text: # 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.interpreters import xla
  from jax.lib import xla_client
  from jax.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: xla.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, xla.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, backend=None):
    """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.
      backend: experimental, optional: the platform on which `dlpack` lives.
    """
    # TODO(phawkins): ideally the user wouldn't need to provide a backend and we
    # would be able to figure it out from the DLPack.
    backend = backend or xla_bridge.get_backend()
    client = getattr(backend, "client", backend)
    buf = xla_client._xla.dlpack_managed_tensor_to_buffer(dlpack, client)
    xla_shape = buf.xla_shape()
    assert not xla_shape.is_tuple()
    aval = core.ShapedArray(xla_shape.dimensions(), xla_shape.numpy_dtype())
    return xla.make_device_array(aval, buf.device(), buf)  # pytype: disable=attribute-error
  