# Copyright 2020 Google LLC
#
# 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
#
#     https://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.

from jax import core
from jax import numpy as jnp
from jax._src import device_array
from jax._src.lib import xla_client
from jax._src.lib import xla_bridge

SUPPORTED_DTYPES = set([jnp.int8, jnp.int16, jnp.int32, jnp.int64,
                        jnp.uint8, jnp.uint16, jnp.uint32, jnp.uint64,
                        jnp.float16, jnp.bfloat16, jnp.float32, jnp.float64])


def to_dlpack(x: device_array.DeviceArrayProtocol, take_ownership: bool = False):
  """Returns a DLPack tensor that encapsulates a DeviceArray `x`.

  Takes ownership of the contents of `x`; leaves `x` in an invalid/deleted
  state.

  Args:
    x: a `DeviceArray`, on either CPU or GPU.
    take_ownership: If ``True``, JAX hands ownership of the buffer to DLPack,
      and the consumer is free to mutate the buffer; the JAX buffer acts as if
      it were deleted. If ``False``, JAX retains ownership of the buffer; it is
      undefined behavior if the DLPack consumer writes to a buffer that JAX
      owns.
  """
  if not isinstance(x, device_array.DeviceArray):
    raise TypeError("Argument to to_dlpack must be a DeviceArray, got {}"
                    .format(type(x)))
  return xla_client._xla.buffer_to_dlpack_managed_tensor(
      x.device_buffer, take_ownership=take_ownership)

def from_dlpack(dlpack):
  """Returns a `DeviceArray` representation of a DLPack tensor `dlpack`.

  The returned `DeviceArray` shares memory with `dlpack`.

  Args:
    dlpack: a DLPack tensor, on either CPU or GPU.
  """
  cpu_backend = xla_bridge.get_backend("cpu")
  try:
    gpu_backend = xla_bridge.get_backend("gpu")
  except RuntimeError:
    gpu_backend = None
  buf = xla_client._xla.dlpack_managed_tensor_to_buffer(
      dlpack, cpu_backend, gpu_backend)

  xla_shape = buf.xla_shape()
  assert not xla_shape.is_tuple()
  aval = core.ShapedArray(xla_shape.dimensions(), xla_shape.numpy_dtype())
  return device_array.make_device_array(aval, buf.device(), buf)  # pytype: disable=attribute-error