EVOLUTION-MANAGER

Edit File: gen_rnn_ops.py

"""Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.
Original C++ source file: rnn_ops.cc
"""

import collections

from tensorflow.python import pywrap_tfe as pywrap_tfe
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 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 typing import TypeVar
_BlockLSTMOutput = collections.namedtuple(
    "BlockLSTM",
    ["i", "cs", "f", "o", "ci", "co", "h"])

def block_lstm(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None):
  r"""Computes the LSTM cell forward propagation for all the time steps.

This is equivalent to applying LSTMBlockCell in a loop, like so:

```python
  for x1 in unpack(x):
    i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
      x1, cs_prev, h_prev, w, wci, wcf, wco, b)
    cs_prev = cs1
    h_prev = h1
    i.append(i1)
    cs.append(cs1)
    f.append(f1)
    o.append(o1)
    ci.append(ci1)
    co.append(co1)
    h.append(h1)
  return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h)
  ```

Returns:
    A tuple of `Tensor` objects (i, cs, f, o, ci, co, h).

i: A `Tensor`. Has the same type as `x`.
    cs: A `Tensor`. Has the same type as `x`.
    f: A `Tensor`. Has the same type as `x`.
    o: A `Tensor`. Has the same type as `x`.
    ci: A `Tensor`. Has the same type as `x`.
    co: A `Tensor`. Has the same type as `x`.
    h: A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "BlockLSTM", name, seq_len_max, x, cs_prev, h_prev, w, wci, wcf,
        wco, b, "forget_bias", forget_bias, "cell_clip", cell_clip,
        "use_peephole", use_peephole)
      _result = _BlockLSTMOutput._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return block_lstm_eager_fallback(
          seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b,
          forget_bias=forget_bias, cell_clip=cell_clip,
          use_peephole=use_peephole, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  if forget_bias is None:
    forget_bias = 1
  forget_bias = _execute.make_float(forget_bias, "forget_bias")
  if cell_clip is None:
    cell_clip = 3
  cell_clip = _execute.make_float(cell_clip, "cell_clip")
  if use_peephole is None:
    use_peephole = False
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "BlockLSTM", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev,
                     h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b,
                     forget_bias=forget_bias, cell_clip=cell_clip,
                     use_peephole=use_peephole, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("forget_bias", _op.get_attr("forget_bias"), "cell_clip",
              _op.get_attr("cell_clip"), "use_peephole",
              _op._get_attr_bool("use_peephole"), "T",
              _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "BlockLSTM", _inputs_flat, _attrs, _result)
  _result = _BlockLSTMOutput._make(_result)
  return _result

BlockLSTM = tf_export("raw_ops.BlockLSTM")(_ops.to_raw_op(block_lstm))

def block_lstm_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias, cell_clip, use_peephole, name, ctx):
  if forget_bias is None:
    forget_bias = 1
  forget_bias = _execute.make_float(forget_bias, "forget_bias")
  if cell_clip is None:
    cell_clip = 3
  cell_clip = _execute.make_float(cell_clip, "cell_clip")
  if use_peephole is None:
    use_peephole = False
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], ctx, [_dtypes.half, _dtypes.float32, ])
  (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T
  seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64)
  _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b]
  _attrs = ("forget_bias", forget_bias, "cell_clip", cell_clip,
  "use_peephole", use_peephole, "T", _attr_T)
  _result = _execute.execute(b"BlockLSTM", 7, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "BlockLSTM", _inputs_flat, _attrs, _result)
  _result = _BlockLSTMOutput._make(_result)
  return _result

_BlockLSTMGradOutput = collections.namedtuple(
    "BlockLSTMGrad",
    ["x_grad", "cs_prev_grad", "h_prev_grad", "w_grad", "wci_grad", "wcf_grad", "wco_grad", "b_grad"])

def block_lstm_grad(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None):
  r"""Computes the LSTM cell backward propagation for the entire time sequence.

This implementation is to be used in conjunction of LSTMBlock.

Args:
    seq_len_max: A `Tensor` of type `int64`.
      Maximum time length actually used by this input. Outputs are padded
      with zeros beyond this length.
    x: A `Tensor`. Must be one of the following types: `half`, `float32`.
      The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
    cs_prev: A `Tensor`. Must have the same type as `x`.
      Value of the initial cell state.
    h_prev: A `Tensor`. Must have the same type as `x`.
      Initial output of cell (to be used for peephole).
    w: A `Tensor`. Must have the same type as `x`. The weight matrix.
    wci: A `Tensor`. Must have the same type as `x`.
      The weight matrix for input gate peephole connection.
    wcf: A `Tensor`. Must have the same type as `x`.
      The weight matrix for forget gate peephole connection.
    wco: A `Tensor`. Must have the same type as `x`.
      The weight matrix for output gate peephole connection.
    b: A `Tensor`. Must have the same type as `x`. The bias vector.
    i: A `Tensor`. Must have the same type as `x`.
      The input gate over the whole time sequence.
    cs: A `Tensor`. Must have the same type as `x`.
      The cell state before the tanh over the whole time sequence.
    f: A `Tensor`. Must have the same type as `x`.
      The forget gate over the whole time sequence.
    o: A `Tensor`. Must have the same type as `x`.
      The output gate over the whole time sequence.
    ci: A `Tensor`. Must have the same type as `x`.
      The cell input over the whole time sequence.
    co: A `Tensor`. Must have the same type as `x`.
      The cell after the tanh over the whole time sequence.
    h: A `Tensor`. Must have the same type as `x`.
      The output h vector over the whole time sequence.
    cs_grad: A `Tensor`. Must have the same type as `x`.
      The current gradient of cs.
    h_grad: A `Tensor`. Must have the same type as `x`.
      The gradient of h vector.
    use_peephole: A `bool`. Whether to use peephole weights.
    name: A name for the operation (optional).

Returns:
    A tuple of `Tensor` objects (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad).

x_grad: A `Tensor`. Has the same type as `x`.
    cs_prev_grad: A `Tensor`. Has the same type as `x`.
    h_prev_grad: A `Tensor`. Has the same type as `x`.
    w_grad: A `Tensor`. Has the same type as `x`.
    wci_grad: A `Tensor`. Has the same type as `x`.
    wcf_grad: A `Tensor`. Has the same type as `x`.
    wco_grad: A `Tensor`. Has the same type as `x`.
    b_grad: A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "BlockLSTMGrad", name, seq_len_max, x, cs_prev, h_prev, w, wci,
        wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, "use_peephole",
        use_peephole)
      _result = _BlockLSTMGradOutput._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return block_lstm_grad_eager_fallback(
          seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o,
          ci, co, h, cs_grad, h_grad, use_peephole=use_peephole, name=name,
          ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "BlockLSTMGrad", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev,
                         h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b,
                         i=i, cs=cs, f=f, o=o, ci=ci, co=co, h=h,
                         cs_grad=cs_grad, h_grad=h_grad,
                         use_peephole=use_peephole, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("use_peephole", _op._get_attr_bool("use_peephole"), "T",
              _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "BlockLSTMGrad", _inputs_flat, _attrs, _result)
  _result = _BlockLSTMGradOutput._make(_result)
  return _result

BlockLSTMGrad = tf_export("raw_ops.BlockLSTMGrad")(_ops.to_raw_op(block_lstm_grad))

def block_lstm_grad_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name, ctx):
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad], ctx, [_dtypes.half, _dtypes.float32, ])
  (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad) = _inputs_T
  seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64)
  _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad]
  _attrs = ("use_peephole", use_peephole, "T", _attr_T)
  _result = _execute.execute(b"BlockLSTMGrad", 8, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "BlockLSTMGrad", _inputs_flat, _attrs, _result)
  _result = _BlockLSTMGradOutput._make(_result)
  return _result

_BlockLSTMGradV2Output = collections.namedtuple(
    "BlockLSTMGradV2",
    ["x_grad", "cs_prev_grad", "h_prev_grad", "w_grad", "wci_grad", "wcf_grad", "wco_grad", "b_grad"])

def block_lstm_grad_v2(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None):
  r"""Computes the LSTM cell backward propagation for the entire time sequence.

This implementation is to be used in conjunction of BlockLSTMV2.

Returns:
    A tuple of `Tensor` objects (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad).

x_grad: A `Tensor`. Has the same type as `x`.
    cs_prev_grad: A `Tensor`. Has the same type as `x`.
    h_prev_grad: A `Tensor`. Has the same type as `x`.
    w_grad: A `Tensor`. Has the same type as `x`.
    wci_grad: A `Tensor`. Has the same type as `x`.
    wcf_grad: A `Tensor`. Has the same type as `x`.
    wco_grad: A `Tensor`. Has the same type as `x`.
    b_grad: A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "BlockLSTMGradV2", name, seq_len_max, x, cs_prev, h_prev, w,
        wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad,
        "use_peephole", use_peephole)
      _result = _BlockLSTMGradV2Output._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return block_lstm_grad_v2_eager_fallback(
          seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o,
          ci, co, h, cs_grad, h_grad, use_peephole=use_peephole, name=name,
          ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "BlockLSTMGradV2", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev,
                           h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b,
                           i=i, cs=cs, f=f, o=o, ci=ci, co=co, h=h,
                           cs_grad=cs_grad, h_grad=h_grad,
                           use_peephole=use_peephole, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("use_peephole", _op._get_attr_bool("use_peephole"), "T",
              _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "BlockLSTMGradV2", _inputs_flat, _attrs, _result)
  _result = _BlockLSTMGradV2Output._make(_result)
  return _result

BlockLSTMGradV2 = tf_export("raw_ops.BlockLSTMGradV2")(_ops.to_raw_op(block_lstm_grad_v2))

def block_lstm_grad_v2_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name, ctx):
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad], ctx, [_dtypes.half, _dtypes.float32, ])
  (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad) = _inputs_T
  seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64)
  _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad]
  _attrs = ("use_peephole", use_peephole, "T", _attr_T)
  _result = _execute.execute(b"BlockLSTMGradV2", 8, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "BlockLSTMGradV2", _inputs_flat, _attrs, _result)
  _result = _BlockLSTMGradV2Output._make(_result)
  return _result

_BlockLSTMV2Output = collections.namedtuple(
    "BlockLSTMV2",
    ["i", "cs", "f", "o", "ci", "co", "h"])

def block_lstmv2(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, cell_clip=0, use_peephole=False, name=None):
  r"""Computes the LSTM cell forward propagation for all the time steps.

This is equivalent to applying LSTMBlockCell in a loop, like so:

Note that unlike LSTMBlockCell (and BlockLSTM) which uses ICFO gate layout,
  this op uses IFCO. So in order for the following snippet to be equivalent
  all gate-related outputs should be reordered.
  ```

Returns:
    A tuple of `Tensor` objects (i, cs, f, o, ci, co, h).

i: A `Tensor`. Has the same type as `x`.
    cs: A `Tensor`. Has the same type as `x`.
    f: A `Tensor`. Has the same type as `x`.
    o: A `Tensor`. Has the same type as `x`.
    ci: A `Tensor`. Has the same type as `x`.
    co: A `Tensor`. Has the same type as `x`.
    h: A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "BlockLSTMV2", name, seq_len_max, x, cs_prev, h_prev, w, wci,
        wcf, wco, b, "cell_clip", cell_clip, "use_peephole", use_peephole)
      _result = _BlockLSTMV2Output._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return block_lstmv2_eager_fallback(
          seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b,
          cell_clip=cell_clip, use_peephole=use_peephole, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  if cell_clip is None:
    cell_clip = 0
  cell_clip = _execute.make_float(cell_clip, "cell_clip")
  if use_peephole is None:
    use_peephole = False
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "BlockLSTMV2", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev,
                       h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b,
                       cell_clip=cell_clip, use_peephole=use_peephole,
                       name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("cell_clip", _op.get_attr("cell_clip"), "use_peephole",
              _op._get_attr_bool("use_peephole"), "T",
              _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "BlockLSTMV2", _inputs_flat, _attrs, _result)
  _result = _BlockLSTMV2Output._make(_result)
  return _result

BlockLSTMV2 = tf_export("raw_ops.BlockLSTMV2")(_ops.to_raw_op(block_lstmv2))

def block_lstmv2_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, cell_clip, use_peephole, name, ctx):
  if cell_clip is None:
    cell_clip = 0
  cell_clip = _execute.make_float(cell_clip, "cell_clip")
  if use_peephole is None:
    use_peephole = False
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], ctx, [_dtypes.half, _dtypes.float32, ])
  (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T
  seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64)
  _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b]
  _attrs = ("cell_clip", cell_clip, "use_peephole", use_peephole, "T",
  _attr_T)
  _result = _execute.execute(b"BlockLSTMV2", 7, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "BlockLSTMV2", _inputs_flat, _attrs, _result)
  _result = _BlockLSTMV2Output._make(_result)
  return _result

_GRUBlockCellOutput = collections.namedtuple(
    "GRUBlockCell",
    ["r", "u", "c", "h"])

def gru_block_cell(x, h_prev, w_ru, w_c, b_ru, b_c, name=None):
  r"""Computes the GRU cell forward propagation for 1 time step.

Returns
      r: Output of the reset gate.
      u: Output of the update gate.
      c: Output of the cell connection gate.
      h: Current state of the GRU cell.

Note on notation of the variables:

Concatenation of a and b is represented by a_b
  Element-wise dot product of a and b is represented by ab
  Element-wise dot product is represented by \circ
  Matrix multiplication is represented by *

Biases are initialized with :
  `b_ru` - constant_initializer(1.0)
  `b_c` - constant_initializer(0.0)

This kernel op implements the following mathematical equations:

```
  x_h_prev = [x, h_prev]

[r_bar u_bar] = x_h_prev * w_ru + b_ru

r = sigmoid(r_bar)
  u = sigmoid(u_bar)

h_prevr = h_prev \circ r

x_h_prevr = [x h_prevr]

c_bar = x_h_prevr * w_c + b_c
  c = tanh(c_bar)

h = (1-u) \circ c + u \circ h_prev
  ```

Returns:
    A tuple of `Tensor` objects (r, u, c, h).

r: A `Tensor`. Has the same type as `x`.
    u: A `Tensor`. Has the same type as `x`.
    c: A `Tensor`. Has the same type as `x`.
    h: A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "GRUBlockCell", name, x, h_prev, w_ru, w_c, b_ru, b_c)
      _result = _GRUBlockCellOutput._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return gru_block_cell_eager_fallback(
          x, h_prev, w_ru, w_c, b_ru, b_c, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "GRUBlockCell", x=x, h_prev=h_prev, w_ru=w_ru, w_c=w_c, b_ru=b_ru,
                        b_c=b_c, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "GRUBlockCell", _inputs_flat, _attrs, _result)
  _result = _GRUBlockCellOutput._make(_result)
  return _result

GRUBlockCell = tf_export("raw_ops.GRUBlockCell")(_ops.to_raw_op(gru_block_cell))

def gru_block_cell_eager_fallback(x, h_prev, w_ru, w_c, b_ru, b_c, name, ctx):
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, h_prev, w_ru, w_c, b_ru, b_c], ctx, [_dtypes.float32, ])
  (x, h_prev, w_ru, w_c, b_ru, b_c) = _inputs_T
  _inputs_flat = [x, h_prev, w_ru, w_c, b_ru, b_c]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"GRUBlockCell", 4, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "GRUBlockCell", _inputs_flat, _attrs, _result)
  _result = _GRUBlockCellOutput._make(_result)
  return _result

_GRUBlockCellGradOutput = collections.namedtuple(
    "GRUBlockCellGrad",
    ["d_x", "d_h_prev", "d_c_bar", "d_r_bar_u_bar"])

def gru_block_cell_grad(x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h, name=None):
  r"""Computes the GRU cell back-propagation for 1 time step.

Args
      x: Input to the GRU cell.
      h_prev: State input from the previous GRU cell.
      w_ru: Weight matrix for the reset and update gate.
      w_c: Weight matrix for the cell connection gate.
      b_ru: Bias vector for the reset and update gate.
      b_c: Bias vector for the cell connection gate.
      r: Output of the reset gate.
      u: Output of the update gate.
      c: Output of the cell connection gate.
      d_h: Gradients of the h_new wrt to objective function.

Returns
      d_x: Gradients of the x wrt to objective function.
      d_h_prev: Gradients of the h wrt to objective function.
      d_c_bar Gradients of the c_bar wrt to objective function.
      d_r_bar_u_bar Gradients of the r_bar & u_bar wrt to objective function.

This kernel op implements the following mathematical equations:

Note on notation of the variables:

Additional notes for clarity:

`w_ru` can be segmented into 4 different matrices.
  ```
  w_ru = [w_r_x w_u_x
          w_r_h_prev w_u_h_prev]
  ```
  Similarly, `w_c` can be segmented into 2 different matrices.
  ```
  w_c = [w_c_x w_c_h_prevr]
  ```
  Same goes for biases.
  ```
  b_ru = [b_ru_x b_ru_h]
  b_c = [b_c_x b_c_h]
  ```
  Another note on notation:
  ```
  d_x = d_x_component_1 + d_x_component_2

where d_x_component_1 = d_r_bar * w_r_x^T + d_u_bar * w_r_x^T
  and d_x_component_2 = d_c_bar * w_c_x^T

d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + d_h \circ u
  where d_h_prev_componenet_1 = d_r_bar * w_r_h_prev^T + d_u_bar * w_r_h_prev^T
  ```

Mathematics behind the Gradients below:
  ```
  d_c_bar = d_h \circ (1-u) \circ (1-c \circ c)
  d_u_bar = d_h \circ (h-c) \circ u \circ (1-u)

d_r_bar_u_bar = [d_r_bar d_u_bar]

[d_x_component_1 d_h_prev_component_1] = d_r_bar_u_bar * w_ru^T

[d_x_component_2 d_h_prevr] = d_c_bar * w_c^T

d_x = d_x_component_1 + d_x_component_2

d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + u
  ```
  Below calculation is performed in the python wrapper for the Gradients
  (not in the gradient kernel.)
  ```
  d_w_ru = x_h_prevr^T * d_c_bar

d_w_c = x_h_prev^T * d_r_bar_u_bar

d_b_ru = sum of d_r_bar_u_bar along axis = 0

d_b_c = sum of d_c_bar along axis = 0
  ```

Args:
    x: A `Tensor`. Must be one of the following types: `float32`.
    h_prev: A `Tensor`. Must have the same type as `x`.
    w_ru: A `Tensor`. Must have the same type as `x`.
    w_c: A `Tensor`. Must have the same type as `x`.
    b_ru: A `Tensor`. Must have the same type as `x`.
    b_c: A `Tensor`. Must have the same type as `x`.
    r: A `Tensor`. Must have the same type as `x`.
    u: A `Tensor`. Must have the same type as `x`.
    c: A `Tensor`. Must have the same type as `x`.
    d_h: A `Tensor`. Must have the same type as `x`.
    name: A name for the operation (optional).

Returns:
    A tuple of `Tensor` objects (d_x, d_h_prev, d_c_bar, d_r_bar_u_bar).

d_x: A `Tensor`. Has the same type as `x`.
    d_h_prev: A `Tensor`. Has the same type as `x`.
    d_c_bar: A `Tensor`. Has the same type as `x`.
    d_r_bar_u_bar: A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "GRUBlockCellGrad", name, x, h_prev, w_ru, w_c, b_ru, b_c, r, u,
        c, d_h)
      _result = _GRUBlockCellGradOutput._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return gru_block_cell_grad_eager_fallback(
          x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "GRUBlockCellGrad", x=x, h_prev=h_prev, w_ru=w_ru, w_c=w_c, b_ru=b_ru,
                            b_c=b_c, r=r, u=u, c=c, d_h=d_h, name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("T", _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "GRUBlockCellGrad", _inputs_flat, _attrs, _result)
  _result = _GRUBlockCellGradOutput._make(_result)
  return _result

GRUBlockCellGrad = tf_export("raw_ops.GRUBlockCellGrad")(_ops.to_raw_op(gru_block_cell_grad))

def gru_block_cell_grad_eager_fallback(x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h, name, ctx):
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h], ctx, [_dtypes.float32, ])
  (x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h) = _inputs_T
  _inputs_flat = [x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"GRUBlockCellGrad", 4, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "GRUBlockCellGrad", _inputs_flat, _attrs, _result)
  _result = _GRUBlockCellGradOutput._make(_result)
  return _result

_LSTMBlockCellOutput = collections.namedtuple(
    "LSTMBlockCell",
    ["i", "cs", "f", "o", "ci", "co", "h"])

def lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None):
  r"""Computes the LSTM cell forward propagation for 1 time step.

This implementation uses 1 weight matrix and 1 bias vector, and there's an
  optional peephole connection.

This kernel op implements the following mathematical equations:

```python
  xh = [x, h_prev]
  [i, f, ci, o] = xh * w + b
  f = f + forget_bias

if not use_peephole:
    wci = wcf = wco = 0

i = sigmoid(cs_prev * wci + i)
  f = sigmoid(cs_prev * wcf + f)
  ci = tanh(ci)

cs = ci .* i + cs_prev .* f
  cs = clip(cs, cell_clip)

o = sigmoid(cs * wco + o)
  co = tanh(cs)
  h = co .* o
  ```

Args:
    x: A `Tensor`. Must be one of the following types: `half`, `float32`.
      The input to the LSTM cell, shape (batch_size, num_inputs).
    cs_prev: A `Tensor`. Must have the same type as `x`.
      Value of the cell state at previous time step.
    h_prev: A `Tensor`. Must have the same type as `x`.
      Output of the previous cell at previous time step.
    w: A `Tensor`. Must have the same type as `x`. The weight matrix.
    wci: A `Tensor`. Must have the same type as `x`.
      The weight matrix for input gate peephole connection.
    wcf: A `Tensor`. Must have the same type as `x`.
      The weight matrix for forget gate peephole connection.
    wco: A `Tensor`. Must have the same type as `x`.
      The weight matrix for output gate peephole connection.
    b: A `Tensor`. Must have the same type as `x`. The bias vector.
    forget_bias: An optional `float`. Defaults to `1`. The forget gate bias.
    cell_clip: An optional `float`. Defaults to `3`.
      Value to clip the 'cs' value to.
    use_peephole: An optional `bool`. Defaults to `False`.
      Whether to use peephole weights.
    name: A name for the operation (optional).

Returns:
    A tuple of `Tensor` objects (i, cs, f, o, ci, co, h).

i: A `Tensor`. Has the same type as `x`.
    cs: A `Tensor`. Has the same type as `x`.
    f: A `Tensor`. Has the same type as `x`.
    o: A `Tensor`. Has the same type as `x`.
    ci: A `Tensor`. Has the same type as `x`.
    co: A `Tensor`. Has the same type as `x`.
    h: A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "LSTMBlockCell", name, x, cs_prev, h_prev, w, wci, wcf, wco, b,
        "forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole",
        use_peephole)
      _result = _LSTMBlockCellOutput._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return lstm_block_cell_eager_fallback(
          x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=forget_bias,
          cell_clip=cell_clip, use_peephole=use_peephole, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  if forget_bias is None:
    forget_bias = 1
  forget_bias = _execute.make_float(forget_bias, "forget_bias")
  if cell_clip is None:
    cell_clip = 3
  cell_clip = _execute.make_float(cell_clip, "cell_clip")
  if use_peephole is None:
    use_peephole = False
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "LSTMBlockCell", x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci,
                         wcf=wcf, wco=wco, b=b, forget_bias=forget_bias,
                         cell_clip=cell_clip, use_peephole=use_peephole,
                         name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("forget_bias", _op.get_attr("forget_bias"), "cell_clip",
              _op.get_attr("cell_clip"), "use_peephole",
              _op._get_attr_bool("use_peephole"), "T",
              _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "LSTMBlockCell", _inputs_flat, _attrs, _result)
  _result = _LSTMBlockCellOutput._make(_result)
  return _result

LSTMBlockCell = tf_export("raw_ops.LSTMBlockCell")(_ops.to_raw_op(lstm_block_cell))

def lstm_block_cell_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias, cell_clip, use_peephole, name, ctx):
  if forget_bias is None:
    forget_bias = 1
  forget_bias = _execute.make_float(forget_bias, "forget_bias")
  if cell_clip is None:
    cell_clip = 3
  cell_clip = _execute.make_float(cell_clip, "cell_clip")
  if use_peephole is None:
    use_peephole = False
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], ctx, [_dtypes.half, _dtypes.float32, ])
  (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T
  _inputs_flat = [x, cs_prev, h_prev, w, wci, wcf, wco, b]
  _attrs = ("forget_bias", forget_bias, "cell_clip", cell_clip,
  "use_peephole", use_peephole, "T", _attr_T)
  _result = _execute.execute(b"LSTMBlockCell", 7, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "LSTMBlockCell", _inputs_flat, _attrs, _result)
  _result = _LSTMBlockCellOutput._make(_result)
  return _result

_LSTMBlockCellGradOutput = collections.namedtuple(
    "LSTMBlockCellGrad",
    ["cs_prev_grad", "dicfo", "wci_grad", "wcf_grad", "wco_grad"])

def lstm_block_cell_grad(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None):
  r"""Computes the LSTM cell backward propagation for 1 timestep.

This implementation is to be used in conjunction of LSTMBlockCell.

Args:
    x: A `Tensor`. Must be one of the following types: `half`, `float32`.
      The input to the LSTM cell, shape (batch_size, num_inputs).
    cs_prev: A `Tensor`. Must have the same type as `x`.
      The previous cell state.
    h_prev: A `Tensor`. Must have the same type as `x`. The previous h state.
    w: A `Tensor`. Must have the same type as `x`. The weight matrix.
    wci: A `Tensor`. Must have the same type as `x`.
      The weight matrix for input gate peephole connection.
    wcf: A `Tensor`. Must have the same type as `x`.
      The weight matrix for forget gate peephole connection.
    wco: A `Tensor`. Must have the same type as `x`.
      The weight matrix for output gate peephole connection.
    b: A `Tensor`. Must have the same type as `x`. The bias vector.
    i: A `Tensor`. Must have the same type as `x`. The input gate.
    cs: A `Tensor`. Must have the same type as `x`.
      The cell state before the tanh.
    f: A `Tensor`. Must have the same type as `x`. The forget gate.
    o: A `Tensor`. Must have the same type as `x`. The output gate.
    ci: A `Tensor`. Must have the same type as `x`. The cell input.
    co: A `Tensor`. Must have the same type as `x`. The cell after the tanh.
    cs_grad: A `Tensor`. Must have the same type as `x`.
      The current gradient of cs.
    h_grad: A `Tensor`. Must have the same type as `x`.
      The gradient of h vector.
    use_peephole: A `bool`. Whether the cell uses peephole connections.
    name: A name for the operation (optional).

Returns:
    A tuple of `Tensor` objects (cs_prev_grad, dicfo, wci_grad, wcf_grad, wco_grad).

cs_prev_grad: A `Tensor`. Has the same type as `x`.
    dicfo: A `Tensor`. Has the same type as `x`.
    wci_grad: A `Tensor`. Has the same type as `x`.
    wcf_grad: A `Tensor`. Has the same type as `x`.
    wco_grad: A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  tld = _ctx._thread_local_data
  if tld.is_eager:
    try:
      _result = pywrap_tfe.TFE_Py_FastPathExecute(
        _ctx, "LSTMBlockCellGrad", name, x, cs_prev, h_prev, w, wci, wcf, wco,
        b, i, cs, f, o, ci, co, cs_grad, h_grad, "use_peephole", use_peephole)
      _result = _LSTMBlockCellGradOutput._make(_result)
      return _result
    except _core._NotOkStatusException as e:
      _ops.raise_from_not_ok_status(e, name)
    except _core._FallbackException:
      pass
    try:
      return lstm_block_cell_grad_eager_fallback(
          x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co,
          cs_grad, h_grad, use_peephole=use_peephole, name=name, ctx=_ctx)
    except _core._SymbolicException:
      pass  # Add nodes to the TensorFlow graph.
  # Add nodes to the TensorFlow graph.
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _, _, _op, _outputs = _op_def_library._apply_op_helper(
        "LSTMBlockCellGrad", x=x, cs_prev=cs_prev, h_prev=h_prev, w=w,
                             wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f,
                             o=o, ci=ci, co=co, cs_grad=cs_grad,
                             h_grad=h_grad, use_peephole=use_peephole,
                             name=name)
  _result = _outputs[:]
  if _execute.must_record_gradient():
    _attrs = ("use_peephole", _op._get_attr_bool("use_peephole"), "T",
              _op._get_attr_type("T"))
    _inputs_flat = _op.inputs
    _execute.record_gradient(
        "LSTMBlockCellGrad", _inputs_flat, _attrs, _result)
  _result = _LSTMBlockCellGradOutput._make(_result)
  return _result

LSTMBlockCellGrad = tf_export("raw_ops.LSTMBlockCellGrad")(_ops.to_raw_op(lstm_block_cell_grad))

def lstm_block_cell_grad_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name, ctx):
  use_peephole = _execute.make_bool(use_peephole, "use_peephole")
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad], ctx, [_dtypes.half, _dtypes.float32, ])
  (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad) = _inputs_T
  _inputs_flat = [x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad]
  _attrs = ("use_peephole", use_peephole, "T", _attr_T)
  _result = _execute.execute(b"LSTMBlockCellGrad", 5, inputs=_inputs_flat,
                             attrs=_attrs, ctx=ctx, name=name)
  if _execute.must_record_gradient():
    _execute.record_gradient(
        "LSTMBlockCellGrad", _inputs_flat, _attrs, _result)
  _result = _LSTMBlockCellGradOutput._make(_result)
  return _result