"""Python wrappers around TensorFlow ops.

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

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.tf_export import tf_export


@tf_export('resampler')
def resampler(data, warp, name=None):
  r"""Resampler op.

  Args:
    data: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
    warp: A `Tensor`. Must have the same type as `data`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "Resampler", data=data, warp=warp, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("T", _op.get_attr("T"))
    _execute.record_gradient(
      "Resampler", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result

  else:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._eager_context.device_name, "Resampler",
        name, _ctx._post_execution_callbacks, data, warp)
      return _result
    except _core._FallbackException:
      return resampler_eager_fallback(
          data, warp, name=name, ctx=_ctx)
    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)


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

_ops.RegisterShape("Resampler")(None)


_resampler_grad_outputs = ["grad_data", "grad_warp"]
_ResamplerGradOutput = _collections.namedtuple(
    "ResamplerGrad", _resampler_grad_outputs)


@tf_export('resampler_grad')
def resampler_grad(data, warp, grad_output, name=None):
  r"""Resampler Grad op.

  Args:
    data: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
    warp: A `Tensor`. Must have the same type as `data`.
    grad_output: A `Tensor`. Must have the same type as `data`.
    name: A name for the operation (optional).

  Returns:
    A tuple of `Tensor` objects (grad_data, grad_warp).

    grad_data: A `Tensor`. Has the same type as `data`.
    grad_warp: A `Tensor`. Has the same type as `data`.
  """
  _ctx = _context._context
  if _ctx is None or not _ctx._eager_context.is_eager:
    _, _, _op = _op_def_lib._apply_op_helper(
        "ResamplerGrad", data=data, warp=warp, grad_output=grad_output,
        name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("T", _op.get_attr("T"))
    _execute.record_gradient(