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."""
  
  # pylint: disable=g-bad-name
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import threading
  
  # Used by py_util.cc to get tracebacks.
  import traceback  # pylint: disable=unused-import
  import weakref
  
  import numpy as np
  import six
  
  from tensorflow.python import pywrap_tensorflow
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_script_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.util import compat
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
  
  # Map from EagerPyFunc token to tuple (tape, eager args, eager outputs);
  # used for differentiation.
  tape_cache = {}
  
  
  class EagerFunc(object):
    """A wrapper for a function owned by an EagerPyFunc."""
  
    def __init__(self, func, Tout, is_grad_func):
      """Constructs an EagerFunc.
  
      Args:
        func: The function to wrap.
        Tout: A list of datatypes for the output; an empty list if the output is
              None.
        is_grad_func: Whether this EagerFunc is the gradient of another
          EagerPyFunc.
      """
      self._func = func
      self._out_dtypes = Tout
      self._is_grad_func = is_grad_func
  
    def _convert(self, value, dtype):
      """Converts `value` to a tensor of type `dtype`, with error checking.
  
      Args:
        value: The tensor to convert.
        dtype: The desired dtype.
  
      Returns:
        A tensor of type `dtype`, or a zeros tensor if value is None and
        this function is in fact a grdient function.
  
      Raises:
        RuntimeError: if `value` is a variable.
      """
  
      if isinstance(value, resource_variable_ops.ResourceVariable):
        raise RuntimeError(
            "Attempting to return a variable from an eagerly executed py_func. "
            "Only numeric data structures like Tensors or NumPy arrays should "
            "be returned; to return the value of a variable, make sure to obtain "
            "the Tensor backing it by calling `.read_value()` on the variable in "
            "question: %s" % value)
      if value is None and self._is_grad_func:
        # Gradient functions may legitimately return a list that contains
        # both Tensors and Python Nones. Unfortuantely this breaks the
        # OpKernel, so for now we replace None objects with zeros, which is
        # mathematically correct but will prevent short-circuiting gradient
        # computations.
        #
        # TODO(akshayka): Make it possible to return a list of both Tensors and
        # Nones from an EagerPyFunc.
        return constant_op.constant(0.0, dtype=dtype)
      return ops.convert_to_tensor(value, dtype=dtype)
  
    def __call__(self, device, token, args):
      """Passes `args` to `self._func`, which is executed eagerly."""
  
      with context.eager_mode(), backprop.GradientTape() as tape:
        for tensor in args:
          tape.watch(tensor)
        ret = self._func(*args)
        # Use tf.identity to copy the returned tensors to device if neccesary.
        with ops.device(device):
          if isinstance(ret, (tuple, list)):
            outputs = [
                array_ops.identity(self._convert(x, dtype=dtype))
                for (x, dtype) in zip(ret, self._out_dtypes)
            ]
          elif ret is None:
            outputs = None
          else:
            outputs = array_ops.identity(
                self._convert(ret, dtype=self._out_dtypes[0]))
      tape_cache[compat.as_bytes(token)] = (tape, args, outputs)
      return outputs
  
  
  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)
      # Only store weakrefs to the functions. The strong reference is stored in
      # the graph.
      self._funcs = weakref.WeakValueDictionary()
  
    def insert(self, func):
      """Registers `func` and returns a unique token for this entry."""
      token = self._next_unique_token()
      # Store a weakref to the function
      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
      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("utf8"))(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, device, args):
      """Calls the registered function for `token` with args.
  
      Args:
        token: A key into this `FuncRegistry` identifying which function to call.
        device: Name of the device on which outputs of `token`'s corresponding
          operation should be placed. Used iff the function registered for `token`
          is an EagerPyFunc.
        args: The arguments to pass to the function registered for `token`.
  
      Returns:
        The output of the function registered for `token`.
  
      Raises:
        ValueError: if no function is registered for `token`.
      """
      func = self._funcs.get(token, None)
      if func is None:
        raise ValueError("callback %s is not found" % token)
      if isinstance(func, EagerFunc):
        # NB: Different invocations of the same py_func will share the same
        # token, and the entries they stash in the tape_cache will collide.
        # In practice, when executing a graph, this should only happen if
        # the py_func is in a while_loop whose iterations are run in parallel
        # or if the graph is being driven by concurrent session.run() calls.
        #
        # TODO(akshayka): Key the tape cache in a thread-safe way.
        return func(device, token, args)
      else:
        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)
  
  
  def _internal_py_func(func,
                        inp,
                        Tout,
                        stateful=None,
                        eager=False,
                        is_grad_func=False,
                        name=None):
    """See documentation for py_func and eager_py_func."""
  
    is_list_or_tuple = False
    if isinstance(Tout, (list, tuple)):
      is_list_or_tuple = True
    else:
      Tout = [Tout]
  
    if eager:
      func = EagerFunc(func, Tout, is_grad_func)
  
    token = _py_funcs.insert(func)
    # We tie the registered function's lifetime with the current default graph,
    # i.e., when the current graph is destroyed, we remove its py funcs.
    graph = ops.get_default_graph()
  
    # pylint: disable=protected-access
    while isinstance(graph, function._FuncGraph):
      # If the py_func was declared inside a _FuncGraph, its lifetime should be
      # bound to that of the outer graph instead.
      graph = graph._outer_graph
  
    # TODO(zhifengc): Consider adding a Graph method to collect
    # `cleanup` objects in one of its member.
    if not hasattr(graph, "_py_funcs_used_in_graph"):
      graph._py_funcs_used_in_graph = []
  
    # Store a reference to the function in the graph to ensure it stays alive
    # as long as the graph lives. When the graph is destroyed, the function
    # is left to the garbage collector for destruction as well.
    graph._py_funcs_used_in_graph.append(func)
    # pylint: enable=protected-access
  
    if eager:
      result = gen_script_ops.eager_py_func(
          input=inp, token=token, Tout=Tout, name=name)
    else:
      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)
    return result if is_list_or_tuple else result[0]
  
  
  # TODO(akshayka): Implement higher-order derivatives.
  @ops.RegisterGradient("EagerPyFunc")
  def _EagerPyFuncGrad(op, *dy):
    """Computes the gradient of an EagerPyFunc."""
  
    token = op.get_attr("token")
  
    def eagerly_executed_grad(*dy):
      tape, eager_inputs, eager_outputs = tape_cache.pop(compat.as_bytes(token))
      return tape.gradient(eager_outputs, eager_inputs, output_gradients=dy)
  
    with ops.control_dependencies(op.outputs):
      return _internal_py_func(
          func=eagerly_executed_grad,
          inp=dy,
          Tout=[tensor.dtype for tensor in op.inputs],
          eager=True,
          is_grad_func=True)
  
  
  def eager_py_func(func, inp, Tout, name=None):
    """Wraps a python function into a TensorFlow op that executes it eagerly.
  
    This function allows expressing computations in a TensorFlow graph as
    Python functions. In particular, it wraps a Python function `func`
    in a once-differentiable TensorFlow operation that executes it with eager
    exeuction enabled. As a consequence, `tf.contrib.eager.py_func` makes it
    possible to express control flow using Python constructs (`if`, `while`,
    `for`, etc.), instead of TensorFlow control flow constructs (`tf.cond`,
    `tf.while_loop`). For example, you might use `tf.contrib.eager.py_func` to
    implement the log huber function:
  
    ```python
    def log_huber(x, m):
      if tf.abs(x) <= m:
        return x**2
      else:
        return m**2 * (1 - 2 * tf.log(m) + tf.log(x**2))
  
    x = tf.placeholder(tf.float32)
    m = tf.placeholder(tf.float32)
  
    y = tf.contrib.eager.py_func(func=log_huber, inp=[x, m], Tout=tf.float32)
    dy_dx = tf.gradients(y, x)[0]
  
    with tf.Session() as sess:
      # The session executes `log_huber` eagerly. Given the feed values below,
      # it will take the first branch, so `y` evaluates to 1.0 and
      # `dy_dx` evaluates to 2.0.
      y, dy_dx = sess.run([y, dy_dx], feed_dict={x: 1.0, m: 2.0})
    ```
  
    You can also use `tf.contrib.eager.py_func` to debug your models at runtime
    using Python tools, i.e., you can isolate portions of your code that
    you want to debug, wrap them in Python functions and insert `pdb` tracepoints
    or print statements as desired, and wrap those functions in
    `tf.contrib.eager.py_func`.
  
    For more information on eager execution, see the
    [Eager guide](https://tensorflow.org/guide/eager).
  
    `tf.contrib.eager.py_func` is similar in spirit to `tf.py_func`, but unlike
    the latter, the former lets you use TensorFlow operations in the wrapped
    Python function. In particular, while `tf.py_func` only runs on CPUs and
    wraps functions that take NumPy arrays as inputs and return NumPy arrays as
    outputs, `tf.contrib.eager.py_func` can be placed on GPUs and wraps functions
    that take Tensors as inputs, execute TensorFlow operations in their bodies,
    and return Tensors as outputs.
  
    Like `tf.py_func`, `tf.contrib.eager.py_func` has the following limitations
    with respect to serialization and distribution:
  
    * 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.contrib.eager.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.contrib.eager.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 `Tensor` objects
        having element types that match the corresponding `tf.Tensor` objects
        in `inp` and returns a list of `Tensor` objects (or a single
        `Tensor`, or `None`) having element types that match the
        corresponding values in `Tout`.
      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; an empty list
        if no value is returned (i.e., if the return value is `None`).
      name: A name for the operation (optional).
  
    Returns:
      A list of `Tensor` or a single `Tensor` which `func` computes; an empty list
      if `func` returns None.
    """
    return _internal_py_func(func=func, inp=inp, Tout=Tout, eager=True, name=name)
  
  
  @tf_export("py_func")
  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
    arguments 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)
    input = tf.placeholder(tf.float32)
    y = tf.py_func(my_func, [input], 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 `ndarray` objects as arguments and
        returns a list of `ndarray` objects (or a single `ndarray`). This function
        must accept as many arguments as there are tensors in `inp`, and these
        argument types will match the corresponding `tf.Tensor` objects
        in `inp`. The returns `ndarray`s must match the number and types defined
        `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.
    """
    if context.executing_eagerly():
      result = func(*[x.numpy() for x in inp])
      result = nest.flatten(result)
  
      result = [x if x is None else ops.convert_to_tensor(x) for x in result]
      if len(result) == 1:
        # Mimic the automatic unwrapping in graph-mode py_func
        result, = result
      return result
  
    return _internal_py_func(
        func=func, inp=inp, Tout=Tout, stateful=stateful, eager=False, name=name)
  
  
  ops.NotDifferentiable("PyFunc")
  ops.NotDifferentiable("PyFuncStateless")
  