Python pickle.TUPLE Examples
The following are 24
code examples of pickle.TUPLE().
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and go to the original project or source file by following the links above each example.
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Example #1
| Source File: pickletester.py From ironpython2 with Apache License 2.0 | 6 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assertEqual(x, y, (proto, x, s, y))
expected = expected_opcode[proto, len(x)]
self.assertEqual(opcode_in_pickle(expected, s), True)
Example #2
| Source File: pickletester.py From ironpython2 with Apache License 2.0 | 6 votes |
|
def test_bad_stack(self):
badpickles = [
'.', # STOP
'0', # POP
'1', # POP_MARK
'2', # DUP
# '(2', # PyUnpickler doesn't raise
'R', # REDUCE
')R',
'a', # APPEND
'Na',
'b', # BUILD
'Nb',
'd', # DICT
'e', # APPENDS
# '(e', # PyUnpickler raises AttributeError
'i__builtin__\nlist\n', # INST
'l', # LIST
'o', # OBJ
'(o',
'p1\n', # PUT
'q\x00', # BINPUT
'r\x00\x00\x00\x00', # LONG_BINPUT
's', # SETITEM
'Ns',
'NNs',
't', # TUPLE
'u', # SETITEMS
# '(u', # PyUnpickler doesn't raise
'}(Nu',
'\x81', # NEWOBJ
')\x81',
'\x85', # TUPLE1
'\x86', # TUPLE2
'N\x86',
'\x87', # TUPLE3
'N\x87',
'NN\x87',
]
for p in badpickles:
self.check_unpickling_error(self.bad_stack_errors, p)
Example #3
| Source File: pickletester.py From CTFCrackTools with GNU General Public License v3.0 | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assertEqual(x, y, (proto, x, s, y))
expected = expected_opcode[proto, len(x)]
self.assertEqual(opcode_in_pickle(expected, s), True)
Example #4
| Source File: pickletester.py From CTFCrackTools-V2 with GNU General Public License v3.0 | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assertEqual(x, y, (proto, x, s, y))
expected = expected_opcode[proto, len(x)]
self.assertEqual(opcode_in_pickle(expected, s), True)
Example #5
| Source File: pickletester.py From medicare-demo with Apache License 2.0 | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assertEqual(x, y, (proto, x, s, y))
expected = expected_opcode[proto, len(x)]
self.assertEqual(opcode_in_pickle(expected, s), True)
Example #6
| Source File: pickletester.py From Project-New-Reign---Nemesis-Main with GNU General Public License v3.0 | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
(3, 0): pickle.EMPTY_TUPLE,
(3, 1): pickle.TUPLE1,
(3, 2): pickle.TUPLE2,
(3, 3): pickle.TUPLE3,
(3, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assert_is_copy(x, y)
expected = expected_opcode[min(proto, 3), len(x)]
self.assertTrue(opcode_in_pickle(expected, s))
Example #7
| Source File: pickletester.py From gcblue with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assertEqual(x, y, (proto, x, s, y))
expected = expected_opcode[proto, len(x)]
self.assertEqual(opcode_in_pickle(expected, s), True)
Example #8
| Source File: pickletester.py From ironpython3 with Apache License 2.0 | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
(3, 0): pickle.EMPTY_TUPLE,
(3, 1): pickle.TUPLE1,
(3, 2): pickle.TUPLE2,
(3, 3): pickle.TUPLE3,
(3, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assert_is_copy(x, y)
expected = expected_opcode[min(proto, 3), len(x)]
self.assertTrue(opcode_in_pickle(expected, s))
Example #9
| Source File: pickletester.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
(3, 0): pickle.EMPTY_TUPLE,
(3, 1): pickle.TUPLE1,
(3, 2): pickle.TUPLE2,
(3, 3): pickle.TUPLE3,
(3, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assert_is_copy(x, y)
expected = expected_opcode[min(proto, 3), len(x)]
self.assertTrue(opcode_in_pickle(expected, s))
Example #10
| Source File: pickletester.py From oss-ftp with MIT License | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assertEqual(x, y, (proto, x, s, y))
expected = expected_opcode[proto, len(x)]
self.assertEqual(opcode_in_pickle(expected, s), True)
Example #11
| Source File: pickletester.py From BinderFilter with MIT License | 5 votes |
|
def test_short_tuples(self):
# Map (proto, len(tuple)) to expected opcode.
expected_opcode = {(0, 0): pickle.TUPLE,
(0, 1): pickle.TUPLE,
(0, 2): pickle.TUPLE,
(0, 3): pickle.TUPLE,
(0, 4): pickle.TUPLE,
(1, 0): pickle.EMPTY_TUPLE,
(1, 1): pickle.TUPLE,
(1, 2): pickle.TUPLE,
(1, 3): pickle.TUPLE,
(1, 4): pickle.TUPLE,
(2, 0): pickle.EMPTY_TUPLE,
(2, 1): pickle.TUPLE1,
(2, 2): pickle.TUPLE2,
(2, 3): pickle.TUPLE3,
(2, 4): pickle.TUPLE,
}
a = ()
b = (1,)
c = (1, 2)
d = (1, 2, 3)
e = (1, 2, 3, 4)
for proto in protocols:
for x in a, b, c, d, e:
s = self.dumps(x, proto)
y = self.loads(s)
self.assertEqual(x, y, (proto, x, s, y))
expected = expected_opcode[proto, len(x)]
self.assertEqual(opcode_in_pickle(expected, s), True)
Example #12
| Source File: cloudpickle.py From LearningApacheSpark with MIT License | 4 votes |
|
def save_dynamic_class(self, obj):
"""
Save a class that can't be stored as module global.
This method is used to serialize classes that are defined inside
functions, or that otherwise can't be serialized as attribute lookups
from global modules.
"""
clsdict = dict(obj.__dict__) # copy dict proxy to a dict
clsdict.pop('__weakref__', None)
# On PyPy, __doc__ is a readonly attribute, so we need to include it in
# the initial skeleton class. This is safe because we know that the
# doc can't participate in a cycle with the original class.
type_kwargs = {'__doc__': clsdict.pop('__doc__', None)}
# If type overrides __dict__ as a property, include it in the type kwargs.
# In Python 2, we can't set this attribute after construction.
__dict__ = clsdict.pop('__dict__', None)
if isinstance(__dict__, property):
type_kwargs['__dict__'] = __dict__
save = self.save
write = self.write
# We write pickle instructions explicitly here to handle the
# possibility that the type object participates in a cycle with its own
# __dict__. We first write an empty "skeleton" version of the class and
# memoize it before writing the class' __dict__ itself. We then write
# instructions to "rehydrate" the skeleton class by restoring the
# attributes from the __dict__.
#
# A type can appear in a cycle with its __dict__ if an instance of the
# type appears in the type's __dict__ (which happens for the stdlib
# Enum class), or if the type defines methods that close over the name
# of the type, (which is common for Python 2-style super() calls).
# Push the rehydration function.
save(_rehydrate_skeleton_class)
# Mark the start of the args tuple for the rehydration function.
write(pickle.MARK)
# Create and memoize an skeleton class with obj's name and bases.
tp = type(obj)
self.save_reduce(tp, (obj.__name__, obj.__bases__, type_kwargs), obj=obj)
# Now save the rest of obj's __dict__. Any references to obj
# encountered while saving will point to the skeleton class.
save(clsdict)
# Write a tuple of (skeleton_class, clsdict).
write(pickle.TUPLE)
# Call _rehydrate_skeleton_class(skeleton_class, clsdict)
write(pickle.REDUCE)
Example #13
| Source File: cloudpickle.py From LearningApacheSpark with MIT License | 4 votes |
|
def save_function_tuple(self, func):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
if is_tornado_coroutine(func):
self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),
obj=func)
return
save = self.save
write = self.write
code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
self._save_subimports(
code,
itertools.chain(f_globals.values(), closure_values or ()),
)
# create a skeleton function object and memoize it
save(_make_skel_func)
save((
code,
len(closure_values) if closure_values is not None else -1,
base_globals,
))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
state = {
'globals': f_globals,
'defaults': defaults,
'dict': dct,
'module': func.__module__,
'closure_values': closure_values,
}
if hasattr(func, '__qualname__'):
state['qualname'] = func.__qualname__
save(state)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
Example #14
| Source File: pickletester.py From ironpython3 with Apache License 2.0 | 4 votes |
|
def test_bad_stack(self):
badpickles = [
b'.', # STOP
b'0', # POP
b'1', # POP_MARK
b'2', # DUP
# b'(2', # PyUnpickler doesn't raise
b'R', # REDUCE
b')R',
b'a', # APPEND
b'Na',
b'b', # BUILD
b'Nb',
b'd', # DICT
b'e', # APPENDS
# b'(e', # PyUnpickler raises AttributeError
b'ibuiltins\nlist\n', # INST
b'l', # LIST
b'o', # OBJ
b'(o',
b'p1\n', # PUT
b'q\x00', # BINPUT
b'r\x00\x00\x00\x00', # LONG_BINPUT
b's', # SETITEM
b'Ns',
b'NNs',
b't', # TUPLE
b'u', # SETITEMS
# b'(u', # PyUnpickler doesn't raise
b'}(Nu',
b'\x81', # NEWOBJ
b')\x81',
b'\x85', # TUPLE1
b'\x86', # TUPLE2
b'N\x86',
b'\x87', # TUPLE3
b'N\x87',
b'NN\x87',
b'\x90', # ADDITEMS
# b'(\x90', # PyUnpickler raises AttributeError
b'\x91', # FROZENSET
b'\x92', # NEWOBJ_EX
b')}\x92',
b'\x93', # STACK_GLOBAL
b'Vlist\n\x93',
b'\x94', # MEMOIZE
]
for p in badpickles:
self.check_unpickling_error(self.bad_stack_errors, p)
Example #15
| Source File: cloudpickle.py From FATE with Apache License 2.0 | 4 votes |
|
def save_function_tuple(self, func):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
if is_tornado_coroutine(func):
self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),
obj=func)
return
save = self.save
write = self.write
code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
self._save_subimports(
code,
itertools.chain(f_globals.values(), closure_values or ()),
)
# create a skeleton function object and memoize it
save(_make_skel_func)
save((
code,
len(closure_values) if closure_values is not None else -1,
base_globals,
))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
state = {
'globals': f_globals,
'defaults': defaults,
'dict': dct,
'module': func.__module__,
'closure_values': closure_values,
}
if hasattr(func, '__qualname__'):
state['qualname'] = func.__qualname__
save(state)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
Example #16
| Source File: cloudpickle.py From BentoML with Apache License 2.0 | 4 votes |
|
def save_function_tuple(self, func):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
if is_tornado_coroutine(func):
self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),
obj=func)
return
save = self.save
write = self.write
code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
self._save_subimports(
code,
itertools.chain(f_globals.values(), closure_values or ()),
)
# create a skeleton function object and memoize it
save(_make_skel_func)
save((
code,
len(closure_values) if closure_values is not None else -1,
base_globals,
))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
state = {
'globals': f_globals,
'defaults': defaults,
'dict': dct,
'closure_values': closure_values,
'module': func.__module__,
'name': func.__name__,
'doc': func.__doc__,
}
if hasattr(func, '__annotations__') and sys.version_info >= (3, 7):
state['annotations'] = func.__annotations__
if hasattr(func, '__qualname__'):
state['qualname'] = func.__qualname__
if hasattr(func, '__kwdefaults__'):
state['kwdefaults'] = func.__kwdefaults__
save(state)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
Example #17
| Source File: cloudpickle.py From FATE with Apache License 2.0 | 4 votes |
|
def save_dynamic_class(self, obj):
"""
Save a class that can't be stored as module global.
This method is used to serialize classes that are defined inside
functions, or that otherwise can't be serialized as attribute lookups
from global modules.
"""
clsdict = dict(obj.__dict__) # copy dict proxy to a dict
clsdict.pop('__weakref__', None)
# On PyPy, __doc__ is a readonly attribute, so we need to include it in
# the initial skeleton class. This is safe because we know that the
# doc can't participate in a cycle with the original class.
type_kwargs = {'__doc__': clsdict.pop('__doc__', None)}
# If type overrides __dict__ as a property, include it in the type kwargs.
# In Python 2, we can't set this attribute after construction.
__dict__ = clsdict.pop('__dict__', None)
if isinstance(__dict__, property):
type_kwargs['__dict__'] = __dict__
save = self.save
write = self.write
# We write pickle instructions explicitly here to handle the
# possibility that the type object participates in a cycle with its own
# __dict__. We first write an empty "skeleton" version of the class and
# memoize it before writing the class' __dict__ itself. We then write
# instructions to "rehydrate" the skeleton class by restoring the
# attributes from the __dict__.
#
# A type can appear in a cycle with its __dict__ if an instance of the
# type appears in the type's __dict__ (which happens for the stdlib
# Enum class), or if the type defines methods that close over the name
# of the type, (which is utils for Python 2-style super() calls).
# Push the rehydration function.
save(_rehydrate_skeleton_class)
# Mark the start of the args tuple for the rehydration function.
write(pickle.MARK)
# Create and memoize an skeleton class with obj's name and bases.
tp = type(obj)
self.save_reduce(tp, (obj.__name__, obj.__bases__, type_kwargs), obj=obj)
# Now save the rest of obj's __dict__. Any references to obj
# encountered while saving will point to the skeleton class.
save(clsdict)
# Write a tuple of (skeleton_class, clsdict).
write(pickle.TUPLE)
# Call _rehydrate_skeleton_class(skeleton_class, clsdict)
write(pickle.REDUCE)
Example #18
| Source File: pickletester.py From Project-New-Reign---Nemesis-Main with GNU General Public License v3.0 | 4 votes |
|
def test_bad_stack(self):
badpickles = [
b'.', # STOP
b'0', # POP
b'1', # POP_MARK
b'2', # DUP
# b'(2', # PyUnpickler doesn't raise
b'R', # REDUCE
b')R',
b'a', # APPEND
b'Na',
b'b', # BUILD
b'Nb',
b'd', # DICT
b'e', # APPENDS
# b'(e', # PyUnpickler raises AttributeError
b'ibuiltins\nlist\n', # INST
b'l', # LIST
b'o', # OBJ
b'(o',
b'p1\n', # PUT
b'q\x00', # BINPUT
b'r\x00\x00\x00\x00', # LONG_BINPUT
b's', # SETITEM
b'Ns',
b'NNs',
b't', # TUPLE
b'u', # SETITEMS
# b'(u', # PyUnpickler doesn't raise
b'}(Nu',
b'\x81', # NEWOBJ
b')\x81',
b'\x85', # TUPLE1
b'\x86', # TUPLE2
b'N\x86',
b'\x87', # TUPLE3
b'N\x87',
b'NN\x87',
b'\x90', # ADDITEMS
# b'(\x90', # PyUnpickler raises AttributeError
b'\x91', # FROZENSET
b'\x92', # NEWOBJ_EX
b')}\x92',
b'\x93', # STACK_GLOBAL
b'Vlist\n\x93',
b'\x94', # MEMOIZE
]
for p in badpickles:
self.check_unpickling_error(self.bad_stack_errors, p)
Example #19
| Source File: cloudpickle.py From FATE with Apache License 2.0 | 4 votes |
|
def save_function_tuple(self, func):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
if is_tornado_coroutine(func):
self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),
obj=func)
return
save = self.save
write = self.write
code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
self._save_subimports(
code,
itertools.chain(f_globals.values(), closure_values or ()),
)
# create a skeleton function object and memoize it
save(_make_skel_func)
save((
code,
len(closure_values) if closure_values is not None else -1,
base_globals,
))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
state = {
'globals': f_globals,
'defaults': defaults,
'dict': dct,
'module': func.__module__,
'closure_values': closure_values,
}
if hasattr(func, '__qualname__'):
state['qualname'] = func.__qualname__
save(state)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
Example #20
| Source File: cloudpickle.py From FATE with Apache License 2.0 | 4 votes |
|
def save_dynamic_class(self, obj):
"""
Save a class that can't be stored as module global.
This method is used to serialize classes that are defined inside
functions, or that otherwise can't be serialized as attribute lookups
from global modules.
"""
clsdict = dict(obj.__dict__) # copy dict proxy to a dict
clsdict.pop('__weakref__', None)
# On PyPy, __doc__ is a readonly attribute, so we need to include it in
# the initial skeleton class. This is safe because we know that the
# doc can't participate in a cycle with the original class.
type_kwargs = {'__doc__': clsdict.pop('__doc__', None)}
# If type overrides __dict__ as a property, include it in the type kwargs.
# In Python 2, we can't set this attribute after construction.
__dict__ = clsdict.pop('__dict__', None)
if isinstance(__dict__, property):
type_kwargs['__dict__'] = __dict__
save = self.save
write = self.write
# We write pickle instructions explicitly here to handle the
# possibility that the type object participates in a cycle with its own
# __dict__. We first write an empty "skeleton" version of the class and
# memoize it before writing the class' __dict__ itself. We then write
# instructions to "rehydrate" the skeleton class by restoring the
# attributes from the __dict__.
#
# A type can appear in a cycle with its __dict__ if an instance of the
# type appears in the type's __dict__ (which happens for the stdlib
# Enum class), or if the type defines methods that close over the name
# of the type, (which is utils for Python 2-style super() calls).
# Push the rehydration function.
save(_rehydrate_skeleton_class)
# Mark the start of the args tuple for the rehydration function.
write(pickle.MARK)
# Create and memoize an skeleton class with obj's name and bases.
tp = type(obj)
self.save_reduce(tp, (obj.__name__, obj.__bases__, type_kwargs), obj=obj)
# Now save the rest of obj's __dict__. Any references to obj
# encountered while saving will point to the skeleton class.
save(clsdict)
# Write a tuple of (skeleton_class, clsdict).
write(pickle.TUPLE)
# Call _rehydrate_skeleton_class(skeleton_class, clsdict)
write(pickle.REDUCE)
Example #21
| Source File: cloudpickle.py From spark-cluster-deployment with Apache License 2.0 | 4 votes |
|
def save_function_tuple(self, func, forced_imports):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
save = self.save
write = self.write
# save the modules (if any)
if forced_imports:
write(pickle.MARK)
save(_modules_to_main)
#print 'forced imports are', forced_imports
forced_names = map(lambda m: m.__name__, forced_imports)
save((forced_names,))
#save((forced_imports,))
write(pickle.REDUCE)
write(pickle.POP_MARK)
code, f_globals, defaults, closure, dct, base_globals = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
# create a skeleton function object and memoize it
save(_make_skel_func)
save((code, len(closure), base_globals))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
save(f_globals)
save(defaults)
save(closure)
save(dct)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
Example #22
| Source File: cloudpickle.py From pywren with Apache License 2.0 | 4 votes |
|
def save_function_tuple(self, func):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
if is_tornado_coroutine(func):
self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),
obj=func)
return
save = self.save
write = self.write
code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
self._save_subimports(
code,
itertools.chain(f_globals.values(), closure_values or ()),
)
# create a skeleton function object and memoize it
save(_make_skel_func)
save((
code,
len(closure_values) if closure_values is not None else -1,
base_globals,
))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
save(f_globals)
save(defaults)
save(dct)
save(func.__module__)
save(closure_values)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
Example #23
| Source File: cloudpickle.py From eggroll with Apache License 2.0 | 4 votes |
|
def save_dynamic_class(self, obj):
"""
Save a class that can't be stored as module global.
This method is used to serialize classes that are defined inside
functions, or that otherwise can't be serialized as attribute lookups
from global modules.
"""
clsdict = dict(obj.__dict__) # copy dict proxy to a dict
clsdict.pop('__weakref__', None)
# On PyPy, __doc__ is a readonly attribute, so we need to include it in
# the initial skeleton class. This is safe because we know that the
# doc can't participate in a cycle with the original class.
type_kwargs = {'__doc__': clsdict.pop('__doc__', None)}
# If type overrides __dict__ as a property, include it in the type kwargs.
# In Python 2, we can't set this attribute after construction.
__dict__ = clsdict.pop('__dict__', None)
if isinstance(__dict__, property):
type_kwargs['__dict__'] = __dict__
save = self.save
write = self.write
# We write pickle instructions explicitly here to handle the
# possibility that the type object participates in a cycle with its own
# __dict__. We first write an empty "skeleton" version of the class and
# memoize it before writing the class' __dict__ itself. We then write
# instructions to "rehydrate" the skeleton class by restoring the
# attributes from the __dict__.
#
# A type can appear in a cycle with its __dict__ if an instance of the
# type appears in the type's __dict__ (which happens for the stdlib
# Enum class), or if the type defines methods that close over the name
# of the type, (which is utils for Python 2-style super() calls).
# Push the rehydration function.
save(_rehydrate_skeleton_class)
# Mark the start of the args tuple for the rehydration function.
write(pickle.MARK)
# Create and memoize an skeleton class with obj's name and bases.
tp = type(obj)
self.save_reduce(tp, (obj.__name__, obj.__bases__, type_kwargs), obj=obj)
# Now save the rest of obj's __dict__. Any references to obj
# encountered while saving will point to the skeleton class.
save(clsdict)
# Write a tuple of (skeleton_class, clsdict).
write(pickle.TUPLE)
# Call _rehydrate_skeleton_class(skeleton_class, clsdict)
write(pickle.REDUCE)
Example #24
| Source File: cloudpickle.py From eggroll with Apache License 2.0 | 4 votes |
|
def save_function_tuple(self, func):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
if is_tornado_coroutine(func):
self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),
obj=func)
return
save = self.save
write = self.write
code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(
func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
self._save_subimports(
code,
itertools.chain(f_globals.values(), closure_values or ()),
)
# create a skeleton function object and memoize it
save(_make_skel_func)
save((
code,
len(closure_values) if closure_values is not None else -1,
base_globals,
))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
state = {
'globals': f_globals,
'defaults': defaults,
'dict': dct,
'module': func.__module__,
'closure_values': closure_values,
}
if hasattr(func, '__qualname__'):
state['qualname'] = func.__qualname__
save(state)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
