From: Siu Kwan Lam <1929845+sklam@users.noreply.github.com>
Origin: https://github.com/numba/numba/pull/8545
Date: Thu, 29 Sep 2022 11:52:38 -0500
Subject: Upgrade cloudpickle to version v2.2.0

from commit
https://github.com/cloudpipe/cloudpickle/commit/f31859b1dd83fa691f4f7f797166b262c9acb8e7
---
 numba/cloudpickle/__init__.py         |   4 +-
 numba/cloudpickle/cloudpickle.py      | 276 +++++++++++++++++++++++-----------
 numba/cloudpickle/cloudpickle_fast.py | 163 ++++++++++++++------
 numba/cloudpickle/compat.py           |   7 +-
 4 files changed, 312 insertions(+), 138 deletions(-)

diff --git a/numba/cloudpickle/__init__.py b/numba/cloudpickle/__init__.py
index 231a097..e82e3ca 100644
--- a/numba/cloudpickle/__init__.py
+++ b/numba/cloudpickle/__init__.py
@@ -1,7 +1,7 @@
 from __future__ import absolute_import
 
 # NOTE: The following imports are adapted to use as a vendored subpackage.
-# from https://github.com/cloudpipe/cloudpickle/blob/d3279a0689b769d5315fc6ff00cd0f5897844526/cloudpickle/init.py
+# from https://github.com/cloudpipe/cloudpickle/commit/f31859b1dd83fa691f4f7f797166b262c9acb8e7
 from .cloudpickle import *  # noqa
 from .cloudpickle_fast import CloudPickler, dumps, dump  # noqa
 
@@ -9,4 +9,4 @@ from .cloudpickle_fast import CloudPickler, dumps, dump  # noqa
 # expose their Pickler subclass at top-level under the  "Pickler" name.
 Pickler = CloudPickler
 
-__version__ = '1.6.0'
+__version__ = '2.2.0'
diff --git a/numba/cloudpickle/cloudpickle.py b/numba/cloudpickle/cloudpickle.py
index f7fd4dc..317be69 100644
--- a/numba/cloudpickle/cloudpickle.py
+++ b/numba/cloudpickle/cloudpickle.py
@@ -40,7 +40,6 @@ 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.
 """
-from __future__ import print_function
 
 import builtins
 import dis
@@ -55,7 +54,8 @@ import typing
 import warnings
 
 from .compat import pickle
-from typing import Generic, Union, Tuple, Callable
+from collections import OrderedDict
+from typing import ClassVar, Generic, Union, Tuple, Callable
 from pickle import _getattribute
 from importlib._bootstrap import _find_spec
 
@@ -65,11 +65,6 @@ try:  # pragma: no branch
 except ImportError:
     _typing_extensions = Literal = Final = None
 
-if sys.version_info >= (3, 5, 3):
-    from typing import ClassVar
-else:  # pragma: no cover
-    ClassVar = None
-
 if sys.version_info >= (3, 8):
     from types import CellType
 else:
@@ -87,13 +82,15 @@ else:
 # 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()
-_DYNAMIC_CLASS_TRACKER_REUSING = weakref.WeakSet()
 
 PYPY = platform.python_implementation() == "PyPy"
 
@@ -118,17 +115,83 @@ def _get_or_create_tracker_id(class_def):
 def _lookup_class_or_track(class_tracker_id, class_def):
     if class_tracker_id is not None:
         with _DYNAMIC_CLASS_TRACKER_LOCK:
-            orig_class_def = class_def
             class_def = _DYNAMIC_CLASS_TRACKER_BY_ID.setdefault(
                 class_tracker_id, class_def)
             _DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
-            # Check if we are reusing a previous class_def
-            if orig_class_def is not class_def:
-                # Remember the class_def is being reused
-                _DYNAMIC_CLASS_TRACKER_REUSING.add(class_def)
     return class_def
 
 
+def register_pickle_by_value(module):
+    """Register a module to make it functions and classes picklable by value.
+
+    By default, functions and classes that are attributes of an importable
+    module are to be pickled by reference, that is relying on re-importing
+    the attribute from the module at load time.
+
+    If `register_pickle_by_value(module)` is called, all its functions and
+    classes are subsequently to be pickled by value, meaning that they can
+    be loaded in Python processes where the module is not importable.
+
+    This is especially useful when developing a module in a distributed
+    execution environment: restarting the client Python process with the new
+    source code is enough: there is no need to re-install the new version
+    of the module on all the worker nodes nor to restart the workers.
+
+    Note: this feature is considered experimental. See the cloudpickle
+    README.md file for more details and limitations.
+    """
+    if not isinstance(module, types.ModuleType):
+        raise ValueError(
+            f"Input should be a module object, got {str(module)} instead"
+        )
+    # In the future, cloudpickle may need a way to access any module registered
+    # for pickling by value in order to introspect relative imports inside
+    # functions pickled by value. (see
+    # https://github.com/cloudpipe/cloudpickle/pull/417#issuecomment-873684633).
+    # This access can be ensured by checking that module is present in
+    # sys.modules at registering time and assuming that it will still be in
+    # there when accessed during pickling. Another alternative would be to
+    # store a weakref to the module. Even though cloudpickle does not implement
+    # this introspection yet, in order to avoid a possible breaking change
+    # later, we still enforce the presence of module inside sys.modules.
+    if module.__name__ not in sys.modules:
+        raise ValueError(
+            f"{module} was not imported correctly, have you used an "
+            f"`import` statement to access it?"
+        )
+    _PICKLE_BY_VALUE_MODULES.add(module.__name__)
+
+
+def unregister_pickle_by_value(module):
+    """Unregister that the input module should be pickled by value."""
+    if not isinstance(module, types.ModuleType):
+        raise ValueError(
+            f"Input should be a module object, got {str(module)} instead"
+        )
+    if module.__name__ not in _PICKLE_BY_VALUE_MODULES:
+        raise ValueError(f"{module} is not registered for pickle by value")
+    else:
+        _PICKLE_BY_VALUE_MODULES.remove(module.__name__)
+
+
+def list_registry_pickle_by_value():
+    return _PICKLE_BY_VALUE_MODULES.copy()
+
+
+def _is_registered_pickle_by_value(module):
+    module_name = module.__name__
+    if module_name in _PICKLE_BY_VALUE_MODULES:
+        return True
+    while True:
+        parent_name = module_name.rsplit(".", 1)[0]
+        if parent_name == module_name:
+            break
+        if parent_name in _PICKLE_BY_VALUE_MODULES:
+            return True
+        module_name = parent_name
+    return False
+
+
 def _whichmodule(obj, name):
     """Find the module an object belongs to.
 
@@ -142,11 +205,14 @@ def _whichmodule(obj, name):
         # Workaround bug in old Python versions: prior to Python 3.7,
         # T.__module__ would always be set to "typing" even when the TypeVar T
         # would be defined in a different module.
-        #
-        # For such older Python versions, we ignore the __module__ attribute of
-        # TypeVar instances and instead exhaustively lookup those instances in
-        # all currently imported modules.
-        module_name = None
+        if name is not None and getattr(typing, name, None) is obj:
+            # Built-in TypeVar defined in typing such as AnyStr
+            return 'typing'
+        else:
+            # User defined or third-party TypeVar: __module__ attribute is
+            # irrelevant, thus trigger a exhaustive search for obj in all
+            # modules.
+            module_name = None
     else:
         module_name = getattr(obj, '__module__', None)
 
@@ -172,18 +238,35 @@ def _whichmodule(obj, name):
     return None
 
 
-def _is_importable(obj, name=None):
-    """Dispatcher utility to test the importability of various constructs."""
-    if isinstance(obj, types.FunctionType):
-        return _lookup_module_and_qualname(obj, name=name) is not None
-    elif issubclass(type(obj), type):
-        return _lookup_module_and_qualname(obj, name=name) is not 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.
+        # 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(
@@ -238,11 +321,13 @@ def _extract_code_globals(co):
     """
     out_names = _extract_code_globals_cache.get(co)
     if out_names is None:
-        names = co.co_names
-        out_names = {names[oparg] for _, oparg in _walk_global_ops(co)}
+        # We use a dict with None values instead of a set to get a
+        # deterministic order (assuming Python 3.6+) 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 one
+        # 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
@@ -250,7 +335,7 @@ def _extract_code_globals(co):
         if co.co_consts:
             for const in co.co_consts:
                 if isinstance(const, types.CodeType):
-                    out_names |= _extract_code_globals(const)
+                    out_names.update(_extract_code_globals(const))
 
         _extract_code_globals_cache[co] = out_names
 
@@ -425,13 +510,12 @@ def _builtin_type(name):
 
 def _walk_global_ops(code):
     """
-    Yield (opcode, argument number) tuples for all
-    global-referencing instructions in *code*.
+    Yield referenced name for all global-referencing instructions in *code*.
     """
     for instr in dis.get_instructions(code):
         op = instr.opcode
         if op in GLOBAL_OPS:
-            yield op, instr.arg
+            yield instr.argval
 
 
 def _extract_class_dict(cls):
@@ -458,15 +542,31 @@ def _extract_class_dict(cls):
 
 if sys.version_info[:2] < (3, 7):  # pragma: no branch
     def _is_parametrized_type_hint(obj):
-        # This is very cheap but might generate false positives.
+        # This is very cheap but might generate false positives. So try to
+        # narrow it down is good as possible.
+        type_module = getattr(type(obj), '__module__', None)
+        from_typing_extensions = type_module == 'typing_extensions'
+        from_typing = type_module == 'typing'
+
         # general typing Constructs
         is_typing = getattr(obj, '__origin__', None) is not None
 
         # typing_extensions.Literal
-        is_litteral = getattr(obj, '__values__', None) is not None
+        is_literal = (
+            (getattr(obj, '__values__', None) is not None)
+            and from_typing_extensions
+        )
 
         # typing_extensions.Final
-        is_final = getattr(obj, '__type__', None) is not None
+        is_final = (
+            (getattr(obj, '__type__', None) is not None)
+            and from_typing_extensions
+        )
+
+        # typing.ClassVar
+        is_classvar = (
+            (getattr(obj, '__type__', None) is not None) and from_typing
+        )
 
         # typing.Union/Tuple for old Python 3.5
         is_union = getattr(obj, '__union_params__', None) is not None
@@ -475,8 +575,8 @@ if sys.version_info[:2] < (3, 7):  # pragma: no branch
             getattr(obj, '__result__', None) is not None and
             getattr(obj, '__args__', None) is not None
         )
-        return any((is_typing, is_litteral, is_final, is_union, is_tuple,
-                    is_callable))
+        return any((is_typing, is_literal, is_final, is_classvar, is_union,
+                    is_tuple, is_callable))
 
     def _create_parametrized_type_hint(origin, args):
         return origin[args]
@@ -486,7 +586,7 @@ else:
 
 
 def parametrized_type_hint_getinitargs(obj):
-    # The distorted type check semantic for typing construct becomes:
+    # The distorted type check sematic for typing construct becomes:
     # ``type(obj) is type(TypeHint)``, which means "obj is a
     # parametrized TypeHint"
     if type(obj) is type(Literal):  # pragma: no branch
@@ -496,43 +596,21 @@ def parametrized_type_hint_getinitargs(obj):
     elif type(obj) is type(ClassVar):
         initargs = (ClassVar, obj.__type__)
     elif type(obj) is type(Generic):
-        parameters = obj.__parameters__
-        if len(obj.__parameters__) > 0:
-            # in early Python 3.5, __parameters__ was sometimes
-            # preferred to __args__
-            initargs = (obj.__origin__, parameters)
-
-        else:
-            initargs = (obj.__origin__, obj.__args__)
+        initargs = (obj.__origin__, obj.__args__)
     elif type(obj) is type(Union):
-        if sys.version_info < (3, 5, 3):  # pragma: no cover
-            initargs = (Union, obj.__union_params__)
-        else:
-            initargs = (Union, obj.__args__)
+        initargs = (Union, obj.__args__)
     elif type(obj) is type(Tuple):
-        if sys.version_info < (3, 5, 3):  # pragma: no cover
-            initargs = (Tuple, obj.__tuple_params__)
-        else:
-            initargs = (Tuple, obj.__args__)
+        initargs = (Tuple, obj.__args__)
     elif type(obj) is type(Callable):
-        if sys.version_info < (3, 5, 3):  # pragma: no cover
-            args = obj.__args__
-            result = obj.__result__
-            if args != Ellipsis:
-                if isinstance(args, tuple):
-                    args = list(args)
-                else:
-                    args = [args]
+        (*args, result) = obj.__args__
+        if len(args) == 1 and args[0] is Ellipsis:
+            args = Ellipsis
         else:
-            (*args, result) = obj.__args__
-            if len(args) == 1 and args[0] is Ellipsis:
-                args = Ellipsis
-            else:
-                args = list(args)
+            args = list(args)
         initargs = (Callable, (args, result))
     else:  # pragma: no cover
         raise pickle.PicklingError(
-            "Cloudpickle Error: Unknown type {}".format(type(obj))
+            f"Cloudpickle Error: Unknown type {type(obj)}"
         )
     return initargs
 
@@ -563,8 +641,11 @@ load = pickle.load
 loads = pickle.loads
 
 
-# hack for __import__ not working as desired
 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]
 
@@ -609,7 +690,7 @@ def instance(cls):
 
 
 @instance
-class _empty_cell_value(object):
+class _empty_cell_value:
     """sentinel for empty closures
     """
     @classmethod
@@ -638,7 +719,7 @@ def _fill_function(*args):
         keys = ['globals', 'defaults', 'dict', 'module', 'closure_values']
         state = dict(zip(keys, args[1:]))
     else:
-        raise ValueError('Unexpected _fill_value arguments: %r' % (args,))
+        raise ValueError(f'Unexpected _fill_value arguments: {args!r}')
 
     # - At pickling time, any dynamic global variable used by func is
     #   serialized by value (in state['globals']).
@@ -682,6 +763,12 @@ def _fill_function(*args):
     return func
 
 
+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
@@ -705,7 +792,7 @@ def _make_skel_func(code, cell_count, base_globals=None):
     """
     # This function is deprecated and should be removed in cloudpickle 1.7
     warnings.warn(
-        "A pickle file created using an old (<=1.4.1) version of cloudpicke "
+        "A pickle file created using an old (<=1.4.1) version of cloudpickle "
         "is currently being loaded. This is not supported by cloudpickle and "
         "will break in cloudpickle 1.7", category=UserWarning
     )
@@ -806,29 +893,33 @@ def _make_typevar(name, bound, constraints, covariant, contravariant,
 
 
 def _decompose_typevar(obj):
-    try:
-        class_tracker_id = _get_or_create_tracker_id(obj)
-    except TypeError:  # pragma: nocover
-        # TypeVar instances are not weakref-able in Python 3.5.3
-        class_tracker_id = None
     return (
         obj.__name__, obj.__bound__, obj.__constraints__,
         obj.__covariant__, obj.__contravariant__,
-        class_tracker_id,
+        _get_or_create_tracker_id(obj),
     )
 
 
 def _typevar_reduce(obj):
-    # TypeVar instances have no __qualname__ hence we pass the name explicitly.
+    # 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 hasattr(typ, '__orig_bases__'):
+    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
@@ -836,13 +927,22 @@ def _get_bases(typ):
     return getattr(typ, bases_attr)
 
 
-def _make_dict_keys(obj):
-    return dict.fromkeys(obj).keys()
+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):
-    return {i: _ for i, _ in enumerate(obj)}.values()
+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):
-    return obj.items()
+def _make_dict_items(obj, is_ordered=False):
+    if is_ordered:
+        return OrderedDict(obj).items()
+    else:
+        return obj.items()
diff --git a/numba/cloudpickle/cloudpickle_fast.py b/numba/cloudpickle/cloudpickle_fast.py
index 2164504..8741dcb 100644
--- a/numba/cloudpickle/cloudpickle_fast.py
+++ b/numba/cloudpickle/cloudpickle_fast.py
@@ -6,7 +6,7 @@ previous Python implementation of the Pickler class. Because this functionality
 is only available for Python versions 3.8+, a lot of backward-compatibility
 code is also removed.
 
-Note that the C Pickler sublassing API is CPython-specific. Therefore, some
+Note that the C Pickler subclassing API is CPython-specific. Therefore, some
 guards present in cloudpickle.py that were written to handle PyPy specificities
 are not present in cloudpickle_fast.py
 """
@@ -23,24 +23,23 @@ import weakref
 import typing
 
 from enum import Enum
-from collections import ChainMap
+from collections import ChainMap, OrderedDict
 
 from .compat import pickle, Pickler
 from .cloudpickle import (
     _extract_code_globals, _BUILTIN_TYPE_NAMES, DEFAULT_PROTOCOL,
-    _find_imported_submodules, _get_cell_contents, _is_importable,
+    _find_imported_submodules, _get_cell_contents, _should_pickle_by_reference,
     _builtin_type, _get_or_create_tracker_id,  _make_skeleton_class,
     _make_skeleton_enum, _extract_class_dict, dynamic_subimport, subimport,
     _typevar_reduce, _get_bases, _make_cell, _make_empty_cell, CellType,
     _is_parametrized_type_hint, PYPY, cell_set,
     parametrized_type_hint_getinitargs, _create_parametrized_type_hint,
     builtin_code_type,
-    _make_dict_keys, _make_dict_values, _make_dict_items,
-    _DYNAMIC_CLASS_TRACKER_REUSING,
+    _make_dict_keys, _make_dict_values, _make_dict_items, _make_function,
 )
 
 
-if pickle.HIGHEST_PROTOCOL >= 5 and not PYPY:
+if pickle.HIGHEST_PROTOCOL >= 5:
     # Shorthands similar to pickle.dump/pickle.dumps
 
     def dump(obj, file, protocol=None, buffer_callback=None):
@@ -124,7 +123,7 @@ def _class_getnewargs(obj):
 
 
 def _enum_getnewargs(obj):
-    members = dict((e.name, e.value) for e in 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)
 
@@ -181,7 +180,7 @@ def _class_getstate(obj):
     clsdict.pop('__weakref__', None)
 
     if issubclass(type(obj), abc.ABCMeta):
-        # If obj is an instance of an ABCMeta subclass, dont pickle the
+        # 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)
@@ -219,7 +218,7 @@ def _class_getstate(obj):
 def _enum_getstate(obj):
     clsdict, slotstate = _class_getstate(obj)
 
-    members = dict((e.name, e.value) for e in obj)
+    members = {e.name: e.value for e in obj}
     # Cleanup the clsdict that will be passed to _rehydrate_skeleton_class:
     # Those attributes are already handled by the metaclass.
     for attrname in ["_generate_next_value_", "_member_names_",
@@ -245,7 +244,46 @@ def _enum_getstate(obj):
 
 def _code_reduce(obj):
     """codeobject reducer"""
-    if hasattr(obj, "co_posonlyargcount"):  # pragma: no branch
+    # If you are not sure about the order of arguments, take a look at help
+    # of the specific type from types, for example:
+    # >>> from types import CodeType
+    # >>> help(CodeType)
+    if hasattr(obj, "co_exceptiontable"):  # pragma: no branch
+        # Python 3.11 and later: there are some new attributes
+        # related to the enhanced exceptions.
+        args = (
+            obj.co_argcount, obj.co_posonlyargcount,
+            obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize,
+            obj.co_flags, obj.co_code, obj.co_consts, obj.co_names,
+            obj.co_varnames, obj.co_filename, obj.co_name, obj.co_qualname,
+            obj.co_firstlineno, obj.co_linetable, obj.co_exceptiontable,
+            obj.co_freevars, obj.co_cellvars,
+        )
+    elif hasattr(obj, "co_linetable"):  # pragma: no branch
+        # Python 3.10 and later: obj.co_lnotab is deprecated and constructor
+        # expects obj.co_linetable instead.
+        args = (
+            obj.co_argcount, obj.co_posonlyargcount,
+            obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize,
+            obj.co_flags, obj.co_code, obj.co_consts, obj.co_names,
+            obj.co_varnames, obj.co_filename, obj.co_name,
+            obj.co_firstlineno, obj.co_linetable, obj.co_freevars,
+            obj.co_cellvars
+        )
+    elif hasattr(obj, "co_nmeta"):  # pragma: no cover
+        # "nogil" Python: modified attributes from 3.9
+        args = (
+            obj.co_argcount, obj.co_posonlyargcount,
+            obj.co_kwonlyargcount, obj.co_nlocals, obj.co_framesize,
+            obj.co_ndefaultargs, obj.co_nmeta,
+            obj.co_flags, obj.co_code, obj.co_consts,
+            obj.co_varnames, obj.co_filename, obj.co_name,
+            obj.co_firstlineno, obj.co_lnotab, obj.co_exc_handlers,
+            obj.co_jump_table, obj.co_freevars, obj.co_cellvars,
+            obj.co_free2reg, obj.co_cell2reg
+        )
+    elif hasattr(obj, "co_posonlyargcount"):
+        # Backward compat for 3.9 and older
         args = (
             obj.co_argcount, obj.co_posonlyargcount,
             obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize,
@@ -255,6 +293,7 @@ def _code_reduce(obj):
             obj.co_cellvars
         )
     else:
+        # Backward compat for even older versions of Python
         args = (
             obj.co_argcount, obj.co_kwonlyargcount, obj.co_nlocals,
             obj.co_stacksize, obj.co_flags, obj.co_code, obj.co_consts,
@@ -340,11 +379,16 @@ def _memoryview_reduce(obj):
 
 
 def _module_reduce(obj):
-    if _is_importable(obj):
+    if _should_pickle_by_reference(obj):
         return subimport, (obj.__name__,)
     else:
-        obj.__dict__.pop('__builtins__', None)
-        return dynamic_subimport, (obj.__name__, vars(obj))
+        # 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):
@@ -397,7 +441,7 @@ def _class_reduce(obj):
         return type, (NotImplemented,)
     elif obj in _BUILTIN_TYPE_NAMES:
         return _builtin_type, (_BUILTIN_TYPE_NAMES[obj],)
-    elif not _is_importable(obj):
+    elif not _should_pickle_by_reference(obj):
         return _dynamic_class_reduce(obj)
     return NotImplemented
 
@@ -420,6 +464,24 @@ 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)
+
+
 # COLLECTIONS OF OBJECTS STATE SETTERS
 # ------------------------------------
 # state setters are called at unpickling time, once the object is created and
@@ -427,7 +489,7 @@ def _dict_items_reduce(obj):
 
 
 def _function_setstate(obj, state):
-    """Update the state of a dynaamic function.
+    """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
@@ -461,10 +523,6 @@ def _function_setstate(obj, state):
 
 
 def _class_setstate(obj, state):
-    # Check if class is being reused and needs bypass setstate logic.
-    if obj in _DYNAMIC_CLASS_TRACKER_REUSING:
-        return obj
-
     state, slotstate = state
     registry = None
     for attrname, attr in state.items():
@@ -500,7 +558,13 @@ class CloudPickler(Pickler):
     _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 = ChainMap(_dispatch_table, copyreg.dispatch_table)
 
@@ -510,7 +574,7 @@ class CloudPickler(Pickler):
         """Reduce a function that is not pickleable via attribute lookup."""
         newargs = self._function_getnewargs(func)
         state = _function_getstate(func)
-        return (types.FunctionType, newargs, state, None, None,
+        return (_make_function, newargs, state, None, None,
                 _function_setstate)
 
     def _function_reduce(self, obj):
@@ -525,7 +589,7 @@ class CloudPickler(Pickler):
         As opposed to cloudpickle.py, There no special handling for builtin
         pypy functions because cloudpickle_fast is CPython-specific.
         """
-        if _is_importable(obj):
+        if _should_pickle_by_reference(obj):
             return NotImplemented
         else:
             return self._dynamic_function_reduce(obj)
@@ -577,6 +641,32 @@ class CloudPickler(Pickler):
                 raise
 
     if pickle.HIGHEST_PROTOCOL >= 5:
+        def __init__(self, file, protocol=None, buffer_callback=None):
+            if protocol is None:
+                protocol = DEFAULT_PROTOCOL
+            Pickler.__init__(
+                self, 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)
+    else:
+        def __init__(self, file, protocol=None):
+            if protocol is None:
+                protocol = DEFAULT_PROTOCOL
+            Pickler.__init__(self, file, protocol=protocol)
+            # 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 = {}
+            assert hasattr(self, 'proto')
+
+    if pickle.HIGHEST_PROTOCOL >= 5 and not PYPY:
+        # Pickler is the C implementation of the CPython pickler and therefore
+        # we rely on reduce_override method to customize the pickler behavior.
+
         # `CloudPickler.dispatch` is only left for backward compatibility - note
         # that when using protocol 5, `CloudPickler.dispatch` is not an
         # extension of `Pickler.dispatch` dictionary, because CloudPickler
@@ -584,7 +674,7 @@ class CloudPickler(Pickler):
         # `dispatch` attribute.  Earlier versions of the protocol 5 CloudPickler
         # used `CloudPickler.dispatch` as a class-level attribute storing all
         # reducers implemented by cloudpickle, but the attribute name was not a
-        # great choice given the meaning of `Cloudpickler.dispatch` when
+        # great choice given the meaning of `CloudPickler.dispatch` when
         # `CloudPickler` extends the pure-python pickler.
         dispatch = dispatch_table
 
@@ -597,17 +687,6 @@ class CloudPickler(Pickler):
         # availability of both notions coincide on CPython's pickle and the
         # pickle5 backport, but it may not be the case anymore when pypy
         # implements protocol 5
-        def __init__(self, file, protocol=None, buffer_callback=None):
-            if protocol is None:
-                protocol = DEFAULT_PROTOCOL
-            Pickler.__init__(
-                self, 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)
 
         def reducer_override(self, obj):
             """Type-agnostic reducing callback for function and classes.
@@ -658,7 +737,7 @@ class CloudPickler(Pickler):
                 return self._function_reduce(obj)
             else:
                 # fallback to save_global, including the Pickler's
-                # distpatch_table
+                # dispatch_table
                 return NotImplemented
 
     else:
@@ -668,16 +747,6 @@ class CloudPickler(Pickler):
         # hard-coded call to save_global when pickling meta-classes.
         dispatch = Pickler.dispatch.copy()
 
-        def __init__(self, file, protocol=None):
-            if protocol is None:
-                protocol = DEFAULT_PROTOCOL
-            Pickler.__init__(self, file, protocol=protocol)
-            # 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 = {}
-            assert hasattr(self, 'proto')
-
         def _save_reduce_pickle5(self, func, args, state=None, listitems=None,
                                  dictitems=None, state_setter=None, obj=None):
             save = self.save
@@ -729,7 +798,7 @@ class CloudPickler(Pickler):
                 )
             elif name is not None:
                 Pickler.save_global(self, obj, name=name)
-            elif not _is_importable(obj, name=name):
+            elif not _should_pickle_by_reference(obj, name=name):
                 self._save_reduce_pickle5(*_dynamic_class_reduce(obj), obj=obj)
             else:
                 Pickler.save_global(self, obj, name=name)
@@ -741,7 +810,7 @@ class CloudPickler(Pickler):
             Determines what kind of function obj is (e.g. lambda, defined at
             interactive prompt, etc) and handles the pickling appropriately.
             """
-            if _is_importable(obj, name=name):
+            if _should_pickle_by_reference(obj, name=name):
                 return Pickler.save_global(self, obj, name=name)
             elif PYPY and isinstance(obj.__code__, builtin_code_type):
                 return self.save_pypy_builtin_func(obj)
diff --git a/numba/cloudpickle/compat.py b/numba/cloudpickle/compat.py
index afa285f..5e9b527 100644
--- a/numba/cloudpickle/compat.py
+++ b/numba/cloudpickle/compat.py
@@ -7,7 +7,12 @@ if sys.version_info < (3, 8):
         from pickle5 import Pickler  # noqa: F401
     except ImportError:
         import pickle  # noqa: F401
+
+        # Use the Python pickler for old CPython versions
         from pickle import _Pickler as Pickler  # noqa: F401
 else:
     import pickle  # noqa: F401
-    from _pickle import Pickler  # noqa: F401
+
+    # Pickler will the C implementation in CPython and the Python
+    # implementation in PyPy
+    from pickle import Pickler  # noqa: F401