Python pickle.TUPLE() Examples
The following are code examples for showing how to use pickle.TUPLE(). They are from open source Python projects. You can vote up the examples you like or vote down the ones you don't like.
Example 1
| Project: pyblish-win Author: pyblish File: pickletester.py GNU Lesser 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 2
| Project: NiujiaoDebugger Author: MrSrc File: pickletester.py 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 3
| Project: ironpython2 Author: IronLanguages File: pickletester.py Apache License 2.0 | 5 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 4
| Project: ironpython2 Author: IronLanguages File: pickletester.py 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 5
| Project: cqp-sdk-for-py37-native Author: crud-boy File: pickletester.py GNU General Public License v2.0 | 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 6
| Project: oss-ftp Author: aliyun File: pickletester.py 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 7
| Project: Hot Author: dsolimando File: pickletester.py 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 8
| Project: Project-New-Reign---Nemesis-Main Author: ShikyoKira File: pickletester.py 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 9
| Project: cells Author: AlesTsurko File: pickletester.py 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,
(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
| Project: NiujiaoDebugger Author: MrSrc File: pickletester.py 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',
b'R', # REDUCE
b')R',
b'a', # APPEND
b'Na',
b'b', # BUILD
b'Nb',
b'd', # DICT
b'e', # APPENDS
b'(e',
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',
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',
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 11
| Project: cqp-sdk-for-py37-native Author: crud-boy File: pickletester.py GNU General Public License v2.0 | 4 votes |
|
def test_bad_stack(self):
badpickles = [
b'.', # STOP
b'0', # POP
b'1', # POP_MARK
b'2', # DUP
b'(2',
b'R', # REDUCE
b')R',
b'a', # APPEND
b'Na',
b'b', # BUILD
b'Nb',
b'd', # DICT
b'e', # APPENDS
b'(e',
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',
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',
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 12
| Project: FATE Author: FederatedAI File: cloudpickle.py 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 13
| Project: FATE Author: FederatedAI File: cloudpickle.py 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 14
| Project: LearningApacheSpark Author: runawayhorse001 File: cloudpickle.py 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 15
| Project: LearningApacheSpark Author: runawayhorse001 File: cloudpickle.py 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 16
| Project: cloudpickle-generators Author: llllllllll File: __init__.py BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def _save_generator_impl(self, frame, gen, filler):
if frame is None:
# frame is None when the generator is fully consumed; take a fast path
self.save_reduce(
_restore_spent_generator,
(gen.__name__, getattr(gen, '__qualname__', None)),
obj=gen,
)
return
f_locals = frame.f_locals
f_code = frame.f_code
# Create a copy of generator function without the closure to serve as a box
# to serialize the code, globals, name, and closure. Cloudpickle already
# handles things like closures and complicated globals so just rely on
# cloudpickle to serialize this function.
gen_func = FunctionType(
f_code,
frame.f_globals,
gen.__name__,
(),
(_empty_cell(),) * len(f_code.co_freevars),
)
try:
gen_func.__qualname__ = gen.__qualname__
except AttributeError:
# there is no __qualname__ on generators in Python < 3.5
pass
save = self.save
write = self.write
# push a function onto the stack to fill up our skeleton generator
# or coroutine
save(filler)
# the start of the tuple to pass to ``_fill_generator`` (or
# ``_fill_coroutine``, ``_fill_async_generator``)
write(pickle.MARK)
save(_create_skeleton_generator)
save((gen_func,))
write(pickle.REDUCE)
self.memoize(gen)
# push the rest of the arguments to ``_fill_generator`` (or
# ``_fill_coroutine``, ``_fill_async_generator``)
save(frame.f_lasti)
save(f_locals)
save(private_frame_data(frame))
# call ``_fill_generator`` (or ``_fill_coroutine``,
# _fill_async_generator``)
write(pickle.TUPLE)
write(pickle.REDUCE)
Example 17
| Project: mmappickle Author: UniNE-CHYN File: numpy.py GNU Lesser General Public License v3.0 | 4 votes |
|
def read(self, offset, length):
self._file.seek(offset)
assert self._file.read(len(self._header)) == self._header
datatype = self._file.read(1)
if datatype == pickle.SHORT_BINBYTES:
datalength = struct.unpack('<B', self._file.read(1))[0]
elif datatype == pickle.BINBYTES:
datalength = struct.unpack('<I', self._file.read(4))[0]
elif datatype == pickle.BINBYTES8:
datalength = struct.unpack('<Q', self._file.read(8))[0]
else:
raise ValueError("Invalid data type")
datastart = self._file.tell()
# Move after data
self._file.seek(datalength, io.SEEK_CUR)
# Then we have the dtype string, which should be short
assert self._file.read(1) == pickle.SHORT_BINUNICODE
dtypelength = struct.unpack('<B', self._file.read(1))[0]
dtype = self._file.read(dtypelength).decode('utf8', 'surrogatepass')
# Skip TUPLE2 and REDUCE, to get to the shape tuple
self._file.seek(2, io.SEEK_CUR)
shapelist = []
while True:
shapeelementtype = self._file.read(1)
if shapeelementtype == pickle.BININT1:
shapeelement = struct.unpack('<B', self._file.read(1))[0]
elif shapeelementtype == pickle.BININT2:
shapeelement = struct.unpack('<H', self._file.read(2))[0]
elif shapeelementtype == pickle.BININT:
shapeelement = struct.unpack('<i', self._file.read(4))[0]
elif shapeelementtype in (pickle.TUPLE1, pickle.TUPLE2, pickle.TUPLE3, pickle.TUPLE):
# End of tuple
break
elif shapeelementtype == pickle.MARK:
continue # ignore mark
else:
assert False, "Invalid element type: 0x{:02x}".format(ord(shapeelementtype))
shapelist.append(shapeelement)
# Skip TUPLE2 and REDUCE
self._file.seek(2, io.SEEK_CUR)
length = self._file.tell() - offset
if self._file.writable():
return numpy.memmap(self._file, dtype=dtype, mode='r+', shape=tuple(shapelist), offset=datastart), length
else:
return numpy.memmap(self._file, dtype=dtype, mode='r', shape=tuple(shapelist), offset=datastart), length
Example 18
| Project: Project-New-Reign---Nemesis-Main Author: ShikyoKira File: pickletester.py 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
| Project: cells Author: AlesTsurko File: pickletester.py MIT License | 4 votes |
|
def test_bad_stack(self):
badpickles = [
b'.', # STOP
b'0', # POP
b'1', # POP_MARK
b'2', # DUP
b'(2',
b'R', # REDUCE
b')R',
b'a', # APPEND
b'Na',
b'b', # BUILD
b'Nb',
b'd', # DICT
b'e', # APPENDS
b'(e',
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',
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',
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 20
| Project: pywren Author: pywren File: cloudpickle.py 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
