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"""

This class is defined to override standard pickle functionality

The goals of it follow:

-Serialize lambdas and nested functions to compiled byte code

-Deal with main module correctly

-Deal with other non-serializable 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/>`_.

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.

"""

from __future__ import print_function

import builtins

import dis

import opcode

import platform

import sys

import types

import weakref

import uuid

import threading

import typing

import warnings

from .compat import pickle

from typing import Generic, Union, Tuple, Callable

from pickle import _getattribute

from importlib._bootstrap import _find_spec

try: # pragma: no branch

import typing_extensions as _typing_extensions

from typing_extensions import Literal, Final

except ImportError:

_typing_extensions = Literal = Final = None

if sys.version_info >= (3, 5, 3):

from typing import ClassVar

else: # pragma: no cover

ClassVar = None

73 ↛ 74line 73 didn't jump to line 74, because the condition on line 73 was never trueif sys.version_info >= (3, 8):

from types import CellType

else:

def f():

a = 1

def g():

return a

return g

CellType = type(f().__closure__[0])

# 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

# Track the provenance of reconstructed dynamic classes to make it possible to

# recontruct 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):

118 ↛ 123line 118 didn't jump to line 123, because the condition on line 118 was never false 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 _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.

"""

if sys.version_info[:2] < (3, 7) and isinstance(obj, typing.TypeVar): # pragma: no branch # noqa

# 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

else:

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.

152 ↛ 166line 152 didn't jump to line 166, because the loop on line 152 didn't complete for module_name, module in sys.modules.copy().items():

# Some modules such as coverage can inject non-module objects inside

# sys.modules

155 ↛ 160line 155 didn't jump to line 160 if (

module_name == '__main__' or

module is None or

not isinstance(module, types.ModuleType)

continue

try:

162 ↛ 152line 162 didn't jump to line 152, because the condition on line 162 was never false if _getattribute(module, name)[0] is obj:

return module_name

except Exception:

pass

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

175 ↛ 183line 175 didn't jump to line 183, because the condition on line 175 was never false 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.

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)

200 ↛ 203line 200 didn't jump to line 203, because the condition on line 200 was never true if module_name is None:

# In this case, obj.__module__ is None AND obj was not found in any

# imported module. obj is thus treated as dynamic.

return None

if module_name == "__main__":

return None

# Note: if module_name is in sys.modules, the corresponding module is

# assumed importable at unpickling time. See #357

module = sys.modules.get(module_name, None)

if module is None:

# The main reason why obj's module would not be imported is that this

# module has been dynamically created, using for example

# types.ModuleType. The other possibility is that module was removed

# from sys.modules after obj was created/imported. But this case is not

# supported, as the standard pickle does not support it either.

return None

try:

obj2, parent = _getattribute(module, name)

except AttributeError:

# obj was not found inside the module it points to

return None

224 ↛ 225line 224 didn't jump to line 225, because the condition on line 224 was never true 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:

names = co.co_names

out_names = {names[oparg] for _, oparg in _walk_global_ops(co)}

# Declaring a function inside another one using the "def ..."

# syntax generates a constant code object corresonding 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 |= _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:

284 ↛ 287line 284 didn't jump to line 287, because the condition on line 284 was never true 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

def cell_set(cell, value):

"""Set the value of a closure cell.

The point of this function is to set the cell_contents attribute of a cell

after its creation. This operation is necessary in case the cell contains a

reference to the function the cell belongs to, as when calling the

function's constructor

``f = types.FunctionType(code, globals, name, argdefs, closure)``,

closure will not be able to contain the yet-to-be-created f.

In Python3.7, cell_contents is writeable, so setting the contents of a cell

can be done simply using

>>> cell.cell_contents = value

In earlier Python3 versions, the cell_contents attribute of a cell is read

only, but this limitation can be worked around by leveraging the Python 3

``nonlocal`` keyword.

In Python2 however, this attribute is read only, and there is no

``nonlocal`` keyword. For this reason, we need to come up with more

complicated hacks to set this attribute.

The chosen approach is to create a function with a STORE_DEREF opcode,

which sets the content of a closure variable. Typically:

>>> def inner(value):

... lambda: cell # the lambda makes cell a closure

... cell = value # cell is a closure, so this triggers a STORE_DEREF

(Note that in Python2, A STORE_DEREF can never be triggered from an inner

function. The function g for example here

>>> def f(var):

... def g():

... var += 1

... return g

will not modify the closure variable ``var```inplace, but instead try to

load a local variable var and increment it. As g does not assign the local

variable ``var`` any initial value, calling f(1)() will fail at runtime.)

Our objective is to set the value of a given cell ``cell``. So we need to

somewhat reference our ``cell`` object into the ``inner`` function so that

this object (and not the smoke cell of the lambda function) gets affected

by the STORE_DEREF operation.

In inner, ``cell`` is referenced as a cell variable (an enclosing variable

that is referenced by the inner function). If we create a new function

cell_set with the exact same code as ``inner``, but with ``cell`` marked as

a free variable instead, the STORE_DEREF will be applied on its closure -

``cell``, which we can specify explicitly during construction! The new

cell_set variable thus actually sets the contents of a specified cell!

Note: we do not make use of the ``nonlocal`` keyword to set the contents of

a cell in early python3 versions to limit possible syntax errors in case

test and checker libraries decide to parse the whole file.

"""

if sys.version_info[:2] >= (3, 7): # pragma: no branch

cell.cell_contents = value

else:

_cell_set = types.FunctionType(

_cell_set_template_code, {}, '_cell_set', (), (cell,),)

_cell_set(value)

def _make_cell_set_template_code():

def _cell_set_factory(value):

368 ↛ exitline 368 didn't run the lambda on line 368 lambda: cell

cell = value

co = _cell_set_factory.__code__

_cell_set_template_code = types.CodeType(

co.co_argcount,

co.co_kwonlyargcount, # Python 3 only argument

co.co_nlocals,

co.co_stacksize,

co.co_flags,

co.co_code,

co.co_consts,

co.co_names,

co.co_varnames,

co.co_filename,

co.co_name,

co.co_firstlineno,

co.co_lnotab,

co.co_cellvars, # co_freevars is initialized with co_cellvars

(), # co_cellvars is made empty

)

return _cell_set_template_code

393 ↛ 397line 393 didn't jump to line 397, because the condition on line 393 was never falseif sys.version_info[:2] < (3, 7):

_cell_set_template_code = _make_cell_set_template_code()

# 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 (opcode, argument number) tuples 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

def _extract_class_dict(cls):

"""Retrieve a copy of the dict of a class without the inherited methods"""

clsdict = dict(cls.__dict__) # copy dict proxy to a dict

434 ↛ 437line 434 didn't jump to line 437, because the condition on line 434 was never false 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

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.

# general typing Constructs

is_typing = getattr(obj, '__origin__', None) is not None

# typing_extensions.Literal

is_litteral = getattr(obj, '__values__', None) is not None

# typing_extensions.Final

is_final = getattr(obj, '__type__', None) is not None

# typing.Union/Tuple for old Python 3.5

is_union = getattr(obj, '__union_params__', None) is not None

is_tuple = getattr(obj, '__tuple_params__', None) is not None

is_callable = (

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))

def _create_parametrized_type_hint(origin, args):

return origin[args]

else:

_is_parametrized_type_hint = None

_create_parametrized_type_hint = None

def parametrized_type_hint_getinitargs(obj):

# 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

initargs = (Literal, obj.__values__)

elif type(obj) is type(Final): # pragma: no branch

initargs = (Final, obj.__type__)

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__)

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__)

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__)

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]

else:

(*args, result) = obj.__args__

if len(args) == 1 and args[0] is Ellipsis:

args = Ellipsis

else:

args = list(args)

initargs = (Callable, (args, result))

else: # pragma: no cover

raise pickle.PicklingError(

"Cloudpickle Error: Unknown type {}".format(type(obj))

)

return initargs

# Tornado support

def is_tornado_coroutine(func):

"""

Return whether *func* is a Tornado coroutine function.

Running coroutines are not supported.

"""

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 _rebuild_tornado_coroutine(func):

from tornado import gen

return gen.coroutine(func)

# including pickles unloading functions in this namespace

load = pickle.load

loads = pickle.loads

# hack for __import__ not working as desired

def subimport(name):

__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 _gen_ellipsis():

return Ellipsis

def _gen_not_implemented():

return NotImplemented

def _get_cell_contents(cell):

try:

return cell.cell_contents

except ValueError:

# sentinel used by ``_fill_function`` which will leave the cell empty

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(object):

"""sentinel for empty closures

"""

@classmethod

def __reduce__(cls):

return cls.__name__

def _fill_function(*args):

"""Fills in the rest of function data into the skeleton function object

The skeleton itself is create by _make_skel_func().

"""

if len(args) == 2:

func = args[0]

state = args[1]

elif len(args) == 5:

# Backwards compat for cloudpickle v0.4.0, after which the `module`

# argument was introduced

func = args[0]

keys = ['globals', 'defaults', 'dict', 'closure_values']

state = dict(zip(keys, args[1:]))

elif len(args) == 6:

# Backwards compat for cloudpickle v0.4.1, after which the function

# state was passed as a dict to the _fill_function it-self.

func = args[0]

keys = ['globals', 'defaults', 'dict', 'module', 'closure_values']

state = dict(zip(keys, args[1:]))

else:

raise ValueError('Unexpected _fill_value arguments: %r' % (args,))

# - At pickling time, any dynamic global variable used by func is

# serialized by value (in state['globals']).

# - At unpickling time, func's __globals__ attribute is initialized by

# first retrieving an empty isolated namespace that will be shared

# with other functions pickled from the same original module

# by the same CloudPickler instance and then updated with the

# content of state['globals'] to populate the shared isolated

# namespace with all the global variables that are specifically

# referenced for this function.

func.__globals__.update(state['globals'])

func.__defaults__ = state['defaults']

func.__dict__ = state['dict']

if 'annotations' in state:

func.__annotations__ = state['annotations']

if 'doc' in state:

func.__doc__ = state['doc']

if 'name' in state:

func.__name__ = state['name']

if 'module' in state:

func.__module__ = state['module']

if 'qualname' in state:

func.__qualname__ = state['qualname']

if 'kwdefaults' in state:

func.__kwdefaults__ = state['kwdefaults']

# _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)

if '_cloudpickle_submodules' in state:

state.pop('_cloudpickle_submodules')

cells = func.__closure__

if cells is not None:

for cell, value in zip(cells, state['closure_values']):

if value is not _empty_cell_value:

cell_set(cell, value)

return func

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')

685 ↛ exitline 685 didn't run the lambda on line 685 return (lambda: cell).__closure__[0]

def _make_cell(value=_empty_cell_value):

cell = _make_empty_cell()

690 ↛ 692line 690 didn't jump to line 692, because the condition on line 690 was never false if value is not _empty_cell_value:

cell_set(cell, value)

return cell

def _make_skel_func(code, cell_count, base_globals=None):

""" Creates a skeleton function object that contains just the provided

code and the correct number of cells in func_closure. All other

func attributes (e.g. func_globals) are empty.

"""

# 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 "

"is currently being loaded. This is not supported by cloudpickle and "

"will break in cloudpickle 1.7", category=UserWarning

)

# This is backward-compatibility code: for cloudpickle versions between

# 0.5.4 and 0.7, base_globals could be a string or None. base_globals

# should now always be a dictionary.

if base_globals is None or isinstance(base_globals, str):

base_globals = {}

base_globals['__builtins__'] = __builtins__

closure = (

tuple(_make_empty_cell() for _ in range(cell_count))

if cell_count >= 0 else

None

)

return types.FunctionType(code, base_globals, None, None, closure)

def _make_skeleton_class(type_constructor, name, bases, type_kwargs,

class_tracker_id, extra):

"""Build dynamic class with an empty __dict__ to be filled once memoized

If class_tracker_id is not None, try to lookup an existing class definition

matching that id. If none is found, track a newly reconstructed class

definition under that id so that other instances stemming from the same

class id will also reuse this class definition.

The "extra" variable is meant to be a dict (or None) that can be used for

forward compatibility shall the need arise.

"""

skeleton_class = types.new_class(

name, bases, {'metaclass': type_constructor},

lambda ns: ns.update(type_kwargs)

)

return _lookup_class_or_track(class_tracker_id, skeleton_class)

def _rehydrate_skeleton_class(skeleton_class, class_dict):

"""Put attributes from `class_dict` back on `skeleton_class`.

See CloudPickler.save_dynamic_class for more info.

"""

registry = None

for attrname, attr in class_dict.items():

if attrname == "_abc_impl":

registry = attr

else:

setattr(skeleton_class, attrname, attr)

if registry is not None:

for subclass in registry:

skeleton_class.register(subclass)

return 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

)

if class_tracker_id is not None:

return _lookup_class_or_track(class_tracker_id, tv)

else: # pragma: nocover

# Only for Python 3.5.3 compat.

return tv

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,

)

def _typevar_reduce(obj):

# TypeVar instances have no __qualname__ hence we pass the name explicitly.

module_and_name = _lookup_module_and_qualname(obj, name=obj.__name__)

if module_and_name is None:

return (_make_typevar, _decompose_typevar(obj))

return (getattr, module_and_name)

def _get_bases(typ):

824 ↛ 826line 824 didn't jump to line 826, because the condition on line 824 was never true if hasattr(typ, '__orig_bases__'):

# For generic types (see PEP 560)

bases_attr = '__orig_bases__'

else:

# For regular class objects

bases_attr = '__bases__'

return getattr(typ, bases_attr)

def _make_dict_keys(obj):

return dict.fromkeys(obj).keys()

def _make_dict_values(obj):

return {i: _ for i, _ in enumerate(obj)}.values()

def _make_dict_items(obj):

return obj.items()

Coverage for pyspark/cloudpickle/cloudpickle.py : 56%

334 statements 201 run 133 missing 24 excluded 15 partial