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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""Base TFDecorator class and utility functions for working with decorators.

There are two ways to create decorators that TensorFlow can introspect into.

This is important for documentation generation purposes, so that function

signatures aren't obscured by the (*args, **kwds) signature that decorators

often provide.

1. Call `tf_decorator.make_decorator` on your wrapper function. If your

decorator is stateless, or can capture all of the variables it needs to work

with through lexical closure, this is the simplest option. Create your wrapper

function as usual, but instead of returning it, return

`tf_decorator.make_decorator(target, your_wrapper)`. This will attach some

decorator introspection metadata onto your wrapper and return it.

Example:

def print_hello_before_calling(target):

def wrapper(*args, **kwargs):

print('hello')

return target(*args, **kwargs)

return tf_decorator.make_decorator(target, wrapper)

2. Derive from TFDecorator. If your decorator needs to be stateful, you can

implement it in terms of a TFDecorator. Store whatever state you need in your

derived class, and implement the `__call__` method to do your work before

calling into your target. You can retrieve the target via

`super(MyDecoratorClass, self).decorated_target`, and call it with whatever

parameters it needs.

Example:

class CallCounter(tf_decorator.TFDecorator):

def __init__(self, target):

super(CallCounter, self).__init__('count_calls', target)

self.call_count = 0

def __call__(self, *args, **kwargs):

self.call_count += 1

return super(CallCounter, self).decorated_target(*args, **kwargs)

def count_calls(target):

return CallCounter(target)

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import functools as _functools

import traceback as _traceback

def make_decorator(target,

decorator_func,

decorator_name=None,

decorator_doc='',

decorator_argspec=None):

"""Make a decorator from a wrapper and a target.

Args:

target: The final callable to be wrapped.

decorator_func: The wrapper function.

decorator_name: The name of the decorator. If `None`, the name of the

function calling make_decorator.

decorator_doc: Documentation specific to this application of

`decorator_func` to `target`.

decorator_argspec: The new callable signature of this decorator.

Returns:

The `decorator_func` argument with new metadata attached.

"""

if decorator_name is None:

frame = _traceback.extract_stack(limit=2)[0]

# frame name is tuple[2] in python2, and object.name in python3

decorator_name = getattr(frame, 'name', frame[2]) # Caller's name

decorator = TFDecorator(decorator_name, target, decorator_doc,

decorator_argspec)

setattr(decorator_func, '_tf_decorator', decorator)

# Objects that are callables (e.g., a functools.partial object) may not have

# the following attributes.

if hasattr(target, '__name__'):

decorator_func.__name__ = target.__name__

if hasattr(target, '__module__'):

decorator_func.__module__ = target.__module__

if hasattr(target, '__doc__'):

decorator_func.__doc__ = decorator.__doc__

decorator_func.__wrapped__ = target

return decorator_func

def unwrap(maybe_tf_decorator):

"""Unwraps an object into a list of TFDecorators and a final target.

Args:

maybe_tf_decorator: Any callable object.

Returns:

A tuple whose first element is an list of TFDecorator-derived objects that

were applied to the final callable target, and whose second element is the

final undecorated callable target. If the `maybe_tf_decorator` parameter is

not decorated by any TFDecorators, the first tuple element will be an empty

list. The `TFDecorator` list is ordered from outermost to innermost

decorators.

"""

decorators = []

cur = maybe_tf_decorator

while True:

if isinstance(cur, TFDecorator):

decorators.append(cur)

elif hasattr(cur, '_tf_decorator'):

decorators.append(getattr(cur, '_tf_decorator'))

else:

break

cur = decorators[-1].decorated_target

return decorators, cur

class TFDecorator(object):

"""Base class for all TensorFlow decorators.

TFDecorator captures and exposes the wrapped target, and provides details

about the current decorator.

"""

def __init__(self,

decorator_name,

target,

decorator_doc='',

decorator_argspec=None):

self._decorated_target = target

self._decorator_name = decorator_name

self._decorator_doc = decorator_doc

self._decorator_argspec = decorator_argspec

if hasattr(target, '__name__'):

self.__name__ = target.__name__

if self._decorator_doc:

self.__doc__ = self._decorator_doc

elif hasattr(target, '__doc__') and target.__doc__:

self.__doc__ = target.__doc__

else:

self.__doc__ = ''

def __get__(self, obj, objtype):

return _functools.partial(self.__call__, obj)

def __call__(self, *args, **kwargs):

return self._decorated_target(*args, **kwargs)

@property

def decorated_target(self):

return self._decorated_target

@property

def decorator_name(self):

return self._decorator_name

@property

def decorator_doc(self):

return self._decorator_doc

@property

def decorator_argspec(self):

return self._decorator_argspec

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