arr[:] = value
+        return arr
+
+    arrays = [np.zeros((10, 10), dtype='float64') for i in range(10)]
+
+    results = Parallel(mmap_mode=mmap_mode, max_nbytes=0, n_jobs=2)(
+        delayed(func)(arr, i) for i, arr in enumerate(arrays))
+
+    for i, arr in enumerate(results):
+        np.testing.assert_array_equal(arr, i)
+
+
+@with_numpy
+def test_weak_array_key_map():
+
+    def assert_empty_after_gc_collect(container, retries=100):
+        for i in range(retries):
+            if len(container) == 0:
+                return
+            gc.collect()
+            sleep(.1)
+        assert len(container) == 0
+
+    a = np.ones(42)
+    m = _WeakArrayKeyMap()
+    m.set(a, 'a')
+    assert m.get(a) == 'a'
+
+    b = a
+    assert m.get(b) == 'a'
+    m.set(b, 'b')
+    assert m.get(a) == 'b'
+
+    del a
+    gc.collect()
+    assert len(m._data) == 1
+    assert m.get(b) == 'b'
+
+    del b
+    assert_empty_after_gc_collect(m._data)
+
+    c = np.ones(42)
+    m.set(c, 'c')
+    assert len(m._data) == 1
+    assert m.get(c) == 'c'
+
+    with raises(KeyError):
+        m.get(np.ones(42))
+
+    del c
+    assert_empty_after_gc_collect(m._data)
+
+    # Check that creating and dropping numpy arrays with potentially the same
+    # object id will not cause the map to get confused.
+    def get_set_get_collect(m, i):
+        a = np.ones(42)
+        with raises(KeyError):
+            m.get(a)
+        m.set(a, i)
+        assert m.get(a) == i
+        return id(a)
+
+    unique_ids = set([get_set_get_collect(m, i) for i in range(1000)])
+    if platform.python_implementation() == 'CPython':
+        # On CPython (at least) the same id is often reused many times for the
+        # temporary arrays created under the local scope of the
+        # get_set_get_collect function without causing any spurious lookups /
+        # insertions in the map. Apparently on Python nogil, the id is not
+        # reused as often.
+        max_len_unique_ids = 400 if getattr(sys.flags, 'nogil', False) else 100
+        assert len(unique_ids) < max_len_unique_ids
+
+
+def test_weak_array_key_map_no_pickling():
+    m = _WeakArrayKeyMap()
+    with raises(pickle.PicklingError):
+        pickle.dumps(m)
+
+
+@with_numpy
+@with_multiprocessing
+def test_direct_mmap(tmpdir):
+    testfile = str(tmpdir.join('arr.dat'))
+    a = np.arange(10, dtype='uint8')
+    a.tofile(testfile)
+
+    def _read_array():
+        with open(testfile) as fd:
+            mm = mmap.mmap(fd.fileno(), 0, access=mmap.ACCESS_READ, offset=0)
+        return np.ndarray((10,), dtype=np.uint8, buffer=mm, offset=0)
+
+    def func(x):
+        return x**2
+
+    arr = _read_array()
+
+    # this is expected to work and gives the reference
+    ref = Parallel(n_jobs=2)(delayed(func)(x) for x in [a])
+
+    # now test that it work with the mmap array
+    results = Parallel(n_jobs=2)(delayed(func)(x) for x in [arr])
+    np.testing.assert_array_equal(results, ref)
+
+    # also test with a mmap array read in the subprocess
+    def worker():
+        return _read_array()
+
+    results = Parallel(n_jobs=2)(delayed(worker)() for _ in range(1))
+    np.testing.assert_array_equal(results[0], arr)

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

@@ -0,0 +1,170 @@
+import asyncio
+import gc
+import shutil
+
+import pytest
+
+from joblib.memory import (AsyncMemorizedFunc, AsyncNotMemorizedFunc,
+                           MemorizedResult, Memory, NotMemorizedResult)
+from joblib.test.common import np, with_numpy
+from joblib.testing import raises
+
+from .test_memory import (corrupt_single_cache_item,
+                          monkeypatch_cached_func_warn)
+
+
+async def check_identity_lazy_async(func, accumulator, location):
+    """ Similar to check_identity_lazy_async for coroutine functions"""
+    memory = Memory(location=location, verbose=0)
+    func = memory.cache(func)
+    for i in range(3):
+        for _ in range(2):
+            value = await func(i)
+            assert value == i
+            assert len(accumulator) == i + 1
+
+
+@pytest.mark.asyncio
+async def test_memory_integration_async(tmpdir):
+    accumulator = list()
+
+    async def f(n):
+        await asyncio.sleep(0.1)
+        accumulator.append(1)
+        return n
+
+    await check_identity_lazy_async(f, accumulator, tmpdir.strpath)
+
+    # Now test clearing
+    for compress in (False, True):
+        for mmap_mode in ('r', None):
+            memory = Memory(location=tmpdir.strpath, verbose=10,
+                            mmap_mode=mmap_mode, compress=compress)
+            # First clear the cache directory, to check that our code can
+            # handle that
+            # NOTE: this line would raise an exception, as the database
+            # file is still open; we ignore the error since we want to
+            # test what happens if the directory disappears
+            shutil.rmtree(tmpdir.strpath, ignore_errors=True)
+            g = memory.cache(f)
+            await g(1)
+            g.clear(warn=False)
+            current_accumulator = len(accumulator)
+            out = await g(1)
+
+        assert len(accumulator) == current_accumulator + 1
+        # Also, check that Memory.eval works similarly
+        evaled = await memory.eval(f, 1)
+        assert evaled == out
+        assert len(accumulator) == current_accumulator + 1
+
+    # Now do a smoke test with a function defined in __main__, as the name
+    # mangling rules are more complex
+    f.__module__ = '__main__'
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    await memory.cache(f)(1)
+
+
+@pytest.mark.asyncio
+async def test_no_memory_async():
+    accumulator = list()
+
+    async def ff(x):
+        await asyncio.sleep(0.1)
+        accumulator.append(1)
+        return x
+
+    memory = Memory(location=None, verbose=0)
+    gg = memory.cache(ff)
+    for _ in range(4):
+        current_accumulator = len(accumulator)
+        await gg(1)
+        assert len(accumulator) == current_accumulator + 1
+
+
+@with_numpy
+@pytest.mark.asyncio
+async def test_memory_numpy_check_mmap_mode_async(tmpdir, monkeypatch):
+    """Check that mmap_mode is respected even at the first call"""
+
+    memory = Memory(location=tmpdir.strpath, mmap_mode='r', verbose=0)
+
+    @memory.cache()
+    async def twice(a):
+        return a * 2
+
+    a = np.ones(3)
+    b = await twice(a)
+    c = await twice(a)
+
+    assert isinstance(c, np.memmap)
+    assert c.mode == 'r'
+
+    assert isinstance(b, np.memmap)
+    assert b.mode == 'r'
+
+    # Corrupts the file,  Deleting b and c mmaps
+    # is necessary to be able edit the file
+    del b
+    del c
+    gc.collect()
+    corrupt_single_cache_item(memory)
+
+    # Make sure that corrupting the file causes recomputation and that
+    # a warning is issued.
+    recorded_warnings = monkeypatch_cached_func_warn(twice, monkeypatch)
+    d = await twice(a)
+    assert len(recorded_warnings) == 1
+    exception_msg = 'Exception while loading results'
+    assert exception_msg in recorded_warnings[0]
+    # Asserts that the recomputation returns a mmap
+    assert isinstance(d, np.memmap)
+    assert d.mode == 'r'
+
+
+@pytest.mark.asyncio
+async def test_call_and_shelve_async(tmpdir):
+    async def f(x, y=1):
+        await asyncio.sleep(0.1)
+        return x ** 2 + y
+
+    # Test MemorizedFunc outputting a reference to cache.
+    for func, Result in zip((AsyncMemorizedFunc(f, tmpdir.strpath),
+                             AsyncNotMemorizedFunc(f),
+                             Memory(location=tmpdir.strpath,
+                                    verbose=0).cache(f),
+                             Memory(location=None).cache(f),
+                             ),
+                            (MemorizedResult, NotMemorizedResult,
+                             MemorizedResult, NotMemorizedResult,
+                             )):
+        for _ in range(2):
+            result = await func.call_and_shelve(2)
+            assert isinstance(result, Result)
+            assert result.get() == 5
+
+        result.clear()
+        with raises(KeyError):
+            result.get()
+        result.clear()  # Do nothing if there is no cache.
+
+
+@pytest.mark.asyncio
+async def test_memorized_func_call_async(memory):
+
+    async def ff(x, counter):
+        await asyncio.sleep(0.1)
+        counter[x] = counter.get(x, 0) + 1
+        return counter[x]
+
+    gg = memory.cache(ff, ignore=['counter'])
+
+    counter = {}
+    assert await gg(2, counter) == 1
+    assert await gg(2, counter) == 1
+
+    x, meta = await gg.call(2, counter)
+    assert x == 2, "f has not been called properly"
+    assert isinstance(meta, dict), (
+        "Metadata are not returned by MemorizedFunc.call."
+    )

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

@@ -0,0 +1,32 @@
+"""
+Pyodide and other single-threaded Python builds will be missing the
+_multiprocessing module. Test that joblib still works in this environment.
+"""
+
+import os
+import subprocess
+import sys
+
+
+def test_missing_multiprocessing(tmp_path):
+    """
+    Test that import joblib works even if _multiprocessing is missing.
+
+    pytest has already imported everything from joblib. The most reasonable way
+    to test importing joblib with modified environment is to invoke a separate
+    Python process. This also ensures that we don't break other tests by
+    importing a bad `_multiprocessing` module.
+    """
+    (tmp_path / "_multiprocessing.py").write_text(
+        'raise ImportError("No _multiprocessing module!")'
+    )
+    env = dict(os.environ)
+    # For subprocess, use current sys.path with our custom version of
+    # multiprocessing inserted.
+    env["PYTHONPATH"] = ":".join([str(tmp_path)] + sys.path)
+    subprocess.check_call(
+        [sys.executable, "-c",
+         "import joblib, math; "
+         "joblib.Parallel(n_jobs=1)("
+         "joblib.delayed(math.sqrt)(i**2) for i in range(10))"
+         ], env=env)

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

@@ -0,0 +1,53 @@
+import sys
+import joblib
+from joblib.testing import check_subprocess_call
+from joblib.test.common import with_multiprocessing
+
+
+def test_version():
+    assert hasattr(joblib, '__version__'), (
+        "There are no __version__ argument on the joblib module")
+
+
+@with_multiprocessing
+def test_no_start_method_side_effect_on_import():
+    # check that importing joblib does not implicitly set the global
+    # start_method for multiprocessing.
+    code = """if True:
+        import joblib
+        import multiprocessing as mp
+        # The following line would raise RuntimeError if the
+        # start_method is already set.
+        mp.set_start_method("loky")
+    """
+    check_subprocess_call([sys.executable, '-c', code])
+
+
+@with_multiprocessing
+def test_no_semaphore_tracker_on_import():
+    # check that importing joblib does not implicitly spawn a resource tracker
+    # or a semaphore tracker
+    code = """if True:
+        import joblib
+        from multiprocessing import semaphore_tracker
+        # The following line would raise RuntimeError if the
+        # start_method is already set.
+        msg = "multiprocessing.semaphore_tracker has been spawned on import"
+        assert semaphore_tracker._semaphore_tracker._fd is None, msg"""
+    if sys.version_info >= (3, 8):
+        # semaphore_tracker was renamed in Python 3.8:
+        code = code.replace("semaphore_tracker", "resource_tracker")
+    check_subprocess_call([sys.executable, '-c', code])
+
+
+@with_multiprocessing
+def test_no_resource_tracker_on_import():
+    code = """if True:
+        import joblib
+        from joblib.externals.loky.backend import resource_tracker
+        # The following line would raise RuntimeError if the
+        # start_method is already set.
+        msg = "loky.resource_tracker has been spawned on import"
+        assert resource_tracker._resource_tracker._fd is None, msg
+    """
+    check_subprocess_call([sys.executable, '-c', code])

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

@@ -0,0 +1,9 @@
+from joblib.compressor import BinaryZlibFile
+from joblib.testing import parametrize
+
+
+@parametrize('filename', ['test', u'test'])  # testing str and unicode names
+def test_binary_zlib_file(tmpdir, filename):
+    """Testing creation of files depending on the type of the filenames."""
+    binary_file = BinaryZlibFile(tmpdir.join(filename).strpath, mode='wb')
+    binary_file.close()

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

@@ -0,0 +1,2056 @@
+"""
+Test the parallel module.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2010-2011 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+import os
+import sys
+import time
+import mmap
+import weakref
+import warnings
+import threading
+from traceback import format_exception
+from math import sqrt
+from time import sleep
+from pickle import PicklingError
+from contextlib import nullcontext
+from multiprocessing import TimeoutError
+import pytest
+
+import joblib
+from joblib import parallel
+from joblib import dump, load
+
+from joblib._multiprocessing_helpers import mp
+
+from joblib.test.common import np, with_numpy
+from joblib.test.common import with_multiprocessing
+from joblib.test.common import IS_PYPY, force_gc_pypy
+from joblib.testing import (parametrize, raises, check_subprocess_call,
+                            skipif, warns)
+
+if mp is not None:
+    # Loky is not available if multiprocessing is not
+    from joblib.externals.loky import get_reusable_executor
+
+from queue import Queue
+
+try:
+    import posix
+except ImportError:
+    posix = None
+
+try:
+    from ._openmp_test_helper.parallel_sum import parallel_sum
+except ImportError:
+    parallel_sum = None
+
+try:
+    import distributed
+except ImportError:
+    distributed = None
+
+from joblib._parallel_backends import SequentialBackend
+from joblib._parallel_backends import ThreadingBackend
+from joblib._parallel_backends import MultiprocessingBackend
+from joblib._parallel_backends import ParallelBackendBase
+from joblib._parallel_backends import LokyBackend
+
+from joblib.parallel import Parallel, delayed
+from joblib.parallel import parallel_config
+from joblib.parallel import parallel_backend
+from joblib.parallel import register_parallel_backend
+from joblib.parallel import effective_n_jobs, cpu_count
+
+from joblib.parallel import mp, BACKENDS, DEFAULT_BACKEND
+
+
+RETURN_GENERATOR_BACKENDS = BACKENDS.copy()
+RETURN_GENERATOR_BACKENDS.pop("multiprocessing", None)
+
+ALL_VALID_BACKENDS = [None] + sorted(BACKENDS.keys())
+# Add instances of backend classes deriving from ParallelBackendBase
+ALL_VALID_BACKENDS += [BACKENDS[backend_str]() for backend_str in BACKENDS]
+if mp is None:
+    PROCESS_BACKENDS = []
+else:
+    PROCESS_BACKENDS = ['multiprocessing', 'loky']
+PARALLEL_BACKENDS = PROCESS_BACKENDS + ['threading']
+
+if hasattr(mp, 'get_context'):
+    # Custom multiprocessing context in Python 3.4+
+    ALL_VALID_BACKENDS.append(mp.get_context('spawn'))
+
+DefaultBackend = BACKENDS[DEFAULT_BACKEND]
+
+
+def get_workers(backend):
+    return getattr(backend, '_pool', getattr(backend, '_workers', None))
+
+
+def division(x, y):
+    return x / y
+
+
+def square(x):
+    return x ** 2
+
+
+class MyExceptionWithFinickyInit(Exception):
+    """An exception class with non trivial __init__
+    """
+    def __init__(self, a, b, c, d):
+        pass
+
+
+def exception_raiser(x, custom_exception=False):
+    if x == 7:
+        raise (MyExceptionWithFinickyInit('a', 'b', 'c', 'd')
+               if custom_exception else ValueError)
+    return x
+
+
+def interrupt_raiser(x):
+    time.sleep(.05)
+    raise KeyboardInterrupt
+
+
+def f(x, y=0, z=0):
+    """ A module-level function so that it can be spawn with
+    multiprocessing.
+    """
+    return x ** 2 + y + z
+
+
+def _active_backend_type():
+    return type(parallel.get_active_backend()[0])
+
+
+def parallel_func(inner_n_jobs, backend):
+    return Parallel(n_jobs=inner_n_jobs, backend=backend)(
+        delayed(square)(i) for i in range(3))
+
+
+###############################################################################
+def test_cpu_count():
+    assert cpu_count() > 0
+
+
+def test_effective_n_jobs():
+    assert effective_n_jobs() > 0
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+@pytest.mark.parametrize(
+    "backend_n_jobs, expected_n_jobs",
+    [(3, 3), (-1, effective_n_jobs(n_jobs=-1)), (None, 1)],
+    ids=["positive-int", "negative-int", "None"]
+)
+@with_multiprocessing
+def test_effective_n_jobs_None(context, backend_n_jobs, expected_n_jobs):
+    # check the number of effective jobs when `n_jobs=None`
+    # non-regression test for https://github.com/joblib/joblib/issues/984
+    with context("threading", n_jobs=backend_n_jobs):
+        # when using a backend, the default of number jobs will be the one set
+        # in the backend
+        assert effective_n_jobs(n_jobs=None) == expected_n_jobs
+    # without any backend, None will default to a single job
+    assert effective_n_jobs(n_jobs=None) == 1
+
+
+###############################################################################
+# Test parallel
+
+@parametrize('backend', ALL_VALID_BACKENDS)
+@parametrize('n_jobs', [1, 2, -1, -2])
+@parametrize('verbose', [2, 11, 100])
+def test_simple_parallel(backend, n_jobs, verbose):
+    assert ([square(x) for x in range(5)] ==
+            Parallel(n_jobs=n_jobs, backend=backend,
+                     verbose=verbose)(
+                delayed(square)(x) for x in range(5)))
+
+
+@parametrize('backend', ALL_VALID_BACKENDS)
+def test_main_thread_renamed_no_warning(backend, monkeypatch):
+    # Check that no default backend relies on the name of the main thread:
+    # https://github.com/joblib/joblib/issues/180#issuecomment-253266247
+    # Some programs use a different name for the main thread. This is the case
+    # for uWSGI apps for instance.
+    monkeypatch.setattr(target=threading.current_thread(), name='name',
+                        value='some_new_name_for_the_main_thread')
+
+    with warnings.catch_warnings(record=True) as warninfo:
+        results = Parallel(n_jobs=2, backend=backend)(
+            delayed(square)(x) for x in range(3))
+        assert results == [0, 1, 4]
+
+    # Due to the default parameters of LokyBackend, there is a chance that
+    # warninfo catches Warnings from worker timeouts. We remove it if it exists
+    warninfo = [w for w in warninfo if "worker timeout" not in str(w.message)]
+
+    # The multiprocessing backend will raise a warning when detecting that is
+    # started from the non-main thread. Let's check that there is no false
+    # positive because of the name change.
+    assert len(warninfo) == 0
+
+
+def _assert_warning_nested(backend, inner_n_jobs, expected):
+    with warnings.catch_warnings(record=True) as warninfo:
+        warnings.simplefilter("always")
+        parallel_func(backend=backend, inner_n_jobs=inner_n_jobs)
+
+    warninfo = [w.message for w in warninfo]
+    if expected:
+        if warninfo:
+            warnings_are_correct = all(
+                'backed parallel loops cannot' in each.args[0]
+                for each in warninfo
+            )
+            # With Python nogil, when the outer backend is threading, we might
+            # see more that one warning
+            warnings_have_the_right_length = (
+                len(warninfo) >= 1 if getattr(sys.flags, 'nogil', False)
+                else len(warninfo) == 1)
+            return warnings_are_correct and warnings_have_the_right_length
+
+        return False
+    else:
+        assert not warninfo
+        return True
+
+
+@with_multiprocessing
+@parametrize('parent_backend,child_backend,expected', [
+    ('loky', 'multiprocessing', True),
+    ('loky', 'loky', False),
+    ('multiprocessing', 'multiprocessing', True),
+    ('multiprocessing', 'loky', True),
+    ('threading', 'multiprocessing', True),
+    ('threading', 'loky', True),
+])
+def test_nested_parallel_warnings(parent_backend, child_backend, expected):
+
+    # no warnings if inner_n_jobs=1
+    Parallel(n_jobs=2, backend=parent_backend)(
+        delayed(_assert_warning_nested)(
+            backend=child_backend, inner_n_jobs=1,
+            expected=False)
+        for _ in range(5))
+
+    #  warnings if inner_n_jobs != 1 and expected
+    res = Parallel(n_jobs=2, backend=parent_backend)(
+        delayed(_assert_warning_nested)(
+            backend=child_backend, inner_n_jobs=2,
+            expected=expected)
+        for _ in range(5))
+
+    # warning handling is not thread safe. One thread might see multiple
+    # warning or no warning at all.
+    if parent_backend == "threading":
+        if IS_PYPY and not any(res):
+            # Related to joblib#1426, should be removed once it is solved.
+            pytest.xfail(reason="This test often fails in PyPy.")
+        assert any(res)
+    else:
+        assert all(res)
+
+
+@with_multiprocessing
+@parametrize('backend', ['loky', 'multiprocessing', 'threading'])
+def test_background_thread_parallelism(backend):
+    is_run_parallel = [False]
+
+    def background_thread(is_run_parallel):
+        with warnings.catch_warnings(record=True) as warninfo:
+            Parallel(n_jobs=2)(
+                delayed(sleep)(.1) for _ in range(4))
+        print(len(warninfo))
+        is_run_parallel[0] = len(warninfo) == 0
+
+    t = threading.Thread(target=background_thread, args=(is_run_parallel,))
+    t.start()
+    t.join()
+    assert is_run_parallel[0]
+
+
+def nested_loop(backend):
+    Parallel(n_jobs=2, backend=backend)(
+        delayed(square)(.01) for _ in range(2))
+
+
+@parametrize('child_backend', BACKENDS)
+@parametrize('parent_backend', BACKENDS)
+def test_nested_loop(parent_backend, child_backend):
+    Parallel(n_jobs=2, backend=parent_backend)(
+        delayed(nested_loop)(child_backend) for _ in range(2))
+
+
+def raise_exception(backend):
+    raise ValueError
+
+
+@with_multiprocessing
+def test_nested_loop_with_exception_with_loky():
+    with raises(ValueError):
+        with Parallel(n_jobs=2, backend="loky") as parallel:
+            parallel([delayed(nested_loop)("loky"),
+                      delayed(raise_exception)("loky")])
+
+
+def test_mutate_input_with_threads():
+    """Input is mutable when using the threading backend"""
+    q = Queue(maxsize=5)
+    Parallel(n_jobs=2, backend="threading")(
+        delayed(q.put)(1) for _ in range(5))
+    assert q.full()
+
+
+@parametrize('n_jobs', [1, 2, 3])
+def test_parallel_kwargs(n_jobs):
+    """Check the keyword argument processing of pmap."""
+    lst = range(10)
+    assert ([f(x, y=1) for x in lst] ==
+            Parallel(n_jobs=n_jobs)(delayed(f)(x, y=1) for x in lst))
+
+
+@parametrize('backend', PARALLEL_BACKENDS)
+def test_parallel_as_context_manager(backend):
+    lst = range(10)
+    expected = [f(x, y=1) for x in lst]
+
+    with Parallel(n_jobs=4, backend=backend) as p:
+        # Internally a pool instance has been eagerly created and is managed
+        # via the context manager protocol
+        managed_backend = p._backend
+
+        # We make call with the managed parallel object several times inside
+        # the managed block:
+        assert expected == p(delayed(f)(x, y=1) for x in lst)
+        assert expected == p(delayed(f)(x, y=1) for x in lst)
+
+        # Those calls have all used the same pool instance:
+        if mp is not None:
+            assert get_workers(managed_backend) is get_workers(p._backend)
+
+    # As soon as we exit the context manager block, the pool is terminated and
+    # no longer referenced from the parallel object:
+    if mp is not None:
+        assert get_workers(p._backend) is None
+
+    # It's still possible to use the parallel instance in non-managed mode:
+    assert expected == p(delayed(f)(x, y=1) for x in lst)
+    if mp is not None:
+        assert get_workers(p._backend) is None
+
+
+@with_multiprocessing
+def test_parallel_pickling():
+    """ Check that pmap captures the errors when it is passed an object
+        that cannot be pickled.
+    """
+    class UnpicklableObject(object):
+        def __reduce__(self):
+            raise RuntimeError('123')
+
+    with raises(PicklingError, match=r"the task to send"):
+        Parallel(n_jobs=2, backend='loky')(delayed(id)(
+            UnpicklableObject()) for _ in range(10))
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('byteorder', ['<', '>', '='])
+def test_parallel_byteorder_corruption(byteorder):
+
+    def inspect_byteorder(x):
+        return x, x.dtype.byteorder
+
+    x = np.arange(6).reshape((2, 3)).view(f'{byteorder}i4')
+
+    initial_np_byteorder = x.dtype.byteorder
+
+    result = Parallel(n_jobs=2, backend='loky')(
+        delayed(inspect_byteorder)(x) for _ in range(3)
+    )
+
+    for x_returned, byteorder_in_worker in result:
+        assert byteorder_in_worker == initial_np_byteorder
+        assert byteorder_in_worker == x_returned.dtype.byteorder
+        np.testing.assert_array_equal(x, x_returned)
+
+
+@parametrize('backend', PARALLEL_BACKENDS)
+def test_parallel_timeout_success(backend):
+    # Check that timeout isn't thrown when function is fast enough
+    assert len(Parallel(n_jobs=2, backend=backend, timeout=30)(
+        delayed(sleep)(0.001) for x in range(10))) == 10
+
+
+@with_multiprocessing
+@parametrize('backend', PARALLEL_BACKENDS)
+def test_parallel_timeout_fail(backend):
+    # Check that timeout properly fails when function is too slow
+    with raises(TimeoutError):
+        Parallel(n_jobs=2, backend=backend, timeout=0.01)(
+            delayed(sleep)(10) for x in range(10))
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_error_capture(backend):
+    # Check that error are captured, and that correct exceptions
+    # are raised.
+    if mp is not None:
+        with raises(ZeroDivisionError):
+            Parallel(n_jobs=2, backend=backend)(
+                [delayed(division)(x, y)
+                    for x, y in zip((0, 1), (1, 0))])
+
+        with raises(KeyboardInterrupt):
+            Parallel(n_jobs=2, backend=backend)(
+                [delayed(interrupt_raiser)(x) for x in (1, 0)])
+
+        # Try again with the context manager API
+        with Parallel(n_jobs=2, backend=backend) as parallel:
+            assert get_workers(parallel._backend) is not None
+            original_workers = get_workers(parallel._backend)
+
+            with raises(ZeroDivisionError):
+                parallel([delayed(division)(x, y)
+                          for x, y in zip((0, 1), (1, 0))])
+
+            # The managed pool should still be available and be in a working
+            # state despite the previously raised (and caught) exception
+            assert get_workers(parallel._backend) is not None
+
+            # The pool should have been interrupted and restarted:
+            assert get_workers(parallel._backend) is not original_workers
+
+            assert ([f(x, y=1) for x in range(10)] ==
+                    parallel(delayed(f)(x, y=1) for x in range(10)))
+
+            original_workers = get_workers(parallel._backend)
+            with raises(KeyboardInterrupt):
+                parallel([delayed(interrupt_raiser)(x) for x in (1, 0)])
+
+            # The pool should still be available despite the exception
+            assert get_workers(parallel._backend) is not None
+
+            # The pool should have been interrupted and restarted:
+            assert get_workers(parallel._backend) is not original_workers
+
+            assert ([f(x, y=1) for x in range(10)] ==
+                    parallel(delayed(f)(x, y=1) for x in range(10))), (
+                parallel._iterating, parallel.n_completed_tasks,
+                parallel.n_dispatched_tasks, parallel._aborting
+            )
+
+        # Check that the inner pool has been terminated when exiting the
+        # context manager
+        assert get_workers(parallel._backend) is None
+    else:
+        with raises(KeyboardInterrupt):
+            Parallel(n_jobs=2)(
+                [delayed(interrupt_raiser)(x) for x in (1, 0)])
+
+    # wrapped exceptions should inherit from the class of the original
+    # exception to make it easy to catch them
+    with raises(ZeroDivisionError):
+        Parallel(n_jobs=2)(
+            [delayed(division)(x, y) for x, y in zip((0, 1), (1, 0))])
+
+    with raises(MyExceptionWithFinickyInit):
+        Parallel(n_jobs=2, verbose=0)(
+            (delayed(exception_raiser)(i, custom_exception=True)
+             for i in range(30)))
+
+
+@with_multiprocessing
+@parametrize('backend', BACKENDS)
+def test_error_in_task_iterator(backend):
+
+    def my_generator(raise_at=0):
+        for i in range(20):
+            if i == raise_at:
+                raise ValueError("Iterator Raising Error")
+            yield i
+
+    with Parallel(n_jobs=2, backend=backend) as p:
+        # The error is raised in the pre-dispatch phase
+        with raises(ValueError, match="Iterator Raising Error"):
+            p(delayed(square)(i) for i in my_generator(raise_at=0))
+
+        # The error is raised when dispatching a new task after the
+        # pre-dispatch (likely to happen in a different thread)
+        with raises(ValueError, match="Iterator Raising Error"):
+            p(delayed(square)(i) for i in my_generator(raise_at=5))
+
+        # Same, but raises long after the pre-dispatch phase
+        with raises(ValueError, match="Iterator Raising Error"):
+            p(delayed(square)(i) for i in my_generator(raise_at=19))
+
+
+def consumer(queue, item):
+    queue.append('Consumed %s' % item)
+
+
+@parametrize('backend', BACKENDS)
+@parametrize('batch_size, expected_queue',
+             [(1, ['Produced 0', 'Consumed 0',
+                   'Produced 1', 'Consumed 1',
+                   'Produced 2', 'Consumed 2',
+                   'Produced 3', 'Consumed 3',
+                   'Produced 4', 'Consumed 4',
+                   'Produced 5', 'Consumed 5']),
+              (4, [  # First Batch
+                  'Produced 0', 'Produced 1', 'Produced 2', 'Produced 3',
+                  'Consumed 0', 'Consumed 1', 'Consumed 2', 'Consumed 3',
+                     # Second batch
+                  'Produced 4', 'Produced 5', 'Consumed 4', 'Consumed 5'])])
+def test_dispatch_one_job(backend, batch_size, expected_queue):
+    """ Test that with only one job, Parallel does act as a iterator.
+    """
+    queue = list()
+
+    def producer():
+        for i in range(6):
+            queue.append('Produced %i' % i)
+            yield i
+
+    Parallel(n_jobs=1, batch_size=batch_size, backend=backend)(
+        delayed(consumer)(queue, x) for x in producer())
+    assert queue == expected_queue
+    assert len(queue) == 12
+
+
+@with_multiprocessing
+@parametrize('backend', PARALLEL_BACKENDS)
+def test_dispatch_multiprocessing(backend):
+    """ Check that using pre_dispatch Parallel does indeed dispatch items
+        lazily.
+    """
+    manager = mp.Manager()
+    queue = manager.list()
+
+    def producer():
+        for i in range(6):
+            queue.append('Produced %i' % i)
+            yield i
+
+    Parallel(n_jobs=2, batch_size=1, pre_dispatch=3, backend=backend)(
+        delayed(consumer)(queue, 'any') for _ in producer())
+
+    queue_contents = list(queue)
+    assert queue_contents[0] == 'Produced 0'
+
+    # Only 3 tasks are pre-dispatched out of 6. The 4th task is dispatched only
+    # after any of the first 3 jobs have completed.
+    first_consumption_index = queue_contents[:4].index('Consumed any')
+    assert first_consumption_index > -1
+
+    produced_3_index = queue_contents.index('Produced 3')  # 4th task produced
+    assert produced_3_index > first_consumption_index
+
+    assert len(queue) == 12
+
+
+def test_batching_auto_threading():
+    # batching='auto' with the threading backend leaves the effective batch
+    # size to 1 (no batching) as it has been found to never be beneficial with
+    # this low-overhead backend.
+
+    with Parallel(n_jobs=2, batch_size='auto', backend='threading') as p:
+        p(delayed(id)(i) for i in range(5000))  # many very fast tasks
+        assert p._backend.compute_batch_size() == 1
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_batching_auto_subprocesses(backend):
+    with Parallel(n_jobs=2, batch_size='auto', backend=backend) as p:
+        p(delayed(id)(i) for i in range(5000))  # many very fast tasks
+
+        # It should be strictly larger than 1 but as we don't want heisen
+        # failures on clogged CI worker environment be safe and only check that
+        # it's a strictly positive number.
+        assert p._backend.compute_batch_size() > 0
+
+
+def test_exception_dispatch():
+    """Make sure that exception raised during dispatch are indeed captured"""
+    with raises(ValueError):
+        Parallel(n_jobs=2, pre_dispatch=16, verbose=0)(
+            delayed(exception_raiser)(i) for i in range(30))
+
+
+def nested_function_inner(i):
+    Parallel(n_jobs=2)(
+        delayed(exception_raiser)(j) for j in range(30))
+
+
+def nested_function_outer(i):
+    Parallel(n_jobs=2)(
+        delayed(nested_function_inner)(j) for j in range(30))
+
+
+@with_multiprocessing
+@parametrize('backend', PARALLEL_BACKENDS)
+@pytest.mark.xfail(reason="https://github.com/joblib/loky/pull/255")
+def test_nested_exception_dispatch(backend):
+    """Ensure errors for nested joblib cases gets propagated
+
+    We rely on the Python 3 built-in __cause__ system that already
+    report this kind of information to the user.
+    """
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs=2, backend=backend)(
+            delayed(nested_function_outer)(i) for i in range(30))
+
+    # Check that important information such as function names are visible
+    # in the final error message reported to the user
+    report_lines = format_exception(excinfo.type, excinfo.value, excinfo.tb)
+    report = "".join(report_lines)
+    assert 'nested_function_outer' in report
+    assert 'nested_function_inner' in report
+    assert 'exception_raiser' in report
+
+    assert type(excinfo.value) is ValueError
+
+
+class FakeParallelBackend(SequentialBackend):
+    """Pretends to run concurrently while running sequentially."""
+
+    def configure(self, n_jobs=1, parallel=None, **backend_args):
+        self.n_jobs = self.effective_n_jobs(n_jobs)
+        self.parallel = parallel
+        return n_jobs
+
+    def effective_n_jobs(self, n_jobs=1):
+        if n_jobs < 0:
+            n_jobs = max(mp.cpu_count() + 1 + n_jobs, 1)
+        return n_jobs
+
+
+def test_invalid_backend():
+    with raises(ValueError, match="Invalid backend:"):
+        Parallel(backend='unit-testing')
+
+    with raises(ValueError, match="Invalid backend:"):
+        with parallel_config(backend='unit-testing'):
+            pass
+
+    with raises(ValueError, match="Invalid backend:"):
+        with parallel_config(backend='unit-testing'):
+            pass
+
+
+@parametrize('backend', ALL_VALID_BACKENDS)
+def test_invalid_njobs(backend):
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs=0, backend=backend)._initialize_backend()
+    assert "n_jobs == 0 in Parallel has no meaning" in str(excinfo.value)
+
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs=0.5, backend=backend)._initialize_backend()
+    assert "n_jobs == 0 in Parallel has no meaning" in str(excinfo.value)
+
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs="2.3", backend=backend)._initialize_backend()
+    assert "n_jobs could not be converted to int" in str(excinfo.value)
+
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs="invalid_str", backend=backend)._initialize_backend()
+    assert "n_jobs could not be converted to int" in str(excinfo.value)
+
+
+@with_multiprocessing
+@parametrize('backend', PARALLEL_BACKENDS)
+@parametrize('n_jobs', ['2', 2.3, 2])
+def test_njobs_converted_to_int(backend, n_jobs):
+    p = Parallel(n_jobs=n_jobs, backend=backend)
+    assert p._effective_n_jobs() == 2
+
+    res = p(delayed(square)(i) for i in range(10))
+    assert all(r == square(i) for i, r in enumerate(res))
+
+
+def test_register_parallel_backend():
+    try:
+        register_parallel_backend("test_backend", FakeParallelBackend)
+        assert "test_backend" in BACKENDS
+        assert BACKENDS["test_backend"] == FakeParallelBackend
+    finally:
+        del BACKENDS["test_backend"]
+
+
+def test_overwrite_default_backend():
+    assert _active_backend_type() == DefaultBackend
+    try:
+        register_parallel_backend("threading", BACKENDS["threading"],
+                                  make_default=True)
+        assert _active_backend_type() == ThreadingBackend
+    finally:
+        # Restore the global default manually
+        parallel.DEFAULT_BACKEND = DEFAULT_BACKEND
+    assert _active_backend_type() == DefaultBackend
+
+
+@skipif(mp is not None, reason="Only without multiprocessing")
+def test_backend_no_multiprocessing():
+    with warns(UserWarning,
+               match="joblib backend '.*' is not available on.*"):
+        Parallel(backend='loky')(delayed(square)(i) for i in range(3))
+
+    # The below should now work without problems
+    with parallel_config(backend='loky'):
+        Parallel()(delayed(square)(i) for i in range(3))
+
+
+def check_backend_context_manager(context, backend_name):
+    with context(backend_name, n_jobs=3):
+        active_backend, active_n_jobs = parallel.get_active_backend()
+        assert active_n_jobs == 3
+        assert effective_n_jobs(3) == 3
+        p = Parallel()
+        assert p.n_jobs == 3
+        if backend_name == 'multiprocessing':
+            assert type(active_backend) is MultiprocessingBackend
+            assert type(p._backend) is MultiprocessingBackend
+        elif backend_name == 'loky':
+            assert type(active_backend) is LokyBackend
+            assert type(p._backend) is LokyBackend
+        elif backend_name == 'threading':
+            assert type(active_backend) is ThreadingBackend
+            assert type(p._backend) is ThreadingBackend
+        elif backend_name.startswith('test_'):
+            assert type(active_backend) is FakeParallelBackend
+            assert type(p._backend) is FakeParallelBackend
+
+
+all_backends_for_context_manager = PARALLEL_BACKENDS[:]
+all_backends_for_context_manager.extend(
+    ['test_backend_%d' % i for i in range(3)]
+)
+
+
+@with_multiprocessing
+@parametrize('backend', all_backends_for_context_manager)
+@parametrize('context', [parallel_backend, parallel_config])
+def test_backend_context_manager(monkeypatch, backend, context):
+    if backend not in BACKENDS:
+        monkeypatch.setitem(BACKENDS, backend, FakeParallelBackend)
+
+    assert _active_backend_type() == DefaultBackend
+    # check that this possible to switch parallel backends sequentially
+    check_backend_context_manager(context, backend)
+
+    # The default backend is restored
+    assert _active_backend_type() == DefaultBackend
+
+    # Check that context manager switching is thread safe:
+    Parallel(n_jobs=2, backend='threading')(
+        delayed(check_backend_context_manager)(context, b)
+        for b in all_backends_for_context_manager if not b)
+
+    # The default backend is again restored
+    assert _active_backend_type() == DefaultBackend
+
+
+class ParameterizedParallelBackend(SequentialBackend):
+    """Pretends to run conncurrently while running sequentially."""
+
+    def __init__(self, param=None):
+        if param is None:
+            raise ValueError('param should not be None')
+        self.param = param
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_parameterized_backend_context_manager(monkeypatch, context):
+    monkeypatch.setitem(BACKENDS, 'param_backend',
+                        ParameterizedParallelBackend)
+    assert _active_backend_type() == DefaultBackend
+
+    with context('param_backend', param=42, n_jobs=3):
+        active_backend, active_n_jobs = parallel.get_active_backend()
+        assert type(active_backend) is ParameterizedParallelBackend
+        assert active_backend.param == 42
+        assert active_n_jobs == 3
+        p = Parallel()
+        assert p.n_jobs == 3
+        assert p._backend is active_backend
+        results = p(delayed(sqrt)(i) for i in range(5))
+    assert results == [sqrt(i) for i in range(5)]
+
+    # The default backend is again restored
+    assert _active_backend_type() == DefaultBackend
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_directly_parameterized_backend_context_manager(context):
+    assert _active_backend_type() == DefaultBackend
+
+    # Check that it's possible to pass a backend instance directly,
+    # without registration
+    with context(ParameterizedParallelBackend(param=43), n_jobs=5):
+        active_backend, active_n_jobs = parallel.get_active_backend()
+        assert type(active_backend) is ParameterizedParallelBackend
+        assert active_backend.param == 43
+        assert active_n_jobs == 5
+        p = Parallel()
+        assert p.n_jobs == 5
+        assert p._backend is active_backend
+        results = p(delayed(sqrt)(i) for i in range(5))
+    assert results == [sqrt(i) for i in range(5)]
+
+    # The default backend is again restored
+    assert _active_backend_type() == DefaultBackend
+
+
+def sleep_and_return_pid():
+    sleep(.1)
+    return os.getpid()
+
+
+def get_nested_pids():
+    assert _active_backend_type() == ThreadingBackend
+    # Assert that the nested backend does not change the default number of
+    # jobs used in Parallel
+    assert Parallel()._effective_n_jobs() == 1
+
+    # Assert that the tasks are running only on one process
+    return Parallel(n_jobs=2)(delayed(sleep_and_return_pid)()
+                              for _ in range(2))
+
+
+class MyBackend(joblib._parallel_backends.LokyBackend):
+    """Backend to test backward compatibility with older backends"""
+    def get_nested_backend(self, ):
+        # Older backends only return a backend, without n_jobs indications.
+        return super(MyBackend, self).get_nested_backend()[0]
+
+
+register_parallel_backend('back_compat_backend', MyBackend)
+
+
+@with_multiprocessing
+@parametrize('backend', ['threading', 'loky', 'multiprocessing',
+                         'back_compat_backend'])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_nested_backend_context_manager(context, backend):
+    # Check that by default, nested parallel calls will always use the
+    # ThreadingBackend
+
+    with context(backend):
+        pid_groups = Parallel(n_jobs=2)(
+            delayed(get_nested_pids)()
+            for _ in range(10)
+        )
+        for pid_group in pid_groups:
+            assert len(set(pid_group)) == 1
+
+
+@with_multiprocessing
+@parametrize('n_jobs', [2, -1, None])
+@parametrize('backend', PARALLEL_BACKENDS)
+@parametrize("context", [parallel_config, parallel_backend])
+def test_nested_backend_in_sequential(backend, n_jobs, context):
+    # Check that by default, nested parallel calls will always use the
+    # ThreadingBackend
+
+    def check_nested_backend(expected_backend_type, expected_n_job):
+        # Assert that the sequential backend at top level, does not change the
+        # backend for nested calls.
+        assert _active_backend_type() == BACKENDS[expected_backend_type]
+
+        # Assert that the nested backend in SequentialBackend does not change
+        # the default number of jobs used in Parallel
+        expected_n_job = effective_n_jobs(expected_n_job)
+        assert Parallel()._effective_n_jobs() == expected_n_job
+
+    Parallel(n_jobs=1)(
+        delayed(check_nested_backend)(DEFAULT_BACKEND, 1)
+        for _ in range(10)
+    )
+
+    with context(backend, n_jobs=n_jobs):
+        Parallel(n_jobs=1)(
+            delayed(check_nested_backend)(backend, n_jobs)
+            for _ in range(10)
+        )
+
+
+def check_nesting_level(context, inner_backend, expected_level):
+    with context(inner_backend) as ctx:
+        if context is parallel_config:
+            backend = ctx["backend"]
+        if context is parallel_backend:
+            backend = ctx[0]
+        assert backend.nesting_level == expected_level
+
+
+@with_multiprocessing
+@parametrize('outer_backend', PARALLEL_BACKENDS)
+@parametrize('inner_backend', PARALLEL_BACKENDS)
+@parametrize("context", [parallel_config, parallel_backend])
+def test_backend_nesting_level(context, outer_backend, inner_backend):
+    # Check that the nesting level for the backend is correctly set
+    check_nesting_level(context, outer_backend, 0)
+
+    Parallel(n_jobs=2, backend=outer_backend)(
+        delayed(check_nesting_level)(context, inner_backend, 1)
+        for _ in range(10)
+    )
+
+    with context(inner_backend, n_jobs=2):
+        Parallel()(delayed(check_nesting_level)(context, inner_backend, 1)
+                   for _ in range(10))
+
+
+@with_multiprocessing
+@parametrize("context", [parallel_config, parallel_backend])
+@parametrize('with_retrieve_callback', [True, False])
+def test_retrieval_context(context, with_retrieve_callback):
+    import contextlib
+
+    class MyBackend(ThreadingBackend):
+        i = 0
+        supports_retrieve_callback = with_retrieve_callback
+
+        @contextlib.contextmanager
+        def retrieval_context(self):
+            self.i += 1
+            yield
+
+    register_parallel_backend("retrieval", MyBackend)
+
+    def nested_call(n):
+        return Parallel(n_jobs=2)(delayed(id)(i) for i in range(n))
+
+    with context("retrieval") as ctx:
+        Parallel(n_jobs=2)(
+            delayed(nested_call)(i)
+            for i in range(5)
+        )
+        if context is parallel_config:
+            assert ctx["backend"].i == 1
+        if context is parallel_backend:
+            assert ctx[0].i == 1
+
+
+###############################################################################
+# Test helpers
+
+@parametrize('batch_size', [0, -1, 1.42])
+def test_invalid_batch_size(batch_size):
+    with raises(ValueError):
+        Parallel(batch_size=batch_size)
+
+
+@parametrize('n_tasks, n_jobs, pre_dispatch, batch_size',
+             [(2, 2, 'all', 'auto'),
+              (2, 2, 'n_jobs', 'auto'),
+              (10, 2, 'n_jobs', 'auto'),
+              (517, 2, 'n_jobs', 'auto'),
+              (10, 2, 'n_jobs', 'auto'),
+              (10, 4, 'n_jobs', 'auto'),
+              (200, 12, 'n_jobs', 'auto'),
+              (25, 12, '2 * n_jobs', 1),
+              (250, 12, 'all', 1),
+              (250, 12, '2 * n_jobs', 7),
+              (200, 12, '2 * n_jobs', 'auto')])
+def test_dispatch_race_condition(n_tasks, n_jobs, pre_dispatch, batch_size):
+    # Check that using (async-)dispatch does not yield a race condition on the
+    # iterable generator that is not thread-safe natively.
+    # This is a non-regression test for the "Pool seems closed" class of error
+    params = {'n_jobs': n_jobs, 'pre_dispatch': pre_dispatch,
+              'batch_size': batch_size}
+    expected = [square(i) for i in range(n_tasks)]
+    results = Parallel(**params)(delayed(square)(i) for i in range(n_tasks))
+    assert results == expected
+
+
+@with_multiprocessing
+def test_default_mp_context():
+    mp_start_method = mp.get_start_method()
+    p = Parallel(n_jobs=2, backend='multiprocessing')
+    context = p._backend_args.get('context')
+    start_method = context.get_start_method()
+    assert start_method == mp_start_method
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_no_blas_crash_or_freeze_with_subprocesses(backend):
+    if backend == 'multiprocessing':
+        # Use the spawn backend that is both robust and available on all
+        # platforms
+        backend = mp.get_context('spawn')
+
+    # Check that on recent Python version, the 'spawn' start method can make
+    # it possible to use multiprocessing in conjunction of any BLAS
+    # implementation that happens to be used by numpy with causing a freeze or
+    # a crash
+    rng = np.random.RandomState(42)
+
+    # call BLAS DGEMM to force the initialization of the internal thread-pool
+    # in the main process
+    a = rng.randn(1000, 1000)
+    np.dot(a, a.T)
+
+    # check that the internal BLAS thread-pool is not in an inconsistent state
+    # in the worker processes managed by multiprocessing
+    Parallel(n_jobs=2, backend=backend)(
+        delayed(np.dot)(a, a.T) for i in range(2))
+
+
+UNPICKLABLE_CALLABLE_SCRIPT_TEMPLATE_NO_MAIN = """\
+from joblib import Parallel, delayed
+
+def square(x):
+    return x ** 2
+
+backend = "{}"
+if backend == "spawn":
+    from multiprocessing import get_context
+    backend = get_context(backend)
+
+print(Parallel(n_jobs=2, backend=backend)(
+      delayed(square)(i) for i in range(5)))
+"""
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_parallel_with_interactively_defined_functions(backend):
+    # When using the "-c" flag, interactive functions defined in __main__
+    # should work with any backend.
+    if backend == "multiprocessing" and mp.get_start_method() != "fork":
+        pytest.skip("Require fork start method to use interactively defined "
+                    "functions with multiprocessing.")
+    code = UNPICKLABLE_CALLABLE_SCRIPT_TEMPLATE_NO_MAIN.format(backend)
+    check_subprocess_call(
+        [sys.executable, '-c', code], timeout=10,
+        stdout_regex=r'\[0, 1, 4, 9, 16\]')
+
+
+UNPICKLABLE_CALLABLE_SCRIPT_TEMPLATE_MAIN = """\
+import sys
+# Make sure that joblib is importable in the subprocess launching this
+# script. This is needed in case we run the tests from the joblib root
+# folder without having installed joblib
+sys.path.insert(0, {joblib_root_folder!r})
+
+from joblib import Parallel, delayed
+
+def run(f, x):
+    return f(x)
+
+{define_func}
+
+if __name__ == "__main__":
+    backend = "{backend}"
+    if backend == "spawn":
+        from multiprocessing import get_context
+        backend = get_context(backend)
+
+    callable_position = "{callable_position}"
+    if callable_position == "delayed":
+        print(Parallel(n_jobs=2, backend=backend)(
+                delayed(square)(i) for i in range(5)))
+    elif callable_position == "args":
+        print(Parallel(n_jobs=2, backend=backend)(
+                delayed(run)(square, i) for i in range(5)))
+    else:
+        print(Parallel(n_jobs=2, backend=backend)(
+                delayed(run)(f=square, x=i) for i in range(5)))
+"""
+
+SQUARE_MAIN = """\
+def square(x):
+    return x ** 2
+"""
+SQUARE_LOCAL = """\
+def gen_square():
+    def square(x):
+        return x ** 2
+    return square
+square = gen_square()
+"""
+SQUARE_LAMBDA = """\
+square = lambda x: x ** 2
+"""
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS + ([] if mp is None else ['spawn']))
+@parametrize('define_func', [SQUARE_MAIN, SQUARE_LOCAL, SQUARE_LAMBDA])
+@parametrize('callable_position', ['delayed', 'args', 'kwargs'])
+def test_parallel_with_unpicklable_functions_in_args(
+        backend, define_func, callable_position, tmpdir):
+    if backend in ['multiprocessing', 'spawn'] and (
+            define_func != SQUARE_MAIN or sys.platform == "win32"):
+        pytest.skip("Not picklable with pickle")
+    code = UNPICKLABLE_CALLABLE_SCRIPT_TEMPLATE_MAIN.format(
+        define_func=define_func, backend=backend,
+        callable_position=callable_position,
+        joblib_root_folder=os.path.dirname(os.path.dirname(joblib.__file__)))
+    code_file = tmpdir.join("unpicklable_func_script.py")
+    code_file.write(code)
+    check_subprocess_call(
+        [sys.executable, code_file.strpath], timeout=10,
+        stdout_regex=r'\[0, 1, 4, 9, 16\]')
+
+
+INTERACTIVE_DEFINED_FUNCTION_AND_CLASS_SCRIPT_CONTENT = """\
+import sys
+import faulthandler
+# Make sure that joblib is importable in the subprocess launching this
+# script. This is needed in case we run the tests from the joblib root
+# folder without having installed joblib
+sys.path.insert(0, {joblib_root_folder!r})
+
+from joblib import Parallel, delayed
+from functools import partial
+
+class MyClass:
+    '''Class defined in the __main__ namespace'''
+    def __init__(self, value):
+        self.value = value
+
+
+def square(x, ignored=None, ignored2=None):
+    '''Function defined in the __main__ namespace'''
+    return x.value ** 2
+
+
+square2 = partial(square, ignored2='something')
+
+# Here, we do not need the `if __name__ == "__main__":` safeguard when
+# using the default `loky` backend (even on Windows).
+
+# To make debugging easier
+faulthandler.dump_traceback_later(30, exit=True)
+
+# The following baroque function call is meant to check that joblib
+# introspection rightfully uses cloudpickle instead of the (faster) pickle
+# module of the standard library when necessary. In particular cloudpickle is
+# necessary for functions and instances of classes interactively defined in the
+# __main__ module.
+
+print(Parallel(backend="loky", n_jobs=2)(
+    delayed(square2)(MyClass(i), ignored=[dict(a=MyClass(1))])
+    for i in range(5)
+))
+""".format(joblib_root_folder=os.path.dirname(
+    os.path.dirname(joblib.__file__)))
+
+
+@with_multiprocessing
+def test_parallel_with_interactively_defined_functions_loky(tmpdir):
+    # loky accepts interactive functions defined in __main__ and does not
+    # require if __name__ == '__main__' even when the __main__ module is
+    # defined by the result of the execution of a filesystem script.
+    script = tmpdir.join('joblib_interactively_defined_function.py')
+    script.write(INTERACTIVE_DEFINED_FUNCTION_AND_CLASS_SCRIPT_CONTENT)
+    check_subprocess_call(
+        [sys.executable, script.strpath],
+        stdout_regex=r'\[0, 1, 4, 9, 16\]',
+        timeout=None,  # rely on faulthandler to kill the process
+    )
+
+
+INTERACTIVELY_DEFINED_SUBCLASS_WITH_METHOD_SCRIPT_CONTENT = """\
+import sys
+# Make sure that joblib is importable in the subprocess launching this
+# script. This is needed in case we run the tests from the joblib root
+# folder without having installed joblib
+sys.path.insert(0, {joblib_root_folder!r})
+
+from joblib import Parallel, delayed, hash
+import multiprocessing as mp
+mp.util.log_to_stderr(5)
+
+class MyList(list):
+    '''MyList is interactively defined by MyList.append is a built-in'''
+    def __hash__(self):
+        # XXX: workaround limitation in cloudpickle
+        return hash(self).__hash__()
+
+l = MyList()
+
+print(Parallel(backend="loky", n_jobs=2)(
+    delayed(l.append)(i) for i in range(3)
+))
+""".format(joblib_root_folder=os.path.dirname(
+    os.path.dirname(joblib.__file__)))
+
+
+@with_multiprocessing
+def test_parallel_with_interactively_defined_bound_method_loky(tmpdir):
+    script = tmpdir.join('joblib_interactive_bound_method_script.py')
+    script.write(INTERACTIVELY_DEFINED_SUBCLASS_WITH_METHOD_SCRIPT_CONTENT)
+    check_subprocess_call([sys.executable, script.strpath],
+                          stdout_regex=r'\[None, None, None\]',
+                          stderr_regex=r'LokyProcess',
+                          timeout=15)
+
+
+def test_parallel_with_exhausted_iterator():
+    exhausted_iterator = iter([])
+    assert Parallel(n_jobs=2)(exhausted_iterator) == []
+
+
+def _cleanup_worker():
+    """Helper function to force gc in each worker."""
+    force_gc_pypy()
+    time.sleep(.1)
+
+
+def check_memmap(a):
+    if not isinstance(a, np.memmap):
+        raise TypeError('Expected np.memmap instance, got %r',
+                        type(a))
+    return a.copy()  # return a regular array instead of a memmap
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_auto_memmap_on_arrays_from_generator(backend):
+    # Non-regression test for a problem with a bad interaction between the
+    # GC collecting arrays recently created during iteration inside the
+    # parallel dispatch loop and the auto-memmap feature of Parallel.
+    # See: https://github.com/joblib/joblib/pull/294
+    def generate_arrays(n):
+        for i in range(n):
+            yield np.ones(10, dtype=np.float32) * i
+    # Use max_nbytes=1 to force the use of memory-mapping even for small
+    # arrays
+    results = Parallel(n_jobs=2, max_nbytes=1, backend=backend)(
+        delayed(check_memmap)(a) for a in generate_arrays(100))
+    for result, expected in zip(results, generate_arrays(len(results))):
+        np.testing.assert_array_equal(expected, result)
+
+    # Second call to force loky to adapt the executor by growing the number
+    # of worker processes. This is a non-regression test for:
+    # https://github.com/joblib/joblib/issues/629.
+    results = Parallel(n_jobs=4, max_nbytes=1, backend=backend)(
+        delayed(check_memmap)(a) for a in generate_arrays(100))
+    for result, expected in zip(results, generate_arrays(len(results))):
+        np.testing.assert_array_equal(expected, result)
+
+
+def identity(arg):
+    return arg
+
+
+@with_numpy
+@with_multiprocessing
+def test_memmap_with_big_offset(tmpdir):
+    fname = tmpdir.join('test.mmap').strpath
+    size = mmap.ALLOCATIONGRANULARITY
+    obj = [np.zeros(size, dtype='uint8'), np.ones(size, dtype='uint8')]
+    dump(obj, fname)
+    memmap = load(fname, mmap_mode='r')
+    result, = Parallel(n_jobs=2)(delayed(identity)(memmap) for _ in [0])
+    assert isinstance(memmap[1], np.memmap)
+    assert memmap[1].offset > size
+    np.testing.assert_array_equal(obj, result)
+
+
+def test_warning_about_timeout_not_supported_by_backend():
+    with warnings.catch_warnings(record=True) as warninfo:
+        Parallel(n_jobs=1, timeout=1)(delayed(square)(i) for i in range(50))
+    assert len(warninfo) == 1
+    w = warninfo[0]
+    assert isinstance(w.message, UserWarning)
+    assert str(w.message) == (
+        "The backend class 'SequentialBackend' does not support timeout. "
+        "You have set 'timeout=1' in Parallel but the 'timeout' parameter "
+        "will not be used.")
+
+
+def set_list_value(input_list, index, value):
+    input_list[index] = value
+    return value
+
+
+@pytest.mark.parametrize('n_jobs', [1, 2, 4])
+def test_parallel_return_order_with_return_as_generator_parameter(n_jobs):
+    # This test inserts values in a list in some expected order
+    # in sequential computing, and then checks that this order has been
+    # respected by Parallel output generator.
+    input_list = [0] * 5
+    result = Parallel(n_jobs=n_jobs, return_as="generator",
+                      backend='threading')(
+        delayed(set_list_value)(input_list, i, i) for i in range(5))
+
+    # Ensure that all the tasks are completed before checking the result
+    result = list(result)
+
+    assert all(v == r for v, r in zip(input_list, result))
+
+
+def _sqrt_with_delay(e, delay):
+    if delay:
+        sleep(30)
+    return sqrt(e)
+
+
+def _test_parallel_unordered_generator_returns_fastest_first(backend, n_jobs):
+    # This test submits 10 tasks, but the second task is super slow. This test
+    # checks that the 9 other tasks return before the slow task is done, when
+    # `return_as` parameter is set to `'generator_unordered'`
+    result = Parallel(n_jobs=n_jobs, return_as="generator_unordered",
+                      backend=backend)(
+        delayed(_sqrt_with_delay)(i**2, (i == 1)) for i in range(10))
+
+    quickly_returned = sorted(next(result) for _ in range(9))
+
+    expected_quickly_returned = [0] + list(range(2, 10))
+
+    assert all(
+        v == r for v, r in zip(expected_quickly_returned, quickly_returned)
+    )
+
+    del result
+    force_gc_pypy()
+
+
+@pytest.mark.parametrize('n_jobs', [2, 4])
+# NB: for this test to work, the backend must be allowed to process tasks
+# concurrently, so at least two jobs with a non-sequential backend are
+# mandatory.
+@with_multiprocessing
+@parametrize('backend', set(RETURN_GENERATOR_BACKENDS) - {"sequential"})
+def test_parallel_unordered_generator_returns_fastest_first(backend, n_jobs):
+    _test_parallel_unordered_generator_returns_fastest_first(backend, n_jobs)
+
+
+@pytest.mark.parametrize('n_jobs', [2, -1])
+@parametrize("context", [parallel_config, parallel_backend])
+@skipif(distributed is None, reason='This test requires dask')
+def test_parallel_unordered_generator_returns_fastest_first_with_dask(
+        n_jobs, context
+):
+    with distributed.Client(
+            n_workers=2, threads_per_worker=2
+    ), context("dask"):
+        _test_parallel_unordered_generator_returns_fastest_first(None, n_jobs)
+
+
+@parametrize('backend', ALL_VALID_BACKENDS)
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_abort_backend(n_jobs, backend):
+    delays = ["a"] + [10] * 100
+    with raises(TypeError):
+        t_start = time.time()
+        Parallel(n_jobs=n_jobs, backend=backend)(
+            delayed(time.sleep)(i) for i in delays)
+    dt = time.time() - t_start
+    assert dt < 20
+
+
+def get_large_object(arg):
+    result = np.ones(int(5 * 1e5), dtype=bool)
+    result[0] = False
+    return result
+
+
+def _test_deadlock_with_generator(backend, return_as, n_jobs):
+    # Non-regression test for a race condition in the backends when the pickler
+    # is delayed by a large object.
+    with Parallel(n_jobs=n_jobs, backend=backend,
+                  return_as=return_as) as parallel:
+        result = parallel(delayed(get_large_object)(i) for i in range(10))
+        next(result)
+        next(result)
+        del result
+        # The gc in pypy can be delayed. Force it to make sure this test does
+        # not cause timeout on the CI.
+        force_gc_pypy()
+
+
+@with_numpy
+@parametrize('backend', RETURN_GENERATOR_BACKENDS)
+@parametrize('return_as', ["generator", "generator_unordered"])
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_deadlock_with_generator(backend, return_as, n_jobs):
+    _test_deadlock_with_generator(backend, return_as, n_jobs)
+
+
+@with_numpy
+@pytest.mark.parametrize('n_jobs', [2, -1])
+@parametrize('return_as', ["generator", "generator_unordered"])
+@parametrize("context", [parallel_config, parallel_backend])
+@skipif(distributed is None, reason='This test requires dask')
+def test_deadlock_with_generator_and_dask(context, return_as, n_jobs):
+    with distributed.Client(
+            n_workers=2, threads_per_worker=2
+    ), context("dask"):
+        _test_deadlock_with_generator(None, return_as, n_jobs)
+
+
+@parametrize('backend', RETURN_GENERATOR_BACKENDS)
+@parametrize('return_as', ["generator", "generator_unordered"])
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_multiple_generator_call(backend, return_as, n_jobs):
+    # Non-regression test that ensures the dispatch of the tasks starts
+    # immediately when Parallel.__call__ is called. This test relies on the
+    # assumption that only one generator can be submitted at a time.
+    with raises(RuntimeError,
+                match="This Parallel instance is already running"):
+        parallel = Parallel(n_jobs, backend=backend, return_as=return_as)
+        g = parallel(delayed(sleep)(1) for _ in range(10))  # noqa: F841
+        t_start = time.time()
+        gen2 = parallel(delayed(id)(i) for i in range(100))  # noqa: F841
+
+    # Make sure that the error is raised quickly
+    assert time.time() - t_start < 2, (
+        "The error should be raised immediatly when submitting a new task "
+        "but it took more than 2s."
+    )
+
+    del g
+    # The gc in pypy can be delayed. Force it to make sure this test does not
+    # cause timeout on the CI.
+    force_gc_pypy()
+
+
+@parametrize('backend', RETURN_GENERATOR_BACKENDS)
+@parametrize('return_as', ["generator", "generator_unordered"])
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_multiple_generator_call_managed(backend, return_as, n_jobs):
+    # Non-regression test that ensures the dispatch of the tasks starts
+    # immediately when Parallel.__call__ is called. This test relies on the
+    # assumption that only one generator can be submitted at a time.
+    with Parallel(n_jobs, backend=backend,
+                  return_as=return_as) as parallel:
+        g = parallel(delayed(sleep)(10) for _ in range(10))  # noqa: F841
+        t_start = time.time()
+        with raises(RuntimeError,
+                    match="This Parallel instance is already running"):
+            g2 = parallel(delayed(id)(i) for i in range(100))  # noqa: F841
+
+        # Make sure that the error is raised quickly
+        assert time.time() - t_start < 2, (
+            "The error should be raised immediatly when submitting a new task "
+            "but it took more than 2s."
+        )
+
+    # The gc in pypy can be delayed. Force it to make sure this test does not
+    # cause timeout on the CI.
+    del g
+    force_gc_pypy()
+
+
+@parametrize('backend', RETURN_GENERATOR_BACKENDS)
+@parametrize('return_as_1', ["generator", "generator_unordered"])
+@parametrize('return_as_2', ["generator", "generator_unordered"])
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_multiple_generator_call_separated(
+        backend, return_as_1, return_as_2, n_jobs
+):
+    # Check that for separated Parallel, both tasks are correctly returned.
+    g = Parallel(n_jobs, backend=backend, return_as=return_as_1)(
+        delayed(sqrt)(i ** 2) for i in range(10)
+    )
+    g2 = Parallel(n_jobs, backend=backend, return_as=return_as_2)(
+        delayed(sqrt)(i ** 2) for i in range(10, 20)
+    )
+
+    if return_as_1 == "generator_unordered":
+        g = sorted(g)
+
+    if return_as_2 == "generator_unordered":
+        g2 = sorted(g2)
+
+    assert all(res == i for res, i in zip(g, range(10)))
+    assert all(res == i for res, i in zip(g2, range(10, 20)))
+
+
+@parametrize('backend, error', [
+    ('loky', True),
+    ('threading', False),
+    ('sequential', False),
+])
+@parametrize('return_as_1', ["generator", "generator_unordered"])
+@parametrize('return_as_2', ["generator", "generator_unordered"])
+def test_multiple_generator_call_separated_gc(
+        backend, return_as_1, return_as_2, error
+):
+
+    if (backend == 'loky') and (mp is None):
+        pytest.skip("Requires multiprocessing")
+
+    # Check that in loky, only one call can be run at a time with
+    # a single executor.
+    parallel = Parallel(2, backend=backend, return_as=return_as_1)
+    g = parallel(delayed(sleep)(10) for i in range(10))
+    g_wr = weakref.finalize(g, lambda: print("Generator collected"))
+    ctx = (
+        raises(RuntimeError, match="The executor underlying Parallel")
+        if error else nullcontext()
+    )
+    with ctx:
+        # For loky, this call will raise an error as the gc of the previous
+        # generator will shutdown the shared executor.
+        # For the other backends, as the worker pools are not shared between
+        # the two calls, this should proceed correctly.
+        t_start = time.time()
+        g = Parallel(2, backend=backend, return_as=return_as_2)(
+            delayed(sqrt)(i ** 2) for i in range(10, 20)
+        )
+
+        # The gc in pypy can be delayed. Force it to test the behavior when it
+        # will eventually be collected.
+        force_gc_pypy()
+
+        if return_as_2 == "generator_unordered":
+            g = sorted(g)
+
+        assert all(res == i for res, i in zip(g, range(10, 20)))
+
+    assert time.time() - t_start < 5
+
+    # Make sure that the computation are stopped for the gc'ed generator
+    retry = 0
+    while g_wr.alive and retry < 3:
+        retry += 1
+        time.sleep(.5)
+    assert time.time() - t_start < 5
+
+    if parallel._effective_n_jobs() != 1:
+        # check that the first parallel object is aborting (the final _aborted
+        # state might be delayed).
+        assert parallel._aborting
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_memmapping_leaks(backend, tmpdir):
+    # Non-regression test for memmapping backends. Ensure that the data
+    # does not stay too long in memory
+    tmpdir = tmpdir.strpath
+
+    # Use max_nbytes=1 to force the use of memory-mapping even for small
+    # arrays
+    with Parallel(n_jobs=2, max_nbytes=1, backend=backend,
+                  temp_folder=tmpdir) as p:
+        p(delayed(check_memmap)(a) for a in [np.random.random(10)] * 2)
+
+        # The memmap folder should not be clean in the context scope
+        assert len(os.listdir(tmpdir)) > 0
+
+        # Cleaning of the memmap folder is triggered by the garbage
+        # collection. With pypy the garbage collection has been observed to be
+        # delayed, sometimes up until the shutdown of the interpreter. This
+        # cleanup job executed in the worker ensures that it's triggered
+        # immediately.
+        p(delayed(_cleanup_worker)() for _ in range(2))
+
+    # Make sure that the shared memory is cleaned at the end when we exit
+    # the context
+    for _ in range(100):
+        if not os.listdir(tmpdir):
+            break
+        sleep(.1)
+    else:
+        raise AssertionError('temporary directory of Parallel was not removed')
+
+    # Make sure that the shared memory is cleaned at the end of a call
+    p = Parallel(n_jobs=2, max_nbytes=1, backend=backend)
+    p(delayed(check_memmap)(a) for a in [np.random.random(10)] * 2)
+    p(delayed(_cleanup_worker)() for _ in range(2))
+
+    for _ in range(100):
+        if not os.listdir(tmpdir):
+            break
+        sleep(.1)
+    else:
+        raise AssertionError('temporary directory of Parallel was not removed')
+
+
+@parametrize('backend',
+             ([None, 'threading'] if mp is None
+              else [None, 'loky', 'threading'])
+             )
+def test_lambda_expression(backend):
+    # cloudpickle is used to pickle delayed callables
+    results = Parallel(n_jobs=2, backend=backend)(
+        delayed(lambda x: x ** 2)(i) for i in range(10))
+    assert results == [i ** 2 for i in range(10)]
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_backend_batch_statistics_reset(backend):
+    """Test that a parallel backend correctly resets its batch statistics."""
+    n_jobs = 2
+    n_inputs = 500
+    task_time = 2. / n_inputs
+
+    p = Parallel(verbose=10, n_jobs=n_jobs, backend=backend)
+    p(delayed(time.sleep)(task_time) for i in range(n_inputs))
+    assert (p._backend._effective_batch_size ==
+            p._backend._DEFAULT_EFFECTIVE_BATCH_SIZE)
+    assert (p._backend._smoothed_batch_duration ==
+            p._backend._DEFAULT_SMOOTHED_BATCH_DURATION)
+
+    p(delayed(time.sleep)(task_time) for i in range(n_inputs))
+    assert (p._backend._effective_batch_size ==
+            p._backend._DEFAULT_EFFECTIVE_BATCH_SIZE)
+    assert (p._backend._smoothed_batch_duration ==
+            p._backend._DEFAULT_SMOOTHED_BATCH_DURATION)
+
+
+@with_multiprocessing
+@parametrize("context", [parallel_config, parallel_backend])
+def test_backend_hinting_and_constraints(context):
+    for n_jobs in [1, 2, -1]:
+        assert type(Parallel(n_jobs=n_jobs)._backend) == DefaultBackend
+
+        p = Parallel(n_jobs=n_jobs, prefer='threads')
+        assert type(p._backend) is ThreadingBackend
+
+        p = Parallel(n_jobs=n_jobs, prefer='processes')
+        assert type(p._backend) is DefaultBackend
+
+        p = Parallel(n_jobs=n_jobs, require='sharedmem')
+        assert type(p._backend) is ThreadingBackend
+
+    # Explicit backend selection can override backend hinting although it
+    # is useless to pass a hint when selecting a backend.
+    p = Parallel(n_jobs=2, backend='loky', prefer='threads')
+    assert type(p._backend) is LokyBackend
+
+    with context('loky', n_jobs=2):
+        # Explicit backend selection by the user with the context manager
+        # should be respected when combined with backend hints only.
+        p = Parallel(prefer='threads')
+        assert type(p._backend) is LokyBackend
+        assert p.n_jobs == 2
+
+    with context('loky', n_jobs=2):
+        # Locally hard-coded n_jobs value is respected.
+        p = Parallel(n_jobs=3, prefer='threads')
+        assert type(p._backend) is LokyBackend
+        assert p.n_jobs == 3
+
+    with context('loky', n_jobs=2):
+        # Explicit backend selection by the user with the context manager
+        # should be ignored when the Parallel call has hard constraints.
+        # In this case, the default backend that supports shared mem is
+        # used an the default number of processes is used.
+        p = Parallel(require='sharedmem')
+        assert type(p._backend) is ThreadingBackend
+        assert p.n_jobs == 1
+
+    with context('loky', n_jobs=2):
+        p = Parallel(n_jobs=3, require='sharedmem')
+        assert type(p._backend) is ThreadingBackend
+        assert p.n_jobs == 3
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_backend_hinting_and_constraints_with_custom_backends(
+    capsys, context
+):
+    # Custom backends can declare that they use threads and have shared memory
+    # semantics:
+    class MyCustomThreadingBackend(ParallelBackendBase):
+        supports_sharedmem = True
+        use_threads = True
+
+        def apply_async(self):
+            pass
+
+        def effective_n_jobs(self, n_jobs):
+            return n_jobs
+
+    with context(MyCustomThreadingBackend()):
+        p = Parallel(n_jobs=2, prefer='processes')  # ignored
+        assert type(p._backend) is MyCustomThreadingBackend
+
+        p = Parallel(n_jobs=2, require='sharedmem')
+        assert type(p._backend) is MyCustomThreadingBackend
+
+    class MyCustomProcessingBackend(ParallelBackendBase):
+        supports_sharedmem = False
+        use_threads = False
+
+        def apply_async(self):
+            pass
+
+        def effective_n_jobs(self, n_jobs):
+            return n_jobs
+
+    with context(MyCustomProcessingBackend()):
+        p = Parallel(n_jobs=2, prefer='processes')
+        assert type(p._backend) is MyCustomProcessingBackend
+
+        out, err = capsys.readouterr()
+        assert out == ""
+        assert err == ""
+
+        p = Parallel(n_jobs=2, require='sharedmem', verbose=10)
+        assert type(p._backend) is ThreadingBackend
+
+        out, err = capsys.readouterr()
+        expected = ("Using ThreadingBackend as joblib backend "
+                    "instead of MyCustomProcessingBackend as the latter "
+                    "does not provide shared memory semantics.")
+        assert out.strip() == expected
+        assert err == ""
+
+    with raises(ValueError):
+        Parallel(backend=MyCustomProcessingBackend(), require='sharedmem')
+
+
+def test_invalid_backend_hinting_and_constraints():
+    with raises(ValueError):
+        Parallel(prefer='invalid')
+
+    with raises(ValueError):
+        Parallel(require='invalid')
+
+    with raises(ValueError):
+        # It is inconsistent to prefer process-based parallelism while
+        # requiring shared memory semantics.
+        Parallel(prefer='processes', require='sharedmem')
+
+    if mp is not None:
+        # It is inconsistent to ask explicitly for a process-based
+        # parallelism while requiring shared memory semantics.
+        with raises(ValueError):
+            Parallel(backend='loky', require='sharedmem')
+        with raises(ValueError):
+            Parallel(backend='multiprocessing', require='sharedmem')
+
+
+def _recursive_backend_info(limit=3, **kwargs):
+    """Perform nested parallel calls and introspect the backend on the way"""
+
+    with Parallel(n_jobs=2) as p:
+        this_level = [(type(p._backend).__name__, p._backend.nesting_level)]
+        if limit == 0:
+            return this_level
+        results = p(delayed(_recursive_backend_info)(limit=limit - 1, **kwargs)
+                    for i in range(1))
+        return this_level + results[0]
+
+
+@with_multiprocessing
+@parametrize('backend', ['loky', 'threading'])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_nested_parallelism_limit(context, backend):
+    with context(backend, n_jobs=2):
+        backend_types_and_levels = _recursive_backend_info()
+
+    if cpu_count() == 1:
+        second_level_backend_type = 'SequentialBackend'
+        max_level = 1
+    else:
+        second_level_backend_type = 'ThreadingBackend'
+        max_level = 2
+
+    top_level_backend_type = backend.title() + 'Backend'
+    expected_types_and_levels = [
+        (top_level_backend_type, 0),
+        (second_level_backend_type, 1),
+        ('SequentialBackend', max_level),
+        ('SequentialBackend', max_level)
+    ]
+    assert backend_types_and_levels == expected_types_and_levels
+
+
+@with_numpy
+@parametrize("context", [parallel_config, parallel_backend])
+@skipif(distributed is None, reason='This test requires dask')
+def test_nested_parallelism_with_dask(context):
+    with distributed.Client(n_workers=2, threads_per_worker=2):
+        # 10 MB of data as argument to trigger implicit scattering
+        data = np.ones(int(1e7), dtype=np.uint8)
+        for i in range(2):
+            with context('dask'):
+                backend_types_and_levels = _recursive_backend_info(data=data)
+            assert len(backend_types_and_levels) == 4
+            assert all(name == 'DaskDistributedBackend'
+                       for name, _ in backend_types_and_levels)
+
+        # No argument
+        with context('dask'):
+            backend_types_and_levels = _recursive_backend_info()
+        assert len(backend_types_and_levels) == 4
+        assert all(name == 'DaskDistributedBackend'
+                   for name, _ in backend_types_and_levels)
+
+
+def _recursive_parallel(nesting_limit=None):
+    """A horrible function that does recursive parallel calls"""
+    return Parallel()(delayed(_recursive_parallel)() for i in range(2))
+
+
+@pytest.mark.no_cover
+@parametrize("context", [parallel_config, parallel_backend])
+@parametrize(
+    'backend', (['threading'] if mp is None else ['loky', 'threading'])
+)
+def test_thread_bomb_mitigation(context, backend):
+    # Test that recursive parallelism raises a recursion rather than
+    # saturating the operating system resources by creating a unbounded number
+    # of threads.
+    with context(backend, n_jobs=2):
+        with raises(BaseException) as excinfo:
+            _recursive_parallel()
+    exc = excinfo.value
+    if backend == "loky":
+        # Local import because loky may not be importable for lack of
+        # multiprocessing
+        from joblib.externals.loky.process_executor import TerminatedWorkerError # noqa
+        if isinstance(exc, (TerminatedWorkerError, PicklingError)):
+            # The recursion exception can itself cause an error when
+            # pickling it to be send back to the parent process. In this
+            # case the worker crashes but the original traceback is still
+            # printed on stderr. This could be improved but does not seem
+            # simple to do and this is not critical for users (as long
+            # as there is no process or thread bomb happening).
+            pytest.xfail("Loky worker crash when serializing RecursionError")
+
+    assert isinstance(exc, RecursionError)
+
+
+def _run_parallel_sum():
+    env_vars = {}
+    for var in ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS',
+                'VECLIB_MAXIMUM_THREADS', 'NUMEXPR_NUM_THREADS',
+                'NUMBA_NUM_THREADS', 'ENABLE_IPC']:
+        env_vars[var] = os.environ.get(var)
+    return env_vars, parallel_sum(100)
+
+
+@parametrize("backend", ([None, 'loky'] if mp is not None else [None]))
+@skipif(parallel_sum is None, reason="Need OpenMP helper compiled")
+def test_parallel_thread_limit(backend):
+    results = Parallel(n_jobs=2, backend=backend)(
+        delayed(_run_parallel_sum)() for _ in range(2)
+    )
+    expected_num_threads = max(cpu_count() // 2, 1)
+    for worker_env_vars, omp_num_threads in results:
+        assert omp_num_threads == expected_num_threads
+        for name, value in worker_env_vars.items():
+            if name.endswith("_THREADS"):
+                assert value == str(expected_num_threads)
+            else:
+                assert name == "ENABLE_IPC"
+                assert value == "1"
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+@skipif(distributed is not None, reason='This test requires dask')
+def test_dask_backend_when_dask_not_installed(context):
+    with raises(ValueError, match='Please install dask'):
+        context('dask')
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_zero_worker_backend(context):
+    # joblib.Parallel should reject with an explicit error message parallel
+    # backends that have no worker.
+    class ZeroWorkerBackend(ThreadingBackend):
+        def configure(self, *args, **kwargs):
+            return 0
+
+        def apply_async(self, func, callback=None):   # pragma: no cover
+            raise TimeoutError("No worker available")
+
+        def effective_n_jobs(self, n_jobs):   # pragma: no cover
+            return 0
+
+    expected_msg = "ZeroWorkerBackend has no active worker"
+    with context(ZeroWorkerBackend()):
+        with pytest.raises(RuntimeError, match=expected_msg):
+            Parallel(n_jobs=2)(delayed(id)(i) for i in range(2))
+
+
+def test_globals_update_at_each_parallel_call():
+    # This is a non-regression test related to joblib issues #836 and #833.
+    # Cloudpickle versions between 0.5.4 and 0.7 introduced a bug where global
+    # variables changes in a parent process between two calls to
+    # joblib.Parallel would not be propagated into the workers.
+    global MY_GLOBAL_VARIABLE
+    MY_GLOBAL_VARIABLE = "original value"
+
+    def check_globals():
+        global MY_GLOBAL_VARIABLE
+        return MY_GLOBAL_VARIABLE
+
+    assert check_globals() == "original value"
+
+    workers_global_variable = Parallel(n_jobs=2)(
+        delayed(check_globals)() for i in range(2))
+    assert set(workers_global_variable) == {"original value"}
+
+    # Change the value of MY_GLOBAL_VARIABLE, and make sure this change gets
+    # propagated into the workers environment
+    MY_GLOBAL_VARIABLE = "changed value"
+    assert check_globals() == "changed value"
+
+    workers_global_variable = Parallel(n_jobs=2)(
+        delayed(check_globals)() for i in range(2))
+    assert set(workers_global_variable) == {"changed value"}
+
+
+##############################################################################
+# Test environment variable in child env, in particular for limiting
+# the maximal number of threads in C-library threadpools.
+#
+
+def _check_numpy_threadpool_limits():
+    import numpy as np
+    # Let's call BLAS on a Matrix Matrix multiplication with dimensions large
+    # enough to ensure that the threadpool managed by the underlying BLAS
+    # implementation is actually used so as to force its initialization.
+    a = np.random.randn(100, 100)
+    np.dot(a, a)
+    from threadpoolctl import threadpool_info
+    return threadpool_info()
+
+
+def _parent_max_num_threads_for(child_module, parent_info):
+    for parent_module in parent_info:
+        if parent_module['filepath'] == child_module['filepath']:
+            return parent_module['num_threads']
+    raise ValueError("An unexpected module was loaded in child:\n{}"
+                     .format(child_module))
+
+
+def check_child_num_threads(workers_info, parent_info, num_threads):
+    # Check that the number of threads reported in workers_info is consistent
+    # with the expectation. We need to be careful to handle the cases where
+    # the requested number of threads is below max_num_thread for the library.
+    for child_threadpool_info in workers_info:
+        for child_module in child_threadpool_info:
+            parent_max_num_threads = _parent_max_num_threads_for(
+                child_module, parent_info)
+            expected = {min(num_threads, parent_max_num_threads), num_threads}
+            assert child_module['num_threads'] in expected
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('n_jobs', [2, 4, -2, -1])
+def test_threadpool_limitation_in_child_loky(n_jobs):
+    # Check that the protection against oversubscription in workers is working
+    # using threadpoolctl functionalities.
+
+    # Skip this test if numpy is not linked to a BLAS library
+    parent_info = _check_numpy_threadpool_limits()
+    if len(parent_info) == 0:
+        pytest.skip(reason="Need a version of numpy linked to BLAS")
+
+    workers_threadpool_infos = Parallel(backend="loky", n_jobs=n_jobs)(
+        delayed(_check_numpy_threadpool_limits)() for i in range(2))
+
+    n_jobs = effective_n_jobs(n_jobs)
+    expected_child_num_threads = max(cpu_count() // n_jobs, 1)
+
+    check_child_num_threads(workers_threadpool_infos, parent_info,
+                            expected_child_num_threads)
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('inner_max_num_threads', [1, 2, 4, None])
+@parametrize('n_jobs', [2, -1])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_threadpool_limitation_in_child_context(
+    context, n_jobs, inner_max_num_threads
+):
+    # Check that the protection against oversubscription in workers is working
+    # using threadpoolctl functionalities.
+
+    # Skip this test if numpy is not linked to a BLAS library
+    parent_info = _check_numpy_threadpool_limits()
+    if len(parent_info) == 0:
+        pytest.skip(reason="Need a version of numpy linked to BLAS")
+
+    with context('loky', inner_max_num_threads=inner_max_num_threads):
+        workers_threadpool_infos = Parallel(n_jobs=n_jobs)(
+            delayed(_check_numpy_threadpool_limits)() for i in range(2))
+
+    n_jobs = effective_n_jobs(n_jobs)
+    if inner_max_num_threads is None:
+        expected_child_num_threads = max(cpu_count() // n_jobs, 1)
+    else:
+        expected_child_num_threads = inner_max_num_threads
+
+    check_child_num_threads(workers_threadpool_infos, parent_info,
+                            expected_child_num_threads)
+
+
+@with_multiprocessing
+@parametrize('n_jobs', [2, -1])
+@parametrize('var_name', ["OPENBLAS_NUM_THREADS",
+                          "MKL_NUM_THREADS",
+                          "OMP_NUM_THREADS"])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_threadpool_limitation_in_child_override(context, n_jobs, var_name):
+    # Check that environment variables set by the user on the main process
+    # always have the priority.
+
+    # Clean up the existing executor because we change the environment of the
+    # parent at runtime and it is not detected in loky intentionally.
+    get_reusable_executor(reuse=True).shutdown()
+
+    def _get_env(var_name):
+        return os.environ.get(var_name)
+
+    original_var_value = os.environ.get(var_name)
+    try:
+        os.environ[var_name] = "4"
+        # Skip this test if numpy is not linked to a BLAS library
+        results = Parallel(n_jobs=n_jobs)(
+            delayed(_get_env)(var_name) for i in range(2))
+        assert results == ["4", "4"]
+
+        with context('loky', inner_max_num_threads=1):
+            results = Parallel(n_jobs=n_jobs)(
+                delayed(_get_env)(var_name) for i in range(2))
+        assert results == ["1", "1"]
+
+    finally:
+        if original_var_value is None:
+            del os.environ[var_name]
+        else:
+            os.environ[var_name] = original_var_value
+
+
+@with_multiprocessing
+@parametrize('n_jobs', [2, 4, -1])
+def test_loky_reuse_workers(n_jobs):
+    # Non-regression test for issue #967 where the workers are not reused when
+    # calling multiple Parallel loops.
+
+    def parallel_call(n_jobs):
+        x = range(10)
+        Parallel(n_jobs=n_jobs)(delayed(sum)(x) for i in range(10))
+
+    # Run a parallel loop and get the workers used for computations
+    parallel_call(n_jobs)
+    first_executor = get_reusable_executor(reuse=True)
+
+    # Ensure that the workers are reused for the next calls, as the executor is
+    # not restarted.
+    for _ in range(10):
+        parallel_call(n_jobs)
+        executor = get_reusable_executor(reuse=True)
+        assert executor == first_executor

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

@@ -0,0 +1,94 @@
+
+try:
+    # Python 2.7: use the C pickle to speed up
+    # test_concurrency_safe_write which pickles big python objects
+    import cPickle as cpickle
+except ImportError:
+    import pickle as cpickle
+import functools
+from pickle import PicklingError
+import time
+
+import pytest
+
+from joblib.testing import parametrize, timeout
+from joblib.test.common import with_multiprocessing
+from joblib.backports import concurrency_safe_rename
+from joblib import Parallel, delayed
+from joblib._store_backends import (
+    concurrency_safe_write,
+    FileSystemStoreBackend,
+    CacheWarning,
+)
+
+
+def write_func(output, filename):
+    with open(filename, 'wb') as f:
+        cpickle.dump(output, f)
+
+
+def load_func(expected, filename):
+    for i in range(10):
+        try:
+            with open(filename, 'rb') as f:
+                reloaded = cpickle.load(f)
+            break
+        except (OSError, IOError):
+            # On Windows you can have WindowsError ([Error 5] Access
+            # is denied or [Error 13] Permission denied) when reading the file,
+            # probably because a writer process has a lock on the file
+            time.sleep(0.1)
+    else:
+        raise
+    assert expected == reloaded
+
+
+def concurrency_safe_write_rename(to_write, filename, write_func):
+    temporary_filename = concurrency_safe_write(to_write,
+                                                filename, write_func)
+    concurrency_safe_rename(temporary_filename, filename)
+
+
+@timeout(0)  # No timeout as this test can be long
+@with_multiprocessing
+@parametrize('backend', ['multiprocessing', 'loky', 'threading'])
+def test_concurrency_safe_write(tmpdir, backend):
+    # Add one item to cache
+    filename = tmpdir.join('test.pkl').strpath
+
+    obj = {str(i): i for i in range(int(1e5))}
+    funcs = [functools.partial(concurrency_safe_write_rename,
+                               write_func=write_func)
+             if i % 3 != 2 else load_func for i in range(12)]
+    Parallel(n_jobs=2, backend=backend)(
+        delayed(func)(obj, filename) for func in funcs)
+
+
+def test_warning_on_dump_failure(tmpdir):
+    # Check that a warning is raised when the dump fails for any reason but
+    # a PicklingError.
+    class UnpicklableObject(object):
+        def __reduce__(self):
+            raise RuntimeError("some exception")
+
+    backend = FileSystemStoreBackend()
+    backend.location = tmpdir.join('test_warning_on_pickling_error').strpath
+    backend.compress = None
+
+    with pytest.warns(CacheWarning, match="some exception"):
+        backend.dump_item("testpath", UnpicklableObject())
+
+
+def test_warning_on_pickling_error(tmpdir):
+    # This is separate from test_warning_on_dump_failure because in the
+    # future we will turn this into an exception.
+    class UnpicklableObject(object):
+        def __reduce__(self):
+            raise PicklingError("not picklable")
+
+    backend = FileSystemStoreBackend()
+    backend.location = tmpdir.join('test_warning_on_pickling_error').strpath
+    backend.compress = None
+
+    with pytest.warns(FutureWarning, match="not picklable"):
+        backend.dump_item("testpath", UnpicklableObject())

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

@@ -0,0 +1,75 @@
+import sys
+import re
+
+from joblib.testing import raises, check_subprocess_call
+
+
+def test_check_subprocess_call():
+    code = '\n'.join(['result = 1 + 2 * 3',
+                      'print(result)',
+                      'my_list = [1, 2, 3]',
+                      'print(my_list)'])
+
+    check_subprocess_call([sys.executable, '-c', code])
+
+    # Now checking stdout with a regex
+    check_subprocess_call([sys.executable, '-c', code],
+                          # Regex needed for platform-specific line endings
+                          stdout_regex=r'7\s{1,2}\[1, 2, 3\]')
+
+
+def test_check_subprocess_call_non_matching_regex():
+    code = '42'
+    non_matching_pattern = '_no_way_this_matches_anything_'
+
+    with raises(ValueError) as excinfo:
+        check_subprocess_call([sys.executable, '-c', code],
+                              stdout_regex=non_matching_pattern)
+    excinfo.match('Unexpected stdout.+{}'.format(non_matching_pattern))
+
+
+def test_check_subprocess_call_wrong_command():
+    wrong_command = '_a_command_that_does_not_exist_'
+    with raises(OSError):
+        check_subprocess_call([wrong_command])
+
+
+def test_check_subprocess_call_non_zero_return_code():
+    code_with_non_zero_exit = '\n'.join([
+        'import sys',
+        'print("writing on stdout")',
+        'sys.stderr.write("writing on stderr")',
+        'sys.exit(123)'])
+
+    pattern = re.compile('Non-zero return code: 123.+'
+                         'Stdout:\nwriting on stdout.+'
+                         'Stderr:\nwriting on stderr', re.DOTALL)
+
+    with raises(ValueError) as excinfo:
+        check_subprocess_call([sys.executable, '-c', code_with_non_zero_exit])
+    excinfo.match(pattern)
+
+
+def test_check_subprocess_call_timeout():
+    code_timing_out = '\n'.join([
+        'import time',
+        'import sys',
+        'print("before sleep on stdout")',
+        'sys.stdout.flush()',
+        'sys.stderr.write("before sleep on stderr")',
+        'sys.stderr.flush()',
+        # We need to sleep for at least 2 * timeout seconds in case the SIGKILL
+        # is triggered.
+        'time.sleep(10)',
+        'print("process should have be killed before")',
+        'sys.stdout.flush()'])
+
+    pattern = re.compile('Non-zero return code:.+'
+                         'Stdout:\nbefore sleep on stdout\\s+'
+                         'Stderr:\nbefore sleep on stderr',
+                         re.DOTALL)
+
+    with raises(ValueError) as excinfo:
+        check_subprocess_call([sys.executable, '-c', code_timing_out],
+                              timeout=1)
+    excinfo.match(pattern)

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

@@ -0,0 +1,27 @@
+import pytest
+
+from joblib._utils import eval_expr
+
+
+@pytest.mark.parametrize(
+    "expr",
+    ["exec('import os')", "print(1)", "import os", "1+1; import os", "1^1"],
+)
+def test_eval_expr_invalid(expr):
+    with pytest.raises(
+        ValueError, match="is not a valid or supported arithmetic"
+    ):
+        eval_expr(expr)
+
+
+@pytest.mark.parametrize(
+    "expr, result",
+    [
+        ("2*6", 12),
+        ("2**6", 64),
+        ("1 + 2*3**(4) / (6 + -7)", -161.0),
+        ("(20 // 3) % 5", 1),
+    ],
+)
+def test_eval_expr_valid(expr, result):
+    assert eval_expr(expr) == result