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Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/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
+      >>> location = 'your_cache_dir_goes_here'
+      >>> mem = Memory(location, verbose=1)
+      >>> 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(location)
+
+"""
+
+# 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__ = "1.5.1"
+
+
+import os
+
+from ._cloudpickle_wrapper import wrap_non_picklable_objects
+from ._parallel_backends import ParallelBackendBase
+from ._store_backends import StoreBackendBase
+from .compressor import register_compressor
+from .hashing import hash
+from .logger import Logger, PrintTime
+from .memory import MemorizedResult, Memory, expires_after, register_store_backend
+from .numpy_pickle import dump, load
+from .parallel import (
+    Parallel,
+    cpu_count,
+    delayed,
+    effective_n_jobs,
+    parallel_backend,
+    parallel_config,
+    register_parallel_backend,
+)
+
+__all__ = [
+    # On-disk result caching
+    "Memory",
+    "MemorizedResult",
+    "expires_after",
+    # Parallel code execution
+    "Parallel",
+    "delayed",
+    "cpu_count",
+    "effective_n_jobs",
+    "wrap_non_picklable_objects",
+    # Context to change the backend globally
+    "parallel_config",
+    "parallel_backend",
+    # Helpers to define and register store/parallel backends
+    "ParallelBackendBase",
+    "StoreBackendBase",
+    "register_compressor",
+    "register_parallel_backend",
+    "register_store_backend",
+    # Helpers kept for backward compatibility
+    "PrintTime",
+    "Logger",
+    "hash",
+    "dump",
+    "load",
+]
+
+
+# Workaround issue discovered in intel-openmp 2019.5:
+# https://github.com/ContinuumIO/anaconda-issues/issues/11294
+os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/_cloudpickle_wrapper.py

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

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/_dask.py

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+from __future__ import absolute_import, division, print_function
+
+import asyncio
+import concurrent.futures
+import contextlib
+import time
+import weakref
+from uuid import uuid4
+
+from ._utils import (
+    _retrieve_traceback_capturing_wrapped_call,
+    _TracebackCapturingWrapper,
+)
+from .parallel import AutoBatchingMixin, ParallelBackendBase, parallel_config
+
+try:
+    import dask
+    import distributed
+except ImportError:
+    dask = None
+    distributed = None
+
+if dask is not None and distributed is not None:
+    from dask.distributed import (
+        Client,
+        as_completed,
+        get_client,
+        rejoin,
+        secede,
+    )
+    from dask.sizeof import sizeof
+    from dask.utils import funcname
+    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()

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/_memmapping_reducer.py

@@ -0,0 +1,715 @@
+"""
+Reducer using memory mapping for numpy arrays
+"""
+# Author: Thomas Moreau <thomas.moreau.2010@gmail.com>
+# Copyright: 2017, Thomas Moreau
+# License: BSD 3 clause
+
+import atexit
+import errno
+import os
+import stat
+import tempfile
+import threading
+import time
+import warnings
+import weakref
+from mmap import mmap
+from multiprocessing import util
+from pickle import HIGHEST_PROTOCOL, PicklingError, dumps, loads, whichmodule
+from uuid import uuid4
+
+try:
+    WindowsError
+except NameError:
+    WindowsError = type(None)
+
+try:
+    import numpy as np
+    from numpy.lib.stride_tricks import as_strided
+except ImportError:
+    np = None
+
+from .backports import make_memmap
+from .disk import delete_folder
+from .externals.loky.backend import resource_tracker
+from .numpy_pickle import dump, load, load_temporary_memmap
+
+# Some system have a ramdisk mounted by default, we can use it instead of /tmp
+# as the default folder to dump big arrays to share with subprocesses.
+SYSTEM_SHARED_MEM_FS = "/dev/shm"
+
+# Minimal number of bytes available on SYSTEM_SHARED_MEM_FS to consider using
+# it as the default folder to dump big arrays to share with subprocesses.
+SYSTEM_SHARED_MEM_FS_MIN_SIZE = int(2e9)
+
+# Folder and file permissions to chmod temporary files generated by the
+# memmapping pool. Only the owner of the Python process can access the
+# temporary files and folder.
+FOLDER_PERMISSIONS = stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR
+FILE_PERMISSIONS = stat.S_IRUSR | stat.S_IWUSR
+
+# Set used in joblib workers, referencing the filenames of temporary memmaps
+# created by joblib to speed up data communication. In child processes, we add
+# a finalizer to these memmaps that sends a maybe_unlink call to the
+# resource_tracker, in order to free main memory as fast as possible.
+JOBLIB_MMAPS = set()
+
+
+def _log_and_unlink(filename):
+    from .externals.loky.backend.resource_tracker import _resource_tracker
+
+    util.debug(
+        "[FINALIZER CALL] object mapping to {} about to be deleted,"
+        " decrementing the refcount of the file (pid: {})".format(
+            os.path.basename(filename), os.getpid()
+        )
+    )
+    _resource_tracker.maybe_unlink(filename, "file")
+
+
+def add_maybe_unlink_finalizer(memmap):
+    util.debug(
+        "[FINALIZER ADD] adding finalizer to {} (id {}, filename {}, pid  {})".format(
+            type(memmap), id(memmap), os.path.basename(memmap.filename), os.getpid()
+        )
+    )
+    weakref.finalize(memmap, _log_and_unlink, memmap.filename)
+
+
+def unlink_file(filename):
+    """Wrapper around os.unlink with a retry mechanism.
+
+    The retry mechanism has been implemented primarily to overcome a race
+    condition happening during the finalizer of a np.memmap: when a process
+    holding the last reference to a mmap-backed np.memmap/np.array is about to
+    delete this array (and close the reference), it sends a maybe_unlink
+    request to the resource_tracker. This request can be processed faster than
+    it takes for the last reference of the memmap to be closed, yielding (on
+    Windows) a PermissionError in the resource_tracker loop.
+    """
+    NUM_RETRIES = 10
+    for retry_no in range(1, NUM_RETRIES + 1):
+        try:
+            os.unlink(filename)
+            break
+        except PermissionError:
+            util.debug(
+                "[ResourceTracker] tried to unlink {}, got PermissionError".format(
+                    filename
+                )
+            )
+            if retry_no == NUM_RETRIES:
+                raise
+            else:
+                time.sleep(0.2)
+        except FileNotFoundError:
+            # In case of a race condition when deleting the temporary folder,
+            # avoid noisy FileNotFoundError exception in the resource tracker.
+            pass
+
+
+resource_tracker._CLEANUP_FUNCS["file"] = unlink_file
+
+
+class _WeakArrayKeyMap:
+    """A variant of weakref.WeakKeyDictionary for unhashable numpy arrays.
+
+    This datastructure will be used with numpy arrays as obj keys, therefore we
+    do not use the __get__ / __set__ methods to avoid any conflict with the
+    numpy fancy indexing syntax.
+    """
+
+    def __init__(self):
+        self._data = {}
+
+    def get(self, obj):
+        ref, val = self._data[id(obj)]
+        if ref() is not obj:
+            # In case of race condition with on_destroy: could never be
+            # triggered by the joblib tests with CPython.
+            raise KeyError(obj)
+        return val
+
+    def set(self, obj, value):
+        key = id(obj)
+        try:
+            ref, _ = self._data[key]
+            if ref() is not obj:
+                # In case of race condition with on_destroy: could never be
+                # triggered by the joblib tests with CPython.
+                raise KeyError(obj)
+        except KeyError:
+            # Insert the new entry in the mapping along with a weakref
+            # callback to automatically delete the entry from the mapping
+            # as soon as the object used as key is garbage collected.
+            def on_destroy(_):
+                del self._data[key]
+
+            ref = weakref.ref(obj, on_destroy)
+        self._data[key] = ref, value
+
+    def __getstate__(self):
+        raise PicklingError("_WeakArrayKeyMap is not pickleable")
+
+
+###############################################################################
+# Support for efficient transient pickling of numpy data structures
+
+
+def _get_backing_memmap(a):
+    """Recursively look up the original np.memmap instance base if any."""
+    b = getattr(a, "base", None)
+    if b is None:
+        # TODO: check scipy sparse datastructure if scipy is installed
+        # a nor its descendants do not have a memmap base
+        return None
+
+    elif isinstance(b, mmap):
+        # a is already a real memmap instance.
+        return a
+
+    else:
+        # Recursive exploration of the base ancestry
+        return _get_backing_memmap(b)
+
+
+def _get_temp_dir(pool_folder_name, temp_folder=None):
+    """Get the full path to a subfolder inside the temporary folder.
+
+    Parameters
+    ----------
+    pool_folder_name : str
+        Sub-folder name used for the serialization of a pool instance.
+
+    temp_folder: str, optional
+        Folder to be used by the pool for memmapping large arrays
+        for sharing memory with worker processes. If None, this will try in
+        order:
+
+        - a folder pointed by the JOBLIB_TEMP_FOLDER environment
+          variable,
+        - /dev/shm if the folder exists and is writable: this is a
+          RAMdisk filesystem available by default on modern Linux
+          distributions,
+        - the default system temporary folder that can be
+          overridden with TMP, TMPDIR or TEMP environment
+          variables, typically /tmp under Unix operating systems.
+
+    Returns
+    -------
+    pool_folder : str
+       full path to the temporary folder
+    use_shared_mem : bool
+       whether the temporary folder is written to the system shared memory
+       folder or some other temporary folder.
+    """
+    use_shared_mem = False
+    if temp_folder is None:
+        temp_folder = os.environ.get("JOBLIB_TEMP_FOLDER", None)
+    if temp_folder is None:
+        if os.path.exists(SYSTEM_SHARED_MEM_FS) and hasattr(os, "statvfs"):
+            try:
+                shm_stats = os.statvfs(SYSTEM_SHARED_MEM_FS)
+                available_nbytes = shm_stats.f_bsize * shm_stats.f_bavail
+                if available_nbytes > SYSTEM_SHARED_MEM_FS_MIN_SIZE:
+                    # Try to see if we have write access to the shared mem
+                    # folder only if it is reasonably large (that is 2GB or
+                    # more).
+                    temp_folder = SYSTEM_SHARED_MEM_FS
+                    pool_folder = os.path.join(temp_folder, pool_folder_name)
+                    if not os.path.exists(pool_folder):
+                        os.makedirs(pool_folder)
+                    use_shared_mem = True
+            except (IOError, OSError):
+                # Missing rights in the /dev/shm partition, fallback to regular
+                # temp folder.
+                temp_folder = None
+    if temp_folder is None:
+        # Fallback to the default tmp folder, typically /tmp
+        temp_folder = tempfile.gettempdir()
+    temp_folder = os.path.abspath(os.path.expanduser(temp_folder))
+    pool_folder = os.path.join(temp_folder, pool_folder_name)
+    return pool_folder, use_shared_mem
+
+
+def has_shareable_memory(a):
+    """Return True if a is backed by some mmap buffer directly or not."""
+    return _get_backing_memmap(a) is not None
+
+
+def _strided_from_memmap(
+    filename,
+    dtype,
+    mode,
+    offset,
+    order,
+    shape,
+    strides,
+    total_buffer_len,
+    unlink_on_gc_collect,
+):
+    """Reconstruct an array view on a memory mapped file."""
+    if mode == "w+":
+        # Do not zero the original data when unpickling
+        mode = "r+"
+
+    if strides is None:
+        # Simple, contiguous memmap
+        return make_memmap(
+            filename,
+            dtype=dtype,
+            shape=shape,
+            mode=mode,
+            offset=offset,
+            order=order,
+            unlink_on_gc_collect=unlink_on_gc_collect,
+        )
+    else:
+        # For non-contiguous data, memmap the total enclosing buffer and then
+        # extract the non-contiguous view with the stride-tricks API
+        base = make_memmap(
+            filename,
+            dtype=dtype,
+            shape=total_buffer_len,
+            offset=offset,
+            mode=mode,
+            order=order,
+            unlink_on_gc_collect=unlink_on_gc_collect,
+        )
+        return as_strided(base, shape=shape, strides=strides)
+
+
+def _reduce_memmap_backed(a, m):
+    """Pickling reduction for memmap backed arrays.
+
+    a is expected to be an instance of np.ndarray (or np.memmap)
+    m is expected to be an instance of np.memmap on the top of the ``base``
+    attribute ancestry of a. ``m.base`` should be the real python mmap object.
+    """
+    # offset that comes from the striding differences between a and m
+    util.debug(
+        "[MEMMAP REDUCE] reducing a memmap-backed array (shape, {}, pid: {})".format(
+            a.shape, os.getpid()
+        )
+    )
+    try:
+        from numpy.lib.array_utils import byte_bounds
+    except (ModuleNotFoundError, ImportError):
+        # Backward-compat for numpy < 2.0
+        from numpy import byte_bounds
+    a_start, a_end = byte_bounds(a)
+    m_start = byte_bounds(m)[0]
+    offset = a_start - m_start
+
+    # offset from the backing memmap
+    offset += m.offset
+
+    # 1D arrays are both F and C contiguous, so only set the flag in
+    # higher dimensions. See https://github.com/joblib/joblib/pull/1704.
+    if m.ndim > 1 and m.flags["F_CONTIGUOUS"]:
+        order = "F"
+    else:
+        # The backing memmap buffer is necessarily contiguous hence C if not
+        # Fortran
+        order = "C"
+
+    if a.flags["F_CONTIGUOUS"] or a.flags["C_CONTIGUOUS"]:
+        # If the array is a contiguous view, no need to pass the strides
+        strides = None
+        total_buffer_len = None
+    else:
+        # Compute the total number of items to map from which the strided
+        # view will be extracted.
+        strides = a.strides
+        total_buffer_len = (a_end - a_start) // a.itemsize
+
+    return (
+        _strided_from_memmap,
+        (
+            m.filename,
+            a.dtype,
+            m.mode,
+            offset,
+            order,
+            a.shape,
+            strides,
+            total_buffer_len,
+            False,
+        ),
+    )
+
+
+def reduce_array_memmap_backward(a):
+    """reduce a np.array or a np.memmap from a child process"""
+    m = _get_backing_memmap(a)
+    if isinstance(m, np.memmap) and m.filename not in JOBLIB_MMAPS:
+        # if a is backed by a memmaped file, reconstruct a using the
+        # memmaped file.
+        return _reduce_memmap_backed(a, m)
+    else:
+        # a is either a regular (not memmap-backed) numpy array, or an array
+        # backed by a shared temporary file created by joblib. In the latter
+        # case, in order to limit the lifespan of these temporary files, we
+        # serialize the memmap as a regular numpy array, and decref the
+        # file backing the memmap (done implicitly in a previously registered
+        # finalizer, see ``unlink_on_gc_collect`` for more details)
+        return (loads, (dumps(np.asarray(a), protocol=HIGHEST_PROTOCOL),))
+
+
+class ArrayMemmapForwardReducer(object):
+    """Reducer callable to dump large arrays to memmap files.
+
+    Parameters
+    ----------
+    max_nbytes: int
+        Threshold to trigger memmapping of large arrays to files created
+        a folder.
+    temp_folder_resolver: callable
+        An callable in charge of resolving a temporary folder name where files
+        for backing memmapped arrays are created.
+    mmap_mode: 'r', 'r+' or 'c'
+        Mode for the created memmap datastructure. See the documentation of
+        numpy.memmap for more details. Note: 'w+' is coerced to 'r+'
+        automatically to avoid zeroing the data on unpickling.
+    verbose: int, optional, 0 by default
+        If verbose > 0, memmap creations are logged.
+        If verbose > 1, both memmap creations, reuse and array pickling are
+        logged.
+    prewarm: bool, optional, False by default.
+        Force a read on newly memmapped array to make sure that OS pre-cache it
+        memory. This can be useful to avoid concurrent disk access when the
+        same data array is passed to different worker processes.
+    """
+
+    def __init__(
+        self,
+        max_nbytes,
+        temp_folder_resolver,
+        mmap_mode,
+        unlink_on_gc_collect,
+        verbose=0,
+        prewarm=True,
+    ):
+        self._max_nbytes = max_nbytes
+        self._temp_folder_resolver = temp_folder_resolver
+        self._mmap_mode = mmap_mode
+        self.verbose = int(verbose)
+        if prewarm == "auto":
+            self._prewarm = not self._temp_folder.startswith(SYSTEM_SHARED_MEM_FS)
+        else:
+            self._prewarm = prewarm
+        self._prewarm = prewarm
+        self._memmaped_arrays = _WeakArrayKeyMap()
+        self._temporary_memmaped_filenames = set()
+        self._unlink_on_gc_collect = unlink_on_gc_collect
+
+    @property
+    def _temp_folder(self):
+        return self._temp_folder_resolver()
+
+    def __reduce__(self):
+        # The ArrayMemmapForwardReducer is passed to the children processes: it
+        # needs to be pickled but the _WeakArrayKeyMap need to be skipped as
+        # it's only guaranteed to be consistent with the parent process memory
+        # garbage collection.
+        # Although this reducer is pickled, it is not needed in its destination
+        # process (child processes), as we only use this reducer to send
+        # memmaps from the parent process to the children processes. For this
+        # reason, we can afford skipping the resolver, (which would otherwise
+        # be unpicklable), and pass it as None instead.
+        args = (self._max_nbytes, None, self._mmap_mode, self._unlink_on_gc_collect)
+        kwargs = {
+            "verbose": self.verbose,
+            "prewarm": self._prewarm,
+        }
+        return ArrayMemmapForwardReducer, args, kwargs
+
+    def __call__(self, a):
+        m = _get_backing_memmap(a)
+        if m is not None and isinstance(m, np.memmap):
+            # a is already backed by a memmap file, let's reuse it directly
+            return _reduce_memmap_backed(a, m)
+
+        if (
+            not a.dtype.hasobject
+            and self._max_nbytes is not None
+            and a.nbytes > self._max_nbytes
+        ):
+            # check that the folder exists (lazily create the pool temp folder
+            # if required)
+            try:
+                os.makedirs(self._temp_folder)
+                os.chmod(self._temp_folder, FOLDER_PERMISSIONS)
+            except OSError as e:
+                if e.errno != errno.EEXIST:
+                    raise e
+
+            try:
+                basename = self._memmaped_arrays.get(a)
+            except KeyError:
+                # Generate a new unique random filename. The process and thread
+                # ids are only useful for debugging purpose and to make it
+                # easier to cleanup orphaned files in case of hard process
+                # kill (e.g. by "kill -9" or segfault).
+                basename = "{}-{}-{}.pkl".format(
+                    os.getpid(), id(threading.current_thread()), uuid4().hex
+                )
+                self._memmaped_arrays.set(a, basename)
+            filename = os.path.join(self._temp_folder, basename)
+
+            # In case the same array with the same content is passed several
+            # times to the pool subprocess children, serialize it only once
+
+            is_new_memmap = filename not in self._temporary_memmaped_filenames
+
+            # add the memmap to the list of temporary memmaps created by joblib
+            self._temporary_memmaped_filenames.add(filename)
+
+            if self._unlink_on_gc_collect:
+                # Bump reference count of the memmap by 1 to account for
+                # shared usage of the memmap by a child process. The
+                # corresponding decref call will be executed upon calling
+                # resource_tracker.maybe_unlink, registered as a finalizer in
+                # the child.
+                # the incref/decref calls here are only possible when the child
+                # and the parent share the same resource_tracker. It is not the
+                # case for the multiprocessing backend, but it does not matter
+                # because unlinking a memmap from a child process is only
+                # useful to control the memory usage of long-lasting child
+                # processes, while the multiprocessing-based pools terminate
+                # their workers at the end of a map() call.
+                resource_tracker.register(filename, "file")
+
+            if is_new_memmap:
+                # Incref each temporary memmap created by joblib one extra
+                # time.  This means that these memmaps will only be deleted
+                # once an extra maybe_unlink() is called, which is done once
+                # all the jobs have completed (or been canceled) in the
+                # Parallel._terminate_backend() method.
+                resource_tracker.register(filename, "file")
+
+            if not os.path.exists(filename):
+                util.debug(
+                    "[ARRAY DUMP] Pickling new array (shape={}, dtype={}) "
+                    "creating a new memmap at {}".format(a.shape, a.dtype, filename)
+                )
+                for dumped_filename in dump(a, filename):
+                    os.chmod(dumped_filename, FILE_PERMISSIONS)
+
+                if self._prewarm:
+                    # Warm up the data by accessing it. This operation ensures
+                    # that the disk access required to create the memmapping
+                    # file are performed in the reducing process and avoids
+                    # concurrent memmap creation in multiple children
+                    # processes.
+                    load(filename, mmap_mode=self._mmap_mode).max()
+
+            else:
+                util.debug(
+                    "[ARRAY DUMP] Pickling known array (shape={}, dtype={}) "
+                    "reusing memmap file: {}".format(
+                        a.shape, a.dtype, os.path.basename(filename)
+                    )
+                )
+
+            # The worker process will use joblib.load to memmap the data
+            return (
+                load_temporary_memmap,
+                (filename, self._mmap_mode, self._unlink_on_gc_collect),
+            )
+        else:
+            # do not convert a into memmap, let pickler do its usual copy with
+            # the default system pickler
+            util.debug(
+                "[ARRAY DUMP] Pickling array (NO MEMMAPPING) (shape={}, "
+                " dtype={}).".format(a.shape, a.dtype)
+            )
+            return (loads, (dumps(a, protocol=HIGHEST_PROTOCOL),))
+
+
+def get_memmapping_reducers(
+    forward_reducers=None,
+    backward_reducers=None,
+    temp_folder_resolver=None,
+    max_nbytes=1e6,
+    mmap_mode="r",
+    verbose=0,
+    prewarm=False,
+    unlink_on_gc_collect=True,
+    **kwargs,
+):
+    """Construct a pair of memmapping reducer linked to a tmpdir.
+
+    This function manage the creation and the clean up of the temporary folders
+    underlying the memory maps and should be use to get the reducers necessary
+    to construct joblib pool or executor.
+    """
+    if forward_reducers is None:
+        forward_reducers = dict()
+    if backward_reducers is None:
+        backward_reducers = dict()
+
+    if np is not None:
+        # Register smart numpy.ndarray reducers that detects memmap backed
+        # arrays and that is also able to dump to memmap large in-memory
+        # arrays over the max_nbytes threshold
+        forward_reduce_ndarray = ArrayMemmapForwardReducer(
+            max_nbytes,
+            temp_folder_resolver,
+            mmap_mode,
+            unlink_on_gc_collect,
+            verbose,
+            prewarm=prewarm,
+        )
+        forward_reducers[np.ndarray] = forward_reduce_ndarray
+        forward_reducers[np.memmap] = forward_reduce_ndarray
+
+        # Communication from child process to the parent process always
+        # pickles in-memory numpy.ndarray without dumping them as memmap
+        # to avoid confusing the caller and make it tricky to collect the
+        # temporary folder
+        backward_reducers[np.ndarray] = reduce_array_memmap_backward
+        backward_reducers[np.memmap] = reduce_array_memmap_backward
+
+    return forward_reducers, backward_reducers
+
+
+class TemporaryResourcesManager(object):
+    """Stateful object able to manage temporary folder and pickles
+
+    It exposes:
+    - a per-context folder name resolving API that memmap-based reducers will
+      rely on to know where to pickle the temporary memmaps
+    - a temporary file/folder management API that internally uses the
+      resource_tracker.
+    """
+
+    def __init__(self, temp_folder_root=None, context_id=None):
+        self._current_temp_folder = None
+        self._temp_folder_root = temp_folder_root
+        self._use_shared_mem = None
+        self._cached_temp_folders = dict()
+        self._id = uuid4().hex
+        self._finalizers = {}
+        if context_id is None:
+            # It would be safer to not assign a default context id (less silent
+            # bugs), but doing this while maintaining backward compatibility
+            # with the previous, context-unaware version get_memmaping_executor
+            # exposes too many low-level details.
+            context_id = uuid4().hex
+        self.set_current_context(context_id)
+
+    def set_current_context(self, context_id):
+        self._current_context_id = context_id
+        self.register_new_context(context_id)
+
+    def register_new_context(self, context_id):
+        # Prepare a sub-folder name specific to a context (usually a unique id
+        # generated by each instance of the Parallel class). Do not create in
+        # advance to spare FS write access if no array is to be dumped).
+        if context_id in self._cached_temp_folders:
+            return
+        else:
+            # During its lifecycle, one Parallel object can have several
+            # executors associated to it (for instance, if a loky worker raises
+            # an exception, joblib shutdowns the executor and instantly
+            # recreates a new one before raising the error - see
+            # ``ensure_ready``.  Because we don't want two executors tied to
+            # the same Parallel object (and thus the same context id) to
+            # register/use/delete the same folder, we also add an id specific
+            # to the current Manager (and thus specific to its associated
+            # executor) to the folder name.
+            new_folder_name = "joblib_memmapping_folder_{}_{}_{}".format(
+                os.getpid(), self._id, context_id
+            )
+            new_folder_path, _ = _get_temp_dir(new_folder_name, self._temp_folder_root)
+            self.register_folder_finalizer(new_folder_path, context_id)
+            self._cached_temp_folders[context_id] = new_folder_path
+
+    def resolve_temp_folder_name(self):
+        """Return a folder name specific to the currently activated context"""
+        return self._cached_temp_folders[self._current_context_id]
+
+    # resource management API
+
+    def register_folder_finalizer(self, pool_subfolder, context_id):
+        # Register the garbage collector at program exit in case caller forgets
+        # to call terminate explicitly: note we do not pass any reference to
+        # ensure that this callback won't prevent garbage collection of
+        # parallel instance and related file handler resources such as POSIX
+        # semaphores and pipes
+        pool_module_name = whichmodule(delete_folder, "delete_folder")
+        resource_tracker.register(pool_subfolder, "folder")
+
+        def _cleanup():
+            # In some cases the Python runtime seems to set delete_folder to
+            # None just before exiting when accessing the delete_folder
+            # function from the closure namespace. So instead we reimport
+            # the delete_folder function explicitly.
+            # https://github.com/joblib/joblib/issues/328
+            # We cannot just use from 'joblib.pool import delete_folder'
+            # because joblib should only use relative imports to allow
+            # easy vendoring.
+            delete_folder = __import__(
+                pool_module_name, fromlist=["delete_folder"]
+            ).delete_folder
+            try:
+                delete_folder(pool_subfolder, allow_non_empty=True)
+                resource_tracker.unregister(pool_subfolder, "folder")
+            except OSError:
+                warnings.warn(
+                    "Failed to delete temporary folder: {}".format(pool_subfolder)
+                )
+
+        self._finalizers[context_id] = atexit.register(_cleanup)
+
+    def _clean_temporary_resources(
+        self, context_id=None, force=False, allow_non_empty=False
+    ):
+        """Clean temporary resources created by a process-based pool"""
+        if context_id is None:
+            # Iterates over a copy of the cache keys to avoid Error due to
+            # iterating over a changing size dictionary.
+            for context_id in list(self._cached_temp_folders):
+                self._clean_temporary_resources(
+                    context_id, force=force, allow_non_empty=allow_non_empty
+                )
+        else:
+            temp_folder = self._cached_temp_folders.get(context_id)
+            if temp_folder and os.path.exists(temp_folder):
+                for filename in os.listdir(temp_folder):
+                    if force:
+                        # Some workers have failed and the ref counted might
+                        # be off. The workers should have shut down by this
+                        # time so forcefully clean up the files.
+                        resource_tracker.unregister(
+                            os.path.join(temp_folder, filename), "file"
+                        )
+                    else:
+                        resource_tracker.maybe_unlink(
+                            os.path.join(temp_folder, filename), "file"
+                        )
+
+                # When forcing clean-up, try to delete the folder even if some
+                # files are still in it. Otherwise, try to delete the folder
+                allow_non_empty |= force
+
+                # Clean up the folder if possible, either if it is empty or
+                # if none of the files in it are in used and allow_non_empty.
+                try:
+                    delete_folder(temp_folder, allow_non_empty=allow_non_empty)
+                    # Forget the folder once it has been deleted
+                    self._cached_temp_folders.pop(context_id, None)
+                    resource_tracker.unregister(temp_folder, "folder")
+
+                    # Also cancel the finalizers  that gets triggered at gc.
+                    finalizer = self._finalizers.pop(context_id, None)
+                    if finalizer is not None:
+                        atexit.unregister(finalizer)
+
+                except OSError:
+                    # Temporary folder cannot be deleted right now.
+                    # This folder will be cleaned up by an atexit
+                    # finalizer registered by the memmapping_reducer.
+                    pass

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/_multiprocessing_helpers.py

@@ -0,0 +1,51 @@
+"""Helper module to factorize the conditional multiprocessing import logic
+
+We use a distinct module to simplify import statements and avoid introducing
+circular dependencies (for instance for the assert_spawning name).
+"""
+
+import os
+import warnings
+
+# Obtain possible configuration from the environment, assuming 1 (on)
+# by default, upon 0 set to None. Should instructively fail if some non
+# 0/1 value is set.
+mp = int(os.environ.get("JOBLIB_MULTIPROCESSING", 1)) or None
+if mp:
+    try:
+        import _multiprocessing  # noqa
+        import multiprocessing as mp
+    except ImportError:
+        mp = None
+
+# 2nd stage: validate that locking is available on the system and
+#            issue a warning if not
+if mp is not None:
+    try:
+        # try to create a named semaphore using SemLock to make sure they are
+        # available on this platform. We use the low level object
+        # _multiprocessing.SemLock to avoid spawning a resource tracker on
+        # Unix system or changing the default backend.
+        import tempfile
+        from _multiprocessing import SemLock
+
+        _rand = tempfile._RandomNameSequence()
+        for i in range(100):
+            try:
+                name = "/joblib-{}-{}".format(os.getpid(), next(_rand))
+                _sem = SemLock(0, 0, 1, name=name, unlink=True)
+                del _sem  # cleanup
+                break
+            except FileExistsError as e:  # pragma: no cover
+                if i >= 99:
+                    raise FileExistsError("cannot find name for semaphore") from e
+    except (FileExistsError, AttributeError, ImportError, OSError) as e:
+        mp = None
+        warnings.warn("%s.  joblib will operate in serial mode" % (e,))
+
+
+# 3rd stage: backward compat for the assert_spawning helper
+if mp is not None:
+    from multiprocessing.context import assert_spawning
+else:
+    assert_spawning = None

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/_parallel_backends.py

@@ -0,0 +1,753 @@
+"""
+Backends for embarrassingly parallel code.
+"""
+
+import contextlib
+import gc
+import os
+import threading
+import warnings
+from abc import ABCMeta, abstractmethod
+
+from ._multiprocessing_helpers import mp
+from ._utils import (
+    _retrieve_traceback_capturing_wrapped_call,
+    _TracebackCapturingWrapper,
+)
+
+if mp is not None:
+    from multiprocessing.pool import ThreadPool
+
+    from .executor import get_memmapping_executor
+
+    # Import loky only if multiprocessing is present
+    from .externals.loky import cpu_count, process_executor
+    from .externals.loky.process_executor import ShutdownExecutorError
+    from .pool import MemmappingPool
+
+
+class ParallelBackendBase(metaclass=ABCMeta):
+    """Helper abc which defines all methods a ParallelBackend must implement"""
+
+    default_n_jobs = 1
+
+    supports_inner_max_num_threads = False
+
+    # This flag was introduced for backward compatibility reasons.
+    # New backends should always set it to True and implement the
+    # `retrieve_result_callback` method.
+    supports_retrieve_callback = False
+
+    @property
+    def supports_return_generator(self):
+        return self.supports_retrieve_callback
+
+    @property
+    def supports_timeout(self):
+        return self.supports_retrieve_callback
+
+    nesting_level = None
+
+    def __init__(
+        self, nesting_level=None, inner_max_num_threads=None, **backend_kwargs
+    ):
+        super().__init__()
+        self.nesting_level = nesting_level
+        self.inner_max_num_threads = inner_max_num_threads
+        self.backend_kwargs = backend_kwargs
+
+    MAX_NUM_THREADS_VARS = [
+        "OMP_NUM_THREADS",
+        "OPENBLAS_NUM_THREADS",
+        "MKL_NUM_THREADS",
+        "BLIS_NUM_THREADS",
+        "VECLIB_MAXIMUM_THREADS",
+        "NUMBA_NUM_THREADS",
+        "NUMEXPR_NUM_THREADS",
+    ]
+
+    TBB_ENABLE_IPC_VAR = "ENABLE_IPC"
+
+    @abstractmethod
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs that can actually run in parallel
+
+        n_jobs is the number of workers requested by the callers. Passing
+        n_jobs=-1 means requesting all available workers for instance matching
+        the number of CPU cores on the worker host(s).
+
+        This method should return a guesstimate of the number of workers that
+        can actually perform work concurrently. The primary use case is to make
+        it possible for the caller to know in how many chunks to slice the
+        work.
+
+        In general working on larger data chunks is more efficient (less
+        scheduling overhead and better use of CPU cache prefetching heuristics)
+        as long as all the workers have enough work to do.
+        """
+
+    def apply_async(self, func, callback=None):
+        """Deprecated: implement `submit` instead."""
+        raise NotImplementedError("Implement `submit` instead.")
+
+    def submit(self, func, callback=None):
+        """Schedule a function to be run and return a future-like object.
+
+        This method should return a future-like object that allow tracking
+        the progress of the task.
+
+        If ``supports_retrieve_callback`` is False, the return value of this
+        method is passed to ``retrieve_result`` instead of calling
+        ``retrieve_result_callback``.
+
+        Parameters
+        ----------
+        func: callable
+            The function to be run in parallel.
+
+        callback: callable
+            A callable that will be called when the task is completed. This callable
+            is a wrapper around ``retrieve_result_callback``. This should be added
+            to the future-like object returned by this method, so that the callback
+            is called when the task is completed.
+
+            For future-like backends, this can be achieved with something like
+            ``future.add_done_callback(callback)``.
+
+        Returns
+        -------
+        future: future-like
+            A future-like object to track the execution of the submitted function.
+        """
+        warnings.warn(
+            "`apply_async` is deprecated, implement and use `submit` instead.",
+            DeprecationWarning,
+        )
+        return self.apply_async(func, callback)
+
+    def retrieve_result_callback(self, out):
+        """Called within the callback function passed to `submit`.
+
+        This method can customise how the result of the function is retrieved
+        from the future-like object.
+
+        Parameters
+        ----------
+        future: future-like
+            The future-like object returned by the `submit` method.
+
+        Returns
+        -------
+        result: object
+            The result of the function executed in parallel.
+        """
+
+    def retrieve_result(self, out, timeout=None):
+        """Hook to retrieve the result when support_retrieve_callback=False.
+
+        The argument `out` is the result of the `submit` call. This method
+        should return the result of the computation or raise an exception if
+        the computation failed.
+        """
+        if self.supports_timeout:
+            return out.get(timeout=timeout)
+        else:
+            return out.get()
+
+    def configure(
+        self, n_jobs=1, parallel=None, prefer=None, require=None, **backend_kwargs
+    ):
+        """Reconfigure the backend and return the number of workers.
+
+        This makes it possible to reuse an existing backend instance for
+        successive independent calls to Parallel with different parameters.
+        """
+        self.parallel = parallel
+        return self.effective_n_jobs(n_jobs)
+
+    def start_call(self):
+        """Call-back method called at the beginning of a Parallel call"""
+
+    def stop_call(self):
+        """Call-back method called at the end of a Parallel call"""
+
+    def terminate(self):
+        """Shutdown the workers and free the shared memory."""
+
+    def compute_batch_size(self):
+        """Determine the optimal batch size"""
+        return 1
+
+    def batch_completed(self, batch_size, duration):
+        """Callback indicate how long it took to run a batch"""
+
+    def abort_everything(self, ensure_ready=True):
+        """Abort any running tasks
+
+        This is called when an exception has been raised when executing a task
+        and all the remaining tasks will be ignored and can therefore be
+        aborted to spare computation resources.
+
+        If ensure_ready is True, the backend should be left in an operating
+        state as future tasks might be re-submitted via that same backend
+        instance.
+
+        If ensure_ready is False, the implementer of this method can decide
+        to leave the backend in a closed / terminated state as no new task
+        are expected to be submitted to this backend.
+
+        Setting ensure_ready to False is an optimization that can be leveraged
+        when aborting tasks via killing processes from a local process pool
+        managed by the backend it-self: if we expect no new tasks, there is no
+        point in re-creating new workers.
+        """
+        # Does nothing by default: to be overridden in subclasses when
+        # canceling tasks is possible.
+        pass
+
+    def get_nested_backend(self):
+        """Backend instance to be used by nested Parallel calls.
+
+        By default a thread-based backend is used for the first level of
+        nesting. Beyond, switch to sequential backend to avoid spawning too
+        many threads on the host.
+        """
+        nesting_level = getattr(self, "nesting_level", 0) + 1
+        if nesting_level > 1:
+            return SequentialBackend(nesting_level=nesting_level), None
+        else:
+            return ThreadingBackend(nesting_level=nesting_level), None
+
+    def _prepare_worker_env(self, n_jobs):
+        """Return environment variables limiting threadpools in external libs.
+
+        This function return a dict containing environment variables to pass
+        when creating a pool of process. These environment variables limit the
+        number of threads to `n_threads` for OpenMP, MKL, Accelerated and
+        OpenBLAS libraries in the child processes.
+        """
+        explicit_n_threads = self.inner_max_num_threads
+        default_n_threads = max(cpu_count() // n_jobs, 1)
+
+        # Set the inner environment variables to self.inner_max_num_threads if
+        # it is given. Else, default to cpu_count // n_jobs unless the variable
+        # is already present in the parent process environment.
+        env = {}
+        for var in self.MAX_NUM_THREADS_VARS:
+            if explicit_n_threads is None:
+                var_value = os.environ.get(var, default_n_threads)
+            else:
+                var_value = explicit_n_threads
+
+            env[var] = str(var_value)
+
+        if self.TBB_ENABLE_IPC_VAR not in os.environ:
+            # To avoid over-subscription when using TBB, let the TBB schedulers
+            # use Inter Process Communication to coordinate:
+            env[self.TBB_ENABLE_IPC_VAR] = "1"
+        return env
+
+    @contextlib.contextmanager
+    def retrieval_context(self):
+        """Context manager to manage an execution context.
+
+        Calls to Parallel.retrieve will be made inside this context.
+
+        By default, this does nothing. It may be useful for subclasses to
+        handle nested parallelism. In particular, it may be required to avoid
+        deadlocks if a backend manages a fixed number of workers, when those
+        workers may be asked to do nested Parallel calls. Without
+        'retrieval_context' this could lead to deadlock, as all the workers
+        managed by the backend may be "busy" waiting for the nested parallel
+        calls to finish, but the backend has no free workers to execute those
+        tasks.
+        """
+        yield
+
+    @staticmethod
+    def in_main_thread():
+        return isinstance(threading.current_thread(), threading._MainThread)
+
+
+class SequentialBackend(ParallelBackendBase):
+    """A ParallelBackend which will execute all batches sequentially.
+
+    Does not use/create any threading objects, and hence has minimal
+    overhead. Used when n_jobs == 1.
+    """
+
+    uses_threads = True
+    supports_timeout = False
+    supports_retrieve_callback = False
+    supports_sharedmem = True
+
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs which are going to run in parallel"""
+        if n_jobs == 0:
+            raise ValueError("n_jobs == 0 in Parallel has no meaning")
+        return 1
+
+    def submit(self, func, callback=None):
+        """Schedule a func to be run"""
+        raise RuntimeError("Should never be called for SequentialBackend.")
+
+    def retrieve_result_callback(self, out):
+        raise RuntimeError("Should never be called for SequentialBackend.")
+
+    def get_nested_backend(self):
+        # import is not top level to avoid cyclic import errors.
+        from .parallel import get_active_backend
+
+        # SequentialBackend should neither change the nesting level, the
+        # default backend or the number of jobs. Just return the current one.
+        return get_active_backend()
+
+
+class PoolManagerMixin(object):
+    """A helper class for managing pool of workers."""
+
+    _pool = None
+
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs which are going to run in parallel"""
+        if n_jobs == 0:
+            raise ValueError("n_jobs == 0 in Parallel has no meaning")
+        elif mp is None or n_jobs is None:
+            # multiprocessing is not available or disabled, fallback
+            # to sequential mode
+            return 1
+        elif n_jobs < 0:
+            n_jobs = max(cpu_count() + 1 + n_jobs, 1)
+        return n_jobs
+
+    def terminate(self):
+        """Shutdown the process or thread pool"""
+        if self._pool is not None:
+            self._pool.close()
+            self._pool.terminate()  # terminate does a join()
+            self._pool = None
+
+    def _get_pool(self):
+        """Used by `submit` to make it possible to implement lazy init"""
+        return self._pool
+
+    def submit(self, func, callback=None):
+        """Schedule a func to be run"""
+        # Here, we need a wrapper to avoid crashes on KeyboardInterruptErrors.
+        # We also call the callback on error, to make sure the pool does not
+        # wait on crashed jobs.
+        return self._get_pool().apply_async(
+            _TracebackCapturingWrapper(func),
+            (),
+            callback=callback,
+            error_callback=callback,
+        )
+
+    def retrieve_result_callback(self, result):
+        """Mimic concurrent.futures results, raising an error if needed."""
+        # In the multiprocessing Pool API, the callback are called with the
+        # result value as an argument so `result`(`out`) is the output of
+        # job.get(). It's either the result or the exception raised while
+        # collecting the result.
+        return _retrieve_traceback_capturing_wrapped_call(result)
+
+    def abort_everything(self, ensure_ready=True):
+        """Shutdown the pool and restart a new one with the same parameters"""
+        self.terminate()
+        if ensure_ready:
+            self.configure(
+                n_jobs=self.parallel.n_jobs,
+                parallel=self.parallel,
+                **self.parallel._backend_kwargs,
+            )
+
+
+class AutoBatchingMixin(object):
+    """A helper class for automagically batching jobs."""
+
+    # In seconds, should be big enough to hide multiprocessing dispatching
+    # overhead.
+    # This settings was found by running benchmarks/bench_auto_batching.py
+    # with various parameters on various platforms.
+    MIN_IDEAL_BATCH_DURATION = 0.2
+
+    # Should not be too high to avoid stragglers: long jobs running alone
+    # on a single worker while other workers have no work to process any more.
+    MAX_IDEAL_BATCH_DURATION = 2
+
+    # Batching counters default values
+    _DEFAULT_EFFECTIVE_BATCH_SIZE = 1
+    _DEFAULT_SMOOTHED_BATCH_DURATION = 0.0
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._effective_batch_size = self._DEFAULT_EFFECTIVE_BATCH_SIZE
+        self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_DURATION
+
+    def compute_batch_size(self):
+        """Determine the optimal batch size"""
+        old_batch_size = self._effective_batch_size
+        batch_duration = self._smoothed_batch_duration
+        if batch_duration > 0 and batch_duration < self.MIN_IDEAL_BATCH_DURATION:
+            # The current batch size is too small: the duration of the
+            # processing of a batch of task is not large enough to hide
+            # the scheduling overhead.
+            ideal_batch_size = int(
+                old_batch_size * self.MIN_IDEAL_BATCH_DURATION / batch_duration
+            )
+            # Multiply by two to limit oscilations between min and max.
+            ideal_batch_size *= 2
+
+            # dont increase the batch size too fast to limit huge batch sizes
+            # potentially leading to starving worker
+            batch_size = min(2 * old_batch_size, ideal_batch_size)
+
+            batch_size = max(batch_size, 1)
+
+            self._effective_batch_size = batch_size
+            if self.parallel.verbose >= 10:
+                self.parallel._print(
+                    f"Batch computation too fast ({batch_duration}s.) "
+                    f"Setting batch_size={batch_size}."
+                )
+        elif batch_duration > self.MAX_IDEAL_BATCH_DURATION and old_batch_size >= 2:
+            # The current batch size is too big. If we schedule overly long
+            # running batches some CPUs might wait with nothing left to do
+            # while a couple of CPUs a left processing a few long running
+            # batches. Better reduce the batch size a bit to limit the
+            # likelihood of scheduling such stragglers.
+
+            # decrease the batch size quickly to limit potential starving
+            ideal_batch_size = int(
+                old_batch_size * self.MIN_IDEAL_BATCH_DURATION / batch_duration
+            )
+            # Multiply by two to limit oscilations between min and max.
+            batch_size = max(2 * ideal_batch_size, 1)
+            self._effective_batch_size = batch_size
+            if self.parallel.verbose >= 10:
+                self.parallel._print(
+                    f"Batch computation too slow ({batch_duration}s.) "
+                    f"Setting batch_size={batch_size}."
+                )
+        else:
+            # No batch size adjustment
+            batch_size = old_batch_size
+
+        if batch_size != old_batch_size:
+            # Reset estimation of the smoothed mean batch duration: this
+            # estimate is updated in the multiprocessing apply_async
+            # CallBack as long as the batch_size is constant. Therefore
+            # we need to reset the estimate whenever we re-tune the batch
+            # size.
+            self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_DURATION
+
+        return batch_size
+
+    def batch_completed(self, batch_size, duration):
+        """Callback indicate how long it took to run a batch"""
+        if batch_size == self._effective_batch_size:
+            # Update the smoothed streaming estimate of the duration of a batch
+            # from dispatch to completion
+            old_duration = self._smoothed_batch_duration
+            if old_duration == self._DEFAULT_SMOOTHED_BATCH_DURATION:
+                # First record of duration for this batch size after the last
+                # reset.
+                new_duration = duration
+            else:
+                # Update the exponentially weighted average of the duration of
+                # batch for the current effective size.
+                new_duration = 0.8 * old_duration + 0.2 * duration
+            self._smoothed_batch_duration = new_duration
+
+    def reset_batch_stats(self):
+        """Reset batch statistics to default values.
+
+        This avoids interferences with future jobs.
+        """
+        self._effective_batch_size = self._DEFAULT_EFFECTIVE_BATCH_SIZE
+        self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_DURATION
+
+
+class ThreadingBackend(PoolManagerMixin, ParallelBackendBase):
+    """A ParallelBackend which will use a thread pool to execute batches in.
+
+    This is a low-overhead backend but it suffers from the Python Global
+    Interpreter Lock if the called function relies a lot on Python objects.
+    Mostly useful when the execution bottleneck is a compiled extension that
+    explicitly releases the GIL (for instance a Cython loop wrapped in a "with
+    nogil" block or an expensive call to a library such as NumPy).
+
+    The actual thread pool is lazily initialized: the actual thread pool
+    construction is delayed to the first call to apply_async.
+
+    ThreadingBackend is used as the default backend for nested calls.
+    """
+
+    supports_retrieve_callback = True
+    uses_threads = True
+    supports_sharedmem = True
+
+    def configure(self, n_jobs=1, parallel=None, **backend_kwargs):
+        """Build a process or thread pool and return the number of workers"""
+        n_jobs = self.effective_n_jobs(n_jobs)
+        if n_jobs == 1:
+            # Avoid unnecessary overhead and use sequential backend instead.
+            raise FallbackToBackend(SequentialBackend(nesting_level=self.nesting_level))
+        self.parallel = parallel
+        self._n_jobs = n_jobs
+        return n_jobs
+
+    def _get_pool(self):
+        """Lazily initialize the thread pool
+
+        The actual pool of worker threads is only initialized at the first
+        call to apply_async.
+        """
+        if self._pool is None:
+            self._pool = ThreadPool(self._n_jobs)
+        return self._pool
+
+
+class MultiprocessingBackend(PoolManagerMixin, AutoBatchingMixin, ParallelBackendBase):
+    """A ParallelBackend which will use a multiprocessing.Pool.
+
+    Will introduce some communication and memory overhead when exchanging
+    input and output data with the with the worker Python processes.
+    However, does not suffer from the Python Global Interpreter Lock.
+    """
+
+    supports_retrieve_callback = True
+    supports_return_generator = False
+
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs which are going to run in parallel.
+
+        This also checks if we are attempting to create a nested parallel
+        loop.
+        """
+        if mp is None:
+            return 1
+
+        if mp.current_process().daemon:
+            # Daemonic processes cannot have children
+            if n_jobs != 1:
+                if inside_dask_worker():
+                    msg = (
+                        "Inside a Dask worker with daemon=True, "
+                        "setting n_jobs=1.\nPossible work-arounds:\n"
+                        "- dask.config.set("
+                        "{'distributed.worker.daemon': False})"
+                        "- set the environment variable "
+                        "DASK_DISTRIBUTED__WORKER__DAEMON=False\n"
+                        "before creating your Dask cluster."
+                    )
+                else:
+                    msg = (
+                        "Multiprocessing-backed parallel loops "
+                        "cannot be nested, setting n_jobs=1"
+                    )
+                warnings.warn(msg, stacklevel=3)
+            return 1
+
+        if process_executor._CURRENT_DEPTH > 0:
+            # Mixing loky and multiprocessing in nested loop is not supported
+            if n_jobs != 1:
+                warnings.warn(
+                    "Multiprocessing-backed parallel loops cannot be nested,"
+                    " below loky, setting n_jobs=1",
+                    stacklevel=3,
+                )
+            return 1
+
+        elif not (self.in_main_thread() or self.nesting_level == 0):
+            # Prevent posix fork inside in non-main posix threads
+            if n_jobs != 1:
+                warnings.warn(
+                    "Multiprocessing-backed parallel loops cannot be nested"
+                    " below threads, setting n_jobs=1",
+                    stacklevel=3,
+                )
+            return 1
+
+        return super(MultiprocessingBackend, self).effective_n_jobs(n_jobs)
+
+    def configure(
+        self,
+        n_jobs=1,
+        parallel=None,
+        prefer=None,
+        require=None,
+        **memmapping_pool_kwargs,
+    ):
+        """Build a process or thread pool and return the number of workers"""
+        n_jobs = self.effective_n_jobs(n_jobs)
+        if n_jobs == 1:
+            raise FallbackToBackend(SequentialBackend(nesting_level=self.nesting_level))
+
+        memmapping_pool_kwargs = {
+            **self.backend_kwargs,
+            **memmapping_pool_kwargs,
+        }
+
+        # Make sure to free as much memory as possible before forking
+        gc.collect()
+        self._pool = MemmappingPool(n_jobs, **memmapping_pool_kwargs)
+        self.parallel = parallel
+        return n_jobs
+
+    def terminate(self):
+        """Shutdown the process or thread pool"""
+        super(MultiprocessingBackend, self).terminate()
+        self.reset_batch_stats()
+
+
+class LokyBackend(AutoBatchingMixin, ParallelBackendBase):
+    """Managing pool of workers with loky instead of multiprocessing."""
+
+    supports_retrieve_callback = True
+    supports_inner_max_num_threads = True
+
+    def configure(
+        self,
+        n_jobs=1,
+        parallel=None,
+        prefer=None,
+        require=None,
+        idle_worker_timeout=None,
+        **memmapping_executor_kwargs,
+    ):
+        """Build a process executor and return the number of workers"""
+        n_jobs = self.effective_n_jobs(n_jobs)
+        if n_jobs == 1:
+            raise FallbackToBackend(SequentialBackend(nesting_level=self.nesting_level))
+
+        memmapping_executor_kwargs = {
+            **self.backend_kwargs,
+            **memmapping_executor_kwargs,
+        }
+
+        # Prohibit the use of 'timeout' in the LokyBackend, as 'idle_worker_timeout'
+        # better describes the backend's behavior.
+        if "timeout" in memmapping_executor_kwargs:
+            raise ValueError(
+                "The 'timeout' parameter is not supported by the LokyBackend. "
+                "Please use the `idle_worker_timeout` parameter instead."
+            )
+        if idle_worker_timeout is None:
+            idle_worker_timeout = self.backend_kwargs.get("idle_worker_timeout", 300)
+
+        self._workers = get_memmapping_executor(
+            n_jobs,
+            timeout=idle_worker_timeout,
+            env=self._prepare_worker_env(n_jobs=n_jobs),
+            context_id=parallel._id,
+            **memmapping_executor_kwargs,
+        )
+        self.parallel = parallel
+        return n_jobs
+
+    def effective_n_jobs(self, n_jobs):
+        """Determine the number of jobs which are going to run in parallel"""
+        if n_jobs == 0:
+            raise ValueError("n_jobs == 0 in Parallel has no meaning")
+        elif mp is None or n_jobs is None:
+            # multiprocessing is not available or disabled, fallback
+            # to sequential mode
+            return 1
+        elif mp.current_process().daemon:
+            # Daemonic processes cannot have children
+            if n_jobs != 1:
+                if inside_dask_worker():
+                    msg = (
+                        "Inside a Dask worker with daemon=True, "
+                        "setting n_jobs=1.\nPossible work-arounds:\n"
+                        "- dask.config.set("
+                        "{'distributed.worker.daemon': False})\n"
+                        "- set the environment variable "
+                        "DASK_DISTRIBUTED__WORKER__DAEMON=False\n"
+                        "before creating your Dask cluster."
+                    )
+                else:
+                    msg = (
+                        "Loky-backed parallel loops cannot be called in a"
+                        " multiprocessing, setting n_jobs=1"
+                    )
+                warnings.warn(msg, stacklevel=3)
+
+            return 1
+        elif not (self.in_main_thread() or self.nesting_level == 0):
+            # Prevent posix fork inside in non-main posix threads
+            if n_jobs != 1:
+                warnings.warn(
+                    "Loky-backed parallel loops cannot be nested below "
+                    "threads, setting n_jobs=1",
+                    stacklevel=3,
+                )
+            return 1
+        elif n_jobs < 0:
+            n_jobs = max(cpu_count() + 1 + n_jobs, 1)
+        return n_jobs
+
+    def submit(self, func, callback=None):
+        """Schedule a func to be run"""
+        future = self._workers.submit(func)
+        if callback is not None:
+            future.add_done_callback(callback)
+        return future
+
+    def retrieve_result_callback(self, future):
+        """Retrieve the result, here out is the future given by submit"""
+        try:
+            return future.result()
+        except ShutdownExecutorError:
+            raise RuntimeError(
+                "The executor underlying Parallel has been shutdown. "
+                "This is likely due to the garbage collection of a previous "
+                "generator from a call to Parallel with return_as='generator'."
+                " Make sure the generator is not garbage collected when "
+                "submitting a new job or that it is first properly exhausted."
+            )
+
+    def terminate(self):
+        if self._workers is not None:
+            # Don't terminate the workers as we want to reuse them in later
+            # calls, but cleanup the temporary resources that the Parallel call
+            # created. This 'hack' requires a private, low-level operation.
+            self._workers._temp_folder_manager._clean_temporary_resources(
+                context_id=self.parallel._id, force=False
+            )
+            self._workers = None
+
+        self.reset_batch_stats()
+
+    def abort_everything(self, ensure_ready=True):
+        """Shutdown the workers and restart a new one with the same parameters"""
+        self._workers.terminate(kill_workers=True)
+        self._workers = None
+
+        if ensure_ready:
+            self.configure(n_jobs=self.parallel.n_jobs, parallel=self.parallel)
+
+
+class FallbackToBackend(Exception):
+    """Raised when configuration should fallback to another backend"""
+
+    def __init__(self, backend):
+        self.backend = backend
+
+
+def inside_dask_worker():
+    """Check whether the current function is executed inside a Dask worker."""
+    # This function can not be in joblib._dask because there would be a
+    # circular import:
+    # _dask imports _parallel_backend that imports _dask ...
+    try:
+        from distributed import get_worker
+    except ImportError:
+        return False
+
+    try:
+        get_worker()
+        return True
+    except ValueError:
+        return False

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/_store_backends.py

@@ -0,0 +1,488 @@
+"""Storage providers backends for Memory caching."""
+
+import collections
+import datetime
+import json
+import operator
+import os
+import os.path
+import re
+import shutil
+import threading
+import time
+import warnings
+from abc import ABCMeta, abstractmethod
+from pickle import PicklingError
+
+from . import numpy_pickle
+from .backports import concurrency_safe_rename
+from .disk import memstr_to_bytes, mkdirp, rm_subdirs
+from .logger import format_time
+
+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)
+            if age_limit.total_seconds() < 0:
+                raise ValueError("age_limit has to be a positive timedelta")
+            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)
+
+        # Automatically add `.gitignore` file to the cache folder.
+        # XXX: the condition is necessary because in `Memory.__init__`, the user
+        # passed `location` param is modified to be either `{location}` or
+        # `{location}/joblib` depending on input type (`pathlib.Path` vs `str`).
+        # The proper resolution of this inconsistency is tracked in:
+        # https://github.com/joblib/joblib/issues/1684
+        cache_directory = (
+            os.path.dirname(location)
+            if os.path.dirname(location) and os.path.basename(location) == "joblib"
+            else location
+        )
+        with open(os.path.join(cache_directory, ".gitignore"), "w") as file:
+            file.write("# Created by joblib automatically.\n")
+            file.write("*\n")
+
+        # 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

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/_utils.py

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

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/backports.py

@@ -0,0 +1,195 @@
+"""
+Backports of fixes for joblib dependencies
+"""
+
+import os
+import re
+import time
+from multiprocessing import util
+from os.path import basename
+
+
+class Version:
+    """Backport from deprecated distutils
+
+    We maintain this backport to avoid introducing a new dependency on
+    `packaging`.
+
+    We might rexplore this choice in the future if all major Python projects
+    introduce a dependency on packaging anyway.
+    """
+
+    def __init__(self, vstring=None):
+        if vstring:
+            self.parse(vstring)
+
+    def __repr__(self):
+        return "%s ('%s')" % (self.__class__.__name__, str(self))
+
+    def __eq__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c == 0
+
+    def __lt__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c < 0
+
+    def __le__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c <= 0
+
+    def __gt__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c > 0
+
+    def __ge__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c >= 0
+
+
+class LooseVersion(Version):
+    """Backport from deprecated distutils
+
+    We maintain this backport to avoid introducing a new dependency on
+    `packaging`.
+
+    We might rexplore this choice in the future if all major Python projects
+    introduce a dependency on packaging anyway.
+    """
+
+    component_re = re.compile(r"(\d+ | [a-z]+ | \.)", re.VERBOSE)
+
+    def __init__(self, vstring=None):
+        if vstring:
+            self.parse(vstring)
+
+    def parse(self, vstring):
+        # I've given up on thinking I can reconstruct the version string
+        # from the parsed tuple -- so I just store the string here for
+        # use by __str__
+        self.vstring = vstring
+        components = [x for x in self.component_re.split(vstring) if x and x != "."]
+        for i, obj in enumerate(components):
+            try:
+                components[i] = int(obj)
+            except ValueError:
+                pass
+
+        self.version = components
+
+    def __str__(self):
+        return self.vstring
+
+    def __repr__(self):
+        return "LooseVersion ('%s')" % str(self)
+
+    def _cmp(self, other):
+        if isinstance(other, str):
+            other = LooseVersion(other)
+        elif not isinstance(other, LooseVersion):
+            return NotImplemented
+
+        if self.version == other.version:
+            return 0
+        if self.version < other.version:
+            return -1
+        if self.version > other.version:
+            return 1
+
+
+try:
+    import numpy as np
+
+    def make_memmap(
+        filename,
+        dtype="uint8",
+        mode="r+",
+        offset=0,
+        shape=None,
+        order="C",
+        unlink_on_gc_collect=False,
+    ):
+        """Custom memmap constructor compatible with numpy.memmap.
+
+        This function:
+        - is a backport the numpy memmap offset fix (See
+          https://github.com/numpy/numpy/pull/8443 for more details.
+          The numpy fix is available starting numpy 1.13)
+        - adds ``unlink_on_gc_collect``, which specifies  explicitly whether
+          the process re-constructing the memmap owns a reference to the
+          underlying file. If set to True, it adds a finalizer to the
+          newly-created memmap that sends a maybe_unlink request for the
+          memmaped file to resource_tracker.
+        """
+        util.debug(
+            "[MEMMAP READ] creating a memmap (shape {}, filename {}, pid {})".format(
+                shape, basename(filename), os.getpid()
+            )
+        )
+
+        mm = np.memmap(
+            filename, dtype=dtype, mode=mode, offset=offset, shape=shape, order=order
+        )
+        if LooseVersion(np.__version__) < "1.13":
+            mm.offset = offset
+        if unlink_on_gc_collect:
+            from ._memmapping_reducer import add_maybe_unlink_finalizer
+
+            add_maybe_unlink_finalizer(mm)
+        return mm
+except ImportError:
+
+    def make_memmap(
+        filename,
+        dtype="uint8",
+        mode="r+",
+        offset=0,
+        shape=None,
+        order="C",
+        unlink_on_gc_collect=False,
+    ):
+        raise NotImplementedError(
+            "'joblib.backports.make_memmap' should not be used "
+            "if numpy is not installed."
+        )
+
+
+if os.name == "nt":
+    # https://github.com/joblib/joblib/issues/540
+    access_denied_errors = (5, 13)
+    from os import replace
+
+    def concurrency_safe_rename(src, dst):
+        """Renames ``src`` into ``dst`` overwriting ``dst`` if it exists.
+
+        On Windows os.replace can yield permission errors if executed by two
+        different processes.
+        """
+        max_sleep_time = 1
+        total_sleep_time = 0
+        sleep_time = 0.001
+        while total_sleep_time < max_sleep_time:
+            try:
+                replace(src, dst)
+                break
+            except Exception as exc:
+                if getattr(exc, "winerror", None) in access_denied_errors:
+                    time.sleep(sleep_time)
+                    total_sleep_time += sleep_time
+                    sleep_time *= 2
+                else:
+                    raise
+        else:
+            raise
+else:
+    from os import replace as concurrency_safe_rename  # noqa

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/compressor.py

@@ -0,0 +1,572 @@
+"""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"
+        )

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/disk.py

@@ -0,0 +1,131 @@
+"""
+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 errno
+import os
+import shutil
+import sys
+import time
+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 / 1024.0)
+
+
+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)

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/executor.py

@@ -0,0 +1,131 @@
+"""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 <thomas.moreau.2010@gmail.com>
+# Copyright: 2017, Thomas Moreau
+# License: BSD 3 clause
+
+from ._memmapping_reducer import TemporaryResourcesManager, get_memmapping_reducers
+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))

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/externals/cloudpickle/__init__.py

@@ -0,0 +1,18 @@
+from . import cloudpickle
+from .cloudpickle import *  # noqa
+
+__doc__ = cloudpickle.__doc__
+
+__version__ = "3.1.1"
+
+__all__ = [  # noqa
+    "__version__",
+    "Pickler",
+    "CloudPickler",
+    "dumps",
+    "loads",
+    "dump",
+    "load",
+    "register_pickle_by_value",
+    "unregister_pickle_by_value",
+]

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/externals/cloudpickle/cloudpickle.py

@@ -0,0 +1,1545 @@
+"""Pickler class to extend the standard pickle.Pickler functionality
+
+The main objective is to make it natural to perform distributed computing on
+clusters (such as PySpark, Dask, Ray...) with interactively defined code
+(functions, classes, ...) written in notebooks or console.
+
+In particular this pickler adds the following features:
+- serialize interactively-defined or locally-defined functions, classes,
+  enums, typevars, lambdas and nested functions to compiled byte code;
+- deal with some other non-serializable objects in an ad-hoc manner where
+  applicable.
+
+This pickler is therefore meant to be used for the communication between short
+lived Python processes running the same version of Python and libraries. In
+particular, it is not meant to be used for long term storage of Python objects.
+
+It does not include an unpickler, as standard Python unpickling suffices.
+
+This module was extracted from the `cloud` package, developed by `PiCloud, Inc.
+<https://web.archive.org/web/20140626004012/http://www.picloud.com/>`_.
+
+Copyright (c) 2012-now, CloudPickle developers and contributors.
+Copyright (c) 2012, Regents of the University of California.
+Copyright (c) 2009 `PiCloud, Inc. <https://web.archive.org/web/20140626004012/http://www.picloud.com/>`_.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the University of California, Berkeley nor the
+      names of its contributors may be used to endorse or promote
+      products derived from this software without specific prior written
+      permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+import _collections_abc
+from collections import ChainMap, OrderedDict
+import abc
+import builtins
+import copyreg
+import dataclasses
+import dis
+from enum import Enum
+import io
+import itertools
+import logging
+import opcode
+import pickle
+from pickle import _getattribute as _pickle_getattribute
+import platform
+import struct
+import sys
+import threading
+import types
+import typing
+import uuid
+import warnings
+import weakref
+
+# The following import is required to be imported in the cloudpickle
+# namespace to be able to load pickle files generated with older versions of
+# cloudpickle. See: tests/test_backward_compat.py
+from types import CellType  # noqa: F401
+
+
+# cloudpickle is meant for inter process communication: we expect all
+# communicating processes to run the same Python version hence we favor
+# communication speed over compatibility:
+DEFAULT_PROTOCOL = pickle.HIGHEST_PROTOCOL
+
+# Names of modules whose resources should be treated as dynamic.
+_PICKLE_BY_VALUE_MODULES = set()
+
+# Track the provenance of reconstructed dynamic classes to make it possible to
+# reconstruct instances from the matching singleton class definition when
+# appropriate and preserve the usual "isinstance" semantics of Python objects.
+_DYNAMIC_CLASS_TRACKER_BY_CLASS = weakref.WeakKeyDictionary()
+_DYNAMIC_CLASS_TRACKER_BY_ID = weakref.WeakValueDictionary()
+_DYNAMIC_CLASS_TRACKER_LOCK = threading.Lock()
+
+PYPY = platform.python_implementation() == "PyPy"
+
+builtin_code_type = None
+if PYPY:
+    # builtin-code objects only exist in pypy
+    builtin_code_type = type(float.__new__.__code__)
+
+_extract_code_globals_cache = weakref.WeakKeyDictionary()
+
+
+def _get_or_create_tracker_id(class_def):
+    with _DYNAMIC_CLASS_TRACKER_LOCK:
+        class_tracker_id = _DYNAMIC_CLASS_TRACKER_BY_CLASS.get(class_def)
+        if class_tracker_id is None:
+            class_tracker_id = uuid.uuid4().hex
+            _DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
+            _DYNAMIC_CLASS_TRACKER_BY_ID[class_tracker_id] = class_def
+    return class_tracker_id
+
+
+def _lookup_class_or_track(class_tracker_id, class_def):
+    if class_tracker_id is not None:
+        with _DYNAMIC_CLASS_TRACKER_LOCK:
+            class_def = _DYNAMIC_CLASS_TRACKER_BY_ID.setdefault(
+                class_tracker_id, class_def
+            )
+            _DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
+    return class_def
+
+
+def register_pickle_by_value(module):
+    """Register a module to make its functions and classes picklable by value.
+
+    By default, functions and classes that are attributes of an importable
+    module are to be pickled by reference, that is relying on re-importing
+    the attribute from the module at load time.
+
+    If `register_pickle_by_value(module)` is called, all its functions and
+    classes are subsequently to be pickled by value, meaning that they can
+    be loaded in Python processes where the module is not importable.
+
+    This is especially useful when developing a module in a distributed
+    execution environment: restarting the client Python process with the new
+    source code is enough: there is no need to re-install the new version
+    of the module on all the worker nodes nor to restart the workers.
+
+    Note: this feature is considered experimental. See the cloudpickle
+    README.md file for more details and limitations.
+    """
+    if not isinstance(module, types.ModuleType):
+        raise ValueError(f"Input should be a module object, got {str(module)} instead")
+    # In the future, cloudpickle may need a way to access any module registered
+    # for pickling by value in order to introspect relative imports inside
+    # functions pickled by value. (see
+    # https://github.com/cloudpipe/cloudpickle/pull/417#issuecomment-873684633).
+    # This access can be ensured by checking that module is present in
+    # sys.modules at registering time and assuming that it will still be in
+    # there when accessed during pickling. Another alternative would be to
+    # store a weakref to the module. Even though cloudpickle does not implement
+    # this introspection yet, in order to avoid a possible breaking change
+    # later, we still enforce the presence of module inside sys.modules.
+    if module.__name__ not in sys.modules:
+        raise ValueError(
+            f"{module} was not imported correctly, have you used an "
+            "`import` statement to access it?"
+        )
+    _PICKLE_BY_VALUE_MODULES.add(module.__name__)
+
+
+def unregister_pickle_by_value(module):
+    """Unregister that the input module should be pickled by value."""
+    if not isinstance(module, types.ModuleType):
+        raise ValueError(f"Input should be a module object, got {str(module)} instead")
+    if module.__name__ not in _PICKLE_BY_VALUE_MODULES:
+        raise ValueError(f"{module} is not registered for pickle by value")
+    else:
+        _PICKLE_BY_VALUE_MODULES.remove(module.__name__)
+
+
+def list_registry_pickle_by_value():
+    return _PICKLE_BY_VALUE_MODULES.copy()
+
+
+def _is_registered_pickle_by_value(module):
+    module_name = module.__name__
+    if module_name in _PICKLE_BY_VALUE_MODULES:
+        return True
+    while True:
+        parent_name = module_name.rsplit(".", 1)[0]
+        if parent_name == module_name:
+            break
+        if parent_name in _PICKLE_BY_VALUE_MODULES:
+            return True
+        module_name = parent_name
+    return False
+
+
+if sys.version_info >= (3, 14):
+    def _getattribute(obj, name):
+        return _pickle_getattribute(obj, name.split('.'))
+else:
+    def _getattribute(obj, name):
+        return _pickle_getattribute(obj, name)[0]
+
+
+def _whichmodule(obj, name):
+    """Find the module an object belongs to.
+
+    This function differs from ``pickle.whichmodule`` in two ways:
+    - it does not mangle the cases where obj's module is __main__ and obj was
+      not found in any module.
+    - Errors arising during module introspection are ignored, as those errors
+      are considered unwanted side effects.
+    """
+    module_name = getattr(obj, "__module__", None)
+
+    if module_name is not None:
+        return module_name
+    # Protect the iteration by using a copy of sys.modules against dynamic
+    # modules that trigger imports of other modules upon calls to getattr or
+    # other threads importing at the same time.
+    for module_name, module in sys.modules.copy().items():
+        # Some modules such as coverage can inject non-module objects inside
+        # sys.modules
+        if (
+            module_name == "__main__"
+            or module_name == "__mp_main__"
+            or module is None
+            or not isinstance(module, types.ModuleType)
+        ):
+            continue
+        try:
+            if _getattribute(module, name) is obj:
+                return module_name
+        except Exception:
+            pass
+    return None
+
+
+def _should_pickle_by_reference(obj, name=None):
+    """Test whether an function or a class should be pickled by reference
+
+    Pickling by reference means by that the object (typically a function or a
+    class) is an attribute of a module that is assumed to be importable in the
+    target Python environment. Loading will therefore rely on importing the
+    module and then calling `getattr` on it to access the function or class.
+
+    Pickling by reference is the only option to pickle functions and classes
+    in the standard library. In cloudpickle the alternative option is to
+    pickle by value (for instance for interactively or locally defined
+    functions and classes or for attributes of modules that have been
+    explicitly registered to be pickled by value.
+    """
+    if isinstance(obj, types.FunctionType) or issubclass(type(obj), type):
+        module_and_name = _lookup_module_and_qualname(obj, name=name)
+        if module_and_name is None:
+            return False
+        module, name = module_and_name
+        return not _is_registered_pickle_by_value(module)
+
+    elif isinstance(obj, types.ModuleType):
+        # We assume that sys.modules is primarily used as a cache mechanism for
+        # the Python import machinery. Checking if a module has been added in
+        # is sys.modules therefore a cheap and simple heuristic to tell us
+        # whether we can assume that a given module could be imported by name
+        # in another Python process.
+        if _is_registered_pickle_by_value(obj):
+            return False
+        return obj.__name__ in sys.modules
+    else:
+        raise TypeError(
+            "cannot check importability of {} instances".format(type(obj).__name__)
+        )
+
+
+def _lookup_module_and_qualname(obj, name=None):
+    if name is None:
+        name = getattr(obj, "__qualname__", None)
+    if name is None:  # pragma: no cover
+        # This used to be needed for Python 2.7 support but is probably not
+        # needed anymore. However we keep the __name__ introspection in case
+        # users of cloudpickle rely on this old behavior for unknown reasons.
+        name = getattr(obj, "__name__", None)
+
+    module_name = _whichmodule(obj, name)
+
+    if module_name is None:
+        # In this case, obj.__module__ is None AND obj was not found in any
+        # imported module. obj is thus treated as dynamic.
+        return None
+
+    if module_name == "__main__":
+        return None
+
+    # Note: if module_name is in sys.modules, the corresponding module is
+    # assumed importable at unpickling time. See #357
+    module = sys.modules.get(module_name, None)
+    if module is None:
+        # The main reason why obj's module would not be imported is that this
+        # module has been dynamically created, using for example
+        # types.ModuleType. The other possibility is that module was removed
+        # from sys.modules after obj was created/imported. But this case is not
+        # supported, as the standard pickle does not support it either.
+        return None
+
+    try:
+        obj2 = _getattribute(module, name)
+    except AttributeError:
+        # obj was not found inside the module it points to
+        return None
+    if obj2 is not obj:
+        return None
+    return module, name
+
+
+def _extract_code_globals(co):
+    """Find all globals names read or written to by codeblock co."""
+    out_names = _extract_code_globals_cache.get(co)
+    if out_names is None:
+        # We use a dict with None values instead of a set to get a
+        # deterministic order and avoid introducing non-deterministic pickle
+        # bytes as a results.
+        out_names = {name: None for name in _walk_global_ops(co)}
+
+        # Declaring a function inside another one using the "def ..." syntax
+        # generates a constant code object corresponding to the one of the
+        # nested function's As the nested function may itself need global
+        # variables, we need to introspect its code, extract its globals, (look
+        # for code object in it's co_consts attribute..) and add the result to
+        # code_globals
+        if co.co_consts:
+            for const in co.co_consts:
+                if isinstance(const, types.CodeType):
+                    out_names.update(_extract_code_globals(const))
+
+        _extract_code_globals_cache[co] = out_names
+
+    return out_names
+
+
+def _find_imported_submodules(code, top_level_dependencies):
+    """Find currently imported submodules used by a function.
+
+    Submodules used by a function need to be detected and referenced for the
+    function to work correctly at depickling time. Because submodules can be
+    referenced as attribute of their parent package (``package.submodule``), we
+    need a special introspection technique that does not rely on GLOBAL-related
+    opcodes to find references of them in a code object.
+
+    Example:
+    ```
+    import concurrent.futures
+    import cloudpickle
+    def func():
+        x = concurrent.futures.ThreadPoolExecutor
+    if __name__ == '__main__':
+        cloudpickle.dumps(func)
+    ```
+    The globals extracted by cloudpickle in the function's state include the
+    concurrent package, but not its submodule (here, concurrent.futures), which
+    is the module used by func. Find_imported_submodules will detect the usage
+    of concurrent.futures. Saving this module alongside with func will ensure
+    that calling func once depickled does not fail due to concurrent.futures
+    not being imported
+    """
+
+    subimports = []
+    # check if any known dependency is an imported package
+    for x in top_level_dependencies:
+        if (
+            isinstance(x, types.ModuleType)
+            and hasattr(x, "__package__")
+            and x.__package__
+        ):
+            # check if the package has any currently loaded sub-imports
+            prefix = x.__name__ + "."
+            # A concurrent thread could mutate sys.modules,
+            # make sure we iterate over a copy to avoid exceptions
+            for name in list(sys.modules):
+                # Older versions of pytest will add a "None" module to
+                # sys.modules.
+                if name is not None and name.startswith(prefix):
+                    # check whether the function can address the sub-module
+                    tokens = set(name[len(prefix) :].split("."))
+                    if not tokens - set(code.co_names):
+                        subimports.append(sys.modules[name])
+    return subimports
+
+
+# relevant opcodes
+STORE_GLOBAL = opcode.opmap["STORE_GLOBAL"]
+DELETE_GLOBAL = opcode.opmap["DELETE_GLOBAL"]
+LOAD_GLOBAL = opcode.opmap["LOAD_GLOBAL"]
+GLOBAL_OPS = (STORE_GLOBAL, DELETE_GLOBAL, LOAD_GLOBAL)
+HAVE_ARGUMENT = dis.HAVE_ARGUMENT
+EXTENDED_ARG = dis.EXTENDED_ARG
+
+
+_BUILTIN_TYPE_NAMES = {}
+for k, v in types.__dict__.items():
+    if type(v) is type:
+        _BUILTIN_TYPE_NAMES[v] = k
+
+
+def _builtin_type(name):
+    if name == "ClassType":  # pragma: no cover
+        # Backward compat to load pickle files generated with cloudpickle
+        # < 1.3 even if loading pickle files from older versions is not
+        # officially supported.
+        return type
+    return getattr(types, name)
+
+
+def _walk_global_ops(code):
+    """Yield referenced name for global-referencing instructions in code."""
+    for instr in dis.get_instructions(code):
+        op = instr.opcode
+        if op in GLOBAL_OPS:
+            yield instr.argval
+
+
+def _extract_class_dict(cls):
+    """Retrieve a copy of the dict of a class without the inherited method."""
+    # Hack to circumvent non-predictable memoization caused by string interning.
+    # See the inline comment in _class_setstate for details.
+    clsdict = {"".join(k): cls.__dict__[k] for k in sorted(cls.__dict__)}
+
+    if len(cls.__bases__) == 1:
+        inherited_dict = cls.__bases__[0].__dict__
+    else:
+        inherited_dict = {}
+        for base in reversed(cls.__bases__):
+            inherited_dict.update(base.__dict__)
+    to_remove = []
+    for name, value in clsdict.items():
+        try:
+            base_value = inherited_dict[name]
+            if value is base_value:
+                to_remove.append(name)
+        except KeyError:
+            pass
+    for name in to_remove:
+        clsdict.pop(name)
+    return clsdict
+
+
+def is_tornado_coroutine(func):
+    """Return whether `func` is a Tornado coroutine function.
+
+    Running coroutines are not supported.
+    """
+    warnings.warn(
+        "is_tornado_coroutine is deprecated in cloudpickle 3.0 and will be "
+        "removed in cloudpickle 4.0. Use tornado.gen.is_coroutine_function "
+        "directly instead.",
+        category=DeprecationWarning,
+    )
+    if "tornado.gen" not in sys.modules:
+        return False
+    gen = sys.modules["tornado.gen"]
+    if not hasattr(gen, "is_coroutine_function"):
+        # Tornado version is too old
+        return False
+    return gen.is_coroutine_function(func)
+
+
+def subimport(name):
+    # We cannot do simply: `return __import__(name)`: Indeed, if ``name`` is
+    # the name of a submodule, __import__ will return the top-level root module
+    # of this submodule. For instance, __import__('os.path') returns the `os`
+    # module.
+    __import__(name)
+    return sys.modules[name]
+
+
+def dynamic_subimport(name, vars):
+    mod = types.ModuleType(name)
+    mod.__dict__.update(vars)
+    mod.__dict__["__builtins__"] = builtins.__dict__
+    return mod
+
+
+def _get_cell_contents(cell):
+    try:
+        return cell.cell_contents
+    except ValueError:
+        # Handle empty cells explicitly with a sentinel value.
+        return _empty_cell_value
+
+
+def instance(cls):
+    """Create a new instance of a class.
+
+    Parameters
+    ----------
+    cls : type
+        The class to create an instance of.
+
+    Returns
+    -------
+    instance : cls
+        A new instance of ``cls``.
+    """
+    return cls()
+
+
+@instance
+class _empty_cell_value:
+    """Sentinel for empty closures."""
+
+    @classmethod
+    def __reduce__(cls):
+        return cls.__name__
+
+
+def _make_function(code, globals, name, argdefs, closure):
+    # Setting __builtins__ in globals is needed for nogil CPython.
+    globals["__builtins__"] = __builtins__
+    return types.FunctionType(code, globals, name, argdefs, closure)
+
+
+def _make_empty_cell():
+    if False:
+        # trick the compiler into creating an empty cell in our lambda
+        cell = None
+        raise AssertionError("this route should not be executed")
+
+    return (lambda: cell).__closure__[0]
+
+
+def _make_cell(value=_empty_cell_value):
+    cell = _make_empty_cell()
+    if value is not _empty_cell_value:
+        cell.cell_contents = value
+    return cell
+
+
+def _make_skeleton_class(
+    type_constructor, name, bases, type_kwargs, class_tracker_id, extra
+):
+    """Build dynamic class with an empty __dict__ to be filled once memoized
+
+    If class_tracker_id is not None, try to lookup an existing class definition
+    matching that id. If none is found, track a newly reconstructed class
+    definition under that id so that other instances stemming from the same
+    class id will also reuse this class definition.
+
+    The "extra" variable is meant to be a dict (or None) that can be used for
+    forward compatibility shall the need arise.
+    """
+    # We need to intern the keys of the type_kwargs dict to avoid having
+    # different pickles for the same dynamic class depending on whether it was
+    # dynamically created or reconstructed from a pickled stream.
+    type_kwargs = {sys.intern(k): v for k, v in type_kwargs.items()}
+
+    skeleton_class = types.new_class(
+        name, bases, {"metaclass": type_constructor}, lambda ns: ns.update(type_kwargs)
+    )
+
+    return _lookup_class_or_track(class_tracker_id, skeleton_class)
+
+
+def _make_skeleton_enum(
+    bases, name, qualname, members, module, class_tracker_id, extra
+):
+    """Build dynamic enum with an empty __dict__ to be filled once memoized
+
+    The creation of the enum class is inspired by the code of
+    EnumMeta._create_.
+
+    If class_tracker_id is not None, try to lookup an existing enum definition
+    matching that id. If none is found, track a newly reconstructed enum
+    definition under that id so that other instances stemming from the same
+    class id will also reuse this enum definition.
+
+    The "extra" variable is meant to be a dict (or None) that can be used for
+    forward compatibility shall the need arise.
+    """
+    # enums always inherit from their base Enum class at the last position in
+    # the list of base classes:
+    enum_base = bases[-1]
+    metacls = enum_base.__class__
+    classdict = metacls.__prepare__(name, bases)
+
+    for member_name, member_value in members.items():
+        classdict[member_name] = member_value
+    enum_class = metacls.__new__(metacls, name, bases, classdict)
+    enum_class.__module__ = module
+    enum_class.__qualname__ = qualname
+
+    return _lookup_class_or_track(class_tracker_id, enum_class)
+
+
+def _make_typevar(name, bound, constraints, covariant, contravariant, class_tracker_id):
+    tv = typing.TypeVar(
+        name,
+        *constraints,
+        bound=bound,
+        covariant=covariant,
+        contravariant=contravariant,
+    )
+    return _lookup_class_or_track(class_tracker_id, tv)
+
+
+def _decompose_typevar(obj):
+    return (
+        obj.__name__,
+        obj.__bound__,
+        obj.__constraints__,
+        obj.__covariant__,
+        obj.__contravariant__,
+        _get_or_create_tracker_id(obj),
+    )
+
+
+def _typevar_reduce(obj):
+    # TypeVar instances require the module information hence why we
+    # are not using the _should_pickle_by_reference directly
+    module_and_name = _lookup_module_and_qualname(obj, name=obj.__name__)
+
+    if module_and_name is None:
+        return (_make_typevar, _decompose_typevar(obj))
+    elif _is_registered_pickle_by_value(module_and_name[0]):
+        return (_make_typevar, _decompose_typevar(obj))
+
+    return (getattr, module_and_name)
+
+
+def _get_bases(typ):
+    if "__orig_bases__" in getattr(typ, "__dict__", {}):
+        # For generic types (see PEP 560)
+        # Note that simply checking `hasattr(typ, '__orig_bases__')` is not
+        # correct.  Subclasses of a fully-parameterized generic class does not
+        # have `__orig_bases__` defined, but `hasattr(typ, '__orig_bases__')`
+        # will return True because it's defined in the base class.
+        bases_attr = "__orig_bases__"
+    else:
+        # For regular class objects
+        bases_attr = "__bases__"
+    return getattr(typ, bases_attr)
+
+
+def _make_dict_keys(obj, is_ordered=False):
+    if is_ordered:
+        return OrderedDict.fromkeys(obj).keys()
+    else:
+        return dict.fromkeys(obj).keys()
+
+
+def _make_dict_values(obj, is_ordered=False):
+    if is_ordered:
+        return OrderedDict((i, _) for i, _ in enumerate(obj)).values()
+    else:
+        return {i: _ for i, _ in enumerate(obj)}.values()
+
+
+def _make_dict_items(obj, is_ordered=False):
+    if is_ordered:
+        return OrderedDict(obj).items()
+    else:
+        return obj.items()
+
+
+# COLLECTION OF OBJECTS __getnewargs__-LIKE METHODS
+# -------------------------------------------------
+
+
+def _class_getnewargs(obj):
+    type_kwargs = {}
+    if "__module__" in obj.__dict__:
+        type_kwargs["__module__"] = obj.__module__
+
+    __dict__ = obj.__dict__.get("__dict__", None)
+    if isinstance(__dict__, property):
+        type_kwargs["__dict__"] = __dict__
+
+    return (
+        type(obj),
+        obj.__name__,
+        _get_bases(obj),
+        type_kwargs,
+        _get_or_create_tracker_id(obj),
+        None,
+    )
+
+
+def _enum_getnewargs(obj):
+    members = {e.name: e.value for e in obj}
+    return (
+        obj.__bases__,
+        obj.__name__,
+        obj.__qualname__,
+        members,
+        obj.__module__,
+        _get_or_create_tracker_id(obj),
+        None,
+    )
+
+
+# COLLECTION OF OBJECTS RECONSTRUCTORS
+# ------------------------------------
+def _file_reconstructor(retval):
+    return retval
+
+
+# COLLECTION OF OBJECTS STATE GETTERS
+# -----------------------------------
+
+
+def _function_getstate(func):
+    # - Put func's dynamic attributes (stored in func.__dict__) in state. These
+    #   attributes will be restored at unpickling time using
+    #   f.__dict__.update(state)
+    # - Put func's members into slotstate. Such attributes will be restored at
+    #   unpickling time by iterating over slotstate and calling setattr(func,
+    #   slotname, slotvalue)
+    slotstate = {
+        # Hack to circumvent non-predictable memoization caused by string interning.
+        # See the inline comment in _class_setstate for details.
+        "__name__": "".join(func.__name__),
+        "__qualname__": "".join(func.__qualname__),
+        "__annotations__": func.__annotations__,
+        "__kwdefaults__": func.__kwdefaults__,
+        "__defaults__": func.__defaults__,
+        "__module__": func.__module__,
+        "__doc__": func.__doc__,
+        "__closure__": func.__closure__,
+    }
+
+    f_globals_ref = _extract_code_globals(func.__code__)
+    f_globals = {k: func.__globals__[k] for k in f_globals_ref if k in func.__globals__}
+
+    if func.__closure__ is not None:
+        closure_values = list(map(_get_cell_contents, func.__closure__))
+    else:
+        closure_values = ()
+
+    # Extract currently-imported submodules used by func. Storing these modules
+    # in a smoke _cloudpickle_subimports attribute of the object's state will
+    # trigger the side effect of importing these modules at unpickling time
+    # (which is necessary for func to work correctly once depickled)
+    slotstate["_cloudpickle_submodules"] = _find_imported_submodules(
+        func.__code__, itertools.chain(f_globals.values(), closure_values)
+    )
+    slotstate["__globals__"] = f_globals
+
+    # Hack to circumvent non-predictable memoization caused by string interning.
+    # See the inline comment in _class_setstate for details.
+    state = {"".join(k): v for k, v in func.__dict__.items()}
+    return state, slotstate
+
+
+def _class_getstate(obj):
+    clsdict = _extract_class_dict(obj)
+    clsdict.pop("__weakref__", None)
+
+    if issubclass(type(obj), abc.ABCMeta):
+        # If obj is an instance of an ABCMeta subclass, don't pickle the
+        # cache/negative caches populated during isinstance/issubclass
+        # checks, but pickle the list of registered subclasses of obj.
+        clsdict.pop("_abc_cache", None)
+        clsdict.pop("_abc_negative_cache", None)
+        clsdict.pop("_abc_negative_cache_version", None)
+        registry = clsdict.pop("_abc_registry", None)
+        if registry is None:
+            # The abc caches and registered subclasses of a
+            # class are bundled into the single _abc_impl attribute
+            clsdict.pop("_abc_impl", None)
+            (registry, _, _, _) = abc._get_dump(obj)
+
+            clsdict["_abc_impl"] = [subclass_weakref() for subclass_weakref in registry]
+        else:
+            # In the above if clause, registry is a set of weakrefs -- in
+            # this case, registry is a WeakSet
+            clsdict["_abc_impl"] = [type_ for type_ in registry]
+
+    if "__slots__" in clsdict:
+        # pickle string length optimization: member descriptors of obj are
+        # created automatically from obj's __slots__ attribute, no need to
+        # save them in obj's state
+        if isinstance(obj.__slots__, str):
+            clsdict.pop(obj.__slots__)
+        else:
+            for k in obj.__slots__:
+                clsdict.pop(k, None)
+
+    clsdict.pop("__dict__", None)  # unpicklable property object
+
+    return (clsdict, {})
+
+
+def _enum_getstate(obj):
+    clsdict, slotstate = _class_getstate(obj)
+
+    members = {e.name: e.value for e in obj}
+    # Cleanup the clsdict that will be passed to _make_skeleton_enum:
+    # Those attributes are already handled by the metaclass.
+    for attrname in [
+        "_generate_next_value_",
+        "_member_names_",
+        "_member_map_",
+        "_member_type_",
+        "_value2member_map_",
+    ]:
+        clsdict.pop(attrname, None)
+    for member in members:
+        clsdict.pop(member)
+        # Special handling of Enum subclasses
+    return clsdict, slotstate
+
+
+# COLLECTIONS OF OBJECTS REDUCERS
+# -------------------------------
+# A reducer is a function taking a single argument (obj), and that returns a
+# tuple with all the necessary data to re-construct obj. Apart from a few
+# exceptions (list, dict, bytes, int, etc.), a reducer is necessary to
+# correctly pickle an object.
+# While many built-in objects (Exceptions objects, instances of the "object"
+# class, etc), are shipped with their own built-in reducer (invoked using
+# obj.__reduce__), some do not. The following methods were created to "fill
+# these holes".
+
+
+def _code_reduce(obj):
+    """code object reducer."""
+    # If you are not sure about the order of arguments, take a look at help
+    # of the specific type from types, for example:
+    # >>> from types import CodeType
+    # >>> help(CodeType)
+
+    # Hack to circumvent non-predictable memoization caused by string interning.
+    # See the inline comment in _class_setstate for details.
+    co_name = "".join(obj.co_name)
+
+    # Create shallow copies of these tuple to make cloudpickle payload deterministic.
+    # When creating a code object during load, copies of these four tuples are
+    # created, while in the main process, these tuples can be shared.
+    # By always creating copies, we make sure the resulting payload is deterministic.
+    co_names = tuple(name for name in obj.co_names)
+    co_varnames = tuple(name for name in obj.co_varnames)
+    co_freevars = tuple(name for name in obj.co_freevars)
+    co_cellvars = tuple(name for name in obj.co_cellvars)
+    if hasattr(obj, "co_exceptiontable"):
+        # Python 3.11 and later: there are some new attributes
+        # related to the enhanced exceptions.
+        args = (
+            obj.co_argcount,
+            obj.co_posonlyargcount,
+            obj.co_kwonlyargcount,
+            obj.co_nlocals,
+            obj.co_stacksize,
+            obj.co_flags,
+            obj.co_code,
+            obj.co_consts,
+            co_names,
+            co_varnames,
+            obj.co_filename,
+            co_name,
+            obj.co_qualname,
+            obj.co_firstlineno,
+            obj.co_linetable,
+            obj.co_exceptiontable,
+            co_freevars,
+            co_cellvars,
+        )
+    elif hasattr(obj, "co_linetable"):
+        # Python 3.10 and later: obj.co_lnotab is deprecated and constructor
+        # expects obj.co_linetable instead.
+        args = (
+            obj.co_argcount,
+            obj.co_posonlyargcount,
+            obj.co_kwonlyargcount,
+            obj.co_nlocals,
+            obj.co_stacksize,
+            obj.co_flags,
+            obj.co_code,
+            obj.co_consts,
+            co_names,
+            co_varnames,
+            obj.co_filename,
+            co_name,
+            obj.co_firstlineno,
+            obj.co_linetable,
+            co_freevars,
+            co_cellvars,
+        )
+    elif hasattr(obj, "co_nmeta"):  # pragma: no cover
+        # "nogil" Python: modified attributes from 3.9
+        args = (
+            obj.co_argcount,
+            obj.co_posonlyargcount,
+            obj.co_kwonlyargcount,
+            obj.co_nlocals,
+            obj.co_framesize,
+            obj.co_ndefaultargs,
+            obj.co_nmeta,
+            obj.co_flags,
+            obj.co_code,
+            obj.co_consts,
+            co_varnames,
+            obj.co_filename,
+            co_name,
+            obj.co_firstlineno,
+            obj.co_lnotab,
+            obj.co_exc_handlers,
+            obj.co_jump_table,
+            co_freevars,
+            co_cellvars,
+            obj.co_free2reg,
+            obj.co_cell2reg,
+        )
+    else:
+        # Backward compat for 3.8 and 3.9
+        args = (
+            obj.co_argcount,
+            obj.co_posonlyargcount,
+            obj.co_kwonlyargcount,
+            obj.co_nlocals,
+            obj.co_stacksize,
+            obj.co_flags,
+            obj.co_code,
+            obj.co_consts,
+            co_names,
+            co_varnames,
+            obj.co_filename,
+            co_name,
+            obj.co_firstlineno,
+            obj.co_lnotab,
+            co_freevars,
+            co_cellvars,
+        )
+    return types.CodeType, args
+
+
+def _cell_reduce(obj):
+    """Cell (containing values of a function's free variables) reducer."""
+    try:
+        obj.cell_contents
+    except ValueError:  # cell is empty
+        return _make_empty_cell, ()
+    else:
+        return _make_cell, (obj.cell_contents,)
+
+
+def _classmethod_reduce(obj):
+    orig_func = obj.__func__
+    return type(obj), (orig_func,)
+
+
+def _file_reduce(obj):
+    """Save a file."""
+    import io
+
+    if not hasattr(obj, "name") or not hasattr(obj, "mode"):
+        raise pickle.PicklingError(
+            "Cannot pickle files that do not map to an actual file"
+        )
+    if obj is sys.stdout:
+        return getattr, (sys, "stdout")
+    if obj is sys.stderr:
+        return getattr, (sys, "stderr")
+    if obj is sys.stdin:
+        raise pickle.PicklingError("Cannot pickle standard input")
+    if obj.closed:
+        raise pickle.PicklingError("Cannot pickle closed files")
+    if hasattr(obj, "isatty") and obj.isatty():
+        raise pickle.PicklingError("Cannot pickle files that map to tty objects")
+    if "r" not in obj.mode and "+" not in obj.mode:
+        raise pickle.PicklingError(
+            "Cannot pickle files that are not opened for reading: %s" % obj.mode
+        )
+
+    name = obj.name
+
+    retval = io.StringIO()
+
+    try:
+        # Read the whole file
+        curloc = obj.tell()
+        obj.seek(0)
+        contents = obj.read()
+        obj.seek(curloc)
+    except OSError as e:
+        raise pickle.PicklingError(
+            "Cannot pickle file %s as it cannot be read" % name
+        ) from e
+    retval.write(contents)
+    retval.seek(curloc)
+
+    retval.name = name
+    return _file_reconstructor, (retval,)
+
+
+def _getset_descriptor_reduce(obj):
+    return getattr, (obj.__objclass__, obj.__name__)
+
+
+def _mappingproxy_reduce(obj):
+    return types.MappingProxyType, (dict(obj),)
+
+
+def _memoryview_reduce(obj):
+    return bytes, (obj.tobytes(),)
+
+
+def _module_reduce(obj):
+    if _should_pickle_by_reference(obj):
+        return subimport, (obj.__name__,)
+    else:
+        # Some external libraries can populate the "__builtins__" entry of a
+        # module's `__dict__` with unpicklable objects (see #316). For that
+        # reason, we do not attempt to pickle the "__builtins__" entry, and
+        # restore a default value for it at unpickling time.
+        state = obj.__dict__.copy()
+        state.pop("__builtins__", None)
+        return dynamic_subimport, (obj.__name__, state)
+
+
+def _method_reduce(obj):
+    return (types.MethodType, (obj.__func__, obj.__self__))
+
+
+def _logger_reduce(obj):
+    return logging.getLogger, (obj.name,)
+
+
+def _root_logger_reduce(obj):
+    return logging.getLogger, ()
+
+
+def _property_reduce(obj):
+    return property, (obj.fget, obj.fset, obj.fdel, obj.__doc__)
+
+
+def _weakset_reduce(obj):
+    return weakref.WeakSet, (list(obj),)
+
+
+def _dynamic_class_reduce(obj):
+    """Save a class that can't be referenced as a module attribute.
+
+    This method is used to serialize classes that are defined inside
+    functions, or that otherwise can't be serialized as attribute lookups
+    from importable modules.
+    """
+    if Enum is not None and issubclass(obj, Enum):
+        return (
+            _make_skeleton_enum,
+            _enum_getnewargs(obj),
+            _enum_getstate(obj),
+            None,
+            None,
+            _class_setstate,
+        )
+    else:
+        return (
+            _make_skeleton_class,
+            _class_getnewargs(obj),
+            _class_getstate(obj),
+            None,
+            None,
+            _class_setstate,
+        )
+
+
+def _class_reduce(obj):
+    """Select the reducer depending on the dynamic nature of the class obj."""
+    if obj is type(None):  # noqa
+        return type, (None,)
+    elif obj is type(Ellipsis):
+        return type, (Ellipsis,)
+    elif obj is type(NotImplemented):
+        return type, (NotImplemented,)
+    elif obj in _BUILTIN_TYPE_NAMES:
+        return _builtin_type, (_BUILTIN_TYPE_NAMES[obj],)
+    elif not _should_pickle_by_reference(obj):
+        return _dynamic_class_reduce(obj)
+    return NotImplemented
+
+
+def _dict_keys_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_keys, (list(obj),)
+
+
+def _dict_values_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_values, (list(obj),)
+
+
+def _dict_items_reduce(obj):
+    return _make_dict_items, (dict(obj),)
+
+
+def _odict_keys_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_keys, (list(obj), True)
+
+
+def _odict_values_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_values, (list(obj), True)
+
+
+def _odict_items_reduce(obj):
+    return _make_dict_items, (dict(obj), True)
+
+
+def _dataclass_field_base_reduce(obj):
+    return _get_dataclass_field_type_sentinel, (obj.name,)
+
+
+# COLLECTIONS OF OBJECTS STATE SETTERS
+# ------------------------------------
+# state setters are called at unpickling time, once the object is created and
+# it has to be updated to how it was at unpickling time.
+
+
+def _function_setstate(obj, state):
+    """Update the state of a dynamic function.
+
+    As __closure__ and __globals__ are readonly attributes of a function, we
+    cannot rely on the native setstate routine of pickle.load_build, that calls
+    setattr on items of the slotstate. Instead, we have to modify them inplace.
+    """
+    state, slotstate = state
+    obj.__dict__.update(state)
+
+    obj_globals = slotstate.pop("__globals__")
+    obj_closure = slotstate.pop("__closure__")
+    # _cloudpickle_subimports is a set of submodules that must be loaded for
+    # the pickled function to work correctly at unpickling time. Now that these
+    # submodules are depickled (hence imported), they can be removed from the
+    # object's state (the object state only served as a reference holder to
+    # these submodules)
+    slotstate.pop("_cloudpickle_submodules")
+
+    obj.__globals__.update(obj_globals)
+    obj.__globals__["__builtins__"] = __builtins__
+
+    if obj_closure is not None:
+        for i, cell in enumerate(obj_closure):
+            try:
+                value = cell.cell_contents
+            except ValueError:  # cell is empty
+                continue
+            obj.__closure__[i].cell_contents = value
+
+    for k, v in slotstate.items():
+        setattr(obj, k, v)
+
+
+def _class_setstate(obj, state):
+    state, slotstate = state
+    registry = None
+    for attrname, attr in state.items():
+        if attrname == "_abc_impl":
+            registry = attr
+        else:
+            # Note: setting attribute names on a class automatically triggers their
+            # interning in CPython:
+            # https://github.com/python/cpython/blob/v3.12.0/Objects/object.c#L957
+            #
+            # This means that to get deterministic pickling for a dynamic class that
+            # was initially defined in a different Python process, the pickler
+            # needs to ensure that dynamic class and function attribute names are
+            # systematically copied into a non-interned version to avoid
+            # unpredictable pickle payloads.
+            #
+            # Indeed the Pickler's memoizer relies on physical object identity to break
+            # cycles in the reference graph of the object being serialized.
+            setattr(obj, attrname, attr)
+
+    if sys.version_info >= (3, 13) and "__firstlineno__" in state:
+        # Set the Python 3.13+ only __firstlineno__  attribute one more time, as it
+        # will be automatically deleted by the `setattr(obj, attrname, attr)` call
+        # above when `attrname` is "__firstlineno__". We assume that preserving this
+        # information might be important for some users and that it not stale in the
+        # context of cloudpickle usage, hence legitimate to propagate. Furthermore it
+        # is necessary to do so to keep deterministic chained pickling as tested in
+        # test_deterministic_str_interning_for_chained_dynamic_class_pickling.
+        obj.__firstlineno__ = state["__firstlineno__"]
+
+    if registry is not None:
+        for subclass in registry:
+            obj.register(subclass)
+
+    return obj
+
+
+# COLLECTION OF DATACLASS UTILITIES
+# ---------------------------------
+# There are some internal sentinel values whose identity must be preserved when
+# unpickling dataclass fields. Each sentinel value has a unique name that we can
+# use to retrieve its identity at unpickling time.
+
+
+_DATACLASSE_FIELD_TYPE_SENTINELS = {
+    dataclasses._FIELD.name: dataclasses._FIELD,
+    dataclasses._FIELD_CLASSVAR.name: dataclasses._FIELD_CLASSVAR,
+    dataclasses._FIELD_INITVAR.name: dataclasses._FIELD_INITVAR,
+}
+
+
+def _get_dataclass_field_type_sentinel(name):
+    return _DATACLASSE_FIELD_TYPE_SENTINELS[name]
+
+
+class Pickler(pickle.Pickler):
+    # set of reducers defined and used by cloudpickle (private)
+    _dispatch_table = {}
+    _dispatch_table[classmethod] = _classmethod_reduce
+    _dispatch_table[io.TextIOWrapper] = _file_reduce
+    _dispatch_table[logging.Logger] = _logger_reduce
+    _dispatch_table[logging.RootLogger] = _root_logger_reduce
+    _dispatch_table[memoryview] = _memoryview_reduce
+    _dispatch_table[property] = _property_reduce
+    _dispatch_table[staticmethod] = _classmethod_reduce
+    _dispatch_table[CellType] = _cell_reduce
+    _dispatch_table[types.CodeType] = _code_reduce
+    _dispatch_table[types.GetSetDescriptorType] = _getset_descriptor_reduce
+    _dispatch_table[types.ModuleType] = _module_reduce
+    _dispatch_table[types.MethodType] = _method_reduce
+    _dispatch_table[types.MappingProxyType] = _mappingproxy_reduce
+    _dispatch_table[weakref.WeakSet] = _weakset_reduce
+    _dispatch_table[typing.TypeVar] = _typevar_reduce
+    _dispatch_table[_collections_abc.dict_keys] = _dict_keys_reduce
+    _dispatch_table[_collections_abc.dict_values] = _dict_values_reduce
+    _dispatch_table[_collections_abc.dict_items] = _dict_items_reduce
+    _dispatch_table[type(OrderedDict().keys())] = _odict_keys_reduce
+    _dispatch_table[type(OrderedDict().values())] = _odict_values_reduce
+    _dispatch_table[type(OrderedDict().items())] = _odict_items_reduce
+    _dispatch_table[abc.abstractmethod] = _classmethod_reduce
+    _dispatch_table[abc.abstractclassmethod] = _classmethod_reduce
+    _dispatch_table[abc.abstractstaticmethod] = _classmethod_reduce
+    _dispatch_table[abc.abstractproperty] = _property_reduce
+    _dispatch_table[dataclasses._FIELD_BASE] = _dataclass_field_base_reduce
+
+    dispatch_table = ChainMap(_dispatch_table, copyreg.dispatch_table)
+
+    # function reducers are defined as instance methods of cloudpickle.Pickler
+    # objects, as they rely on a cloudpickle.Pickler attribute (globals_ref)
+    def _dynamic_function_reduce(self, func):
+        """Reduce a function that is not pickleable via attribute lookup."""
+        newargs = self._function_getnewargs(func)
+        state = _function_getstate(func)
+        return (_make_function, newargs, state, None, None, _function_setstate)
+
+    def _function_reduce(self, obj):
+        """Reducer for function objects.
+
+        If obj is a top-level attribute of a file-backed module, this reducer
+        returns NotImplemented, making the cloudpickle.Pickler fall back to
+        traditional pickle.Pickler routines to save obj. Otherwise, it reduces
+        obj using a custom cloudpickle reducer designed specifically to handle
+        dynamic functions.
+        """
+        if _should_pickle_by_reference(obj):
+            return NotImplemented
+        else:
+            return self._dynamic_function_reduce(obj)
+
+    def _function_getnewargs(self, func):
+        code = func.__code__
+
+        # base_globals represents the future global namespace of func at
+        # unpickling time. Looking it up and storing it in
+        # cloudpickle.Pickler.globals_ref allow functions sharing the same
+        # globals at pickling time to also share them once unpickled, at one
+        # condition: since globals_ref is an attribute of a cloudpickle.Pickler
+        # instance, and that a new cloudpickle.Pickler is created each time
+        # cloudpickle.dump or cloudpickle.dumps is called, functions also need
+        # to be saved within the same invocation of
+        # cloudpickle.dump/cloudpickle.dumps (for example:
+        # cloudpickle.dumps([f1, f2])). There is no such limitation when using
+        # cloudpickle.Pickler.dump, as long as the multiple invocations are
+        # bound to the same cloudpickle.Pickler instance.
+        base_globals = self.globals_ref.setdefault(id(func.__globals__), {})
+
+        if base_globals == {}:
+            # Add module attributes used to resolve relative imports
+            # instructions inside func.
+            for k in ["__package__", "__name__", "__path__", "__file__"]:
+                if k in func.__globals__:
+                    base_globals[k] = func.__globals__[k]
+
+        # Do not bind the free variables before the function is created to
+        # avoid infinite recursion.
+        if func.__closure__ is None:
+            closure = None
+        else:
+            closure = tuple(_make_empty_cell() for _ in range(len(code.co_freevars)))
+
+        return code, base_globals, None, None, closure
+
+    def dump(self, obj):
+        try:
+            return super().dump(obj)
+        except RuntimeError as e:
+            if len(e.args) > 0 and "recursion" in e.args[0]:
+                msg = "Could not pickle object as excessively deep recursion required."
+                raise pickle.PicklingError(msg) from e
+            else:
+                raise
+
+    def __init__(self, file, protocol=None, buffer_callback=None):
+        if protocol is None:
+            protocol = DEFAULT_PROTOCOL
+        super().__init__(file, protocol=protocol, buffer_callback=buffer_callback)
+        # map functions __globals__ attribute ids, to ensure that functions
+        # sharing the same global namespace at pickling time also share
+        # their global namespace at unpickling time.
+        self.globals_ref = {}
+        self.proto = int(protocol)
+
+    if not PYPY:
+        # pickle.Pickler is the C implementation of the CPython pickler and
+        # therefore we rely on reduce_override method to customize the pickler
+        # behavior.
+
+        # `cloudpickle.Pickler.dispatch` is only left for backward
+        # compatibility - note that when using protocol 5,
+        # `cloudpickle.Pickler.dispatch` is not an extension of
+        # `pickle._Pickler.dispatch` dictionary, because `cloudpickle.Pickler`
+        # subclasses the C-implemented `pickle.Pickler`, which does not expose
+        # a `dispatch` attribute.  Earlier versions of `cloudpickle.Pickler`
+        # used `cloudpickle.Pickler.dispatch` as a class-level attribute
+        # storing all reducers implemented by cloudpickle, but the attribute
+        # name was not a great choice given because it would collide with a
+        # similarly named attribute in the pure-Python `pickle._Pickler`
+        # implementation in the standard library.
+        dispatch = dispatch_table
+
+        # Implementation of the reducer_override callback, in order to
+        # efficiently serialize dynamic functions and classes by subclassing
+        # the C-implemented `pickle.Pickler`.
+        # TODO: decorrelate reducer_override (which is tied to CPython's
+        # implementation - would it make sense to backport it to pypy? - and
+        # pickle's protocol 5 which is implementation agnostic. Currently, the
+        # availability of both notions coincide on CPython's pickle, but it may
+        # not be the case anymore when pypy implements protocol 5.
+
+        def reducer_override(self, obj):
+            """Type-agnostic reducing callback for function and classes.
+
+            For performance reasons, subclasses of the C `pickle.Pickler` class
+            cannot register custom reducers for functions and classes in the
+            dispatch_table attribute. Reducers for such types must instead
+            implemented via the special `reducer_override` method.
+
+            Note that this method will be called for any object except a few
+            builtin-types (int, lists, dicts etc.), which differs from reducers
+            in the Pickler's dispatch_table, each of them being invoked for
+            objects of a specific type only.
+
+            This property comes in handy for classes: although most classes are
+            instances of the ``type`` metaclass, some of them can be instances
+            of other custom metaclasses (such as enum.EnumMeta for example). In
+            particular, the metaclass will likely not be known in advance, and
+            thus cannot be special-cased using an entry in the dispatch_table.
+            reducer_override, among other things, allows us to register a
+            reducer that will be called for any class, independently of its
+            type.
+
+            Notes:
+
+            * reducer_override has the priority over dispatch_table-registered
+            reducers.
+            * reducer_override can be used to fix other limitations of
+              cloudpickle for other types that suffered from type-specific
+              reducers, such as Exceptions. See
+              https://github.com/cloudpipe/cloudpickle/issues/248
+            """
+            t = type(obj)
+            try:
+                is_anyclass = issubclass(t, type)
+            except TypeError:  # t is not a class (old Boost; see SF #502085)
+                is_anyclass = False
+
+            if is_anyclass:
+                return _class_reduce(obj)
+            elif isinstance(obj, types.FunctionType):
+                return self._function_reduce(obj)
+            else:
+                # fallback to save_global, including the Pickler's
+                # dispatch_table
+                return NotImplemented
+
+    else:
+        # When reducer_override is not available, hack the pure-Python
+        # Pickler's types.FunctionType and type savers. Note: the type saver
+        # must override Pickler.save_global, because pickle.py contains a
+        # hard-coded call to save_global when pickling meta-classes.
+        dispatch = pickle.Pickler.dispatch.copy()
+
+        def _save_reduce_pickle5(
+            self,
+            func,
+            args,
+            state=None,
+            listitems=None,
+            dictitems=None,
+            state_setter=None,
+            obj=None,
+        ):
+            save = self.save
+            write = self.write
+            self.save_reduce(
+                func,
+                args,
+                state=None,
+                listitems=listitems,
+                dictitems=dictitems,
+                obj=obj,
+            )
+            # backport of the Python 3.8 state_setter pickle operations
+            save(state_setter)
+            save(obj)  # simple BINGET opcode as obj is already memoized.
+            save(state)
+            write(pickle.TUPLE2)
+            # Trigger a state_setter(obj, state) function call.
+            write(pickle.REDUCE)
+            # The purpose of state_setter is to carry-out an
+            # inplace modification of obj. We do not care about what the
+            # method might return, so its output is eventually removed from
+            # the stack.
+            write(pickle.POP)
+
+        def save_global(self, obj, name=None, pack=struct.pack):
+            """Main dispatch method.
+
+            The name of this method is somewhat misleading: all types get
+            dispatched here.
+            """
+            if obj is type(None):  # noqa
+                return self.save_reduce(type, (None,), obj=obj)
+            elif obj is type(Ellipsis):
+                return self.save_reduce(type, (Ellipsis,), obj=obj)
+            elif obj is type(NotImplemented):
+                return self.save_reduce(type, (NotImplemented,), obj=obj)
+            elif obj in _BUILTIN_TYPE_NAMES:
+                return self.save_reduce(
+                    _builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj
+                )
+
+            if name is not None:
+                super().save_global(obj, name=name)
+            elif not _should_pickle_by_reference(obj, name=name):
+                self._save_reduce_pickle5(*_dynamic_class_reduce(obj), obj=obj)
+            else:
+                super().save_global(obj, name=name)
+
+        dispatch[type] = save_global
+
+        def save_function(self, obj, name=None):
+            """Registered with the dispatch to handle all function types.
+
+            Determines what kind of function obj is (e.g. lambda, defined at
+            interactive prompt, etc) and handles the pickling appropriately.
+            """
+            if _should_pickle_by_reference(obj, name=name):
+                return super().save_global(obj, name=name)
+            elif PYPY and isinstance(obj.__code__, builtin_code_type):
+                return self.save_pypy_builtin_func(obj)
+            else:
+                return self._save_reduce_pickle5(
+                    *self._dynamic_function_reduce(obj), obj=obj
+                )
+
+        def save_pypy_builtin_func(self, obj):
+            """Save pypy equivalent of builtin functions.
+
+            PyPy does not have the concept of builtin-functions. Instead,
+            builtin-functions are simple function instances, but with a
+            builtin-code attribute.
+            Most of the time, builtin functions should be pickled by attribute.
+            But PyPy has flaky support for __qualname__, so some builtin
+            functions such as float.__new__ will be classified as dynamic. For
+            this reason only, we created this special routine. Because
+            builtin-functions are not expected to have closure or globals,
+            there is no additional hack (compared the one already implemented
+            in pickle) to protect ourselves from reference cycles. A simple
+            (reconstructor, newargs, obj.__dict__) tuple is save_reduced.  Note
+            also that PyPy improved their support for __qualname__ in v3.6, so
+            this routing should be removed when cloudpickle supports only PyPy
+            3.6 and later.
+            """
+            rv = (
+                types.FunctionType,
+                (obj.__code__, {}, obj.__name__, obj.__defaults__, obj.__closure__),
+                obj.__dict__,
+            )
+            self.save_reduce(*rv, obj=obj)
+
+        dispatch[types.FunctionType] = save_function
+
+
+# Shorthands similar to pickle.dump/pickle.dumps
+
+
+def dump(obj, file, protocol=None, buffer_callback=None):
+    """Serialize obj as bytes streamed into file
+
+    protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
+    pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
+    speed between processes running the same Python version.
+
+    Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
+    compatibility with older versions of Python (although this is not always
+    guaranteed to work because cloudpickle relies on some internal
+    implementation details that can change from one Python version to the
+    next).
+    """
+    Pickler(file, protocol=protocol, buffer_callback=buffer_callback).dump(obj)
+
+
+def dumps(obj, protocol=None, buffer_callback=None):
+    """Serialize obj as a string of bytes allocated in memory
+
+    protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
+    pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
+    speed between processes running the same Python version.
+
+    Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
+    compatibility with older versions of Python (although this is not always
+    guaranteed to work because cloudpickle relies on some internal
+    implementation details that can change from one Python version to the
+    next).
+    """
+    with io.BytesIO() as file:
+        cp = Pickler(file, protocol=protocol, buffer_callback=buffer_callback)
+        cp.dump(obj)
+        return file.getvalue()
+
+
+# Include pickles unloading functions in this namespace for convenience.
+load, loads = pickle.load, pickle.loads
+
+# Backward compat alias.
+CloudPickler = Pickler

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/externals/cloudpickle/cloudpickle_fast.py

@@ -0,0 +1,14 @@
+"""Compatibility module.
+
+It can be necessary to load files generated by previous versions of cloudpickle
+that rely on symbols being defined under the `cloudpickle.cloudpickle_fast`
+namespace.
+
+See: tests/test_backward_compat.py
+"""
+
+from . import cloudpickle
+
+
+def __getattr__(name):
+    return getattr(cloudpickle, name)

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/externals/loky/__init__.py

@@ -0,0 +1,45 @@
+r"""The :mod:`loky` module manages a pool of worker that can be re-used across time.
+It provides a robust and dynamic implementation os the
+:class:`ProcessPoolExecutor` and a function :func:`get_reusable_executor` which
+hide the pool management under the hood.
+"""
+
+from concurrent.futures import (
+    ALL_COMPLETED,
+    FIRST_COMPLETED,
+    FIRST_EXCEPTION,
+    CancelledError,
+    Executor,
+    TimeoutError,
+    as_completed,
+    wait,
+)
+
+from ._base import Future
+from .backend.context import cpu_count
+from .backend.reduction import set_loky_pickler
+from .reusable_executor import get_reusable_executor
+from .cloudpickle_wrapper import wrap_non_picklable_objects
+from .process_executor import BrokenProcessPool, ProcessPoolExecutor
+
+
+__all__ = [
+    "get_reusable_executor",
+    "cpu_count",
+    "wait",
+    "as_completed",
+    "Future",
+    "Executor",
+    "ProcessPoolExecutor",
+    "BrokenProcessPool",
+    "CancelledError",
+    "TimeoutError",
+    "FIRST_COMPLETED",
+    "FIRST_EXCEPTION",
+    "ALL_COMPLETED",
+    "wrap_non_picklable_objects",
+    "set_loky_pickler",
+]
+
+
+__version__ = "3.5.5"

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/externals/loky/_base.py

@@ -0,0 +1,28 @@
+###############################################################################
+# Modification of concurrent.futures.Future
+#
+# author: Thomas Moreau and Olivier Grisel
+#
+# adapted from concurrent/futures/_base.py (17/02/2017)
+#  * Do not use yield from
+#  * Use old super syntax
+#
+# Copyright 2009 Brian Quinlan. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+from concurrent.futures import Future as _BaseFuture
+from concurrent.futures._base import LOGGER
+
+
+# To make loky._base.Future instances awaitable  by concurrent.futures.wait,
+# derive our custom Future class from _BaseFuture. _invoke_callback is the only
+# modification made to this class in loky.
+# TODO investigate why using `concurrent.futures.Future` directly does not
+# always work in our test suite.
+class Future(_BaseFuture):
+    def _invoke_callbacks(self):
+        for callback in self._done_callbacks:
+            try:
+                callback(self)
+            except BaseException:
+                LOGGER.exception(f"exception calling callback for {self!r}")

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/joblib/externals/loky/backend/__init__.py

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