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Python pickle.REDUCE属性代码示例

本文整理汇总了Python中pickle.REDUCE属性的典型用法代码示例。如果您正苦于以下问题:Python pickle.REDUCE属性的具体用法?Python pickle.REDUCE怎么用?Python pickle.REDUCE使用的例子?那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在pickle的用法示例。


在下文中一共展示了pickle.REDUCE属性的13个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: base64unpickle

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
def base64unpickle(value):
    """
    Decodes value from Base64 to plain format and deserializes (with pickle) its content

    >>> base64unpickle('gAJVBmZvb2JhcnEALg==')
    'foobar'
    """

    retVal = None

    def _(self):
        if len(self.stack) > 1:
            func = self.stack[-2]
            if func not in PICKLE_REDUCE_WHITELIST:
                raise Exception, "abusing reduce() is bad, Mkay!"
        self.load_reduce()

    def loads(str):
        file = StringIO.StringIO(str)
        unpickler = pickle.Unpickler(file)
        unpickler.dispatch[pickle.REDUCE] = _
        return unpickler.load()

    try:
        retVal = loads(base64decode(value))
    except TypeError: 
        retVal = loads(base64decode(bytes(value)))

    return retVal 
开发者ID:ym2011,项目名称:POC-EXP,代码行数:31,代码来源:convert.py

示例2: save_exc_inst

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
def save_exc_inst(self, obj):
        if isinstance(obj, CallError):
            func, args = obj.__reduce__()
            self.save(func)
            self.save(args)
            self.write(py_pickle.REDUCE)
        else:
            py_pickle.Pickler.save_inst(self, obj) 
开发者ID:dw,项目名称:mitogen,代码行数:10,代码来源:core.py

示例3: base64unpickle

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
def base64unpickle(value, unsafe=False):
    """
    Decodes value from Base64 to plain format and deserializes (with pickle) its content

    >>> base64unpickle('gAJVBmZvb2JhcnEBLg==')
    'foobar'
    """

    retVal = None

    def _(self):
        if len(self.stack) > 1:
            func = self.stack[-2]
            if func not in PICKLE_REDUCE_WHITELIST:
                raise Exception("abusing reduce() is bad, Mkay!")
        self.load_reduce()

    def loads(str):
        f = StringIO.StringIO(str)
        if unsafe:
            unpickler = picklePy.Unpickler(f)
            unpickler.dispatch[picklePy.REDUCE] = _
        else:
            unpickler = pickle.Unpickler(f)
        return unpickler.load()

    try:
        retVal = loads(base64decode(value))
    except TypeError:
        retVal = loads(base64decode(bytes(value)))

    return retVal 
开发者ID:sabri-zaki,项目名称:EasY_HaCk,代码行数:34,代码来源:convert.py

示例4: save_dynamic_class

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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) 
开发者ID:FederatedAI,项目名称:FATE,代码行数:57,代码来源:cloudpickle.py

示例5: save_function_tuple

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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 
开发者ID:FederatedAI,项目名称:FATE,代码行数:54,代码来源:cloudpickle.py

示例6: save_dynamic_class

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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) 
开发者ID:runawayhorse001,项目名称:LearningApacheSpark,代码行数:58,代码来源:cloudpickle.py

示例7: save_function_tuple

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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 
开发者ID:runawayhorse001,项目名称:LearningApacheSpark,代码行数:55,代码来源:cloudpickle.py

示例8: save_function_tuple

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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 
开发者ID:bentoml,项目名称:BentoML,代码行数:61,代码来源:cloudpickle.py

示例9: save_function_tuple

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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 
开发者ID:adobe-research,项目名称:spark-cluster-deployment,代码行数:48,代码来源:cloudpickle.py

示例10: save_function_tuple

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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 
开发者ID:pywren,项目名称:pywren,代码行数:50,代码来源:cloudpickle.py

示例11: save_reduce

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
def save_reduce(self, func, args, state=None,
                    listitems=None, dictitems=None, obj=None):
        # Assert that args is a tuple or None
        if not isinstance(args, tuple):
            raise pickle.PicklingError("args from reduce() should be a tuple")

        # Assert that func is callable
        if not hasattr(func, '__call__'):
            raise pickle.PicklingError("func from reduce should be callable")

        save = self.save
        write = self.write

        # Protocol 2 special case: if func's name is __newobj__, use NEWOBJ
        if self.proto >= 2 and getattr(func, "__name__", "") == "__newobj__":
            cls = args[0]
            if not hasattr(cls, "__new__"):
                raise pickle.PicklingError(
                    "args[0] from __newobj__ args has no __new__")
            if obj is not None and cls is not obj.__class__:
                raise pickle.PicklingError(
                    "args[0] from __newobj__ args has the wrong class")
            args = args[1:]
            save(cls)

            save(args)
            write(pickle.NEWOBJ)
        else:
            save(func)
            save(args)
            write(pickle.REDUCE)

        if obj is not None:
            self.memoize(obj)

        # More new special cases (that work with older protocols as
        # well): when __reduce__ returns a tuple with 4 or 5 items,
        # the 4th and 5th item should be iterators that provide list
        # items and dict items (as (key, value) tuples), or None.

        if listitems is not None:
            self._batch_appends(listitems)

        if dictitems is not None:
            self._batch_setitems(dictitems)

        if state is not None:
            save(state)
            write(pickle.BUILD) 
开发者ID:pywren,项目名称:pywren,代码行数:51,代码来源:cloudpickle.py

示例12: save_dynamic_class

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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) 
开发者ID:WeBankFinTech,项目名称:eggroll,代码行数:57,代码来源:cloudpickle.py

示例13: save_function_tuple

# 需要导入模块: import pickle [as 别名]
# 或者: from pickle import REDUCE [as 别名]
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 
开发者ID:WeBankFinTech,项目名称:eggroll,代码行数:55,代码来源:cloudpickle.py


注:本文中的pickle.REDUCE属性示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。