"""Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.
"""

import collections as _collections
import six as _six

from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import errors as _errors
from tensorflow.python.framework import tensor_shape as _tensor_shape

from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.deprecation import deprecated_endpoints
from tensorflow.python.util import dispatch as _dispatch
from tensorflow.python.util.tf_export import tf_export
from tensorflow.python.util.tf_export import kwarg_only as _kwarg_only
from tensorflow.tools.docs import doc_controls as _doc_controls


def abort(error_msg="", exit_without_error=False, name=None):
  r"""Raise a exception to abort the process when called.

  If exit_without_error is true, the process will exit normally,
  otherwise it will exit with a SIGABORT signal.

  Returns nothing but an exception.

  Args:
    error_msg: An optional `string`. Defaults to `""`.
      A string which is the message associated with the exception.
    exit_without_error: An optional `bool`. Defaults to `False`.
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "Abort",
        name, _ctx._post_execution_callbacks, "error_msg", error_msg,
        "exit_without_error", exit_without_error)
      return _result
    except _core._FallbackException:
      try:
        return abort_eager_fallback(
            error_msg=error_msg, exit_without_error=exit_without_error,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if error_msg is None:
    error_msg = ""
  error_msg = _execute.make_str(error_msg, "error_msg")
  if exit_without_error is None:
    exit_without_error = False
  exit_without_error = _execute.make_bool(exit_without_error, "exit_without_error")
  _, _, _op = _op_def_lib._apply_op_helper(
        "Abort", error_msg=error_msg, exit_without_error=exit_without_error,
                 name=name)
  return _op
  _result = None
  return _result

def Abort(error_msg="", exit_without_error=False, name=None):
  return abort(error_msg=error_msg, exit_without_error=exit_without_error, name=name)
Abort.__doc__ = abort.__doc__
Abort = _doc_controls.do_not_generate_docs(_kwarg_only(Abort))
tf_export("raw_ops.Abort")(Abort)


def abort_eager_fallback(error_msg="", exit_without_error=False, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function abort
  """
  _ctx = ctx if ctx else _context.context()
  if error_msg is None:
    error_msg = ""
  error_msg = _execute.make_str(error_msg, "error_msg")
  if exit_without_error is None:
    exit_without_error = False
  exit_without_error = _execute.make_bool(exit_without_error, "exit_without_error")
  _inputs_flat = []
  _attrs = ("error_msg", error_msg, "exit_without_error", exit_without_error)
  _result = _execute.execute(b"Abort", 0, inputs=_inputs_flat, attrs=_attrs,
                             ctx=_ctx, name=name)
  _result = None
  return _result


def control_trigger(name=None):
  r"""Does nothing. Serves as a control trigger for scheduling.

  Only useful as a placeholder for control edges.

  Args:
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "ControlTrigger", name, _ctx._post_execution_callbacks)
      return _result
    except _core._FallbackException:
      try:
        return control_trigger_eager_fallback(
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "ControlTrigger", name=name)
  return _op
  _result = None
  return _result

def ControlTrigger(name=None):
  return control_trigger(name=name)
ControlTrigger.__doc__ = control_trigger.__doc__
ControlTrigger = _doc_controls.do_not_generate_docs(_kwarg_only(ControlTrigger))
tf_export("raw_ops.ControlTrigger")(ControlTrigger)


def control_trigger_eager_fallback(name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function control_trigger
  """
  _ctx = ctx if ctx else _context.context()
  _inputs_flat = []
  _attrs = None
  _result = _execute.execute(b"ControlTrigger", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result


def enter(data, frame_name, is_constant=False, parallel_iterations=10, name=None):
  r"""Creates or finds a child frame, and makes `data` available to the child frame.

  This op is used together with `Exit` to create loops in the graph.
  The unique `frame_name` is used by the `Executor` to identify frames. If
  `is_constant` is true, `output` is a constant in the child frame; otherwise
  it may be changed in the child frame. At most `parallel_iterations` iterations
  are run in parallel in the child frame.

  Args:
    data: A `Tensor`. The tensor to be made available to the child frame.
    frame_name: A `string`. The name of the child frame.
    is_constant: An optional `bool`. Defaults to `False`.
      If true, the output is constant within the child frame.
    parallel_iterations: An optional `int`. Defaults to `10`.
      The number of iterations allowed to run in parallel.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "Enter",
        name, _ctx._post_execution_callbacks, data, "frame_name", frame_name,
        "is_constant", is_constant, "parallel_iterations",
        parallel_iterations)
      return _result
    except _core._FallbackException:
      try:
        return enter_eager_fallback(
            data, frame_name=frame_name, is_constant=is_constant,
            parallel_iterations=parallel_iterations, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  frame_name = _execute.make_str(frame_name, "frame_name")
  if is_constant is None:
    is_constant = False
  is_constant = _execute.make_bool(is_constant, "is_constant")
  if parallel_iterations is None:
    parallel_iterations = 10
  parallel_iterations = _execute.make_int(parallel_iterations, "parallel_iterations")
  _, _, _op = _op_def_lib._apply_op_helper(
        "Enter", data=data, frame_name=frame_name, is_constant=is_constant,
                 parallel_iterations=parallel_iterations, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"), "frame_name", _op.get_attr("frame_name"),
            "is_constant", _op.get_attr("is_constant"), "parallel_iterations",
            _op.get_attr("parallel_iterations"))
  _execute.record_gradient(
      "Enter", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def Enter(data, frame_name, is_constant=False, parallel_iterations=10, name=None):
  return enter(data=data, frame_name=frame_name, is_constant=is_constant, parallel_iterations=parallel_iterations, name=name)
Enter.__doc__ = enter.__doc__
Enter = _doc_controls.do_not_generate_docs(_kwarg_only(Enter))
tf_export("raw_ops.Enter")(Enter)


def enter_eager_fallback(data, frame_name, is_constant=False, parallel_iterations=10, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function enter
  """
  _ctx = ctx if ctx else _context.context()
  frame_name = _execute.make_str(frame_name, "frame_name")
  if is_constant is None:
    is_constant = False
  is_constant = _execute.make_bool(is_constant, "is_constant")
  if parallel_iterations is None:
    parallel_iterations = 10
  parallel_iterations = _execute.make_int(parallel_iterations, "parallel_iterations")
  _attr_T, (data,) = _execute.args_to_matching_eager([data], _ctx)
  _inputs_flat = [data]
  _attrs = ("T", _attr_T, "frame_name", frame_name, "is_constant",
  is_constant, "parallel_iterations", parallel_iterations)
  _result = _execute.execute(b"Enter", 1, inputs=_inputs_flat, attrs=_attrs,
                             ctx=_ctx, name=name)
  _execute.record_gradient(
      "Enter", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def _exit(data, name=None):
  r"""Exits the current frame to its parent frame.

  Exit makes its input `data` available to the parent frame.

  Args:
    data: A `Tensor`. The tensor to be made available to the parent frame.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "Exit",
        name, _ctx._post_execution_callbacks, data)
      return _result
    except _core._FallbackException:
      try:
        return _exit_eager_fallback(
            data, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "Exit", data=data, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"))
  _execute.record_gradient(
      "Exit", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def Exit(data, name=None):
  return _exit(data=data, name=name)
Exit.__doc__ = _exit.__doc__
Exit = _doc_controls.do_not_generate_docs(_kwarg_only(Exit))
tf_export("raw_ops.Exit")(Exit)


def _exit_eager_fallback(data, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function _exit
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, (data,) = _execute.args_to_matching_eager([data], _ctx)
  _inputs_flat = [data]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"Exit", 1, inputs=_inputs_flat, attrs=_attrs,
                             ctx=_ctx, name=name)
  _execute.record_gradient(
      "Exit", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def loop_cond(input, name=None):
  r"""Forwards the input to the output.

  This operator represents the loop termination condition used by the
  "pivot" switches of a loop.

  Args:
    input: A `Tensor` of type `bool`.
      A boolean scalar, representing the branch predicate of the Switch op.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `bool`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "LoopCond",
        name, _ctx._post_execution_callbacks, input)
      return _result
    except _core._FallbackException:
      try:
        return loop_cond_eager_fallback(
            input, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "LoopCond", input=input, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = None
  _execute.record_gradient(
      "LoopCond", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def LoopCond(input, name=None):
  return loop_cond(input=input, name=name)
LoopCond.__doc__ = loop_cond.__doc__
LoopCond = _doc_controls.do_not_generate_docs(_kwarg_only(LoopCond))
tf_export("raw_ops.LoopCond")(LoopCond)


def loop_cond_eager_fallback(input, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function loop_cond
  """
  _ctx = ctx if ctx else _context.context()
  input = _ops.convert_to_tensor(input, _dtypes.bool)
  _inputs_flat = [input]
  _attrs = None
  _result = _execute.execute(b"LoopCond", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "LoopCond", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


_merge_outputs = ["output", "value_index"]
_MergeOutput = _collections.namedtuple(
    "Merge", _merge_outputs)


def merge(inputs, name=None):
  r"""Forwards the value of an available tensor from `inputs` to `output`.

  `Merge` waits for at least one of the tensors in `inputs` to become available.
  It is usually combined with `Switch` to implement branching.

  `Merge` forwards the first tensor to become available to `output`, and sets
  `value_index` to its index in `inputs`.

  Args:
    inputs: A list of at least 1 `Tensor` objects with the same type.
      The input tensors, exactly one of which will become available.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (output, value_index).

    output: A `Tensor`. Has the same type as `inputs`.
    value_index: A `Tensor` of type `int32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "Merge",
        name, _ctx._post_execution_callbacks, inputs)
      _result = _MergeOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return merge_eager_fallback(
            inputs, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'merge' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _, _, _op = _op_def_lib._apply_op_helper(
        "Merge", inputs=inputs, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"), "N", _op.get_attr("N"))
  _execute.record_gradient(
      "Merge", _inputs_flat, _attrs, _result, name)
  _result = _MergeOutput._make(_result)
  return _result

def Merge(inputs, name=None):
  return merge(inputs=inputs, name=name)
Merge.__doc__ = merge.__doc__
Merge = _doc_controls.do_not_generate_docs(_kwarg_only(Merge))
tf_export("raw_ops.Merge")(Merge)


def merge_eager_fallback(inputs, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function merge
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'merge' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _attr_T, inputs = _execute.args_to_matching_eager(list(inputs), _ctx)
  _inputs_flat = list(inputs)
  _attrs = ("T", _attr_T, "N", _attr_N)
  _result = _execute.execute(b"Merge", 2, inputs=_inputs_flat, attrs=_attrs,
                             ctx=_ctx, name=name)
  _execute.record_gradient(
      "Merge", _inputs_flat, _attrs, _result, name)
  _result = _MergeOutput._make(_result)
  return _result


def next_iteration(data, name=None):
  r"""Makes its input available to the next iteration.

  Args:
    data: A `Tensor`. The tensor to be made available to the next iteration.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "NextIteration", name, _ctx._post_execution_callbacks, data)
      return _result
    except _core._FallbackException:
      try:
        return next_iteration_eager_fallback(
            data, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "NextIteration", data=data, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"))
  _execute.record_gradient(
      "NextIteration", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def NextIteration(data, name=None):
  return next_iteration(data=data, name=name)
NextIteration.__doc__ = next_iteration.__doc__
NextIteration = _doc_controls.do_not_generate_docs(_kwarg_only(NextIteration))
tf_export("raw_ops.NextIteration")(NextIteration)


def next_iteration_eager_fallback(data, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function next_iteration
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, (data,) = _execute.args_to_matching_eager([data], _ctx)
  _inputs_flat = [data]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"NextIteration", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "NextIteration", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


@_dispatch.add_dispatch_list
@tf_export('no_op')
def no_op(name=None):
  r"""Does nothing. Only useful as a placeholder for control edges.

  Args:
    name: A name for the operation (optional).

  Returns:
    The created Operation.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "NoOp",
        name, _ctx._post_execution_callbacks)
      return _result
    except _core._FallbackException:
      try:
        return no_op_eager_fallback(
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              no_op, name=name)
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  try:
    _, _, _op = _op_def_lib._apply_op_helper(
        "NoOp", name=name)
  except (TypeError, ValueError):
    result = _dispatch.dispatch(
          no_op, name=name)
    if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
      return result
    raise
  return _op
  _result = None
  return _result

def NoOp(name=None):
  return no_op(name=name)
NoOp.__doc__ = no_op.__doc__
NoOp = _doc_controls.do_not_generate_docs(_kwarg_only(NoOp))
tf_export("raw_ops.NoOp")(NoOp)


def no_op_eager_fallback(name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function no_op
  """
  _ctx = ctx if ctx else _context.context()
  _inputs_flat = []
  _attrs = None
  _result = _execute.execute(b"NoOp", 0, inputs=_inputs_flat, attrs=_attrs,
                             ctx=_ctx, name=name)
  _result = None
  return _result


def ref_enter(data, frame_name, is_constant=False, parallel_iterations=10, name=None):
  r"""Creates or finds a child frame, and makes `data` available to the child frame.

  The unique `frame_name` is used by the `Executor` to identify frames. If
  `is_constant` is true, `output` is a constant in the child frame; otherwise
  it may be changed in the child frame. At most `parallel_iterations` iterations
  are run in parallel in the child frame.

  Args:
    data: A mutable `Tensor`.
      The tensor to be made available to the child frame.
    frame_name: A `string`. The name of the child frame.
    is_constant: An optional `bool`. Defaults to `False`.
      If true, the output is constant within the child frame.
    parallel_iterations: An optional `int`. Defaults to `10`.
      The number of iterations allowed to run in parallel.
    name: A name for the operation (optional).

  Returns:
    A mutable `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    raise RuntimeError("ref_enter op does not support eager execution. Arg 'output' is a ref.")
  # Add nodes to the TensorFlow graph.
  frame_name = _execute.make_str(frame_name, "frame_name")
  if is_constant is None:
    is_constant = False
  is_constant = _execute.make_bool(is_constant, "is_constant")
  if parallel_iterations is None:
    parallel_iterations = 10
  parallel_iterations = _execute.make_int(parallel_iterations, "parallel_iterations")
  _, _, _op = _op_def_lib._apply_op_helper(
        "RefEnter", data=data, frame_name=frame_name, is_constant=is_constant,
                    parallel_iterations=parallel_iterations, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"), "frame_name", _op.get_attr("frame_name"),
            "is_constant", _op.get_attr("is_constant"), "parallel_iterations",
            _op.get_attr("parallel_iterations"))
  _execute.record_gradient(
      "RefEnter", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def RefEnter(data, frame_name, is_constant=False, parallel_iterations=10, name=None):
  return ref_enter(data=data, frame_name=frame_name, is_constant=is_constant, parallel_iterations=parallel_iterations, name=name)
RefEnter.__doc__ = ref_enter.__doc__
RefEnter = _doc_controls.do_not_generate_docs(_kwarg_only(RefEnter))
tf_export("raw_ops.RefEnter")(RefEnter)


def ref_enter_eager_fallback(data, frame_name, is_constant=False, parallel_iterations=10, name=None, ctx=None):
  raise RuntimeError("ref_enter op does not support eager execution. Arg 'output' is a ref.")

def ref_exit(data, name=None):
  r"""Exits the current frame to its parent frame.

  Exit makes its input `data` available to the parent frame.

  Args:
    data: A mutable `Tensor`.
      The tensor to be made available to the parent frame.
    name: A name for the operation (optional).

  Returns:
    A mutable `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    raise RuntimeError("ref_exit op does not support eager execution. Arg 'output' is a ref.")
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "RefExit", data=data, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"))
  _execute.record_gradient(
      "RefExit", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def RefExit(data, name=None):
  return ref_exit(data=data, name=name)
RefExit.__doc__ = ref_exit.__doc__
RefExit = _doc_controls.do_not_generate_docs(_kwarg_only(RefExit))
tf_export("raw_ops.RefExit")(RefExit)


def ref_exit_eager_fallback(data, name=None, ctx=None):
  raise RuntimeError("ref_exit op does not support eager execution. Arg 'output' is a ref.")

_ref_merge_outputs = ["output", "value_index"]
_RefMergeOutput = _collections.namedtuple(
    "RefMerge", _ref_merge_outputs)


def ref_merge(inputs, name=None):
  r"""Forwards the value of an available tensor from `inputs` to `output`.

  `Merge` waits for at least one of the tensors in `inputs` to become available.
  It is usually combined with `Switch` to implement branching.

  `Merge` forwards the first tensor for become available to `output`, and sets
  `value_index` to its index in `inputs`.

  Args:
    inputs: A list of at least 1 mutable `Tensor` objects with the same type.
      The input tensors, exactly one of which will become available.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (output, value_index).

    output: A mutable `Tensor`. Has the same type as `inputs`.
    value_index: A `Tensor` of type `int32`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    raise RuntimeError("ref_merge op does not support eager execution. Arg 'output' is a ref.")
  # Add nodes to the TensorFlow graph.
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'ref_merge' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _, _, _op = _op_def_lib._apply_op_helper(
        "RefMerge", inputs=inputs, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"), "N", _op.get_attr("N"))
  _execute.record_gradient(
      "RefMerge", _inputs_flat, _attrs, _result, name)
  _result = _RefMergeOutput._make(_result)
  return _result

def RefMerge(inputs, name=None):
  return ref_merge(inputs=inputs, name=name)
RefMerge.__doc__ = ref_merge.__doc__
RefMerge = _doc_controls.do_not_generate_docs(_kwarg_only(RefMerge))
tf_export("raw_ops.RefMerge")(RefMerge)


def ref_merge_eager_fallback(inputs, name=None, ctx=None):
  raise RuntimeError("ref_merge op does not support eager execution. Arg 'output' is a ref.")

def ref_next_iteration(data, name=None):
  r"""Makes its input available to the next iteration.

  Args:
    data: A mutable `Tensor`.
      The tensor to be made available to the next iteration.
    name: A name for the operation (optional).

  Returns:
    A mutable `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    raise RuntimeError("ref_next_iteration op does not support eager execution. Arg 'output' is a ref.")
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "RefNextIteration", data=data, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"))
  _execute.record_gradient(
      "RefNextIteration", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def RefNextIteration(data, name=None):
  return ref_next_iteration(data=data, name=name)
RefNextIteration.__doc__ = ref_next_iteration.__doc__
RefNextIteration = _doc_controls.do_not_generate_docs(_kwarg_only(RefNextIteration))
tf_export("raw_ops.RefNextIteration")(RefNextIteration)


def ref_next_iteration_eager_fallback(data, name=None, ctx=None):
  raise RuntimeError("ref_next_iteration op does not support eager execution. Arg 'output' is a ref.")

def ref_select(index, inputs, name=None):
  r"""Forwards the `index`th element of `inputs` to `output`.

  Args:
    index: A `Tensor` of type `int32`.
      A scalar that determines the input that gets selected.
    inputs: A list of at least 1 mutable `Tensor` objects with the same type.
      A list of ref tensors, one of which will be forwarded to `output`.
    name: A name for the operation (optional).

  Returns:
    A mutable `Tensor`. Has the same type as `inputs`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    raise RuntimeError("ref_select op does not support eager execution. Arg 'output' is a ref.")
  # Add nodes to the TensorFlow graph.
  if not isinstance(inputs, (list, tuple)):
    raise TypeError(
        "Expected list for 'inputs' argument to "
        "'ref_select' Op, not %r." % inputs)
  _attr_N = len(inputs)
  _, _, _op = _op_def_lib._apply_op_helper(
        "RefSelect", index=index, inputs=inputs, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"), "N", _op.get_attr("N"))
  _execute.record_gradient(
      "RefSelect", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def RefSelect(index, inputs, name=None):
  return ref_select(index=index, inputs=inputs, name=name)
RefSelect.__doc__ = ref_select.__doc__
RefSelect = _doc_controls.do_not_generate_docs(_kwarg_only(RefSelect))
tf_export("raw_ops.RefSelect")(RefSelect)


def ref_select_eager_fallback(index, inputs, name=None, ctx=None):
  raise RuntimeError("ref_select op does not support eager execution. Arg 'output' is a ref.")

_ref_switch_outputs = ["output_false", "output_true"]
_RefSwitchOutput = _collections.namedtuple(
    "RefSwitch", _ref_switch_outputs)


def ref_switch(data, pred, name=None):
  r"""Forwards the ref tensor `data` to the output port determined by `pred`.

  If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise,
  the data goes to `output_false`.

  See also `Switch` and `Merge`.

  Args:
    data: A mutable `Tensor`.
      The ref tensor to be forwarded to the appropriate output.
    pred: A `Tensor` of type `bool`.
      A scalar that specifies which output port will receive data.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (output_false, output_true).

    output_false: A mutable `Tensor`. Has the same type as `data`.
    output_true: A mutable `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    raise RuntimeError("ref_switch op does not support eager execution. Arg 'output_true' is a ref.")
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "RefSwitch", data=data, pred=pred, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"))
  _execute.record_gradient(
      "RefSwitch", _inputs_flat, _attrs, _result, name)
  _result = _RefSwitchOutput._make(_result)
  return _result

def RefSwitch(data, pred, name=None):
  return ref_switch(data=data, pred=pred, name=name)
RefSwitch.__doc__ = ref_switch.__doc__
RefSwitch = _doc_controls.do_not_generate_docs(_kwarg_only(RefSwitch))
tf_export("raw_ops.RefSwitch")(RefSwitch)


def ref_switch_eager_fallback(data, pred, name=None, ctx=None):
  raise RuntimeError("ref_switch op does not support eager execution. Arg 'output_true' is a ref.")

_switch_outputs = ["output_false", "output_true"]
_SwitchOutput = _collections.namedtuple(
    "Switch", _switch_outputs)


def switch(data, pred, name=None):
  r"""Forwards `data` to the output port determined by `pred`.

  If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise,
  the data goes to `output_false`.

  See also `RefSwitch` and `Merge`.

  Args:
    data: A `Tensor`. The tensor to be forwarded to the appropriate output.
    pred: A `Tensor` of type `bool`.
      A scalar that specifies which output port will receive data.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (output_false, output_true).

    output_false: A `Tensor`. Has the same type as `data`.
    output_true: A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "Switch",
        name, _ctx._post_execution_callbacks, data, pred)
      _result = _SwitchOutput._make(_result)
      return _result
    except _core._FallbackException:
      try:
        return switch_eager_fallback(
            data, pred, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "Switch", data=data, pred=pred, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op.get_attr("T"))
  _execute.record_gradient(
      "Switch", _inputs_flat, _attrs, _result, name)
  _result = _SwitchOutput._make(_result)
  return _result

def Switch(data, pred, name=None):
  return switch(data=data, pred=pred, name=name)
Switch.__doc__ = switch.__doc__
Switch = _doc_controls.do_not_generate_docs(_kwarg_only(Switch))
tf_export("raw_ops.Switch")(Switch)


def switch_eager_fallback(data, pred, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function switch
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, (data,) = _execute.args_to_matching_eager([data], _ctx)
  pred = _ops.convert_to_tensor(pred, _dtypes.bool)
  _inputs_flat = [data, pred]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"Switch", 2, inputs=_inputs_flat, attrs=_attrs,
                             ctx=_ctx, name=name)
  _execute.record_gradient(
      "Switch", _inputs_flat, _attrs, _result, name)
  _result = _SwitchOutput._make(_result)
  return _result

def _InitOpDefLibrary(op_list_proto_bytes):
  op_list = _op_def_pb2.OpList()
  op_list.ParseFromString(op_list_proto_bytes)
  _op_def_registry.register_op_list(op_list)
  op_def_lib = _op_def_library.OpDefLibrary()
  op_def_lib.add_op_list(op_list)
  return op_def_lib
# op {
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# op {
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_op_def_lib = _InitOpDefLibrary(b"\n@\n\005Abort\"\027\n\terror_msg\022\006string\032\002\022\000\"\036\n\022exit_without_error\022\004bool\032\002(\000\n\020\n\016ControlTrigger\ny\n\005Enter\022\t\n\004data\"\001T\032\013\n\006output\"\001T\"\t\n\001T\022\004type\"\024\n\nframe_name\022\006string\"\027\n\013is_constant\022\004bool\032\002(\000\"\036\n\023parallel_iterations\022\003int\032\002\030\n\n)\n\004Exit\022\t\n\004data\"\001T\032\013\n\006output\"\001T\"\t\n\001T\022\004type\n!\n\010LoopCond\022\t\n\005input\030\n\032\n\n\006output\030\n\nN\n\005Merge\022\016\n\006inputs\"\001T*\001N\032\013\n\006output\"\001T\032\017\n\013value_index\030\003\"\t\n\001T\022\004type\"\014\n\001N\022\003int(\0010\001\n2\n\rNextIteration\022\t\n\004data\"\001T\032\013\n\006output\"\001T\"\t\n\001T\022\004type\n\006\n\004NoOp\n\202\001\n\010RefEnter\022\014\n\004data\"\001T\200\001\001\032\016\n\006output\"\001T\200\001\001\"\t\n\001T\022\004type\"\024\n\nframe_name\022\006string\"\027\n\013is_constant\022\004bool\032\002(\000\"\036\n\023parallel_iterations\022\003int\032\002\030\n\n2\n\007RefExit\022\014\n\004data\"\001T\200\001\001\032\016\n\006output\"\001T\200\001\001\"\t\n\001T\022\004type\nW\n\010RefMerge\022\021\n\006inputs\"\001T*\001N\200\001\001\032\016\n\006output\"\001T\200\001\001\032\017\n\013value_index\030\003\"\t\n\001T\022\004type\"\014\n\001N\022\003int(\0010\001\n;\n\020RefNextIteration\022\014\n\004data\"\001T\200\001\001\032\016\n\006output\"\001T\200\001\001\"\t\n\001T\022\004type\nR\n\tRefSelect\022\t\n\005index\030\003\022\021\n\006inputs\"\001T*\001N\200\001\001\032\016\n\006output\"\001T\200\001\001\"\t\n\001T\022\004type\"\014\n\001N\022\003int(\0010\001\n\\\n\tRefSwitch\022\014\n\004data\"\001T\200\001\001\022\010\n\004pred\030\n\032\024\n\014output_false\"\001T\200\001\001\032\023\n\013output_true\"\001T\200\001\001\"\t\n\001T\022\004type\230\001\001\nM\n\006Switch\022\t\n\004data\"\001T\022\010\n\004pred\030\n\032\021\n\014output_false\"\001T\032\020\n\013output_true\"\001T\"\t\n\001T\022\004type")