rocm_jax/jax/experimental/jax2tf/tests/shape_poly_test.py

# Copyright 2020 The JAX Authors.
#
# 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.
"""Tests for the shape-polymorphic jax2tf conversion."""
import contextlib
import unittest

from absl.testing import absltest, parameterized
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union

import collections
import functools
from functools import partial
import operator as op
import re

import jax
from jax import core
from jax.experimental import jax2tf
from jax.experimental.jax2tf import shape_poly
from jax.experimental import pjit
from jax import lax
import jax.numpy as jnp
from jax import random
from jax._src import test_util as jtu
from jax._src import util
from jax._src.lax import lax as lax_internal
from jax._src.lax import control_flow as lax_control_flow
from jax._src.lib import xla_client
import numpy as np

from jax.experimental.jax2tf.tests import tf_test_util

import tensorflow as tf  # type: ignore[import]

from jax.config import config
from jax._src.config import numpy_dtype_promotion

config.parse_flags_with_absl()

# Import after parsing flags
from jax.experimental.jax2tf.tests import primitive_harness
from jax.experimental.jax2tf.tests.primitive_harness import Harness, CustomArg, RandArg, StaticArg
from jax.experimental.jax2tf.tests.jax2tf_limitations import Jax2TfLimitation

PS = jax2tf.PolyShape
_f32 = np.float32
_i32 = np.int32

expect_error_associative_scan = (
    (None, None) if (not config.jax2tf_default_native_serialization or
                                         jtu.device_under_test() == "tpu") else
    (NotImplementedError,
     "associative scan over axis of non-constant size"))


class DimExprTest(tf_test_util.JaxToTfTestCase):

  def test_parse_poly_spec(self):
    self.assertEqual((2, 3), shape_poly._parse_spec(None, (2, 3)))
    self.assertEqual((2, 3), shape_poly._parse_spec("2, 3", (2, 3)))
    self.assertEqual((2, 3), shape_poly._parse_spec("2, _", (2, 3)))
    self.assertEqual((2, 3), shape_poly._parse_spec("2, ...", (2, 3)))
    self.assertEqual((2, 3), shape_poly._parse_spec("...", (2, 3)))
    self.assertEqual((2, 3), shape_poly._parse_spec(" ( 2 , 3 ) ", (2, 3)))

    a, b = shape_poly._parse_spec("a, b", (2, 3))
    self.assertEqual((a, 3), shape_poly._parse_spec("(a, ...) ", (None, 3)))
    tshape = tf.TensorShape([None, 3])
    self.assertEqual((a, 3), shape_poly._parse_spec("(a, ...) ", tshape))

  a, b = shape_poly._parse_spec("a, b", (2, 3))
  @parameterized.named_parameters(
      dict(testcase_name=f"_{dim_spec=}",
           dim_spec=dim_spec, dim_poly=dim_poly)
      for dim_spec, dim_poly in [
          ("2*a*b", 2 * a * b),
          ("-2 * a^2 * b + b^2", -2 * a * a * b + b * b),
          ("-2 * a^2 * b + -1 *b^2*a", -2 * a * a * b - a * b * b),
          ("3 * a * b * a + -2", 3 * a * b * a - 2),
          ("a + 1", a + 1),
          ("a + -1", a - 1),
  ])
  def test_parse_poly_spec_poly(self,
                                dim_spec="3 * a * b * a + -2",
                                dim_poly=3 * a * b * a - 2):
    # For internal usage only (the polymorphic_shapes of VJP) we need to
    # parse polynomials.
    self.assertEqual((dim_poly,), shape_poly._parse_spec(dim_spec, (2,)))
    self.assertEqual((dim_poly,), shape_poly._parse_spec(str(dim_poly), (2,)))

  @parameterized.named_parameters(
      dict(testcase_name=f"_{dim_spec=}",
           dim_spec=dim_spec, dim_poly=dim_poly)
      for dim_spec, dim_poly in [
          ("2*a*b", 2 * a * b),
          ("-2 * a^2 * b + b^2", -2 * a * a * b + b * b),
          ("-2 * a^2 * b + -1 *b^2*a", -2 * a * a * b - a * b * b),
          ("3 * a * b * a + -2", 3 * a * b * a - 2),
          ("a + 1", a + 1),
          ("a + -1", a - 1),
  ])
  def test_parse_poly_spec_shapeenv(self,
                                dim_spec="3 * a * b * a + -2",
                                dim_poly=3 * a * b * a - 2):
    # For internal usage only (the polymorphic_shapes of VJP) we need to
    # parse polynomials.
    self.assertEqual((dim_poly,), shape_poly._parse_spec(dim_spec, (2,)))
    self.assertEqual((dim_poly,), shape_poly._parse_spec(str(dim_poly), (2,)))

  def test_dim_vars(self):
    a, b, a1 = shape_poly._parse_spec("a, b, a", (2, 3, 2))
    self.assertEqual(True, a == a)
    self.assertEqual(True, a == a1)
    self.assertEqual(False, a != a)

    self.assertFalse(a == b)
    self.assertTrue(a != b)

    self.assertLen({a, a}, 1)
    self.assertLen({a, b}, 2)
    self.assertIn(a, {a, b})
    self.assertIn(b, {a, b})
    self.assertIn(a, [a, b])
    self.assertIn(b, [a, b])

  def test_get_vars(self):
    a, b = shape_poly._parse_spec("a, b", (2, 3))

    self.assertEqual({"a"}, a.get_vars())
    self.assertEqual({"a", "b"}, (a * b * a).get_vars())

  def test_evaluate(self):
    a, b = shape_poly._parse_spec("a, b", (2, 3))

    self.assertEqual(1, (a * a - b).evaluate(dict(a=2, b=3)))
    self.assertEqual(1, ((a * a) // b).evaluate(dict(a=2, b=3)))
    self.assertEqual(4, ((a * a) % b).evaluate(dict(a=5, b=7)))

  def test_dim_vars_symbolic_equal(self):
    a, b = shape_poly._parse_spec("a, b", (2, 3))
    self.assertTrue(core.symbolic_equal_dim(a, a))
    self.assertFalse(core.symbolic_equal_dim(a, 1))
    self.assertFalse(core.symbolic_equal_dim(a, b))

    self.assertTrue(core.symbolic_equal_one_of_dim(a, [2, a]))
    self.assertFalse(core.symbolic_equal_one_of_dim(a, [2, b]))
    self.assertFalse(core.symbolic_equal_one_of_dim(a, []))

    self.assertTrue(core.symbolic_equal_one_of_dim(2, [a, 3, 2]))
    self.assertFalse(core.symbolic_equal_one_of_dim(1, [2, b]))
    self.assertFalse(core.symbolic_equal_one_of_dim(3, []))

    self.assertTrue(core.symbolic_equal_dim(1, jnp.add(0, 1)))  # A DeviceArray
    with self.assertRaisesRegex(TypeError,
                                re.escape("Shapes must be 1D sequences of concrete values of integer type, got (1, 'a').")):
      self.assertTrue(core.symbolic_equal_dim(1, "a"))

  def test_poly_bounds(self):
    a, b = shape_poly._parse_spec("a, b", (2, 3))
    bounded_le4 = 5 - a
    bounded_ge2 = b + 1
    bounded_ge0_le4 = a % 5
    self.assertEqual(a.bounds(), (1, np.PINF))
    self.assertEqual(bounded_le4.bounds(), (np.NINF, 4))
    self.assertEqual(bounded_ge2.bounds(), (2, np.PINF))
    self.assertEqual(bounded_ge0_le4.bounds(), (0, 4))

    # Additions
    self.assertEqual((bounded_ge0_le4 + bounded_le4).bounds(), (np.NINF, 8))
    self.assertEqual((bounded_ge0_le4 + bounded_ge2).bounds(), (2, np.PINF))
    self.assertEqual((bounded_le4 + bounded_ge2).bounds(), (np.NINF, np.PINF))

    # Subtractions
    self.assertEqual((bounded_ge0_le4 - bounded_le4).bounds(), (-4, np.PINF))
    self.assertEqual((- bounded_ge0_le4 + bounded_le4).bounds(), (np.NINF, 4))
    self.assertEqual((bounded_ge0_le4 - bounded_ge2).bounds(), (np.NINF, 2))
    self.assertEqual((- bounded_ge0_le4 + bounded_ge2).bounds(), (-2, np.PINF))
    self.assertEqual((bounded_le4 - bounded_ge2).bounds(), (np.NINF, 2))
    self.assertEqual((- bounded_le4 + bounded_ge2).bounds(), (-2, np.PINF))

    # Multiplications
    self.assertEqual((2 * a - 3).bounds(), (-1, np.PINF))
    self.assertEqual((-2 * a - 3).bounds(), (np.NINF, -5))
    self.assertEqual((3 * a * b * b + 5 * a - 7).bounds(), (1, np.PINF))
    self.assertEqual((3 * a * b * b - 5 * a - 7).bounds(), (np.NINF, np.PINF))
    self.assertEqual((a + b - a * b + a * b * a).bounds(), (np.NINF, np.PINF))
    self.assertEqual((a + 2 * b - a).bounds(), (2, np.PINF))
    self.assertEqual((a + 2 * b - a).bounds(), (2, np.PINF))

    # mod
    self.assertEqual(((b + 1) % 2).bounds(), (0, 1))
    self.assertEqual(((b + 1) % -2).bounds(), (-1, 0))
    self.assertEqual(((b - 4) % 2).bounds(), (0, 1))
    self.assertEqual(((b + 1) % a).bounds(), (0, np.PINF))
    self.assertEqual((11 % (a + 1)).bounds(), (0, np.PINF))
    self.assertEqual((-11 % (a + 1)).bounds(), (0, np.PINF))
    self.assertEqual((b % (a - 2)).bounds(), (np.NINF, np.PINF))

    # floordiv
    self.assertEqual(((a + 4) // 2).bounds(), (2, np.PINF))
    self.assertEqual(((a + 4) // -2).bounds(), (np.NINF, -3))
    self.assertEqual(((a + 5) // 2).bounds(), (3, np.PINF))
    self.assertEqual(((a + 5) // -2).bounds(), (np.NINF, -3))
    self.assertEqual((11 // (a + 1)).bounds(), (0, 5))
    self.assertEqual((-11 // (a + 1)).bounds(), (-6, -1))
    self.assertEqual((-11 // (- a)).bounds(), (0, 11))  # finite negative dividend, infinite divisor
    self.assertEqual(((b + 1) // (a + 1)).bounds(), (0, np.PINF))
    self.assertEqual((-b // (a + 1)).bounds(), (np.NINF, -1))




    # Generate test cases for floordiv and mod: (a + N) // +-2, (N - a) // +-2
    # and then evaluate them for a = 1, 5, 10000
    div_mod_atoms = [
        operation(op1 + n, div)
        for op1 in (a, a + 10, a + 11, -a, -a + 10, -a + 11)
        for n in (-3, -1, 0, 1, 3)
        for div in (-2, 2, a + 4, -4 - a)  # Either negative, or positive
        for operation in (op.floordiv, op.mod)
        ]
    for atom in div_mod_atoms:
      lb, ub = atom.bounds()
      self.assertLessEqual(lb, ub)
      for a_val in (1, 5, 10000):
        atom_val = atom.evaluate(dict(a=a_val))
        self.assertGreaterEqual(atom_val, lb)
        self.assertLessEqual(atom_val, ub)

  def test_poly_equal(self):
    a, b = shape_poly._parse_spec("a, b", (2, 3))
    poly3 = a + 3 - a
    self.assertTrue(poly3 == 3)
    self.assertTrue(poly3 == np.array(3, np.int64))
    self.assertTrue(poly3 == np.array(3, np.int64)[()])
    self.assertFalse((poly3 + 1) == 3)
    self.assertFalse(poly3 == poly3 + 1)
    self.assertTrue((2 * a * b * a + 3).eq(1 + b * a * a + a * a * b + 2))
    self.assertFalse((2 * a * b * a + 3).eq(a * b * a + 3))

    self.assertFalse((a * b * a + 3).eq(a * b * a + 4))
    self.assertFalse((2 * a * b * a).eq(a * b * a))
    self.assertFalse((2 * a * b * a + 1).eq(a * b * a))
    self.assertFalse((3 * a * b * a - 1).eq(a * b * a))

    self.assertFalse((3 * a * b * a - 2).eq(a * b * a))

    self.assertTrue(a % b == a % b)
    self.assertTrue(a % b - a % b == 0)
    self.assertTrue(a // b == a // b)
    self.assertTrue(a // b - a // b == 0)

    self.assertTrue(a % b == (2 * a // 2) % (a + b - a))
    self.assertTrue(a // b == (2 * a // 2) // (a + b - a))

    self.assertTrue(a, a + (a + b) // b - (b + a) // b)

    # Test the normalization (a // b) * b == a - a % b
    self.assertTrue((a // 2) * 2 == a - a % 2)
    self.assertTrue((a // 2) + (a // 2) == a - a % 2)
    self.assertTrue((a // 2) * 6 == 3 * a - 3 * (a % 2))
    self.assertTrue((a // b) * b == a - a % b)
    self.assertTrue(2 * (a // b) * b * b == 2 * b * a - 2 * b * (a % b))
    self.assertTrue(a // (2 * b) * 2 * b == a - a % (2 * b))

  def test_poly_compare(self):
    a, b = shape_poly._parse_spec("a, b", (2, 3))
    poly = 4 * a + b + 3
    self.assertTrue(poly.ge(0))
    self.assertTrue(poly.ge(8))
    self.assertTrue(poly.ge(poly))
    self.assertTrue(poly.ge(poly - 1))

    with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "inconclusive"):
      poly.ge(9)

    with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "inconclusive"):
      (4 * a - b).ge(0)

  def test_poly_compare_overload(self):
    a, b = shape_poly._parse_spec("a, b", (2, 3))
    poly = 4 * a + b + 3
    self.assertTrue(poly >= 0)
    self.assertTrue(poly >= 8)
    self.assertTrue(poly > 7)
    self.assertTrue(poly >= poly)
    self.assertTrue(poly >= poly - 1)

    with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "inconclusive"):
      poly >= 9

    with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "inconclusive"):
      (4 * a - b) >= 0

  def test_core_greater_equal(self):
    a, b = shape_poly._parse_spec("a, b", (2, 3))
    self.assertTrue(core.greater_equal_dim(a, a))
    self.assertTrue(core.greater_equal_dim(a, 0))
    self.assertTrue(core.greater_equal_dim(a, 1))

    self.assertTrue(core.greater_equal_shape((a, 2), (1, 1)))

    with self.assertRaisesRegex(core.InconclusiveDimensionOperation,
                                "Symbolic dimension comparison .* is inconclusive"):
      core.greater_equal_dim(a, 2)

    with self.assertRaisesRegex(core.InconclusiveDimensionOperation,
                                "Symbolic dimension comparison .* is inconclusive"):
      core.greater_equal_dim(a, b)

  def test_poly_int_results(self):
    # Whenever the result is an integer, it should be represented as an
    # Python integer, not a symbolic dimension.
    a, b = shape_poly._parse_spec("a, b", (2, 3))
    self.assertEqual(a + 2 - a, 2)
    self.assertIsInstance(a + 2 - a, int)
    self.assertEqual(a + (2 - a), 2)
    self.assertIsInstance(a + (2 - a), int)
    self.assertEqual(a * 2 // a, 2)
    self.assertIsInstance(a * 2 // a, int)

  @parameterized.named_parameters(
      dict(testcase_name=f"_D={dividend}_d={divisor}_q={quotient}_r={remainder}",
           dividend=dividend, divisor=divisor, quotient=quotient,
           remainder=remainder)
      for dividend, divisor, quotient, remainder in [
          (a, 1, a, 0),
          (3 * a, 3, a, 0),
          (3 * a + 3, 3, a + 1, 0),
          (3 * a + 2, 3, a, 2),
          (3 * a + 5, 3, a + 1, 2),
          (3 * a - 2, 3, a - 1, 1),
          (3 * a * a * b + 2 * b * b * a, a * b, 3 * a + 2 * b, 0),
          (a * a - b * b, a + b, a - b, 0),
          (a, b, "floordiv(a, b)", "mod(a, b)"),
          (3 * a, 2, "floordiv(3*a, 2)", "mod(3*a, 2)"),
          (2 * a * b + b * b, a + b, "floordiv(2*a*b + b^2, a + b)", "mod(2*a*b + b^2, a + b)"),
          (3, a, "floordiv(3, a)", "mod(3, a)"),
  ])
  def test_poly_divmod(self, *, dividend, quotient, divisor, remainder):
    if isinstance(quotient, str):
      d1, d2 = divmod(dividend, divisor)
      self.assertEqual((quotient, remainder), (str(d1), str(d2)))
    else:
      self.assertEqual((quotient, remainder), divmod(dividend, divisor))

  def test_dilate_shape(self):
    """0 if d == 0 else 1 + dilation * (d - 1))"""
    a, = shape_poly._parse_spec("a,", (2,))

    self.assertEqual((4, 7), core.dilate_shape((2, 3), (3, 3)))
    self.assertEqual((0, 7), core.dilate_shape((0, 3), (3, 3)))
    self.assertEqual((a, 7), core.dilate_shape((a, 3), (1, 3)))
    self.assertEqual((2 * a - 1, 7), core.dilate_shape((a, 3), (2, 3)))

  def test_stride_shape(self):
    """(s - window_size) // window_stride + 1"""
    a, stride = shape_poly._parse_spec("a, s", (2, 3))

    self.assertEqual((8, 9), core.stride_shape((10, 20), window_size=(3, 3), window_stride=(1, 2)))
    self.assertEqual((a, 9), core.stride_shape((a, 20), (1, 3), (1, 2)))

    self.assertEqual((a - 1, 9), core.stride_shape((a, 20), (2, 3), (1, 2)))
    self.assertEqual((a + 1, 9), core.stride_shape((a * stride + 2, 20), (2, 3), (stride, 2)))

    (stride0, stride1) = core.stride_shape((a, 20), (1, 3), (2, 2))
    self.assertEqual("floordiv(a + -1, 2) + 1", str(stride0))
    self.assertEqual(9, stride1)


class PolyHarness(Harness):
  """Tests a function with shape polymorphism.

  Converts `fun` with shape polymorphism, creates a `tf.ConcreteFunction`
  given `input_signature` and checks the inferred output shapes to match
  `expected_output_shapes`, then checks that the JAX and the TF functions
  produce the same results.
  """
  def __init__(self,
               group_name: str, name: str,
               fun: Callable,
               *,
               arg_descriptors: Sequence[primitive_harness.ArgDescriptor] = (),
               polymorphic_shapes: Optional[Sequence[Any]] = None,
               input_signature: Optional[Sequence[tf.TensorSpec]] = None,
               poly_axes: Optional[Sequence[Optional[Union[int, Sequence[int]]]]] = None,
               expected_output_signature: Optional[tf.TensorSpec] = None,
               enable_xla: bool = True,
               expect_error: Tuple[Optional[Any], Optional[str]] = (None, None),
               skip_jax_run: bool = False,
               check_result: bool = True,
               tol: Optional[float] = None):
    """Args:

      group_name, name: The name for the harness. See `Harness.__init__`.
      fun: the function to be converted, possbily after partial application to
        static arguments from `arg_descriptors`. See `Harness.__init__`.
      arg_descriptors: The argument descriptors. See `Harness.__init__`. May
        be missing, in which case `skip_jax_run` should be `True` and
        `poly_axes` cannot be used.
      polymorphic_shapes: For `jax2tf.convert`. If missing, generated from
        `poly_axes`.
      input_signature: For `tf.function.get_concrete_function`. If missing,
        generated from `poly_axes`.
      poly_axes: If present, used to generate `polymorphic_shapes` and
        `input_signature`. Must correspond to the non-static arguments, and for
        each one it must specify which axes are polymorphic: None, or an int
        (for the index of the polymorphic axis), or a tuple of ints
        (for multiple polymorphic axes). For each argument, we use its
        `poly_axes` entry to generate the polymorphic_shapes specification,
        creating dimension variables `b0`, `b1, ..., for each of its polymorphic
        axes. This means that separate arguments will share the same dimension
        variable names, in the order in which the axes are listed in
        `poly_axes`. We also generate the input_signature from `poly_axes`.
      expected_output_signature: the expected inferred output shape.
      enable_xla: For `jax2tf.convert`.
      expect_error: a pair of an Exception type and a regular expression to
        match the expected exception string.
      skip_jax_run: If True, then neither the JAX nor the TF functions are
        executed.
      check_result: specifies if we want to check that the result of the shape
        polymorphic conversion produces the same result and the JAX function.
      tol: the tolerance to use for checking results.
    """
    super().__init__(group_name, name, fun, arg_descriptors,
                     dtype=np.float32)
    self.poly_axes = poly_axes
    self.polymorphic_shapes = polymorphic_shapes
    self.input_signature = input_signature
    self.expected_output_signature = expected_output_signature
    self.skip_jax_run = skip_jax_run
    self.expect_error = expect_error
    self.enable_xla = enable_xla
    self.tol = tol
    self.check_result = check_result

  # Replicate the harness for both enable and disable xla
  def both_enable_and_disable_xla(self) -> Tuple["PolyHarness", "PolyHarness"]:
    assert self.enable_xla
    other = PolyHarness(self.group_name,
                        f"{self.name}_enable_xla=False",
                        self.fun,
                        arg_descriptors=self.arg_descriptors,
                        poly_axes=self.poly_axes,
                        polymorphic_shapes=self.polymorphic_shapes,
                        input_signature=self.input_signature,
                        expected_output_signature=self.expected_output_signature,
                        expect_error=self.expect_error,
                        tol=self.tol,
                        enable_xla=False)
    self.name = f"{self.name}_enable_xla=True"
    return (self, other)

  def run_test(self, tst: tf_test_util.JaxToTfTestCase):
    # Make polymorphic_shapes and input_signature from poly_axes.
    if self.poly_axes is None:
      polymorphic_shapes = self.polymorphic_shapes
      input_signature = self.input_signature
      assert input_signature is not None
      if not self.skip_jax_run:
        args = self.dyn_args_maker(tst.rng())

    else:
      assert isinstance(self.poly_axes, Sequence)
      # Make poly_axes: Sequence[Sequence[int]], one top-level element for each argument
      poly_axes = tuple(map(lambda pa: pa if isinstance(pa, Sequence) or pa is None else (pa,),
                            self.poly_axes))
      args = self.dyn_args_maker(tst.rng())

      assert self.polymorphic_shapes is None
      assert self.input_signature is None
      assert args is not None and len(args) == len(poly_axes)
      # Make the polymorphic_shapes and input_signature
      polymorphic_shapes = []
      input_signature = []
      for arg, poly_axis in zip(args, poly_axes):
        if poly_axis is None:
          polymorphic_shapes.append(None)
          input_signature.append(tf.TensorSpec(np.shape(arg), arg.dtype))
        else:
          def make_arg_polymorphic_shapes(poly_axis: Sequence[int]) -> Tuple[str, tf.TensorSpec]:
            idx = -1
            dims = []
            tensorspec_dims: List[Optional[int]] = []
            for i, d in enumerate(arg.shape):
              if i in poly_axis:
                idx += 1
                dims.append(f"b{idx}")
                tensorspec_dims.append(None)
              else:
                dims.append(str(d))
                tensorspec_dims.append(d)
            return ", ".join(dims), tf.TensorSpec(tensorspec_dims, arg.dtype)

          arg_polymorphic_shapes, arg_tensorspec = make_arg_polymorphic_shapes(poly_axis)
          polymorphic_shapes.append(arg_polymorphic_shapes)
          input_signature.append(arg_tensorspec)

    expect_error_type, expect_error_regex = self.expect_error
    if self.skip_jax_run and self.arg_descriptors == ():
      f_jax = self.fun
    else:
      f_jax = self.dyn_fun

    with contextlib.ExitStack() as stack:
      if expect_error_type is not None:
        stack.enter_context(tst.assertRaisesRegex(expect_error_type, expect_error_regex))

      f_tf = jax2tf.convert(f_jax, polymorphic_shapes=polymorphic_shapes,
                            enable_xla=self.enable_xla)
      # Run in tf.Eager mode first, because it is friendlier to debuggers
      res_tf = f_tf(*args) if not self.skip_jax_run else None
      f_tf_func = tf.function(
          f_tf, autograph=False, input_signature=input_signature)
      # Create tf.ConcreteFunction and check inferred output signature
      concrete_f_tf = f_tf_func.get_concrete_function(*input_signature)

    if expect_error_type is not None:
      return

    if self.expected_output_signature:
      # Strangely, output_shapes can be a single shape for a function with a
      # single result, or a list/tuple of shapes.
      expected_output_signature = self.expected_output_signature
      concrete_output_tf_shape = concrete_f_tf.output_shapes
      if not isinstance(concrete_output_tf_shape, (tuple, list)):  # Single result
        assert not isinstance(self.expected_output_signature, (tuple, list))
        expected_output_signature = [self.expected_output_signature]
        concrete_output_tf_shape = [concrete_output_tf_shape]
      for expected, found in util.safe_zip(expected_output_signature,
                                           concrete_output_tf_shape):
        tst.assertEqual(tuple(expected.shape), tuple(found))

    # Run the JAX and the TF functions and compare the results
    if not self.skip_jax_run:
      res_jax = f_jax(*args)
      if self.check_result:
        tst.assertAllClose(res_jax, res_tf, atol=self.tol, rtol=self.tol)


def check_shape_poly(tst, f_jax: Callable, *,
                     arg_descriptors: Sequence[primitive_harness.ArgDescriptor] = (),
                     skip_jax_run: bool = False,
                     poly_axes = None,
                     polymorphic_shapes: Optional[Sequence[Any]] = None,
                     input_signature: Optional[Sequence[tf.TensorSpec]] = None,
                     expected_output_signature: Optional[tf.TensorSpec] = None,
                     expect_error=(None, None)):
  # Makes and tests a harness. See PolyHarness documentation.
  h = PolyHarness("", "", f_jax,
                  arg_descriptors=arg_descriptors,
                  skip_jax_run=skip_jax_run, poly_axes=poly_axes,
                  polymorphic_shapes=polymorphic_shapes,
                  input_signature=input_signature,
                  expected_output_signature=expected_output_signature,
                  expect_error=expect_error)
  h.run_test(tst)


class ShapePolyTest(tf_test_util.JaxToTfTestCase):

  def test_simple_unary(self):
    """Test shape polymorphism for a simple case, unary function."""

    def f_jax(x):
      return x + jnp.sin(x)

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((2, 3), _f32)],
                     input_signature=[tf.TensorSpec([2, 3])],
                     polymorphic_shapes=None,
                     expected_output_signature=tf.TensorSpec([2, 3]))

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((2, 3), _f32)],
                     input_signature=[tf.TensorSpec([2, None])],
                     polymorphic_shapes=["_, h"],
                     expected_output_signature=tf.TensorSpec([2, None]))

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((3, 3), _f32)],
                     input_signature=[tf.TensorSpec([None, None])],
                     polymorphic_shapes=["h, h"],
                     expected_output_signature=tf.TensorSpec([None, None]))

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((3, 3), _f32)],
                     input_signature=[tf.TensorSpec([None, None])],
                     polymorphic_shapes="h, h",
                     expected_output_signature=tf.TensorSpec([None, None]))

  def test_simple_binary(self):
    """Test shape polymorphism for a simple case, binary function."""

    def f_jax(x, y):
      return x + jnp.sin(y)

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((2, 3), _f32), RandArg((2, 3), _f32)],
                     input_signature=[tf.TensorSpec([2, 3]), tf.TensorSpec([2, 3])],
                     polymorphic_shapes=None,
                     expected_output_signature=tf.TensorSpec([2, 3]))

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((2, 3), _f32), RandArg((2, 3), _f32)],
                     input_signature=[tf.TensorSpec([2, None]), tf.TensorSpec([2, 3])],
                     polymorphic_shapes="_, h",
                     expected_output_signature=(
                         # for native serialization we cannot refine the inferred shape of the
                         # output if the input is more specific than polymorphic_shapes.
                         tf.TensorSpec([2, 3]) if not config.jax2tf_default_native_serialization
                         else tf.TensorSpec([2, None])))

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((3, 3), _f32), RandArg((3, 3), _f32)],
                     input_signature=[tf.TensorSpec([None, None]), tf.TensorSpec([None, None])],
                     polymorphic_shapes=PS("h", "h"),
                     expected_output_signature=tf.TensorSpec([None, None]))

  def test_arange(self):
    def f_jax(x):
      return x + jnp.arange(x.shape[0], dtype=np.float32)
    x = np.ones((3,), dtype=np.float32)
    self.assertAllClose(jax2tf.convert(f_jax, polymorphic_shapes="b")(x),
                        f_jax(x))

  def test_argmax(self):
    def f_jax(x):  # x: f32[b, 4, 5]
      return lax.argmax(x, axis=1, index_dtype=np.int32)
    x = np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5))
    self.assertAllClose(jax2tf.convert(f_jax, polymorphic_shapes="(b, _, _)")(x),
                        f_jax(x))

  @parameterized.named_parameters([
      dict(testcase_name=f"_expr={name}", expr=expr)
      for name, expr in [
          ("d + 2", lambda d: d + 2),
          ("2 - d", lambda d: 2 - d),
          ("d * 2", lambda d: d * 2),
          ("d * d", lambda d: d * d),
          ("(- d) * d", lambda d: (- d) * d),
          ("d * d - d", lambda d: d * d - d),
          # Division
          ("d // 2", lambda d: d // 2),
          ("(d + 1) // 2", lambda d: (d + 1) // 2),
          ("d // -2", lambda d: d // -2),
          ("(d + 1) // -2", lambda d: (d + 1) // -2),
          ("(-d) // 2", lambda d: (-d) // 2),
          ("(-d - 1) // 2", lambda d: (-d - 1) // 2),
          ("(-d) // -2", lambda d: (-d) // -2),
          ("(-d - 1) // -2", lambda d: (-d - 1) // -2),
          # Remainder
          ("d % 2", lambda d: d % 2),
          ("(d + 1) % 2", lambda d: (d + 1) % 2),
          ("d % -2", lambda d: d % -2),
          ("(d + 1) % -2", lambda d: (d + 1) % -2),
          ("(-d) % 2", lambda d: (-d) % 2),
          ("(-d - 1) % 2", lambda d: (-d - 1) % 2),
          ("(-d) % -2", lambda d: (-d) % -2),
          ("(-d - 1) % -2", lambda d: (-d - 1) % -2),
      ]
  ])
  def test_non_trivial_dim_expr(self, expr=lambda d: d % -2):
    # Check the lowering for shape expressions
    check_shape_poly(
       self,
       lambda x: x[0] * 0 + expr(x.shape[0]),
       arg_descriptors=[RandArg((3,), np.int64)],
       poly_axes=[0])

  def test_static_shape_result(self):
    """The result has static shape."""

    def f_jax(x):
      return jnp.sum(x + jnp.sin(x), axis=0)

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((2, 3), _f32)],
                     input_signature=[tf.TensorSpec([2, 3])],
                     polymorphic_shapes=None,
                     expected_output_signature=tf.TensorSpec([3]))

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((2, 3), _f32)],
                     input_signature=[tf.TensorSpec([None, 3])],
                     polymorphic_shapes="b, _",
                     expected_output_signature=tf.TensorSpec([3]))

  def test_forgot_polymorphic_shapes_error(self):
    msg_re = "polymorphic shape None in axis .* must contain a dimension variable for unknown dimension in argument shape .*. Perhaps you forgot to add the polymorphic_shapes"
    with self.assertRaisesRegex(ValueError, msg_re):
      check_shape_poly(self,
                       jnp.sin,
                       arg_descriptors=[RandArg((1, 3,), _f32)],
                       input_signature=[tf.TensorSpec([1, None])],
                       polymorphic_shapes=None)

  def test_kwargs(self):
    """Test shape polymorphism for a function with kwargs."""

    x = np.ones(3, dtype=np.float32)
    y = np.ones(1, dtype=np.float32)
    def f_jax(x, *, y):
      return x + jnp.sin(y)

    f_tf: Callable[..., Any] = jax2tf.convert(f_jax, polymorphic_shapes=["b, ..."])
    self.assertAllClose(f_jax(x, y=y), f_tf(x, y=y))

  def test_arg_avals(self):
    """Test conversion of actual arguments to abstract values."""

    def check_avals(*, arg_shapes: Sequence[Sequence[Optional[int]]],
                    polymorphic_shapes: Sequence[Optional[Union[str, PS]]],
                    expected_avals: Optional[Sequence[core.ShapedArray]] = None,
                    expected_shapeenv: Optional[Dict[str, int]] = None,
                    eager_mode: bool = False):
      # Use eager mode only for when all arg_shapes are known, in order to
      # check expected_shapeenv.
      arg_dtypes = (_f32,) * len(arg_shapes)
      def f_tf(*args_tf):
        avals = tuple(map(shape_poly.arg_aval, arg_shapes, arg_dtypes, polymorphic_shapes))
        dim_vars, get_dim_values_jax = shape_poly.prepare_dim_var_env(avals)
        dim_values, _ = jax2tf.jax2tf._interpret_fun_jax(get_dim_values_jax,
                                                         args_tf, avals, "")
        if expected_avals is not None:
          self.assertEqual(expected_avals, avals)
        return dict(zip(dim_vars, dim_values))
      if eager_mode:
        # If we want to check the shape_env then all arg_shapes must be known
        assert all(all(d is not None for d in a_s)
                   for a_s in arg_shapes)
        shape_env = f_tf(*[tf.ones(a_s, dtype=_f32) for a_s in arg_shapes])
        if expected_shapeenv is not None:
          for v, val in expected_shapeenv.items():
            self.assertEqual(val, shape_env.get(v))
      else:
        f_tf = tf.function(autograph=False)(f_tf)
        f_tf.get_concrete_function(*[tf.TensorSpec(a_s, _f32)
                                   for a_s in arg_shapes])
        assert not expected_shapeenv, "Should use eager_mode=True"

    def shaped_array(shape_spec: str, actual_shape: core.Shape):
      return core.ShapedArray(
          shape_poly._parse_spec(shape_spec, actual_shape), np.float32)

    # Known shapes for the arguments
    check_avals(
        arg_shapes=[(2, 3)],
        polymorphic_shapes=[None],
        expected_avals=(shaped_array("2, 3", [2, 3]),))

    check_avals(
        arg_shapes=[(2, 3)],
        polymorphic_shapes=["(2, 3)"],
        expected_avals=(shaped_array("2, 3", [2, 3]),))

    check_avals(
        arg_shapes=[(2, 3)],
        polymorphic_shapes=["(_, 3)"],
        expected_avals=(shaped_array("2, 3", [2, 3]),))

    check_avals(
        arg_shapes=[(2, 3)],
        polymorphic_shapes=[PS("_", 3)],
        expected_avals=(shaped_array("2, 3", [2, 3]),))

    check_avals(
        arg_shapes=[(2, 3)],
        polymorphic_shapes=["..."],
        expected_avals=(shaped_array("2, 3", [2, 3]),))

    check_avals(
        arg_shapes=[(2, 3)],
        polymorphic_shapes=[PS(...)],
        expected_avals=(shaped_array("2, 3", [2, 3]),))

    # Partially known shapes for the arguments
    check_avals(
        arg_shapes=[(None, 3)],
        polymorphic_shapes=[PS("b", ...)],
        expected_avals=(shaped_array("(b, 3)", (2, 3)),))

    check_avals(
        arg_shapes=[(None, None)],
        polymorphic_shapes=["h, h"],
        expected_avals=(shaped_array("(h, h)", (2, 2)),))

    check_avals(
        arg_shapes=[(2, None)],
        polymorphic_shapes=["h, h"],
        expected_avals=(shaped_array("(h, h)", (2, 2)),))

    check_avals(
        arg_shapes=[(None, 3, 4)],
        polymorphic_shapes=["(c, b, a)"],
        expected_avals=(shaped_array("(c, b, a)", (2, 3, 4)),),
    )

    # Check cases when the specifications are polynomials
    check_avals(
        arg_shapes=[(2, 3)],
        polymorphic_shapes=[PS("a + 1", "b + 2")],
        eager_mode=True,
        expected_shapeenv=dict(a=1, b=1))

    check_avals(
        arg_shapes=[(7, 5)],
        polymorphic_shapes=[PS("2 * a + b", "b + 2")],
        eager_mode=True,
        expected_shapeenv=dict(a=2, b=3))

    check_avals(
        arg_shapes=[(7, 11, 4)],
        polymorphic_shapes=[PS("2 * a + b", "b * b + 2", "b + 1")],
        eager_mode=True,
        expected_shapeenv=dict(a=2, b=3))

    check_avals(
        arg_shapes=[(7, 11, 19, 7)],
        polymorphic_shapes=[PS("2 * a + b", "b * b + 2", "b + c * c", "2 * c + -1")],
        eager_mode=True,
        expected_shapeenv=dict(a=2, b=3, c=4))

    with self.assertRaisesRegex(ValueError,
                                "Cannot solve for values of dimension variables {'b'}"):
      check_avals(
          arg_shapes=[(4, 36, 3)],
          polymorphic_shapes=[PS("b * b", "b * d * d", "d")])

    with self.assertRaisesRegex(ValueError,
                                "Dimension variable b must have integer value >= 1"):
      check_avals(
          arg_shapes=[(5, 36)],
          polymorphic_shapes=[PS("3 * b", ...)],
          eager_mode=True)

    with self.assertRaisesRegex(ValueError,
                                "Dimension variable b must have integer value >= 1"):
      check_avals(
          arg_shapes=[(10, 3)],
          polymorphic_shapes=[PS("3 * b + 10", ...)],
          eager_mode=True)

    with self.assertRaisesRegex(ValueError,
                                "Dimension variable b must have integer value >= 1"):
      check_avals(
          arg_shapes=[(7, 3)],
          polymorphic_shapes=[PS("3 * b + 10", ...)],
          eager_mode=True)


    for invalid_syntax in [")(", "2a", "a@", "a - 2", "'a'", "('a', ...)"]:
      with self.assertRaisesRegex(ValueError,
                                  re.escape("has invalid syntax")):
        check_avals(
            arg_shapes=[(2,)], polymorphic_shapes=[invalid_syntax])

    for invalid_syntax in [5.0, ["a list"], ("a tuple",), re.compile(".")]:
      with self.assertRaisesRegex(ValueError,
                                  re.escape("Invalid polymorphic shape element")):
        check_avals(
            arg_shapes=[(2,)], polymorphic_shapes=[PS([invalid_syntax])])

    with self.assertRaisesRegex(
        ValueError,
        re.escape("polymorphic shape '..., 3' can contain Ellipsis only at the end.")):
      check_avals(
          arg_shapes=[(2, 3)],
          polymorphic_shapes=["..., 3"])

    with self.assertRaisesRegex(
        ValueError,
        re.escape(
            "polymorphic shape '2, 3, 4, ...' of rank 3 must match the rank 2 of argument shape (2, 3).")
    ):
      check_avals(
          arg_shapes=[(2, 3)],
          polymorphic_shapes=["2, 3, 4, ..."])

    with self.assertRaisesRegex(
        ValueError,
        re.escape(
            "polymorphic shape (Ellipsis, 3) can contain Ellipsis only at the end.")):
      check_avals(
          arg_shapes=[(2, 3)],
          polymorphic_shapes=[PS(..., 3)])

    with self.assertRaisesRegex(
        ValueError,
        re.escape(
            "polymorphic shape None in axis 1 must contain a dimension variable for unknown dimension in argument shape (2, None)"
        )):
      check_avals(
          arg_shapes=[(2, None)],
          polymorphic_shapes=[None])

    with self.assertRaisesRegex(
        ValueError,
        re.escape("polymorphic shape '()' of rank 0 must match the rank 2 of argument shape (2, 3)")):
      check_avals(
          arg_shapes=[(2, 3)], polymorphic_shapes=["()"])

    with self.assertRaisesRegex(
        ValueError,
        re.escape(
            "polymorphic shape '(_, _)' in axis 1 must contain a dimension variable "
            "for unknown dimension in argument shape (2, None)"
        )):
      check_avals(
          arg_shapes=[(2, None)],
          polymorphic_shapes=["(_, _)"])

    with self.assertRaisesRegex(
        ValueError,
        re.escape(
            "polymorphic shape '(2, 13)' in axis 1 must match the known dimension size 3 "
            "for argument shape (2, 3)"
        )):
      check_avals(
          arg_shapes=[(2, 3)],
          polymorphic_shapes=["(2, 13)"])

    with self.assertRaisesRegex(
        ValueError,
        re.escape(
            "polymorphic shape '(2, 3)' in axis 1 must contain a dimension variable for "
            "unknown dimension in argument shape (2, None)"
        )):
      check_avals(
          arg_shapes=[(2, None)],
          polymorphic_shapes=["(2, 3)"])

    with self.assertRaisesRegex(
        ValueError,
        "Found inconsistency when solving.*"):
      check_avals(
          arg_shapes=[(2, 3)],
          polymorphic_shapes=["(a, a)"],
          eager_mode=True)

    # Same error across multiple arguments
    with self.assertRaisesRegex(
        ValueError,
        "Found inconsistency when solving.*"):
      check_avals(
          arg_shapes=[(2, 3), (5,)],
          polymorphic_shapes=["a, ...", "a"],
          eager_mode=True)

  def test_pytree(self):
    """Arguments and polymorphic_shapes are pytrees."""

    # Arguments are of the form [([x00, x01], [x10]), dict(a=ya, b=yb)]
    def add_all_jax(x_pair_of_list, y_dict):
      x_list_0, x_list_1 = x_pair_of_list
      return functools.reduce(op.add,
                              x_list_0 + x_list_1 + [y_dict["a"], y_dict["b"]])

    check_shape_poly(self,
                     add_all_jax,
                     skip_jax_run=True,
                     input_signature=[([tf.TensorSpec([None]), tf.TensorSpec([None])],
                                       [tf.TensorSpec([None])]),
                                      dict(a=tf.TensorSpec([None]),
                                           b=tf.TensorSpec([None]))],
                     polymorphic_shapes=[(["v", "v"], ["v"]),
                                         dict(a="v", b="v")],
                     expected_output_signature=tf.TensorSpec([None]))

    # Now partial polymorphic_shapes; the parts of the polymorphic_shapes that
    # are not specified must have full input_signatures.
    check_shape_poly(self,
                     add_all_jax,
                     skip_jax_run=True,
                     input_signature=[([tf.TensorSpec([4]), tf.TensorSpec([4])], [tf.TensorSpec([4])]),
                                      dict(a=tf.TensorSpec([4]), b=tf.TensorSpec([4]))],
                     polymorphic_shapes=[(["(4,)", "(_,)"], [("4,")]),
                                         dict(a="(_,)", b="(4,)")],
                     expected_output_signature=tf.TensorSpec([4]))

  def test_with_nested_jit(self):
    def f_jax(x):  # x: f32[w, h]
      # x + (np.sin(x) + np.broadcast_to(np.arange(x.shape[1]), x.shape))
      return jnp.sin(x) + jnp.arange(x.shape[1], dtype=x.dtype)
    check_shape_poly(self,
                     lambda x: x + jax.jit(f_jax)(x),
                     arg_descriptors=[RandArg((3, 4), _f32)],
                     poly_axes=[(0, 1)])

  def test_non_trivial_polynomials_spec(self):
    if config.jax_dynamic_shapes:
      raise unittest.SkipTest("--jax_dynamic_shapes supports only trivial polynomials")
    # We can handle non-trivial polynomials in the input shape,
    # as long as all variables also occur in trivial polynoamials
    check_shape_poly(self,
                     lambda x, y: x + y.reshape((-1,)),
                     arg_descriptors=[RandArg((9,), _f32), RandArg((3, 3), _f32)],
                     input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None, None])],
                     polymorphic_shapes=["b * b", "b, b"])

  def test_unused_args(self):
    # Tests with functions that do not use their inputs.

    # First arg unused, not polymorphic
    check_shape_poly(self,
                     lambda x_unused, y: y * 2.0,
                     arg_descriptors=[RandArg((2, 3), _f32), RandArg((3,), _f32)],
                     input_signature=[tf.TensorSpec([]), tf.TensorSpec([None])],
                     polymorphic_shapes=[None, "b"])

    # Some args unused, not polymorphic
    check_shape_poly(self,
                     lambda x_unused, y, z_unused, w: jnp.concatenate([y, w]),
                     arg_descriptors=[RandArg((3,), _f32), RandArg((4,), _f32),
                           RandArg((5,), _f32), RandArg((6,), _f32)],
                     input_signature=[tf.TensorSpec([]), tf.TensorSpec([None]),
                         tf.TensorSpec([]), tf.TensorSpec([None])],
                     polymorphic_shapes=[None, "b1", None, "b2"])

    # A polymorphic arg is not used, but the dimension var appears
    # in a used arg also
    check_shape_poly(self,
                     lambda x_unused, y: y * 2.0,
                     arg_descriptors=[RandArg((3,), _f32), RandArg((3,), _f32)],
                     input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
                     polymorphic_shapes=["b", "b"])

    # A polymorphic arg is not used, and the dimension var does not appear
    # elsewhere.
    check_shape_poly(self,
        lambda x_unused, y: y * 2.0,
        arg_descriptors=[RandArg((4,), _f32), RandArg((3,), _f32)],
        input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
        polymorphic_shapes=["b1", "b2"])

    # A polymorphic arg is not used, and the dimension var does appear
    # elsewhere but not as a trivial monomial.
    check_shape_poly(self,
        lambda x_unused, y: y * 2.0,
        arg_descriptors=[RandArg((3,), _f32), RandArg((9,), _f32)],
        input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
        polymorphic_shapes=["b1", "b1 * b1"])

    # It is not sufficient to just use the shape of an input; it is still unused
    check_shape_poly(self,
        lambda x_unused, y: y + x_unused.shape[0],
        arg_descriptors=[RandArg((3,), _f32), RandArg((9,), _f32)],
        input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
        polymorphic_shapes=["b1", "b2"])

  def test_with_custom_vjp(self):
    """Shape-polymorphic custom VJP."""

    @jax.custom_vjp
    def f(x):
      # x: [b1, b2, d1, d2] (a batch of matrices)
      # res: [b1, b2, d1, d1]
      return jnp.matmul(x, jnp.transpose(x, axes=(0, 1, 3, 2)))

    # f_fwd: a -> (b, residual)
    def f_fwd(x):
      # x: [b1, b2, d1, d2]
      # b: [b1, b2, d1, d1]
      # res: [b1, b2, d1, d1]
      # residual: [b1, b2, d1, d2]
      return f(x), 3. * x

    # f_bwd: (residual, CT b) -> [CT a]
    def f_bwd(residual, ct_b):
      # residual: [b1, b2, d1, d2]
      # ct_b: [b1, b2, d1, d1]
      # ct_a: [b1, b2, d1, d2]
      return jnp.matmul(ct_b, residual),

    f.defvjp(f_fwd, f_bwd)
    x = np.ones((2, 3, 4, 5), dtype=np.float32)
    res_jax = f(x)
    res_jax_grad = jax.grad(lambda x: jnp.sum(f(x)))(x)

    f_tf = jax2tf.convert(f, polymorphic_shapes=["(batch1, batch2, d1, d2)"])
    self.assertAllClose(res_jax, f_tf(x))

    xv = tf.Variable(x, dtype=np.float32)

    def tf_value_and_grad(xv):
      with tf.GradientTape() as tape:
        tape.watch(xv)
        res_tf = f_tf(xv)
        res_tf_grad = tape.gradient(res_tf, xv)
        return res_tf, res_tf_grad

    res_tf, res_tf_grad = tf_value_and_grad(xv)
    self.assertAllClose(res_jax, res_tf)
    self.assertAllClose(res_jax_grad, res_tf_grad)

    # Now use TF tracing for the gradient
    tf_grad = tf.function(
       tf_value_and_grad, autograph=False).get_concrete_function(
           tf.TensorSpec([3, 4, 8, 9]))

    # for native serialization we cannot refine the inferred shape of the
    # output if the input is more specific than polymorphic_shapes.
    if config.jax2tf_default_native_serialization:
      self.assertEqual((None, None, None, None), tuple(tf_grad.output_shapes[0]))
      self.assertEqual((None, None, None, None), tuple(tf_grad.output_shapes[1]))
    else:
      self.assertEqual((3, 4, 8, 8), tuple(tf_grad.output_shapes[0]))
      self.assertEqual((3, 4, 8, 9), tuple(tf_grad.output_shapes[1]))

  def test_gradients_pytree(self):
    """Shape polymorphism with gradients and pytrees for inputs and outputs."""

    def f(x):
      # x: dict(x=[b, 3, 4])
      # res: dict(res=[b, 3, 4])
      return dict(res=x["x"] * 2.)

    check_shape_poly(self,
                     f,
                     skip_jax_run=True,
                     input_signature=[dict(x=tf.TensorSpec([None, 3, 4]))],
                     polymorphic_shapes=[dict(x=("b, 3, 4"))])

    f_tf = jax2tf.convert(f, polymorphic_shapes=[dict(x=("b, 3, 4"))])
    x = dict(x=np.ones((2, 3, 4), dtype=np.float32))
    xv = tf.Variable(x["x"], dtype=np.float32)

    def tf_value_and_grad(xv):
      # xv: [b, 3, 4]
      # res_value: dict(res=[b, 3, 4])
      # res_grad: dict(grad=[b, 3, 4])
      with tf.GradientTape() as tape:
        tape.watch(xv)
        res_tf = f_tf(dict(x=xv))
        res_tf_grad = tape.gradient(res_tf, xv)
        return res_tf, dict(grad=res_tf_grad)

    res_tf, res_tf_grad = tf_value_and_grad(xv)
    # Now use TF tracing for the gradient
    tf_grad = tf.function(
        tf_value_and_grad,
        autograph=False).get_concrete_function(tf.TensorSpec([None, 3, 4]))
    # The shape of the value
    self.assertEqual((None, 3, 4), tuple(tf_grad.output_shapes[0]["res"]))
    # The shape of the gradient should match the input
    self.assertEqual((None, 3, 4), tuple(tf_grad.output_shapes[1]["grad"]))

  def test_grad_not_var_output(self):
    # Output of the function has poly shapes, non-variable
    if config.jax2tf_default_native_serialization:
      raise unittest.SkipTest("Not supported with native serialization")
    def f_jax(x):  # :[b, 3]
      return jnp.reshape(x, (-1,))  # : [3b]
    x = np.arange(12, dtype=np.float32).reshape((4, 3))
    xv = tf.Variable(x)

    f_tf = jax2tf.convert(f_jax, with_gradient=True,
                          polymorphic_shapes=["b, ..."])

    with tf.GradientTape() as tape:
      res_tf = f_tf(xv)
    grad_tf = tape.gradient(res_tf, xv)
    self.assertAllClose(np.ones(x.shape, dtype=np.float32), grad_tf.numpy())


  def test_cond(self):
    # Test the primitive under conditional
    def f(x, y):
      # x: f32[B, H], y : f32[H]
      return lax.cond(
          jnp.sum(x) > 0.,
          lambda _: x + y,
          lambda _: jnp.zeros_like(x),
          operand=None)

    x = np.ones((2, 3))
    y = np.ones((3,))
    res_jax = f(x, y)
    self.assertAllClose(
        res_jax,
        jax2tf.convert(f, polymorphic_shapes=["(b, h)", "h"])(x, y))

  def test_while(self):
    def f(x):
      # x: f32[B], iter: i32
      return lax.while_loop(lambda x_iter: x_iter[1] < 5,
                            lambda x_iter: (x_iter[0] + jnp.arange(x_iter[0].shape[0], dtype=np.float32), x_iter[1] + 1),
                            (x, 0))

    x = np.ones((3,), dtype=np.float32)
    res_tf = jax2tf.convert(f, polymorphic_shapes=["(b,)"])(x)
    self.assertAllClose(f(x), res_tf)

  @jtu.sample_product(with_function=[False, True])
  def test_grad_int(self, with_function=True):
    # https://github.com/google/jax/issues/7093
    # Also issue #6975.
    x_shape = (2, 3, 4)
    xi = np.arange(np.prod(x_shape), dtype=np.int16).reshape(x_shape)
    yf = xi.astype(np.float32)
    xi_yf = (xi, yf)
    zb = np.array([True, False], dtype=np.bool_)
    def f_jax(xi_yf, zb):  # xi: s16[2, 3, 4], yf: f32[2, 3, 4], zb: bool[2]
      xi, yf = xi_yf
      # Return a tuple:
      #   (1) float constant, with 0 tangent;
      #   (2) a tuple with:
      #     (2.1) the integer input;
      #     (2.2) the boolean input;
      #     (2.3) a float depending on both inputs.
      # TODO: there is a problem if we add a None output
      return (jnp.zeros(xi.shape, dtype=jnp.float32),
              (xi, zb, xi.astype(np.float32) * 2. * yf))

    args = (xi_yf, zb)

    f_tf = jax2tf.convert(f_jax, polymorphic_shapes=[("b1, b2, 4", "b1, b2, 4"), "b1"])
    if with_function:
      f_tf = tf.function(f_tf, autograph=False)

    res_tf, g_tf = tf_test_util.ComputeTfValueAndGrad(f_tf, args)
    self.assertAllClose(g_tf[0][0], np.zeros_like(xi))
    self.assertAllClose(g_tf[0][1], (xi * 2).astype(yf.dtype))
    self.assertAllClose(g_tf[1], np.zeros_like(zb))

  def test_prng(self):
    # The PRNG implementation uses opaque types, test shape polymorphism
    try:
      prev_custom_prng = config.jax_enable_custom_prng
      config.update("jax_enable_custom_prng", True)

      def f_jax(x):  # x: f32[b1, b2]
        key = random.PRNGKey(123)  #  key
        # Exercise key operations that have custom lowering rules
        broadcast_keys = lax.broadcast_in_dim(key, x.shape, ())  # key[b1, b2]
        gather_keys = lax.broadcast_in_dim(broadcast_keys[0], (1, x.shape[1]), (1,))  # : key[1, b2]
        slice_keys1 = lax.slice(broadcast_keys, (0, 0), (1, x.shape[1]), (1, 1))  # key[1, b2]
        slice_keys2 = lax.dynamic_slice(broadcast_keys, (0, 0), slice_sizes=(1, x.shape[1]))  # key[1, b2]
        upd1 = lax.dynamic_update_slice(slice_keys2, slice_keys1, start_indices=(0, 0))  # key[1, b2]
        _ = lax.dynamic_update_slice(upd1, gather_keys, start_indices=(0, 0))
        return x

      check_shape_poly(self, f_jax,
                       arg_descriptors=[RandArg((3, 4), _f32)],
                       input_signature=[tf.TensorSpec([None, None], dtype=tf.float32)],
                       polymorphic_shapes=["b1, b2"])
    finally:
      config.update("jax_enable_custom_prng", prev_custom_prng)


  def test_saved_model(self):
    f_jax = jnp.sin
    f_tf = jax2tf.convert(f_jax, polymorphic_shapes=["(b, ...)"])
    x = np.array([0.7, 0.8], dtype=np.float32)
    restored_f, _ = tf_test_util.SaveAndLoadFunction(
        f_tf, input_signature=[tf.TensorSpec([None], x.dtype)])
    self.assertAllClose(f_jax(x), restored_f(x))
    # Ensure that restored_f works at other batch size as well
    y = np.concatenate([x, x])
    self.assertAllClose(f_jax(y), restored_f(y))

  def test_saved_model_int_function(self):
    # TODO(https://github.com/google/jax/issues/14437)
    if config.jax2tf_default_native_serialization:
      raise unittest.SkipTest("Gradient function does not use the dimension  variables")

    def f_jax(x):  # x:s32[b, 3, 4]
      return jnp.reshape(x, (-1,))  # : s32[b * 12]
    f_tf = jax2tf.convert(f_jax, polymorphic_shapes=["(b, ...)"])
    f_tf = tf.function(f_tf, autograph=False)
    x_shape = (2, 3, 4)
    x = np.arange(np.prod(x_shape), dtype=np.int32).reshape(x_shape)

    # When saving the model with gradients, we trace the gradient function
    # and we used to get an error when creating zeros_like_aval for a
    # polymorphic shape
    restored_f, _ = tf_test_util.SaveAndLoadFunction(
        f_tf, input_signature=[tf.TensorSpec((None,) + x.shape[1:], x.dtype)])
    f_jax_rt = jax2tf.call_tf(restored_f)
    res_jax_rt = f_jax_rt(x)
    self.assertAllClose(f_jax(x), res_jax_rt)

  def test_saved_model_constant_gradient(self):
    # TODO(https://github.com/google/jax/issues/14437)
    if config.jax2tf_default_native_serialization:
      raise unittest.SkipTest("Gradient function does not use the dimension  variables")

    def f_jax(x):  # A function whose gradient is a constant
      return x

    f_tf = jax2tf.convert(f_jax, polymorphic_shapes=["(b, ...)"])
    x = np.array([0.7, 0.8], dtype=np.float32)
    restored_f, _ = tf_test_util.SaveAndLoadFunction(
        f_tf, input_signature=[tf.TensorSpec([None], x.dtype)])
    self.assertAllClose(f_jax(x), restored_f(x))


  def test_readme_examples(self):
    """Some of the examples from the README."""

    jax2tf.convert(lambda x: jnp.reshape(x, (x.shape[0] * x.shape[1],)),
                   polymorphic_shapes=["(b, 4)"])(np.ones((3, 4)))

    jax2tf.convert(lambda x: jnp.reshape(x, (np.prod(x.shape),)),
                   polymorphic_shapes=["(b, 4)"])(np.ones((3, 4)))

    jax2tf.convert(lambda x: x + x.shape[0] + jnp.sin(x.shape[0]),
                   polymorphic_shapes=["b"])(np.ones(3))

    jax2tf.convert(lambda x: jnp.sum(x, axis=0) / x.shape[0],
                   polymorphic_shapes=["(v, _)"])(np.ones((3, 4)))

    with self.assertRaisesRegex(TypeError,
                                "prod requires ndarray or scalar arguments"):
      jax2tf.convert(lambda x: jnp.prod(x.shape) + x,
                     polymorphic_shapes=["(b, 4)"])(np.ones((3, 4)))

    jax2tf.convert(lambda x: jnp.prod(jnp.array(x.shape)) + x,
                   polymorphic_shapes=["(b, 4)"])(np.ones((3, 4)))

    four_ones = np.ones((4,))
    with self.assertRaisesRegex(
        TypeError,
        re.escape("add got incompatible shapes for broadcasting: (v,), (4,)")):
      jax2tf.convert(lambda x, y: x + y,
                     polymorphic_shapes=["(v,)", "(4,)"])(four_ones, four_ones)

    # We get the error even if we use correct actual arguments
    with self.assertRaisesRegex(
        TypeError,
        re.escape("add got incompatible shapes for broadcasting: (v,), (4,)")):
      jax2tf.convert(
          lambda x, y: x + y, polymorphic_shapes=["(v,)", "(4,)"])(four_ones,
                                                                   four_ones)

    with self.assertRaisesRegex(TypeError,
                                re.escape("dot_general requires contracting dimensions to have the same shape, got (4,) and (v,)")):
      jax2tf.convert(lambda x: jnp.matmul(x, x),
                     polymorphic_shapes=["(v, 4)"])(np.ones((4, 4)))

    with self.assertRaisesRegex(core.InconclusiveDimensionOperation,
                                re.compile("Cannot divide evenly the sizes of shapes \\(b, 5, 7\\) and \\(2, -1\\)",
                                           re.DOTALL)):
      jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)),
                     polymorphic_shapes=["(b, _, _)"])(np.ones((4, 5, 7)))

    jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)),
                   polymorphic_shapes=["(b, _, _)"])(np.ones((4, 5, 6)))
    jax2tf.convert(lambda x: jnp.reshape(x, (-1, x.shape[0])),
                   polymorphic_shapes=["(b1, b2, ...)"])(np.ones((4, 5, 6)))

    if not config.jax2tf_default_native_serialization:
      # Does not support 2*b constraints
      jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)),
                     polymorphic_shapes=["(2*b, ...)"])(np.ones((4, 5, 7)))

    with self.assertRaisesRegex(
        core.InconclusiveDimensionOperation,
        re.escape("Symbolic dimension comparison 'a + 1' >= 'b' is inconclusive")):
      jax2tf.convert(lambda x: 0 if x.shape[0] + 1 >= x.shape[1] else 1,
                     polymorphic_shapes=["(a, b)"])(np.ones((4, 4)))

    # Unsoundness: not checking that the dimension variable is 0
    def f1_jax(x):  # f32[b]
      # We have to use "x"
      return jnp.concatenate([x, jnp.array([0. if x.shape[0] == 0 else 1.],
                                           dtype=np.float32)])

    x0 = np.array([], np.float32)
    # JAX with static shapes sees that the x.shape[0] == 0
    self.assertEqual(jnp.array([0.], dtype=np.float32), f1_jax(x0))

    # jax2tf catches the broken assumption b >= 1 if the converted function is executed
    # eagerly.
    with self.assertRaisesRegex(
        ValueError,
        "Dimension variable b must have integer value >= 1. Found value 0 when solving .*"):
      jax2tf.convert(f1_jax, polymorphic_shapes=["b"],
                     native_serialization=False)(x0)

    # TODO(https://github.com/google/jax/issues/14437)
    # In native serialization, or if we trace to a TF graph, we miss this
    res1_tf = jax2tf.convert(f1_jax, polymorphic_shapes=["b"],
                             native_serialization=True)(x0)
    self.assertEqual(jnp.array([1.], dtype=np.float32), res1_tf)

    f1_tf = tf.function(
        jax2tf.convert(f1_jax, polymorphic_shapes=["b"],
                       native_serialization=False)
    ).get_concrete_function(tf.TensorSpec([None], dtype=np.float32))
    self.assertEqual(jnp.array([1.], dtype=np.float32), f1_tf(x0))

    # Unsoundness: not checking that the actual dimensions denoted by the same
    # dimension variables have equal sizes.
    def f2_jax(x):  # f32[b, b]
      # We have to use "x"
      return jnp.sum(x) + (0. if x.shape[0] != x.shape[1] else 1.)

    x45 = np.ones((4, 5), dtype=np.float32)
    # JAX with static shapes sees that x.shape[0] != x.shape[1]
    self.assertEqual(jnp.sum(x45), f2_jax(x45))

    # jax2tf catches the broken assumption b >= 1 if the converted function is executed
    # eagerly.
    with self.assertRaisesRegex(ValueError,
                                "Found inconsistency when solving b == .*"):
      jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"],
                     native_serialization=False)(x45)

    # TODO(https://github.com/google/jax/issues/14437)
    # In native serialization, or if we trace to a TF graph, we miss this
    res2_tf = jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"],
                             native_serialization=True)(x45)
    self.assertEqual(1. + jnp.sum(x45), res2_tf)

    f2_tf = tf.function(
        jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"],
                       native_serialization=False)
    ).get_concrete_function(tf.TensorSpec([None, None], dtype=np.float32))
    self.assertEqual(1. + jnp.sum(x45), f2_tf(x45))

    x = np.ones((5,), dtype=np.float32)
    with self.assertRaisesRegex(
        ValueError,
        "Cannot solve for values of dimension variables"):
      jax2tf.convert(lambda x: jnp.sum(x), polymorphic_shapes=["a + b"],
                     native_serialization=False)(x)
    with self.assertRaisesRegex(
        ValueError,
        "dimension variables cannot be computed from the static shapes of the array arguments"):
      jax2tf.convert(lambda x: jnp.sum(x), polymorphic_shapes=["a + b"],
                     native_serialization=True)(x)

  def test_dynamic_shapes(self):
    # Test dim_as_value with dynamic shapes.
    def f(x):
      return jnp.sum(x, axis=0) * x.shape[0]

    x = np.arange(3.)
    self.assertAllClose(9., jax2tf.convert(f, polymorphic_shapes=["(b,)"])(x))
    self.assertAllClose(
        9.,
        jax2tf.convert(jax.jit(f), polymorphic_shapes=["(b,)"])(x))
    self.assertAllClose(
        9.,
        tf.function(jax2tf.convert(f, polymorphic_shapes=["(b,)"]))(x))

    res_primal, res_tangent = jax2tf.convert(
        lambda x, xt: jax.jvp(f, (x,), (xt,)),
        polymorphic_shapes=["b", "b"])(x, np.array([0.1, 0.2, 0.3]))
    self.assertAllClose((9., 1.8), (res_primal, res_tangent))

    # TODO(https://github.com/google/jax/issues/14437)
    if not config.jax2tf_default_native_serialization:
      self.assertAllClose(
          np.array([3., 3., 3.]),
          jax2tf.convert(jax.grad(f), polymorphic_shapes=["b"])(x))

    xv = np.arange(24.).reshape((2, 3, 4))
    res_vmap = jax.vmap(f, in_axes=1)(xv)
    # Implement by iteration
    res_iter = jnp.stack([f(xv[:, i, :]) for i in range(xv.shape[1])])
    self.assertAllClose(res_iter, res_vmap)

    res_vmap_tf = jax2tf.convert(jax.vmap(f, in_axes=1),
                                 polymorphic_shapes=["b1, b2, ..."])(xv)
    self.assertAllClose(res_iter, res_vmap_tf.numpy())

  def test_with_hash_collision_vmap(self):
    # Batching caches based on Jaxpr, and Jaxpr include _DimExpr. If we have
    # a collision for the hashing of a _DimExpr, then Python will call the
    # equality, which will raise InconclusiveDimensionOperation.

    def f_jax(x):
      return jnp.reshape(x, (2, -1,))
    try:
      # Override the hashing to create collisions
      orig_hash = getattr(shape_poly._DimExpr, "__hash__")
      def collision_hash(obj):
        return hash(5)

      setattr(shape_poly._DimExpr, "__hash__", collision_hash)
      xs = np.ones((3, 5, 6), dtype=np.float32)
      f_toconvert = jax.vmap(pjit.pjit(f_jax))
      res_1 = jax2tf.convert(f_toconvert)(xs)
      res_2 = jax2tf.convert(f_toconvert,
                             polymorphic_shapes = "b1, b2, ...")(xs)
      self.assertAllClose(res_1, res_2)
    finally:
      setattr(shape_poly._DimExpr, "__hash__", orig_hash)

  @parameterized.named_parameters([
      dict(testcase_name=f"_{op_name}",
           op=op)
      for op, op_name in [
          (jnp.array, "array"),
          (jnp.sin, "sin"),
          (lambda x: x, "id"),
          (core.dimension_as_value, "dimension_as_value"),
      ]
  ])
  def test_poly_unary_op(self, *, op=jnp.array):
    def f_jax(x):  # x: f32[b]
      poly = 2 * x.shape[0]
      return (op(poly), x)  # Make sure we are using x

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((3,), _f32)],
                     poly_axes=[0],
                     expected_output_signature=(tf.TensorSpec([]), tf.TensorSpec((None,), _f32)))

  @parameterized.named_parameters([
      dict(testcase_name=f"_{op.__name__}_other={other}:{type(other)}{'_other_jnp_array' if other_jnp_array else ''}{'_swap' if swap else ''}",
           op=op, other=other,
           other_jnp_array=other_jnp_array, swap=swap)
      for op in [op.add, op.mul, op.sub,
                 op.mod, op.floordiv, op.truediv]
      for other in [
          2, np.int32(2), 2., np.float32(2),
          np.array(2, dtype=np.int32), np.arange(1, 5, dtype=np.int32),
          np.array(2., dtype=np.float32), np.arange(1., 7., dtype=np.float32)
      ]
      for other_jnp_array in (
          [True, False] if np.shape(other) == (7,) else [False])  # type: ignore
      for swap in [False, True]  # The poly is the left op by default
  ])
  def test_poly_binary_op(self, *, op=op.add,
                          other=np.arange(2, dtype=np.int32),
                          other_jnp_array=False,
                          swap=True):
    # Test arithmetic operations with poly and a variety of other operand types
    def f_jax(x):  # x: f32[b]
      poly = 2 * x.shape[0]  # This will allow divisions with 2
      other_wrapped = jnp.array(other) if other_jnp_array else other
      ops = (poly, other_wrapped) if not swap else (other_wrapped, poly)
      res = op(*ops)

      # If the other op is an integer then the result is a symbolic dim
      try:
        op.index(other)
        other_isint = True
      except Exception:
        other_isint = False

      if (hasattr(poly, "dimension_as_value") and
          other_isint and
          op.__name__ != "truediv"):
        # If we running under jax2tf and "other" is an integer the result
        # should be a symbolic dimension
        self.assertTrue(isinstance(res, int) or hasattr(res, "dimension_as_value"))

      if config.jax_enable_x64:
        # Outside jax2tf, x.shape[0] is a Python (64-bit) integer and for most
        # operations here JAX is not involved at all because the other operand
        # is a Python or NumPy constant. So the result will be 64-bits. But under
        # jax2tf, x.shape[0] is rewritten to jnp.array(x.shape[0]) which when
        # used with int32 or float32 values will produce 32-bit values.
        return (lax.convert_element_type(res, np.float32), x)
      return (res, x)  # Make sure we are using x

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((3,), np.int32)],
                     poly_axes=[0])

  def test_mean0(self):
    def f_jax(x):  # x: f32[b, 4]
      return jnp.sum(x, axis=0) / x.shape[0]
    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((3, 4), _f32)],
                     poly_axes=[0],
                     expected_output_signature=tf.TensorSpec([4]))


  def test_shape_as_array(self):
    def f_jax(x):
      # The entire x.shape is passed to jnp.array
      return x + jnp.sum(jnp.array(x.shape)).astype(np.int32)

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((3, 4), _f32)],
                     poly_axes=[0])

  def test_dim_as_value_weak_type(self):
    def f_jax(x):  # x: f32[b]
      d0 = jnp.array(x.shape[0])  # in JAX should have weak_type=True
      if isinstance(d0, core.Tracer):
        self.assertTrue(d0.aval.weak_type), d0

      # And an implicit conversion to array
      d1 = x.shape[0] + jnp.array(4)
      if isinstance(d1, core.Tracer):
        self.assertTrue(d1.aval.weak_type), d1
      return d0 + np.array(5., dtype=np.float32) + d1 + x[0]

    with numpy_dtype_promotion("strict"):
      # strict type promotion is sensitive to weak_types
      check_shape_poly(self,
                       f_jax,
                       arg_descriptors=[RandArg((3,), _f32)],
                       poly_axes=[0])

  def test_vmap_while(self):
    def cond_func(x):  # x: f32[3]
      return jnp.sum(x) >= 0.
    def body_func(x):  # x: f32[3]
      return x - 1.
    def f_jax(x):
      return lax.while_loop(cond_func, body_func, x)

    check_shape_poly(self,
                     jax.vmap(f_jax),
                     arg_descriptors=[RandArg((3,), _f32)],
                     input_signature=[tf.TensorSpec((None, 3), dtype=tf.float32)],
                     polymorphic_shapes=["b, ..."],
                     expected_output_signature=tf.TensorSpec((None, 3), dtype=tf.float32)
                     )

  def test_vmap_error(self):
    # vmap is careful to give nice error messages when mapped axes have
    # different sizes, but this can be foiled by InconsistentDimensionOperation
    x = y = np.ones((3, 5), dtype=np.float32)
    with self.assertRaisesRegex(ValueError,
                                "vmap got inconsistent sizes for array axes to be mapped"):
      jax2tf.convert(jax.vmap(lambda x, y: x + y),
                     polymorphic_shapes=["b, ...", None])(x, y)

    z = x
    with self.assertRaisesRegex(ValueError,
                                "vmap got inconsistent sizes for array axes to be mapped"):
      jax2tf.convert(jax.vmap(lambda x, y, z: x + y + z),
                     polymorphic_shapes=["b, ...", "c, ...", None])(x, y, z)

  def test_reshape_compiled(self):
    # We compile the result of conversion for two shapes, hence we need to
    # involve the TF compiler twice, but we trace only once with shape polymorphism
    traced = False

    def f_jax(x):
      nonlocal traced
      traced = True
      y = jnp.sin(x)
      return y.reshape([x.shape[0], -1])

    x = self.rng().rand(4, 2, 3)
    res_jax = f_jax(x)

    traced = False
    # If we get_concrete_function we trace once
    f_tf = tf.function(
        jax2tf.convert(f_jax, polymorphic_shapes=[PS("b", ...)]),
        autograph=False,
        jit_compile=True).get_concrete_function(
            tf.TensorSpec([None, 2, 3], x.dtype))
    self.assertTrue(traced)
    traced = False
    self.assertAllClose(res_jax, f_tf(x))
    self.assertFalse(traced)  # We are not tracing again

    x = self.rng().rand(6, 2, 3)
    res_jax = f_jax(x)
    traced = False

    self.assertAllClose(res_jax, f_tf(x))
    self.assertFalse(traced)  # We are not tracing again


# List containing either harnesses, or lists of harnesses
_POLY_SHAPE_TEST_HARNESSES = [
    PolyHarness("add", "",
                jnp.add,
                arg_descriptors=[RandArg((3, 4), _f32), RandArg((2, 3, 4), _f32)],
                poly_axes=[0, 1]),
    PolyHarness("add_transpose", "",
                jax.grad(lambda x: jnp.sum(jnp.sum(x, axis=0, keepdims=False) + jnp.sin(x))),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("arange", "start",
                lambda op: jnp.arange(2 * op.shape[0], dtype=_f32) + op[0],
                arg_descriptors=[RandArg((3,), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    PolyHarness("arange", "start_no_dtype",
                lambda op: jnp.arange(op.shape[0]) + op[0],
                arg_descriptors=[RandArg((3,), _f32)],
                poly_axes=[0]),
    PolyHarness("arange", "error1",
                lambda op: jnp.arange(op.shape[0], 10),
                arg_descriptors=[RandArg((3,), _f32)],
                poly_axes=[0],
                expect_error=(ValueError, "jax.numpy.arange supports non-constant arguments only in single-argument form")),
    PolyHarness("arange", "error2",
                lambda op: jnp.arange(1, op.shape[0]),
                arg_descriptors=[RandArg((3,), _f32)],
                poly_axes=[0],
                expect_error=(ValueError, "jax.numpy.arange supports non-constant arguments only in single-argument form")),
    PolyHarness("arange", "error3",
                lambda op: jnp.arange(1, 5, op.shape[0]),
                arg_descriptors=[RandArg((3,), _f32)],
                poly_axes=[0],
                expect_error=(ValueError, "jax.numpy.arange supports non-constant arguments only in single-argument form")),
    # Reduce the poly dimension
    PolyHarness("argmax", "0",
                lambda op: lax.argmax(op, axis=0, index_dtype=np.int32),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    # Reduce the non-poly dimension
    PolyHarness("argmax", "1",
                lambda op: lax.argmax(op, axis=1, index_dtype=np.int32),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    PolyHarness("jnp.argsort", "",
                lambda op: jnp.argsort(op),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                poly_axes=[0]),
    [
        PolyHarness("average",
                    f"{axis=}_weights=None",
                    lambda x, axis: jnp.average(x, axis=axis, returned=False, weights=None),
                    arg_descriptors=[RandArg((7, 8, 4), _f32), StaticArg(axis)],
                    poly_axes=[0])
        for axis in [None, 0, 1]
    ],
    [
        PolyHarness("average",
                    f"{axis=}_weights=Some",
                    lambda x, weights, axis: jnp.average(x, axis=axis, returned=False, weights=weights),
                    arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 8, 4), _f32), StaticArg(axis)],
                    poly_axes=[0, 0])
        for axis in [None, 0, 1]
    ],
    PolyHarness("jnp.bincount", "length=constant",
                lambda x: jnp.bincount(x % 2, length=4),
                arg_descriptors=[RandArg((12,), np.int32)],
                poly_axes=[0]),
    PolyHarness("jnp.bincount", "length=poly",
                lambda x: jnp.bincount(x % 4, length=x.shape[0]),
                arg_descriptors=[RandArg((12,), np.int32)],
                poly_axes=[0]),
    PolyHarness("broadcast_to", "",
                lambda x: jnp.broadcast_to(x, [x.shape[0], x.shape[0], 4]),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("broadcast_in_dim", "0",
                lambda x: lax.broadcast_in_dim(x, [x.shape[0], 4, 5, 6],
                                               broadcast_dimensions=(0, 2, 3)),
                arg_descriptors=[RandArg((3, 1, 6), _f32)],
                poly_axes=[0]),
    PolyHarness("broadcast_in_dim", "poly",
                lambda x: lax.broadcast_in_dim(x, [x.shape[0], x.shape[0] + x.shape[0], 4],
                                               broadcast_dimensions=(0, 1, 2)),
                arg_descriptors=[RandArg((3, 1, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("broadcast_in_dim", "poly2",
                lambda x: lax.broadcast_in_dim(x, [x.shape[0], 5, 6, x.shape[2], 4],
                                               broadcast_dimensions=(0, 2, 3)),
                arg_descriptors=[RandArg((3, 1, 4), _f32)],
                poly_axes=[(0, 2)]),
    PolyHarness("broadcast_in_dim", "transpose",
                jax.grad(lambda x: jnp.sum(
                    lax.broadcast_in_dim(jnp.sin(x), [2, x.shape[0], 5, x.shape[2], 4],
                                         broadcast_dimensions=(1, 2, 3)))),
                arg_descriptors=[RandArg((3, 1, 4), _f32)],
                poly_axes=[(0, 2)]),
    PolyHarness("clamp", "",
                lax.clamp,
                arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((3, 4, 5), _f32),
                                 RandArg((3, 4, 5), _f32)],
                poly_axes=[0, 0, 0]),
    PolyHarness("collapse", "",
                lambda x: lax.collapse(x, 1, 4),
                arg_descriptors=[RandArg((3, 4, 5, 6, 7), _f32)],
                poly_axes=[(0, 1, 3)]),
    PolyHarness("concatenate", "",
                lambda x: jnp.concatenate([x, x], axis=0),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                poly_axes=[(0, 1)]),
    PolyHarness("concatenate", "grad",
                jax.grad(lambda x: jnp.sum(jnp.concatenate([x, jnp.sin(x)], axis=0))),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                poly_axes=[(0, 1)]),

    PolyHarness("conv_general_dilated", "1d_stride=1",
                lambda lhs, rhs: lax.conv_general_dilated(
                    lhs, rhs,
                    window_strides=(1,),
                    padding="SAME",
                    rhs_dilation=None,
                    dimension_numbers=lax.ConvDimensionNumbers(lhs_spec=(0, 2, 1),
                                                               rhs_spec=(2, 1, 0),
                                                               out_spec=(0, 2, 1))),
                arg_descriptors=[RandArg((1, 12, 16), _f32), RandArg((4, 16, 16), _f32)],
                poly_axes=[1, None]).both_enable_and_disable_xla(),
    # The same example from above, but with stride=2.
    PolyHarness("conv_general_dilated", "1d_stride=2_even",
                lambda lhs, rhs: lax.conv_general_dilated(
                    lhs, rhs,
                    window_strides=(2,),
                    padding="SAME",
                    rhs_dilation=None,
                    dimension_numbers=lax.ConvDimensionNumbers(lhs_spec=(0, 2, 1),
                                                               rhs_spec=(2, 1, 0),
                                                               out_spec=(0, 2, 1))),
                arg_descriptors=[RandArg((1, 12, 16), _f32), RandArg((4, 16, 16), _f32)],
                poly_axes=[1, None],
              ).both_enable_and_disable_xla(),
    # The same example from above, but with stride=2 and odd input size.
    PolyHarness("conv_general_dilated", "1d_stride=2_odd",
                lambda lhs, rhs: lax.conv_general_dilated(
                    lhs, rhs,
                    window_strides=(2,),
                    padding="SAME",
                    rhs_dilation=None,
                    dimension_numbers=lax.ConvDimensionNumbers(lhs_spec=(0, 2, 1),
                                                               rhs_spec=(2, 1, 0),
                                                               out_spec=(0, 2, 1))),
                arg_descriptors=[RandArg((1, 13, 16), _f32), RandArg((4, 16, 16), _f32)],
                poly_axes=[1, None],
                ).both_enable_and_disable_xla(),
    # Issue #11402
    PolyHarness("conv_general_dilated", "1d_2",
                lambda lhs, rhs: lax.conv_transpose(lhs, rhs,
                                                    strides=(2,),
                                                    padding="SAME",
                                                    rhs_dilation=None,
                                                    transpose_kernel=False),
                arg_descriptors=[RandArg((5, 12, 16), _f32), RandArg((4, 16, 16), _f32)],
                poly_axes=[0, None],
                tol=1e-5).both_enable_and_disable_xla(),
    # Issue #11402
    PolyHarness("conv_general_dilated", "1d_3",
                lambda lhs, rhs: lax.conv_transpose(lhs, rhs,
                                                    strides=(2,),
                                                    padding="SAME",
                                                    rhs_dilation=None,
                                                    transpose_kernel=False),
                arg_descriptors=[RandArg((5, 12, 16), _f32), RandArg((4, 16, 16), _f32)],
                poly_axes=[1, None],
                tol=1e-5).both_enable_and_disable_xla(),
    PolyHarness("conv_general_dilated", "",
                lambda lhs, rhs: lax.conv_general_dilated(
                    lhs, rhs,
                    window_strides=(2, 3),
                    padding=((0, 0), (0, 0)),
                    lhs_dilation=(1, 1),
                    rhs_dilation=(1, 2),
                    dimension_numbers=("NCHW", "OIHW", "NCHW"),
                    feature_group_count=1,
                    batch_group_count=1,
                    precision=None),
                arg_descriptors=[RandArg((7, 3, 9, 10), _f32), RandArg((3, 3, 4, 5), _f32)],
                poly_axes=[0, None]).both_enable_and_disable_xla(),
    PolyHarness("cummax", "",
                lambda x: lax_control_flow.cummax(x, axis=1, reverse=False),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("jnp.cumsum", "reduce_axis=poly",
                lambda x: jnp.cumsum(x, axis=0),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0],
                expect_error=(
                  (None, None) if (not config.jax2tf_default_native_serialization or
                                   jtu.device_under_test() == "tpu") else
                  (NotImplementedError,
                   "associative scan over axis of non-constant size"))),
    PolyHarness("jnp.cumsum", "reduce_axis=static",
                lambda x: jnp.cumsum(x, axis=1),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("delta", "0",
                lambda x: lax_internal._delta(_f32, x.shape, axes=(0, 1)) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                poly_axes=[0]),
    PolyHarness("dot_general", "",
                lambda lhs, rhs: lax.dot_general(lhs, rhs,
                                                 dimension_numbers=(((2,), (1,)), ((0,), (0,)))),
                arg_descriptors=[RandArg((3, 4, 4), _f32), RandArg((3, 4), _f32)],
                poly_axes=[0, 0]),
    PolyHarness("dynamic_slice", "idx=tuple_int",
                # x:shape: (b, 4)
                lambda x: lax.dynamic_slice(x, (0, 1), (x.shape[0], 2)),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    PolyHarness("dynamic_slice", "idx=tuple_arg",
                # x:shape: (b, 4)
                lambda x, i0: lax.dynamic_slice(x, (i0, np.int32(1)), (x.shape[0], 2)),
                arg_descriptors=[RandArg((3, 4), _f32), np.array(-2, dtype=np.int32)],
                poly_axes=[0, None]).both_enable_and_disable_xla(),
    PolyHarness("dynamic_slice", "idx=array",
                # x:shape: (b, 4)
                lambda x, idx: lax.dynamic_slice(x, idx, (x.shape[0], 2)),
                arg_descriptors=[RandArg((3, 4), _f32), np.array([-2, -1], dtype=np.int32)],
                poly_axes=[0, None]).both_enable_and_disable_xla(),
    PolyHarness("dynamic_slice_in_dim", "idx=0",
                # x:shape: (b, 4)
                lambda x: lax.dynamic_slice_in_dim(x, 0, x.shape[0], axis=0),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    PolyHarness("dynamic_update_slice", "idx=tuple_int",
                # x:shape: (b, 4)
                lambda x: lax.dynamic_update_slice(x, x, (0, 0)),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    PolyHarness("dynamic_update_slice", "idx=tuple_arg",
                # x:shape: (b, 4)
                lambda x, i0: lax.dynamic_update_slice(x, x, (i0, np.int32(0))),
                arg_descriptors=[RandArg((3, 4), _f32), np.array(-2, dtype=np.int32)],
                poly_axes=[0, None]).both_enable_and_disable_xla(),
    PolyHarness("dynamic_update_slice", "idx=array",
                # x:shape: (b, 4)
                lambda x, idx: lax.dynamic_update_slice(x, x, idx),
                arg_descriptors=[RandArg((3, 4), _f32), np.array([-2, -1], dtype=np.int32)],
                poly_axes=[0, None]).both_enable_and_disable_xla(),
    PolyHarness("einsum", "0",
                lambda x: jnp.einsum("...i->...", x),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("einsum", "0_alt",
                lambda x: jnp.einsum(x, (..., 1), [...]),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("einsum", "1",
                lambda x, y: jnp.einsum("...ij,...jk->...ik", x, y),
                arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((3, 5, 6), _f32)],
                poly_axes=[0, 0]),
    PolyHarness("einsum", "1_alt",
                lambda x, y: jnp.einsum(x, [..., 0, 1], y, (..., 1, 2), [..., 0, 2]),
                arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((3, 5, 6), _f32)],
                poly_axes=[0, 0]),
    PolyHarness("einsum", "2",
                lambda x, y: jnp.einsum("...ij,jk->...ik", x, y),
                arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((5, 6), _f32)],
                poly_axes=[0, None]),
    PolyHarness("einsum", "2_alt",
                lambda x, y: jnp.einsum(x, [..., 0, 1], y, [1, 2], [..., 0, 2]),
                arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((5, 6), _f32)],
                poly_axes=[0, None]),
    PolyHarness("einsum", "3",
                # Reduced dimension is polymorphic
                lambda x, y: jnp.einsum("ij,jk->ik", x, y),
                arg_descriptors=[RandArg((3, 4), _f32), RandArg((4, 5), _f32)],
                poly_axes=[1, 0]),
    PolyHarness("einsum", "3_alt",
                # Reduced dimension is polymorphic
                lambda x, y: jnp.einsum(x, [0, 1], y, [1, 2], [0, 2]),
                arg_descriptors=[RandArg((3, 4), _f32), RandArg((4, 5), _f32)],
                poly_axes=[1, 0]),
    PolyHarness("einsum", "4",
                # Reduced dimension is polymorphic, and is 2*b
                lambda x, y: jnp.einsum("ij,jk->ik",
                                        jnp.concatenate([x, x], axis=1),
                                        jnp.concatenate([y, y], axis=0)),
                arg_descriptors=[RandArg((3, 4), _f32), RandArg((4, 5), _f32)],
                poly_axes=[1, 0]),
    PolyHarness("einsum", "4_alt",
                # Reduced dimension is polymorphic, and is 2*b
                lambda x, y: jnp.einsum(jnp.concatenate([x, x], axis=1), [0, 1],
                                        jnp.concatenate([y, y], axis=0), [1, 2],
                                        [0, 2]),
                arg_descriptors=[RandArg((3, 4), _f32), RandArg((4, 5), _f32)],
                poly_axes=[1, 0]),
    PolyHarness("einsum", "multiple_contractions",
                lambda x, y, z: jnp.einsum("ab,bc,cd->ad", x, y, z),
                arg_descriptors=[RandArg((3, 2), _f32), RandArg((2, 3), _f32), RandArg((3, 4), _f32)],
                poly_axes=[0, None, None]),
    PolyHarness("einsum", "incompatible_contractions_error",
                lambda x, y: jnp.einsum("ab,cb->ac", x, y),
                arg_descriptors=[RandArg((2, 3), _f32), RandArg((2, 3), _f32)],
                polymorphic_shapes=["(2, b0)", "(2, b1)"],
                input_signature=[tf.TensorSpec((2, None)), tf.TensorSpec((2, None))],
                expect_error=(AssertionError,
                              "Incompatible reduction dimensions")),
    PolyHarness("eye", "N=poly_M=None",
                lambda x: jnp.eye(x.shape[0]) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                poly_axes=[0]),
    PolyHarness("eye", "N=poly_M=poly",
                lambda x: jnp.eye(x.shape[0], M=x.shape[0] + 2) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                poly_axes=[0]),
    PolyHarness("full", "",
                lambda x: lax.full((x.shape[0], 2), 3.) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                poly_axes=[0]),
    # operand is non-poly, index is poly
    PolyHarness("getitem", "op=static_idx=poly",
                lambda a, i: a[i],
                arg_descriptors=[RandArg((3, 4), _f32), np.array([2, 2], np.int32)],
                poly_axes=[None, 0]).both_enable_and_disable_xla(),
    # operand is poly, index is integer
    PolyHarness("getitem", "op=poly_idx=const",
                lambda a: a[1],
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    # operand is poly, index is dim poly
    PolyHarness("getitem", "op=poly_idx=dim",
                lambda a: a[jnp.array(a.shape[0] - 2)],
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    # Both the operand and the index are poly
    PolyHarness("getitem", "op=poly_idx=poly",
                lambda a, i: a[i],
                arg_descriptors=[RandArg((3, 4), _f32), np.array([1, 2, 0], np.int32)],
                poly_axes=[0, 0]).both_enable_and_disable_xla(),
    # op is poly and index is an entire slice
    PolyHarness("getitem", "op=poly_idx=slice-all",
                lambda a: a[:],
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    # op is poly and index is a partial slice
    PolyHarness("getitem", "op=poly_idx=slice-ct-1",
                lambda a: a[:2],
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0],
                expect_error=(IndexError, "Cannot use NumPy slice indexing on an array dimension")
                ).both_enable_and_disable_xla(),
    PolyHarness("getitem", "op=poly_idx=slice-ct-2",
                lambda a: a[:, :2],
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    PolyHarness("getitem", "op=poly_idx=slice-None-1",
                lambda a: a[:a.shape[0]],
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    PolyHarness("getitem", "op=poly_idx=slice-poly",
                lambda a: a[:a.shape[0] - 1],
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0],
                expect_error=(IndexError, "Array slice indices must have static")).both_enable_and_disable_xla(),
    PolyHarness("image_resize", "linear_0",
                lambda x: jax.image.resize(x, (x.shape[0], 2 * x.shape[1], 2 * x.shape[2], x.shape[3]),
                                           method="linear"),
                arg_descriptors=[RandArg((3, 16, 32, 3), _f32)],
                poly_axes=[(1, 2)]),
    PolyHarness("image_resize", "linear_to_fixed_dim",
                lambda x: jax.image.resize(x, (x.shape[0], 64, 64, x.shape[3]),
                                           method="linear"),
                arg_descriptors=[RandArg((3, 16, 32, 3), _f32)],
                poly_axes=[(1, 2)]),
    PolyHarness("image_resize", "nearest_0",
                lambda x: jax.image.resize(x, (x.shape[0], 2 * x.shape[1], 2 * x.shape[2], x.shape[3]),
                                           method="nearest"),
                arg_descriptors=[RandArg((3, 5, 7, 3), _f32)],
                poly_axes=[(1, 2)]),
    PolyHarness("index_in_dim", "0",
                lambda x: lax.index_in_dim(x, -1, axis=0, keepdims=False),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("index_in_dim", "idx=neg",
                lambda x: lax.index_in_dim(x, -1, axis=0, keepdims=False),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("index_in_dim", "idx=last",
                lambda x: lax.index_in_dim(x, x.shape[0] - 1, axis=0, keepdims=False),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("jnp.insert", "insert=constant",
                lambda x: jnp.insert(x, jnp.arange(3, dtype=_i32), np.array([3, 4, 5], dtype=_i32)),
                arg_descriptors=[RandArg((12,), _i32)],
                poly_axes=[0],
                expect_error=expect_error_associative_scan),
    PolyHarness("jnp.insert", "insert=poly",
                lambda x: jnp.insert(x, jnp.arange(x.shape[0], dtype=_i32), x, axis=0),
                arg_descriptors=[RandArg((12, 3), _i32)],
                poly_axes=[(0, 1)],
                expect_error=expect_error_associative_scan),
    PolyHarness("iota", "",
                lambda x: x + lax.iota(_f32, x.shape[0]),
                arg_descriptors=[RandArg((3,), _f32)],
                poly_axes=[0]),
    PolyHarness("matmul", "0",
                jnp.matmul,
                arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 4, 5), _f32)],
                poly_axes=[0, 0],
                tol=1e-5),
    PolyHarness("matmul", "1",
                jnp.matmul,
                arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((4, 5), _f32)],
                poly_axes=[0, None],
                tol=1e-5),
    [
        PolyHarness("mean",
                    f"{axis=}_{keepdims=}_where=None",
                    lambda x, axis, keepdims: jnp.mean(x, axis=axis, keepdims=keepdims, where=None),
                    arg_descriptors=[RandArg((7, 8, 4), _f32), StaticArg(axis), StaticArg(keepdims)],
                    poly_axes=[0])
        for keepdims in [False, True]
        for axis in [None, (0,), (0, 1), (1,)]
    ],
    [
        PolyHarness("mean",
                    f"{axis=}_{keepdims=}_where=Some",
                    lambda x, where, axis, keepdims: jnp.mean(x, axis=axis, keepdims=keepdims, where=where),
                    arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 8, 4), np.bool_),
                                     StaticArg(axis), StaticArg(keepdims)],
                    poly_axes=[0, 0])
        for keepdims in [False, True]
        for axis in [None, (0,), (0, 1), (1,)]
    ],
    PolyHarness("jnp.nonzero", "size=constant",
                lambda x: jnp.nonzero(x % 3, size=10, fill_value=100),
                arg_descriptors=[RandArg((3, 2, 4), _i32)],
                poly_axes=[0],
                expect_error=expect_error_associative_scan),
    PolyHarness("jnp.nonzero", "size=poly",
                lambda x: jnp.nonzero(x % 3, size=x.shape[0] * 2, fill_value=100),
                arg_descriptors=[RandArg((3, 2, 4), _i32)],
                poly_axes=[0],
                expect_error=expect_error_associative_scan),
    PolyHarness("ones", "",
                lambda x: jnp.ones(x.shape, dtype=_f32) + x,
                arg_descriptors=[RandArg((3, 2, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("pad", "",
                lax.pad,
                arg_descriptors=[RandArg((3, 2, 5), _f32), np.float32(5.),
                                 StaticArg(((0, 0, 0), (0, 0, 0), (1, 1, 1)))],
                poly_axes=[0, None]),
    PolyHarness("pad", "poly_padding_config",
                lambda x: lax.pad(x, _f32(0.),
                                  ((x.shape[0], x.shape[1], x.shape[0]),
                                   (0, 0, 0))),
                arg_descriptors=[RandArg((3, 2), _f32)],
                poly_axes=[0]),
    PolyHarness("jnp.pad", "mode=constant",
                lambda x: jnp.pad(x, [[x.shape[0], 0], [x.shape[1], 1]],
                                  mode="constant"),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("jnp.pad", "mode=constant_bminus1",
                # We slice first the unknown dimension to make it of size b - 1
                # which may be 0.
                lambda x: jnp.pad(lax.dynamic_slice_in_dim(x, 1, x.shape[0] - 1,
                                                           axis=0),
                                  [[x.shape[0], 0], [x.shape[1], 1]],
                                  mode="constant"),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("jnp.pad", "mode=edge",
                lambda x: jnp.pad(x, [[x.shape[0], 0], [x.shape[1], 1]],
                                  mode="edge"),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("percentile", "axis=None",
                lambda x: jnp.percentile(x, 50, axis=None),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("nanquantile", "axis=None",
                lambda x: jnp.nanquantile(x, .5, axis=None),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("percentile", "axis=0",
                lambda x: jnp.percentile(x, 50, axis=0),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("nanquantile", "axis=0",
                lambda x: jnp.nanquantile(x, .5, axis=0),
                arg_descriptors=[RandArg((3, 5), _f32)],
                poly_axes=[0]),
    PolyHarness("random_gamma", "",
                lambda key, a: jax.random.gamma(key, a),
                arg_descriptors=[RandArg((3, 2), np.uint32), RandArg((3, 3), _f32)],
                poly_axes=[0, 0]),
    # The known dimensions product must be even.
    PolyHarness("random_categorical", "axis=0",
                lambda key, a: jax.random.categorical(key, a, axis=0),
                arg_descriptors=[RandArg((2,), np.uint32), RandArg((3, 8), _f32)],
                poly_axes=[None, 0]),
    PolyHarness("random_categorical", "axis=1",
                lambda key, a: jax.random.categorical(key, a, axis=1),
                arg_descriptors=[RandArg((2,), np.uint32), RandArg((3, 5, 8), _f32)],
                poly_axes=[None, (0, 1)]),
    PolyHarness("random_categorical", "axis=1_then_reshape",
                lambda key, a: jax.random.categorical(key, a, axis=1).reshape((-1)),
                arg_descriptors=[RandArg((2,), np.uint32), RandArg((3, 5, 8), _f32)],
                poly_axes=[None, (0, 1)]),
    PolyHarness("random_categorical", "0_dim",  # One axis has 0 size
                lambda key, a: jax.random.categorical(key, a, axis=1),
                arg_descriptors=[RandArg((2,), np.uint32), RandArg((3, 5, 0), _f32)],
                poly_axes=[None, (0, 1)]),
    # Works when the known dimensions are known to be even or odd.
    PolyHarness("random_uniform", "even_1",
                lambda key, a: jax.random.uniform(key, a.shape, dtype=_f32) + a,
                arg_descriptors=[RandArg((2,), np.uint32), RandArg((3, 4), _f32)],
                poly_axes=[None, 0]),
    PolyHarness("random_uniform", "even_2",
                lambda key, a: jax.random.uniform(key, (2 * a.shape[0], a.shape[1]),
                                                  dtype=_f32) + jnp.concatenate([a, a], axis=0),
                arg_descriptors=[RandArg((2,), np.uint32), RandArg((3, 1), _f32)],
                poly_axes=[None, 0]),
    PolyHarness("random_uniform", "error_not_even",
                lambda key, a: jax.random.uniform(key, a.shape, dtype=_f32),
                arg_descriptors=[RandArg((2,), np.uint32), RandArg((3, 5), _f32)],
                poly_axes=[None, 0],
                expect_error=(core.InconclusiveDimensionOperation,
                              "the product of the known dimensions must be even")),
    PolyHarness("reduce_window", "min",
                # x.shape = (b, 8)
                lambda x: lax.reduce_window(x, np.array(1., _f32), lax.min,
                                            (2, 2), (1, 1), "VALID"),
                arg_descriptors=[RandArg((3, 8), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "add_0",
                # x.shape = (b, 8)
                lambda x: lax.reduce_window(x, 0, lax.add, (2, 2), (1, 1),
                                            "VALID"),
                arg_descriptors=[RandArg((3, 8), _f32)],
                poly_axes=[0]).both_enable_and_disable_xla(),
    # https://github.com/google/jax/issues/11804
    # Use the reshape trick to simulate a polymorphic dimension of 16*b.
    # (See test "conv_general_dilated.1d_1" above for more details.)
    PolyHarness("reduce_window", "add_1",
                # x.shape = (1, 16*b, 1)
                lambda x: lax.reduce_window(
                    jnp.reshape(x, (1, -1, 1)),
                    0., lax.add, (1, 4, 1), (1, 2, 1), "SAME"),
                arg_descriptors=[RandArg((1, 128, 16), _f32)],
                poly_axes=[1]).both_enable_and_disable_xla(),
    # TODO(necula): not yet supported, but also unlikely to come up.
    # PolyHarness("random_uniform", "odd",
    #               lambda key, a: jax.random.uniform(key, (2 * a.shape[0] + 1, a.shape[1]),
    #                                                 dtype=_f32),
    #               [RandArg((2,), np.uint32), RandArg((3, 5), _f32)],
    #               poly_axes=[None, 0]),
    [
        PolyHarness("reduce", reduce_op.__name__,
                    lambda x: reduce_op(x, axis=-1, keepdims=True),  # type: ignore
                    arg_descriptors=[RandArg((3, 5), _f32)],
                    poly_axes=[0])
        for reduce_op in [jnp.all, jnp.any, jnp.max, jnp.min, jnp.prod, jnp.sum]
    ],
    # Repeat f32[b, 2] * 3
    PolyHarness("repeat", "repeats=int_axis=0",
                lambda x: jnp.repeat(x, repeats=3, axis=0),
                arg_descriptors=[RandArg((3, 2), _f32)],
                poly_axes=[0]),
    # Repeat f32[b, 2] * b
    PolyHarness("repeat", "repeats=poly_axis=0",
                lambda x: jnp.repeat(x, repeats=x.shape[0], axis=0),
                arg_descriptors=[RandArg((3, 2), _f32)],
                poly_axes=[0]),
    # Repeat f32[b, 2] * b
    PolyHarness("repeat", "repeats=poly_axis=None",
                lambda x: jnp.repeat(x, repeats=x.shape[0], axis=None),
                arg_descriptors=[RandArg((3, 2), _f32)],
                poly_axes=[0]),
    # Repeat f32 * b
    PolyHarness("repeat", "repeats=poly_axis=None_scalar",
                lambda x, y: jnp.repeat(x, repeats=y.shape[0], axis=None) + y,
                arg_descriptors=[RandArg((), _f32), RandArg((3, 1), _f32)],
                poly_axes=[None, 0]),
    PolyHarness("repeat", "repeats=poly_axis=None_total_repeat_length1",
                lambda x: jnp.repeat(x, repeats=x.shape[0], axis=None, total_repeat_length=8),
                arg_descriptors=[RandArg((3, 2), _f32)],
                poly_axes=[0],
                expect_error=(ValueError, "jnp.repeat with a non-constant `repeats` is supported only .*")),
    PolyHarness("reshape", "0",
                lambda x: x.reshape([x.shape[0], -1]),
                arg_descriptors=[RandArg((3, 2, 3), _f32)],
                poly_axes=[0]),
    PolyHarness("reshape", "1",
                lambda x: x.reshape([x.shape[0], -1]),
                arg_descriptors=[RandArg((3, 2, 3), _f32)],
                poly_axes=[(0, 1)]),
    PolyHarness("reshape", "2",
                lambda x: x.reshape([x.shape[0], -1, x.shape[3], x.shape[2]]),
                arg_descriptors=[RandArg((3, 4, 5, 6, 7), _f32)],
                poly_axes=[(0, 2, 3)]),
    PolyHarness("reshape", "3",
                lambda x: jnp.reshape(x, [2, -1]),
                arg_descriptors=[RandArg((3, 4, 5, 6, 7), _f32)],
                poly_axes=[(0, 2)]),
    PolyHarness("reshape", "_issue_9975",
                # The newshape is a scalar
                lambda x: jnp.reshape(x, x.shape[0] * x.shape[1]),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("reshape", "error",
                lambda x: x.reshape([x.shape[0], -1, 3]),
                arg_descriptors=[RandArg((3, 2, 4), _f32)],
                poly_axes=[0],
                skip_jax_run=True,
                expect_error=(core.InconclusiveDimensionOperation,
                              re.escape(
                                "Cannot divide evenly the sizes of shapes (b0, 2, 4) and (b0, -1, 3)"))),
    PolyHarness("roll", "axis=0",
                lambda x: jnp.roll(x, 2, axis=0),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("roll", "axis=None",
                lambda x: jnp.roll(x, 2),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("scatter_add", "",
                partial(lax.scatter_add, indices_are_sorted=False, unique_indices=True),
                arg_descriptors=[RandArg((7, 4), _f32),
                                 np.array([[1], [2]], np.int32),  # indices: [2, 1]
                                 RandArg((7, 2), _f32),  # updates: [7, 2]
                                 StaticArg(lax.ScatterDimensionNumbers((0,), (1,), (1,)))],
                poly_axes=[0, None, 0]),
    PolyHarness("scatter_add", "clip0",
                partial(lax.scatter_add, indices_are_sorted=False, unique_indices=True, mode=lax.GatherScatterMode.CLIP),
                arg_descriptors=[RandArg((7, 4), _f32),  # [b, 4]
                                 np.array([[1], [2]], np.int32),  # indices: [2, 1]
                                 RandArg((7, 2), _f32),  # updates: [b, 2]
                                 StaticArg(lax.ScatterDimensionNumbers((0,), (1,), (1,)))],
                poly_axes=[0, None, 0]),
    PolyHarness("scatter_add", "clip1",
                partial(lax.scatter_add, indices_are_sorted=False, unique_indices=True, mode=lax.GatherScatterMode.CLIP),
                arg_descriptors=[RandArg((7, 4), _f32),  # [b, 4]
                                 np.array([[1, 2], [-2, 0], [6, 4], [7, -1], [1, 0], [3, 0], [0, 5]], np.int32),  # indices: [b, 2]
                                 RandArg((7, 1), _f32),  # updates: [b, 1]
                                 StaticArg(lax.ScatterDimensionNumbers((1,), (0,), (0, 1,)))],
                poly_axes=[0, 0, 0]),
    PolyHarness("select", "0",
                # x.shape = (b, 3)
                lambda x: lax.select(x > 5., x, x),
                arg_descriptors=[RandArg((7, 3), _f32)],
                poly_axes=[0]),
    PolyHarness("select", "1",
                # x.shape = (b, 3); y.shape = (3,)
                jax.vmap(lambda x, y: lax.select(x > 5., x, y), in_axes=[0, None]),
                arg_descriptors=[RandArg((7, 3), _f32), RandArg((3,), _f32)],
                poly_axes=[0, None]),
    PolyHarness("slice", "entire_axis",
                lambda x: lax.slice(x, start_indices=(0, 1), limit_indices=(x.shape[0], 3)),
                arg_descriptors=[RandArg((7, 3), _f32)],
                poly_axes=[0]),
    PolyHarness("slice_in_dim", "entire_axis",
                lambda x: lax.slice_in_dim(x, 0, x.shape[0], stride=1, axis=0),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("slice_in_dim", "start=neg",
                lambda x: lax.slice_in_dim(x, -1, x.shape[0], stride=1, axis=0),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("slice_in_dim", "limit=neg",
                lambda x: lax.slice_in_dim(x, 0, -1, stride=1, axis=0),
                arg_descriptors=[RandArg((3, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("slice_in_dim", "stride=2_even",
                lambda x: lax.slice_in_dim(x, 0, x.shape[0], stride=2, axis=0),
                arg_descriptors=[RandArg((12, 4), _f32)],
                poly_axes=[0]),
    PolyHarness("slice_in_dim", "stride=2_odd",
                lambda x: lax.slice_in_dim(x, 0, x.shape[0], stride=2, axis=0),
                arg_descriptors=[RandArg((13, 4), _f32)],
                poly_axes=[0]),
    # Not yet, the slice_in_dim does int(stride)
    # PolyHarness("slice_in_dim", "stride=sym",
    #             lambda x: lax.slice_in_dim(x, 0, x.shape[0], stride=x.shape[0] // 4, axis=0),
    #             arg_descriptors=[RandArg((13, 4), _f32)],
    #             poly_axes=[0]),
    PolyHarness("squeeze", "axis=empty",
                jnp.squeeze,
                arg_descriptors=[RandArg((5,), _f32), StaticArg(())],
                poly_axes=[0]),
    PolyHarness("squeeze", "axis=None",
                jnp.squeeze,
                arg_descriptors=[RandArg((5,), _f32), StaticArg(None)],
                poly_axes=[0],
                expect_error=(ValueError, "jnp.squeeze with axis=None is not supported with shape polymorphism")),
    PolyHarness("squeeze", "axis=1",
                jnp.squeeze,
                arg_descriptors=[RandArg((4, 1), _f32), StaticArg((1,))],
                poly_axes=[0]),
    PolyHarness("squeeze", "axis=1_2",
                jnp.squeeze,
                arg_descriptors=[RandArg((4, 1, 1), _f32), StaticArg((1, 2))],
                poly_axes=[0]),
    PolyHarness("squeeze", "error",
                jnp.squeeze,
                arg_descriptors=[RandArg((3, 33), _f32), StaticArg(-1)],
                poly_axes=[(0, 1)],
                skip_jax_run=True,
                expect_error=(ValueError,
                              re.escape(
                                "cannot select an axis to squeeze out which has size not equal to one, got shape=(b0, b1) and dimensions=(1,)"))
                ),
    PolyHarness("take", "",
                lambda a, i: jnp.take(a, i, axis=1),
                arg_descriptors=[RandArg((3, 4, 5), _f32), np.array([1, 2], np.int32)],
                poly_axes=[0, None]).both_enable_and_disable_xla(),
    PolyHarness("take_along_axis", "0",
                lambda x, y: jnp.take_along_axis(x, y, axis=0),
                arg_descriptors=[RandArg((5, 2), _f32), RandArg((5, 1), np.int32)],
                poly_axes=[0, 0]),
    PolyHarness("take_along_axis", "1",
                lambda x, y: jnp.take_along_axis(x, y, axis=1),
                arg_descriptors=[RandArg((5, 2), _f32), RandArg((5, 1), np.int32)],
                poly_axes=[0, 0]),
    PolyHarness("tile", "0",
                lambda x: jnp.tile(x, (1, 2)),
                arg_descriptors=[RandArg((4, 3), _f32)],
                poly_axes=[0]),
    PolyHarness("tile", "1",
                # The repetitions are polys
                lambda x: jnp.tile(x, (1, x.shape[0])),
                arg_descriptors=[RandArg((4, 2), _f32)],
                poly_axes=[0]),
    PolyHarness("tri", "N=poly_M=None",
                lambda x: jnp.tri(x.shape[0]) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                poly_axes=[0]),
    PolyHarness("tri", "N=poly_M=poly",
                lambda x: jnp.tri(x.shape[0], M=x.shape[0] + 2) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                poly_axes=[0]),
    [
        PolyHarness("var",
                    f"{axis=}_{keepdims=}_where=None",
                    lambda x, axis, keepdims: jnp.var(x, axis=axis, keepdims=keepdims, where=None),
                    arg_descriptors=[RandArg((7, 8, 4), _f32), StaticArg(axis), StaticArg(keepdims)],
                    poly_axes=[0])
        for keepdims in [False, True]
        for axis in [None, (0,), (0, 1), (1,)]
    ],
    [
        PolyHarness("var",
                    f"{axis=}_{keepdims=}_where=Some",
                    lambda x, where, axis, keepdims: jnp.var(x, axis=axis, keepdims=keepdims, where=where),
                    arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 8, 4), np.bool_), StaticArg(axis), StaticArg(keepdims)],
                    poly_axes=[0, 0])
        for keepdims in [False, True]
        for axis in [None, (0,), (0, 1), (1,)]
    ],
    PolyHarness("where", "",
                jnp.where,
                arg_descriptors=[RandArg((2,), np.bool_), RandArg((), _f32), RandArg((2,), _f32)],
                poly_axes=[0, None, 0]),
]

def _get_jax2tf_limitations(
    device, h: primitive_harness.Harness) -> Sequence[Jax2TfLimitation]:
  # And the jax2tf limitations
  def applicable_jax2tf_limitation(l: Jax2TfLimitation) -> bool:
    # The CheckShapePolymorphism uses tf.function, so we care about "graph"
    return l.filter(device=device, dtype=h.dtype, mode="graph")

  limitations = Jax2TfLimitation.limitations_for_harness(h)
  return tuple(filter(applicable_jax2tf_limitation, limitations))

### We add to the test harnesses some that are obtained from the
### primitive harnesses by applying vmap to the function and then asserting
### that we can convert shape polymorphically the result.

def _make_vmap_primitive_harnesses() -> Sequence[PolyHarness]:
  """For each harness group, pick a single dtype.

  See PolyHarness for documentation.

  Ignore harnesses that fail in graph mode in jax2tf.
  """
  all_h = primitive_harness.all_harnesses
  res = []

  # Index by group
  harness_groups: Dict[
    str, Sequence[primitive_harness.Harness]] = collections.defaultdict(list)
  device = jtu.device_under_test()

  for h in all_h:
    # Drop the JAX limitations
    if not h.filter(device_under_test=device, include_jax_unimpl=False):
      continue
    # And the jax2tf limitations that are known to result in TF error.
    if any(l.expect_tf_error for l in _get_jax2tf_limitations(device, h)):
      continue
    # TODO(marcvanzee): We currently exclude tests with enable_xla=False because
    # this doesn't work with vmap due to a call to lax.gather. We should include
    # them once vmap works with enable_xla=False.
    if not h.params.get("enable_xla", True):
      continue
    harness_groups[h.group_name].append(h)

  selected_harnesses = []
  for group_name, hlist in harness_groups.items():
    # Pick the dtype with the most harnesses in this group. Some harness
    # groups only test different use cases at a few dtypes.
    c = collections.Counter([h.dtype for h in hlist])
    (dtype, _), = c.most_common(1)
    selected_harnesses.extend([h for h in hlist if h.dtype == dtype])

  # We do not yet support shape polymorphism for vmap for some primitives
  _NOT_SUPPORTED_YET = frozenset([
      # In linalg._lu_python we do reshape(-1, ...)
      "lu",
      "custom_linear_solve",

      # We do *= shapes in the batching rule for conv_general_dilated
      "conv_general_dilated",

      "tridiagonal_solve",  # batching not implemented in JAX
      "iota",  # vmap does not make sense for 0-argument functions
      "rng_bit_generator",  # vmap not implemented
  ])

  batch_size = 3
  for h in selected_harnesses:
    if h.group_name in _NOT_SUPPORTED_YET:
      continue

    def make_batched_arg_descriptor(
        ad: primitive_harness.ArgDescriptor) -> Optional[primitive_harness.ArgDescriptor]:
      if isinstance(ad, RandArg):
        return RandArg((batch_size,) + ad.shape, ad.dtype)
      elif isinstance(ad, CustomArg):
        def wrap_custom(rng):
          arg = ad.make(rng)
          return np.stack([arg] * batch_size)

        return CustomArg(wrap_custom)
      else:
        assert isinstance(ad, np.ndarray), ad
        return np.stack([ad] * batch_size)

    new_args = [make_batched_arg_descriptor(ad)
                for ad in h.arg_descriptors
                if not isinstance(ad, StaticArg)]

    # This test does not make sense for nullary functions
    if not new_args:
      continue

    # We do not check the result of harnesses that require custom assertions.
    check_result = all(not l.custom_assert and not l.skip_comparison and l.tol is None
                       for l in _get_jax2tf_limitations(device, h))
    if h.group_name == "cumsum":
      # TODO(necula): why do we need to adjust the cumsum tolerance?
      tol = 1e-5
    else:
      tol = None
    vmap_harness = PolyHarness("vmap_" + h.group_name, h.name,
                               jax.vmap(h.dyn_fun, in_axes=0, out_axes=0),
                               arg_descriptors=new_args,
                               poly_axes=[0] * len(new_args),
                               check_result=check_result,
                               tol=tol)
    vmap_harness.original_harness = h
    res.append(vmap_harness)
  return res

_POLY_SHAPE_TEST_HARNESSES.append(_make_vmap_primitive_harnesses())

def _flatten_harnesses(harnesses):
  res = []
  for h in harnesses:
    if isinstance(h, Sequence):
      res.extend(h)
    else:
      res.append(h)
  return res


class ShapePolyPrimitivesTest(tf_test_util.JaxToTfTestCase):
  """Tests for primitives that take shape values as parameters."""

  # This test runs for all _POLY_SHAPE_PRIMITIVE_HARNESSES.

  # For each primitive "xxx" the test will be called "test_harness_xxx_...".
  # If you want to run this test for only one harness that includes "foo"
  # in the name (after test_harness), add parameter `one_containing="foo"`
  # to parameterized below.
  @primitive_harness.parameterized(
      _flatten_harnesses(_POLY_SHAPE_TEST_HARNESSES),
      #one_containing="",
  )
  def test_harness(self, harness: PolyHarness):
    # Exclude some harnesses that are known to fail for native serialization
    if config.jax2tf_default_native_serialization:
      if not harness.enable_xla:
        raise unittest.SkipTest("disabled for native_serialization and enable_xla=False")

      # Set of harness.group_name:platform that are implemented with custom call
      custom_call_harnesses = {
          "vmap_cholesky:cpu", "vmap_cholesky:gpu", "vmap_eig:cpu",
          "vmap_eigh:cpu", "vmap_eigh:gpu", "vmap_fft:cpu",
          "householder_product:cpu", "householder_product:gpu",
          "vmap_geqrf:cpu", "vmap_geqrf:gpu",
          "vmap_lu:cpu", "vmap_lu:gpu", "vmap_qr:cpu", "vmap_qr:gpu",
          "vmap_svd:cpu", "vmap_svd:gpu",
          "random_gamma:gpu", "vmap_random_gamma:gpu",
          "random_categorical:gpu", "vmap_random_categorical:gpu",
          "random_randint:gpu", "vmap_random_randint:gpu",
          "random_uniform:gpu", "vmap_random_uniform:gpu",
          "vmap_random_split:gpu"}
      if f"{harness.group_name}:{jtu.device_under_test()}" in custom_call_harnesses:
        raise unittest.SkipTest("native serialization with shape polymorphism not implemented for custom calls; b/261671778")

      # Set of harness.group_name or harness.group_name:platform that are implemented with HLO fallback lowering rules
      fallback_lowering_harnesses = {
          "vmap_approx_top_k", "vmap_bessel_i0e", "vmap_eigh:tpu",
          "vmap_erf_inv", "vmap_igamma", "vmap_igammac", "vmap_lu",
          "vmap_regularized_incomplete_beta", "vmap_qr:tpu",
          "vmap_random_gamma:cpu", "random_gamma:tpu",
          "vmap_random_gamma:tpu", "vmap_svd:tpu"}
      if (harness.group_name in fallback_lowering_harnesses or
          f"{harness.group_name}:{jtu.device_under_test()}" in fallback_lowering_harnesses):
        raise unittest.SkipTest(
            "native serialization with shape polymorphism not implemented for JAX primitives still using HLO fallback lowering; b/261682623")

      # Set of harness.group_name that are unsupported in serialization
      require_stablehlo_feature_support = {
          # Tan (b/274462307) and TopK (openxla/stablehlo#1255) require support.
          "vmap_tan", "vmap_top_k",
          # Crash due to openxla/stablehlo#1328
          "vmap_random_randint", "vmap_random_uniform"
      }
      if harness.group_name in require_stablehlo_feature_support:
        raise unittest.SkipTest(
            "native lowering with shape polymorphism requires additional StableHLO feature support")
      if (jtu.device_under_test() == "tpu" and
          harness.fullname in [
              "jnp.cumsum_reduce_axis=poly",
              "jnp.insert_insert=constant", "jnp.insert_insert=poly",
              "jnp.nonzero_size=constant", "jnp.nonzero_size=poly"]):
        # https://github.com/openxla/stablehlo/issues/1258
        raise unittest.SkipTest(
            "native serialization with shape polymorphism not implemented for window_reductions on TPU")
      if (jtu.device_under_test() == "gpu" and
          harness.fullname in [
              "jnp.cumsum_reduce_axis=poly",
              "jnp.insert_insert=constant", "jnp.insert_insert=poly",
              "jnp.nonzero_size=constant", "jnp.nonzero_size=poly"]):
        raise unittest.SkipTest(
            "TODO(b/271645610): investigate inconclusive dimension operation for cumsum on gpu")

    harness.run_test(self)


if __name__ == "__main__":
  absltest.main(testLoader=jtu.JaxTestLoader())