text: # -*- coding: UTF-8 -*-
  # File: registry.py
  
  
  import tensorflow as tf
  from functools import wraps
  import six
  import re
  import copy
  
  from ..tfutils.argscope import get_arg_scope
  from ..tfutils.model_utils import get_shape_str
  from ..utils import logger
  
  # make sure each layer is only logged once
  _LAYER_LOGGED = set()
  _LAYER_REGISTRY = {}
  
  __all__ = ['layer_register']
  
  def _register(name, func):
      if name in _LAYER_REGISTRY:
          raise ValueError("Layer named {} is already registered!".format(name))
      if name in ['tf']:
          raise ValueError(logger.error("A layer cannot be named {}".format(name)))
  
      _LAYER_REGISTRY[name] = func
  
  
  def get_registered_layer(name):
      """
      Args:
          name (str): the name of the layer, e.g. 'Conv2D'
      Returns:
          the wrapped layer function, or None if not registered.
      """
      return _LAYER_REGISTRY.get(name, None)
  
  
  def disable_layer_logging():
      """
      Disable the shape logging for all layers from this moment on. Can be
      useful when creating multiple towers.
      """
      class ContainEverything:
          def __contains__(self, x):
              return True
      # can use nonlocal in python3, but how
      globals()['_LAYER_LOGGED'] = ContainEverything()
  
  
  def layer_register(
          log_shape=False,
          use_scope=True):
      """
      Args:
          log_shape (bool): log input/output shape of this layer
          use_scope (bool or None):
              Whether to call this layer with an extra first argument as scope.
              When set to None, it can be called either with or without
              the scope name argument.
              It will try to figure out by checking if the first argument
              is string or not.
  
      Returns:
          A decorator used to register a layer.
  
      Examples:
  
      .. code-block:: python
  
          @layer_register(use_scope=True)
          def add10(x):
              return x + tf.get_variable('W', shape=[10])
      """
  
      def wrapper(func):
          @wraps(func)
          def wrapped_func(*args, **kwargs):
              assert args[0] is not None, args
              if use_scope:
                  name, inputs = args[0], args[1]
                  args = args[1:]  # actual positional args used to call func
                  assert isinstance(name, six.string_types), "First argument for \"{}\" should be a string. ".format(
                      func.__name__) + "Did you forget to specify the name of the layer?"
              else:
                  assert not log_shape
                  if isinstance(args[0], six.string_types):
                      if use_scope is False:
                          logger.warn(
                              "Please call layer {} without the first scope name argument, "
                              "or register the layer with use_scope=None to allow "
                              "two calling methods.".format(func.__name__))
                      name, inputs = args[0], args[1]
                      args = args[1:]  # actual positional args used to call func
                  else:
                      inputs = args[0]
                      name = None
  
              if not (isinstance(inputs, (tf.Tensor, tf.Variable)) or
                      (isinstance(inputs, (list, tuple)) and
                          isinstance(inputs[0], (tf.Tensor, tf.Variable)))):
                  raise ValueError("Invalid inputs to layer: " + str(inputs))
  
              # use kwargs from current argument scope
              actual_args = copy.copy(get_arg_scope()[func.__name__])
              # explicit kwargs overwrite argscope
              actual_args.update(kwargs)
              # if six.PY3:
              #     # explicit positional args also override argscope. only work in PY3
              #     posargmap = inspect.signature(func).bind_partial(*args).arguments
              #     for k in six.iterkeys(posargmap):
              #         if k in actual_args:
              #             del actual_args[k]
  
              if name is not None:        # use scope
                  with tf.variable_scope(name) as scope:
                      # this name is only used to surpress logging, doesn't hurt to do some heuristics
                      scope_name = re.sub('tower[0-9]+/', '', scope.name)
                      do_log_shape = log_shape and scope_name not in _LAYER_LOGGED
                      if do_log_shape:
                          logger.info("{} input: {}".format(scope.name, get_shape_str(inputs)))
  
                      # run the actual function
                      outputs = func(*args, **actual_args)
  
                      if do_log_shape:
                          # log shape info and add activation
                          logger.info("{} output: {}".format(
                              scope.name, get_shape_str(outputs)))
                          _LAYER_LOGGED.add(scope_name)
              else:
                  # run the actual function
                  outputs = func(*args, **actual_args)
              return outputs
  
          wrapped_func.symbolic_function = func   # attribute to access the underlying function object
          wrapped_func.use_scope = use_scope
          _register(func.__name__, wrapped_func)
          return wrapped_func
  
      return wrapper
  