text: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # 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.
  # ==============================================================================
  
  """Script Language Operators. See the @{$python/script_ops} guide.
  
  @@py_func
  """
  
  # pylint: disable=g-bad-name
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import threading
  
  import numpy as np
  import six
  
  from tensorflow.python import pywrap_tensorflow
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_script_ops
  
  
  class FuncRegistry(object):
    """A helper class to keep track of registered py functions.
  
    FuncRegistry keeps a map from unique tokens (string) to python
    functions, which takes numpy arrays and outputs numpy arrays.
    """
  
    def __init__(self):
      self._lock = threading.Lock()
      self._unique_id = 0  # GUARDED_BY(self._lock)
      self._funcs = {}
  
    def insert(self, func):
      """Registers `func` and returns a unique token for this entry."""
      token = self._next_unique_token()
      self._funcs[token] = func
      return token
  
    def remove(self, token):
      """Removes the registered function corresponding to `token`."""
      self._funcs.pop(token, None)
  
    @staticmethod
    def _convert(value, dtype=None):
      """Converts an arg to numpy, avoiding dangerous string and unicode dtypes.
  
      Numpy pads with zeros when using string and unicode dtypes if different
      components of a tensor have different lengths.  This is bad: ignoring the
      padding is wrong for text data, and removing the padding is wrong for binary
      data.  To avoid this bug, we redo the conversion using an object dtype.
      Additionally, we convert unicode strings to (byte-)strings for Python3
      compatibility.
  
      Args:
        value: Value to convert to a numpy array.
        dtype: (Optional.) Desired NumPy type for the returned value.
  
      Returns:
        A numpy array.
      """
      result = np.asarray(value, dtype=dtype, order="C")
      if result.dtype.char == "S" and result is not value:
        return np.asarray(value, order="C", dtype=object)
      elif result.dtype.char == "U" and result is not value:
        value = np.vectorize(lambda x: x.encode())(value)
        return np.asarray(value, order="C", dtype=object)
      elif result.dtype.char == "U":
        return result.astype(np.bytes_)
      else:
        return result
  
    def __call__(self, token, args):
      """Calls the registered function for `token` with args."""
      func = self._funcs[token]
      if func is None:
        raise ValueError("callback %s is not found" % token)
      ret = func(*args)
      # Strings seem to lead to a memory leak here if they're not wrapped in a
      # list.
      if isinstance(ret, six.binary_type):
        ret = [ret]
      # Ensures that we return either a single numpy array or a list of numpy
      # arrays.
      if isinstance(ret, (tuple, list)):
        return [self._convert(x) for x in ret]
      else:
        return self._convert(ret)
  
    def size(self):
      """Returns how many functions are currently registered."""
      return len(self._funcs)
  
    def _next_unique_token(self):
      """Returns a unique token."""
      with self._lock:
        uid = self._unique_id
        self._unique_id += 1
      return "pyfunc_%d" % uid
  
  # Global registry for py functions.
  _py_funcs = FuncRegistry()
  
  pywrap_tensorflow.InitializePyTrampoline(_py_funcs)
  
  
  class CleanupFunc(object):
    """A helper class to remove a registered function from _py_funcs."""
  
    def __init__(self, token):
      self._token = token
  
    def __del__(self):
      _py_funcs.remove(self._token)
  
  
  def py_func(func, inp, Tout, stateful=True, name=None):
    """Wraps a python function and uses it as a TensorFlow op.
  
    Given a python function `func`, which takes numpy arrays as its
    inputs and returns numpy arrays as its outputs, wrap this function as an
    operation in a TensorFlow graph. The following snippet constructs a simple
    TensorFlow graph that invokes the `np.sinh()` NumPy function as a operation
    in the graph:
  
    ```python
    def my_func(x):
      # x will be a numpy array with the contents of the placeholder below
      return np.sinh(x)
    inp = tf.placeholder(tf.float32)
    y = tf.py_func(my_func, [inp], tf.float32)
    ```
  
    **N.B.** The `tf.py_func()` operation has the following known limitations:
  
    * The body of the function (i.e. `func`) will not be serialized in a
      `GraphDef`. Therefore, you should not use this function if you need to
      serialize your model and restore it in a different environment.
  
    * The operation must run in the same address space as the Python program
      that calls `tf.py_func()`. If you are using distributed TensorFlow, you
      must run a `tf.train.Server` in the same process as the program that calls
      `tf.py_func()` and you must pin the created operation to a device in that
      server (e.g. using `with tf.device():`).
  
    Args:
      func: A Python function, which accepts a list of NumPy `ndarray` objects
        having element types that match the corresponding `tf.Tensor` objects
        in `inp`, and returns a list of `ndarray` objects (or a single `ndarray`)
        having element types that match the corresponding values in `Tout`.
        Important Note: Input and output numpy `ndarray`s of `func` are not
        guaranteed to be copies. In some cases their underlying memory will be
        shared with the corresponding TensorFlow tensors.
        In-place modification or storing `func` input or return values in
        python datastructures without explicit (np.)copy
        can have non-deterministic consequences.
      inp: A list of `Tensor` objects.
      Tout: A list or tuple of tensorflow data types or a single tensorflow data
        type if there is only one, indicating what `func` returns.
      stateful: (Boolean.) If True, the function should be considered stateful.
        If a function is stateless, when given the same input it will return the
        same output and have no observable side effects. Optimizations such as
        common subexpression elimination are only performed on stateless
        operations.
      name: A name for the operation (optional).
  
    Returns:
      A list of `Tensor` or a single `Tensor` which `func` computes.
    """
    token = _py_funcs.insert(func)
    # We tie the registered function's life-time with the current
    # default graph. I.e., when the current graph is destroyed, we
    # should remove its py funcs.
    g = ops.get_default_graph()
  
    # pylint: disable=protected-access
    while isinstance(g, function._FuncGraph):
      # If the py_func was declared inside a _FuncGraph, its lifetime should be
      # bound to that of the outer graph instead.
      g = g._outer_graph
  
    cleanup = CleanupFunc(token)
  
    # TODO(zhifengc): Consider adding a Graph method to collect
    # `cleanup` objects in one of its member.
    if not hasattr(g, "_cleanup_py_funcs_used_in_graph"):
      g._cleanup_py_funcs_used_in_graph = []
  
    # When g is destroyed, elements in _cleanup_py_funcs_used_in_graph
    # will be destroyed and their __del__ will remove the 'token' from
    # the funcs registry.
    g._cleanup_py_funcs_used_in_graph.append(cleanup)
    # pylint: enable=protected-access
  
    if isinstance(Tout, (list, tuple)):
      is_list_or_tuple = True
    else:
      Tout = [Tout]
      is_list_or_tuple = False
    # pylint: disable=protected-access
    if stateful:
      result = gen_script_ops._py_func(
          input=inp, token=token, Tout=Tout, name=name)
    else:
      result = gen_script_ops._py_func_stateless(
          input=inp, token=token, Tout=Tout, name=name)
    # pylint: enable=protected-access
    return result if is_list_or_tuple else result[0]
  
  
  ops.NotDifferentiable("PyFunc")
  ops.NotDifferentiable("PyFuncStateless")
  