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.
  
  import unittest
  
  from absl.testing import absltest, parameterized
  import numpy as np
  
  import jax
  from jax.config import config
  import jax.dlpack
  import jax.numpy as jnp
  from jax import test_util as jtu
  
  config.parse_flags_with_absl()
  
  try:
    import torch
    import torch.utils.dlpack
  except ImportError:
    torch = None
  
  try:
    import cupy
  except ImportError:
    cupy = None
  
  
  dlpack_dtypes = [jnp.int8, jnp.int16, jnp.int32, jnp.int64,
                     jnp.uint8, jnp.uint16, jnp.uint32, jnp.uint64,
                     jnp.float16, jnp.float32, jnp.float64]
  all_dtypes = dlpack_dtypes + [jnp.bool_, jnp.bfloat16]
  torch_dtypes = [jnp.int8, jnp.int16, jnp.int32, jnp.int64,
                  jnp.uint8, jnp.float16, jnp.float32, jnp.float64]
  
  nonempty_nonscalar_array_shapes = [(4,), (3, 4), (2, 3, 4)]
  empty_array_shapes = []
  empty_array_shapes += [(0,), (0, 4), (3, 0),]
  nonempty_nonscalar_array_shapes += [(3, 1), (1, 4), (2, 1, 4)]
  
  nonempty_array_shapes = [()] + nonempty_nonscalar_array_shapes
  all_shapes = nonempty_array_shapes + empty_array_shapes
  
  class DLPackTest(jtu.JaxTestCase):
    def setUp(self):
      if jtu.device_under_test() == "tpu":
        self.skipTest("DLPack not supported on TPU")
  
    @parameterized.named_parameters(jtu.cases_from_list(
       {"testcase_name": "_{}".format(
          jtu.format_shape_dtype_string(shape, dtype)),
       "shape": shape, "dtype": dtype}
       for shape in all_shapes
       for dtype in dlpack_dtypes))
    def testJaxRoundTrip(self, shape, dtype):
      rng = jtu.rand_default()
      np = rng(shape, dtype)
      x = jnp.array(np)
      dlpack = jax.dlpack.to_dlpack(x)
      y = jax.dlpack.from_dlpack(dlpack)
      self.assertAllClose(np.astype(x.dtype), y, check_dtypes=True)
  
      self.assertRaisesRegex(RuntimeError,
                             "DLPack tensor may be consumed at most once",
                             lambda: jax.dlpack.from_dlpack(dlpack))
  
    @parameterized.named_parameters(jtu.cases_from_list(
       {"testcase_name": "_{}".format(
          jtu.format_shape_dtype_string(shape, dtype)),
       "shape": shape, "dtype": dtype}
       for shape in all_shapes
       for dtype in torch_dtypes))
    @unittest.skipIf(not torch, "Test requires PyTorch")
    def testTorchToJax(self, shape, dtype):
      rng = jtu.rand_default()
      np = rng(shape, dtype)
      x = torch.from_numpy(np)
      x = x.cuda() if jtu.device_under_test() == "gpu" else x
      dlpack = torch.utils.dlpack.to_dlpack(x)
      y = jax.dlpack.from_dlpack(dlpack)
      self.assertAllClose(np, y, check_dtypes=True)
  
    @parameterized.named_parameters(jtu.cases_from_list(
       {"testcase_name": "_{}".format(
          jtu.format_shape_dtype_string(shape, dtype)),
       "shape": shape, "dtype": dtype}
       for shape in all_shapes
       for dtype in torch_dtypes))
    @unittest.skipIf(not torch, "Test requires PyTorch")
    # TODO(phawkins): the dlpack destructor issues errors in jaxlib 0.1.38.
    def testJaxToTorch(self, shape, dtype):
      rng = jtu.rand_default()
      np = rng(shape, dtype)
      x = jnp.array(np)
      dlpack = jax.dlpack.to_dlpack(x)
      y = torch.utils.dlpack.from_dlpack(dlpack)
      self.assertAllClose(np, y.numpy(), check_dtypes=True)
  
  
  class CudaArrayInterfaceTest(jtu.JaxTestCase):
  
    def setUp(self):
      if jtu.device_under_test() != "gpu":
        self.skipTest("__cuda_array_interface__ is only supported on GPU")
  
    @parameterized.named_parameters(jtu.cases_from_list(
       {"testcase_name": "_{}".format(
          jtu.format_shape_dtype_string(shape, dtype)),
       "shape": shape, "dtype": dtype}
       for shape in all_shapes
       for dtype in dlpack_dtypes))
    @unittest.skipIf(not cupy, "Test requires CuPy")
    def testJaxToCuPy(self, shape, dtype):
      rng = jtu.rand_default()
      x = rng(shape, dtype)
      y = jnp.array(x)
      z = cupy.asarray(y)
      self.assertEqual(y.__cuda_array_interface__["data"][0],
                       z.__cuda_array_interface__["data"][0])
      self.assertAllClose(x, cupy.asnumpy(z), check_dtypes=True)
  
  
  if __name__ == "__main__":
    absltest.main()
  