old_contents: # Copyright 2019 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 functools
  from functools import partial
  import itertools
  import operator
  
  import jaxlib.mlir.ir as ir
  
  from jaxlib import xla_client
  
  from .mhlo_helpers import custom_call
  
  try:
    from .cuda import _cuda_prng
    for _name, _value in _cuda_prng.registrations().items():
      xla_client.register_custom_call_target(_name, _value, platform="CUDA")
  except ImportError:
    _cuda_prng = None
  
  try:
    from .rocm import _hip_prng
    for _name, _value in _hip_prng.registrations().items():
      xla_client.register_custom_call_target(_name, _value, platform="ROCM")
  except ImportError:
    _hip_prng = None
  
  _prod = lambda xs: functools.reduce(operator.mul, xs, 1)
  
  
  def _threefry2x32_lowering(prng, platform, keys, data):
    """ThreeFry2x32 kernel for GPU."""
    assert len(keys) == 2, keys
    assert len(data) == 2, data
    assert (ir.RankedTensorType(keys[0].type).element_type ==
            ir.IntegerType.get_unsigned(32)), keys[0].type
    typ = keys[0].type
    dims = ir.RankedTensorType(typ).shape
  
    for x in itertools.chain(keys, data):
      assert x.type == typ, (x.type, typ)
    ndims = len(dims)
  
    opaque = prng.threefry2x32_descriptor(_prod(dims))
    layout = tuple(range(ndims - 1, -1, -1))
    return custom_call(
        f"{platform}_threefry2x32",
        [typ, typ],
        [keys[0], keys[1], data[0], data[1]],
        backend_config=opaque,
        operand_layouts=[layout] * 4,
        result_layouts=[layout] * 2)
  
  cuda_threefry2x32 = partial(_threefry2x32_lowering, _cuda_prng, "cuda")
  rocm_threefry2x32 = partial(_threefry2x32_lowering, _hip_prng, "rocm")
  
new_contents: # Copyright 2019 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 functools
  from functools import partial
  import itertools
  import operator
  
  import jaxlib.mlir.ir as ir
  
  from jaxlib import xla_client
  
  from .mhlo_helpers import custom_call
  
  try:
    from .cuda import _cuda_prng
    for _name, _value in _cuda_prng.registrations().items():
      xla_client.register_custom_call_target(_name, _value, platform="CUDA")
  except ImportError:
    _cuda_prng = None
  
  try:
    from .rocm import _hip_prng
    for _name, _value in _hip_prng.registrations().items():
      xla_client.register_custom_call_target(_name, _value, platform="ROCM")
  except ImportError:
    _hip_prng = None
  
  _prod = lambda xs: functools.reduce(operator.mul, xs, 1)
  
  
  def _threefry2x32_lowering(prng, platform, keys, data):
    """ThreeFry2x32 kernel for GPU."""
    assert len(keys) == 2, keys
    assert len(data) == 2, data
    assert (ir.RankedTensorType(keys[0].type).element_type ==
            ir.IntegerType.get_unsigned(32)), keys[0].type
    typ = keys[0].type
    dims = ir.RankedTensorType(typ).shape
  
    for x in itertools.chain(keys, data):
      assert x.type == typ, (x.type, typ)
    ndims = len(dims)
  
    opaque = prng.threefry2x32_descriptor(_prod(dims))
    layout = tuple(range(ndims - 1, -1, -1))
    return custom_call(
        f"{platform}_threefry2x32",
        [typ, typ],
        [keys[0], keys[1], data[0], data[1]],
        backend_config=opaque,
        operand_layouts=[layout] * 4,
        result_layouts=[layout] * 2)
  
  cuda_threefry2x32 = partial(_threefry2x32_lowering, _cuda_prng, "cuda")
  rocm_threefry2x32 = partial(_threefry2x32_lowering, _hip_prng, "hip")
  
subject: Fix unit test failures on ROCm.