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."""

from __future__ import annotations

from collections.abc import Sequence
import contextlib
import math
from typing import Any, Callable
import unittest

from absl import logging
from absl.testing import absltest

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

import jax
from jax.experimental import jax2tf
from jax.experimental import export
from jax.experimental.export import shape_poly
from jax.experimental import pjit
from jax import lax
import jax.numpy as jnp
from jax import random
from jax import tree_util
from jax._src import config
from jax._src import core
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]

config.parse_flags_with_absl()

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

_f32 = np.float32
_i32 = np.int32

expect_error_associative_scan = (
    NotImplementedError,
    "associative scan over axis of non-constant size",
)


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[test_harnesses.ArgDescriptor] = (),
               polymorphic_shapes: Sequence[str | None] = (),
               input_signature: Sequence[tf.TensorSpec] | None = None,
               expected_output_signature: tf.TensorSpec | None = None,
               enable_xla: bool = True,
               expect_error: tuple[Any | None, str | None] = (None, None),
               skip_jax_run: bool = False,
               check_result: bool = True,
               tol: float | None = None,
               limitations: Sequence[Jax2TfLimitation] = (),
               override_jax_config_flags: dict[str, Any] = {}):
    """Args:

      group_name, name: The name for the harness. See `Harness.__init__`.
      fun: the function to be converted, possibly 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
        `input_signature` must be present.
      polymorphic_shapes: For `jax2tf.convert`.
      input_signature: For `tf.function.get_concrete_function`. If missing,
        generated from `polymorphic_shapes`.
      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.
      limitations: if given, then apply the custom_assert and tolerance from the
        Jax2TfLimitations.
      override_jax_config_flags: jax.config flags to override for the duration
        of the test.
    """
    super().__init__(group_name, name, fun, arg_descriptors,
                     dtype=np.float32)
    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
    self.limitations = limitations
    self.override_jax_config_flags = override_jax_config_flags

  # 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,
                        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) -> jax.Array | None:
    def log_message(extra: str):
      return f"[{tst._testMethodName}]: {extra}"

    # Check that we have overridden the jax.config flags
    for fname, fvalue in self.override_jax_config_flags.items():
      tst.assertEqual(getattr(jax.config, fname), fvalue, (
          f"Flag {fname} current value {getattr(jax.config, fname)} != {fvalue}"))

    tst.assertIsNotNone(self.polymorphic_shapes)
    polymorphic_shapes = self.polymorphic_shapes
    if not self.skip_jax_run:
      args = self.dyn_args_maker(tst.rng())
    else:
      tst.assertIsNotNone(self.input_signature)

    if self.input_signature is None:
      tst.assertEqual(
        len(polymorphic_shapes), len(args),
        f"polymorphic_shapes {polymorphic_shapes} of length "
        f"{len(polymorphic_shapes)} must match number of arguments {len(args)}")
      args_specs = export.args_specs(args, polymorphic_shapes)
      input_signature = [
        tf.TensorSpec(
            [d if isinstance(d, int) else None for d in a.shape],
            dtype=a.dtype) for a in args_specs]
    else:
      input_signature = self.input_signature  # type: ignore

    expect_error_type, expect_error_regex = self.expect_error
    if self.skip_jax_run and not 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 None

    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:
        res_tf = tf.nest.map_structure(lambda t: t.numpy(), res_tf)  # type: ignore
        custom_assert_lims = [
            l for l in self.limitations if l.custom_assert is not None]
        assert len(custom_assert_lims) <= 1, custom_assert_lims
        tol = None
        if self.tol is not None:
          tol = self.tol
        elif self.limitations:
          max_lim = self.limitations[0].get_max_tolerance_limitation(
              self.limitations)
          if max_lim is not None:
            tol = max_lim.tol

        if not custom_assert_lims:
          tst.assertAllClose(res_jax, res_tf, atol=tol, rtol=tol)
        else:
          logging.info(log_message(
              f"Running custom_assert with tol={tol} due "
              f"to {custom_assert_lims[0]}"))
          custom_assert_lims[0].custom_assert(tst, res_jax, res_tf, args=args,  # type: ignore
                                              tol=tol, err_msg=None)
        return res_tf
      else:
        return None
    else:
      return None


def check_shape_poly(tst, f_jax: Callable, *,
                     arg_descriptors: Sequence[test_harnesses.ArgDescriptor] = (),
                     skip_jax_run: bool = False,
                     polymorphic_shapes: Sequence[str | None] = (),
                     input_signature: Sequence[tf.TensorSpec] | None = None,
                     expected_output_signature: tf.TensorSpec | None = None,
                     expect_error=(None, None)) -> jax.Array | None:
  # Makes and tests a harness. See PolyHarness documentation.
  h = PolyHarness("", "", f_jax,
                  arg_descriptors=arg_descriptors,
                  skip_jax_run=skip_jax_run,
                  polymorphic_shapes=polymorphic_shapes,
                  input_signature=input_signature,
                  expected_output_signature=expected_output_signature,
                  expect_error=expect_error)
  return 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)],
                     polymorphic_shapes=[None],
                     expected_output_signature=tf.TensorSpec([2, 3]))

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

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

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

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

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

  @jtu.parameterized_filterable(
    # make_args invoked with op.shape[0]: start, stop, step, dtype
    kwargs=[
      dict(testcase_name=name, make_args=make_args, expect_error=expect_error, expect_msg=expect_msg)
      for name, make_args, expect_error, expect_msg in [
          # make_args invoked with op.shape[0]: start, stop, step, dtype
          ("float_start", lambda b: (0., b, None),
           ValueError, "must be either dimension expressions or integers"),
          ("float_step", lambda b: (0, b, 0.5),
           ValueError, "must be either dimension expressions or integers"),
          ("step_0", lambda b: (0, b, 0),
           ValueError, "has step == 0"),
          ("inconclusive_step_sign", lambda b: (0, b, b - 2),
           core.InconclusiveDimensionOperation,
           "must be resolved statically if it is > 0 or < 0"),
      ]
    ]
  )
  def test_arange_error(self, make_args=lambda b: (0., b, 2),
                        expect_error=ValueError,
                        expect_msg="must be either dimension expressions or integers"):
    def f_jax(x):  # x: i32[b]
      return x[0] + jnp.arange(*(make_args(x.shape[0])))
    x = np.ones((3,), dtype=np.int32)
    with self.assertRaisesRegex(expect_error, expect_msg):
      check_shape_poly(self, f_jax, arg_descriptors=[x],
                       polymorphic_shapes=["b"])


  @jtu.parameterized_filterable(
    kwargs=[
      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)],
      polymorphic_shapes=["b"])

  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)],
                     polymorphic_shapes=[None],
                     expected_output_signature=tf.TensorSpec([3]))

    check_shape_poly(self,
                     f_jax,
                     arg_descriptors=[RandArg((2, 3), _f32)],
                     polymorphic_shapes=["b, _"],
                     expected_output_signature=tf.TensorSpec([3]))

  def test_forgot_polymorphic_shapes_error(self):
    msg_re = "syntax error in symbolic shape"
    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_non_native(self):
    """Test conversion of actual arguments to abstract values."""

    def check_avals(*, arg_shapes: Sequence[Sequence[int | None]],
                    polymorphic_shapes: Sequence[str | None],
                    expected_avals: Sequence[core.ShapedArray] | None = None,
                    expected_shapeenv: dict[str, int] | None = 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(
            lambda s, dt, spec: core.ShapedArray(
                export.symbolic_shape(spec, like=s),
                dt),
            arg_shapes, arg_dtypes, polymorphic_shapes))
        dim_vars = shape_poly.all_dim_vars(avals)
        dim_values, _ = jax2tf.jax2tf._interpret_fun_jax(
            partial(shape_poly.compute_dim_vars_from_arg_shapes,
                    avals,
                    args_kwargs_tree=tree_util.tree_flatten((avals, {}))[1]),
            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(
          export.symbolic_shape(shape_spec, like=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=["..."],
        expected_avals=(shaped_array("2, 3", [2, 3]),))

    # Partially known shapes for the arguments
    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=["a + 1, b + 2"],
        eager_mode=True,
        expected_shapeenv=dict(a=1, b=1))

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

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

  def test_arg_avals_errors(self):
    """Test error reporting for shape polymorphism."""
    def conv_and_run(*, arg_shape: core.Shape,
                     polymorphic_shape: str):
      arg = np.arange(math.prod(arg_shape), dtype=np.float32).reshape(arg_shape)
      check_shape_poly(self, lambda x: x,
                       arg_descriptors=[arg],
                       polymorphic_shapes=[polymorphic_shape])

    with self.assertRaisesRegex(ValueError,
                                re.escape("polymorphic shape spec should be")):
      conv_and_run(arg_shape=(2,), polymorphic_shape=5.)

    with self.assertRaisesRegex(ValueError,
                                re.escape("pytree structure error: different types")):
      conv_and_run(arg_shape=(2,), polymorphic_shape=["a list"])

    with self.assertRaisesRegex(ValueError,
                                re.escape("pytree structure error: different types")):
      conv_and_run(arg_shape=(2,), polymorphic_shape=("a tuple",))

    with self.assertRaisesRegex(ValueError,
                                "Cannot solve for values of dimension variables {'b'}"):
      conv_and_run(arg_shape=(4, 36, 3), polymorphic_shape="b * b, b * d * d, d")

    with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
                                "Division had remainder 2 when computing the value of 'b'"):
      conv_and_run(arg_shape=(5, 36), polymorphic_shape="3 * b, ...")

    with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
                                "Expected value >= 1 for dimension variable 'b'"):
      conv_and_run(arg_shape=(10, 3), polymorphic_shape="3 * b + 10, ...")

    with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
                                "Expected value >= 1 for dimension variable 'b'"):
      conv_and_run(arg_shape=(7, 3), polymorphic_shape="3 * b + 10, ...")

    with self.assertRaisesRegex(
        tf.errors.InvalidArgumentError,
        re.escape(
          "Found inconsistency between dimension size "
          "args[0].shape[1] (= 3) and the specification 'a' (= 2)")):
      conv_and_run(arg_shape=(2, 3), polymorphic_shape="(a, a)")

  # Tests details of the shape constraints errors.
  # This test exists also in jax_export_test.py.
  @jtu.parameterized_filterable(
    testcase_name=lambda kw: kw["shape"],
    kwargs=[
      dict(shape=(8, 2, 9),  # a = 2, b = 3, c = 4
           poly_spec="(a + 2*b, a, a + b + c)"),
      dict(shape=(2, 2, 6),  # a = 2, b = 0, c = 4
           poly_spec="(a + 2*b, a, a + b + c)",
           expect_error=(
             "Input shapes do not match the polymorphic shapes specification. "
             "Expected value >= 1 for dimension variable 'b'. "
             "Using the following polymorphic shapes specifications: args[0].shape = (2*b + a, a, c + b + a). "
             "Obtained dimension variables: 'a' = 2 from specification 'a' for dimension args[0].shape[1] (= 2), "
             "'b' = 0 from specification '2*b + a' for dimension args[0].shape[0] (= 2), . "
             "Please see https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md#shape-assertion-errors for more details."
           )),
      dict(shape=(3, 2, 6),  # a = 2, b = 0.5, c = 4 - b is not integer
           poly_spec="(a + 2*b, a, a + b + c)",
           expect_error=(
             "Input shapes do not match the polymorphic shapes specification. "
             "Division had remainder 1 when computing the value of 'b'. "
             "Using the following polymorphic shapes specifications: args[0].shape = (2*b + a, a, c + b + a). "
             "Obtained dimension variables: 'a' = 2 from specification 'a' for dimension args[0].shape[1] (= 2), . "
             "Please see https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md#shape-assertion-errors for more details."
           )),
      dict(shape=(8, 2, 6),  # a = 2, b = 3 - inconsistency
           poly_spec="(a + 2*b, a, a + b)",
           expect_error=(
             "Input shapes do not match the polymorphic shapes specification. "
             "Found inconsistency between dimension size args[0].shape[0] (= 8) and the specification '2*b + a' (= 10). "
             "Using the following polymorphic shapes specifications: args[0].shape = (2*b + a, a, b + a). "
             "Obtained dimension variables: 'a' = 2 from specification 'a' for dimension args[0].shape[1] (= 2), "
             "'b' = 4 from specification 'b + a' for dimension args[0].shape[2] (= 6), . "
             "Please see https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md#shape-assertion-errors for more details."
           )),
      dict(shape=(7, 2, 36),  # a = 2, b = 3, c = 6 - cannot solve c
           poly_spec="(2 * a + b, a, c * c)",
           expect_error=(
             "Cannot solve for values of dimension variables {'c'}. "
             "We can only solve linear uni-variate constraints. "
             "Using the following polymorphic shapes specifications: args[0].shape = (b + 2*a, a, c^2). "
             "Unprocessed specifications: 'c^2' for dimension size args[0].shape[2]. "
             "Please see https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md#dimension-variables-must-be-solvable-from-the-input-shapes for more details."
           )),
  ])
  def test_shape_constraints_errors(self, *,
      shape, poly_spec: str, expect_error: str | None = None):
    def f_jax(x):  # x: f32[a + 2*b, a, a + b + c]
      return 0.

    x = np.arange(math.prod(shape), dtype=np.float32).reshape(shape)
    with contextlib.ExitStack() as stack:
      if expect_error is not None:
        stack.push(self.assertRaisesRegex(Exception, re.escape(expect_error)))
      _ = check_shape_poly(self, f_jax,
                           arg_descriptors=[x],
                           polymorphic_shapes=[poly_spec])

  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"]])

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

    # Prefix polymorphic shapes
    check_shape_poly(self,
                     add_all_jax,
                     skip_jax_run=True,
                     input_signature=input_signature,
                     polymorphic_shapes="v",
                     expected_output_signature=tf.TensorSpec([None]))

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

    check_shape_poly(self,
                     add_all_jax,
                     skip_jax_run=True,
                     input_signature=input_signature,
                     polymorphic_shapes=[("v", "v"), "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]))

  @jtu.parameterized_filterable(
    kwargs=[
      dict(testcase_name=name, polymorphic_shapes=polymorphic_shapes)
      for name, polymorphic_shapes in [
          ("1", ("b", "b", "b")),
          ("2", dict(a="b")),
          ("3", (dict(a="b"), "b")),
      ]]
  )
  def test_pytree_errors(self, polymorphic_shapes=("b", "b", "b")):
    """Arguments and polymorphic_shapes are not-matching pytrees."""

    # Arguments are of the form [([x00, x01], [x10]), dict(a=ya, b=yb)]
    x = np.arange(4, dtype=_f32)
    args = (([x, x], [x]), dict(a=x, b=x))
    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"]])

    with self.assertRaisesRegex(ValueError, "pytree structure error"):
      jax2tf.convert(add_all_jax,
                     polymorphic_shapes=polymorphic_shapes)(*args)

  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)],
                     polymorphic_shapes=["a, b"])

  @jtu.parameterized_filterable(
    kwargs=[
      dict(testcase_name=str(polymorphic_shapes), polymorphic_shapes=polymorphic_shapes)
      # The polymorphic_shapes should have three comma-separated DimExpr matching
      # 16, 24, 32
      for polymorphic_shapes in [
          "b1+6,b1+14,b2",  # b1=10, b2=32
          "2*b1,4*b2,b1+b2+18",  # b1=8,b2=6
          "b1+2*b2,4*b2,b1*b1+16",  # b1=4,b2=6
      ]
  ])
  def test_non_trivial_polynomials_spec(self,
                                        polymorphic_shapes="2*b1,4*b2,b1+b2+18"):
    # We can handle non-trivial polynomials in the input shape,
    # as long as all variables also occur in trivial expressions
    check_shape_poly(self,
        lambda x: 2 * x.shape[0] + 3 * x.shape[1] + 4 * x.shape[2],
        arg_descriptors=[RandArg((16, 24, 32), _f32)],
        polymorphic_shapes=[polymorphic_shapes])

  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)],
                     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)],
                     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)],
                     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)],
        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)],
        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)],
        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.value:
      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):
    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,
        check_shape_poly(self, f, arg_descriptors=[x, y],
                         polymorphic_shapes=["(b, h)", "h"]))

  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 = check_shape_poly(self, f, arg_descriptors=[x],
                              polymorphic_shapes=["(b,)"])
    self.assertAllClose(f(x), res_tf)

  @jtu.parameterized_filterable(
    kwargs=[dict(with_function=v) for v in [True, False]]
  )
  def test_grad_int(self, with_function=False):
    # https://github.com/google/jax/issues/7093
    # Also issue #6975.
    x_shape = (2, 3, 4)
    xi = np.arange(math.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]
                           # results: f32[2, 3, 4], s16[2, 3, 4], bool[2], f32[2, 3, 4]
      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
    with config.enable_custom_prng(True):

      def f_jax(x):  # x: f32[b1, b2]
        key = random.PRNGKey(123)  #  key: key<fry>[]
        # Exercise key operations that have custom lowering rules
        broadcast_keys = lax.broadcast_in_dim(key, x.shape, ())  # key<fry>[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))

        # We need to test the special case for vmap(while)
        xs = broadcast_keys
        counts = jnp.arange(broadcast_keys.shape[0], dtype=np.int32)
        def f_vmap_jax(counts, xs):  # counts: i32[b1], xs: key<fry>[b1, b2]
          def inner(count, x):  # count i32, x: key<fry>[b2]
            return lax.fori_loop(0, count, lambda _, acc: acc, x)
          return jax.vmap(inner)(counts, xs)

        _ = f_vmap_jax(counts, xs)
        return x

      check_shape_poly(self, f_jax,
                       arg_descriptors=[RandArg((3, 4), _f32)],
                       polymorphic_shapes=["b1, b2"])

  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):

    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(math.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):
    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, (math.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)))

    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)))

    # Checking that the dimension variable is >= 1
    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)
    self.assertEqual(jnp.array([0.], dtype=np.float32), f1_jax(x0))

    # In graph serialization eager mode we catch the error
    with self.assertRaisesRegex(
        tf.errors.InvalidArgumentError,
        re.escape("Expected value >= 1 for dimension variable 'b'. "
                  "Using the following polymorphic shapes specifications: args[0].shape = (b,). "
                  "Obtained dimension variables: 'b' = 0")):
      jax2tf.convert(f1_jax, polymorphic_shapes=["b"],
                     native_serialization=False)(x0)

    # In graph serialization graph mode we also catch it (except on TPU)
    f1_tf = tf.function(
        jax2tf.convert(f1_jax, polymorphic_shapes=["b"],
                       native_serialization=False),
      autograph=False,
    ).get_concrete_function(tf.TensorSpec([None], dtype=np.float32))
    # In graph serialization graph mode we also catch it (except on TPU, where
    # the behavior is as for jit_compile=1)
    if jtu.test_device_matches(["tpu"]):
      self.assertEqual(jnp.array([1.], dtype=np.float32), f1_tf(x0))
    else:
      with self.assertRaisesRegex(
          tf.errors.InvalidArgumentError,
          re.escape("Expected value >= 1 for dimension variable")):
        _ = f1_tf(x0)

    # In graph serialization with jit_compile=True we do not catch the error
    # and we return the wrong result
    f1_tf = tf.function(
        jax2tf.convert(f1_jax, polymorphic_shapes=["b"],
                       native_serialization=False),
      autograph=False,
      jit_compile=True
    )
    self.assertEqual(jnp.array([1.], dtype=np.float32), f1_tf(x0))

    # We also catch the error with native serialization
    with self.assertRaisesRegex(
        tf.errors.InvalidArgumentError,
        re.escape(
          "Expected value >= 1 for dimension variable 'b'. "
          "Using the following polymorphic shapes specifications: args[0].shape = (b,). "
          "Obtained dimension variables: 'b' = 0")):
      _ = jax2tf.convert(f1_jax, polymorphic_shapes=["b"],
                         native_serialization=True)(x0)

    # 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))

    # In graph serialization eager mode, we catch the broken assumption b >= 1
    with self.assertRaisesRegex(
        tf.errors.InvalidArgumentError,
        re.escape(
          "Found inconsistency between dimension size args[0].shape[1] (= 5) "
          "and the specification 'b' (= 4)")):
      jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"],
                     native_serialization=False)(x45)

    # In graph serialization graph mode we also catch it (except on TPU, where
    # the behavior is as for jit_compile=1)

    f2_tf = tf.function(
        jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"],
                       native_serialization=False),
        autograph=False,
    ).get_concrete_function(tf.TensorSpec([None, None], dtype=np.float32))
    if jtu.test_device_matches(["tpu"]):
      self.assertEqual(1. + jnp.sum(x45), f2_tf(x45))
    else:
      with self.assertRaisesRegex(
          tf.errors.InvalidArgumentError,
          r"Found inconsistency"):
        _ = f2_tf(x45)

    # In graph serialization with jit_compile=True we do not catch the error
    # and we return the wrong result
    f2_tf = tf.function(
        jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"],
                       native_serialization=False),
      autograph=False,
      jit_compile=True
    )
    self.assertEqual(1. + jnp.sum(x45), f2_tf(x45))

    # We also catch the error with native serialization
    with self.assertRaisesRegex(
        tf.errors.InvalidArgumentError,
        re.escape(
          "Found inconsistency between dimension size args[0].shape[1] (= 5) "
          "and the specification 'b' (= 4)")):
      _ = jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"],
                         native_serialization=True)(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"])(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.,
                        check_shape_poly(self, f,
                                         arg_descriptors=[x],
                                         polymorphic_shapes=["(b,)"]))
    self.assertAllClose(
        9.,
        check_shape_poly(self, jax.jit(f),
                         arg_descriptors=[x], polymorphic_shapes=["(b,)"]))

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

    self.assertAllClose(
        np.array([3., 3., 3.]),
        check_shape_poly(self, jax.grad(f),
                         arg_descriptors=[x],
                         polymorphic_shapes=["b"]))

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

  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)

  @jtu.parameterized_filterable(
    kwargs=[
      dict(testcase_name=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)],
                     polymorphic_shapes=["b"],
                     expected_output_signature=(tf.TensorSpec([]), tf.TensorSpec((None,), _f32)))

  @jtu.parameterized_filterable(
    kwargs=[
      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.enable_x64.value:
        # 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)],
                     polymorphic_shapes=["b"])

  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)],
                     polymorphic_shapes=["b, _"],
                     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)],
                     polymorphic_shapes=["b, _"])

  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 config.numpy_dtype_promotion("strict"):
      # strict type promotion is sensitive to weak_types
      check_shape_poly(self,
                       f_jax,
                       arg_descriptors=[RandArg((3,), _f32)],
                       polymorphic_shapes=["b"])

  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((5, 3), _f32)],
                     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=["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

  def test_eval_poly_shapes(self):
    def f1(x, y):  # x: f32[a, 5] y: f[a, 5] -> f32[a, 10]
      return jnp.concatenate([x, y], axis=1)
    def f2(x, z):  # x: f32[a, 5] z: f32[a, 10]
      return jnp.concatenate([x, jax.lax.slice_in_dim(z, 0, 5, axis=1)],
                             axis=1),

    x = np.arange(np.prod((3, 5)), dtype=np.float32).reshape((3, 5))
    y = x

    x_polymorphic_shape = "a, _"
    y_polymorphic_shape = x_polymorphic_shape
    z_spec, z_polymorphic_shape = jax2tf.eval_polymorphic_shape(
        f1,
        polymorphic_shapes=[x_polymorphic_shape, y_polymorphic_shape])(x, y)
    self.assertEqual(np.float32, z_spec.dtype)
    self.assertEqual("(a, 10)", z_polymorphic_shape)

    # We can use the z_polymorphic_shape for jax2tf.convert
    z = jax2tf.convert(
        f1,
        polymorphic_shapes=[x_polymorphic_shape, y_polymorphic_shape])(x, y)
    res = jax2tf.convert(
        f2,
        polymorphic_shapes=[x_polymorphic_shape, z_polymorphic_shape])(x, z)
    self.assertAllClose(f2(x, f1(x, y)), res)

  def test_eval_poly_shapes_tuple_output(self):
    def f1(x, y):  # x: f32[a, 5] y: f[b, 5] -> (f32[a, 5], f32[a + b, 5])
      return (x, jnp.concatenate([x, y], axis=0))
    def f2(z, w):  # z: f32[a, 5] w: f32[a + b, 5] -> f32[2*a + b, 10]
      return jnp.concatenate([z, w], axis=0)
    x = np.arange(np.prod((3, 5)), dtype=np.float32).reshape((3, 5))
    y = np.arange(np.prod((4, 5)), dtype=np.float32).reshape((4, 5))

    x_polymorphic_shape = "a, _"
    y_polymorphic_shape = "b, _"
    zw_specs, zw_polymorphic_shapes = jax2tf.eval_polymorphic_shape(
        f1,
        polymorphic_shapes=[x_polymorphic_shape, y_polymorphic_shape])(x, y)
    self.assertEqual(np.float32, zw_specs[0].dtype)
    self.assertEqual(np.float32, zw_specs[1].dtype)
    self.assertEqual(("(a, 5)", "(b + a, 5)"), zw_polymorphic_shapes)

    # We can use the zw_polymorphic_shapes for jax2tf.convert
    z, w = jax2tf.convert(
        f1,
        polymorphic_shapes=[x_polymorphic_shape, y_polymorphic_shape])(x, y)
    res = jax2tf.convert(f2, polymorphic_shapes=zw_polymorphic_shapes)(z, w)
    self.assertAllClose(f2(* f1(x, y)), res)


# 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)],
                polymorphic_shapes=["b, ...", "_, b, _"]),
    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)],
                polymorphic_shapes=["b, ..."]),
    [
      # make_args invoked with op.shape[0] and produces the arange args:
      #   start, stop, step, dtype
      PolyHarness("arange", kwargs["testcase_name"],  # type: ignore
                  lambda x: jnp.arange(*(kwargs["make_args"](x.shape[0]))),  # type: ignore
                  arg_descriptors=[RandArg((6,), np.float32)],
                  polymorphic_shapes=["b"])
      for kwargs in [
        # Positive step
        dict(testcase_name="b", make_args=lambda b: (b, None, None, None)),
        dict(testcase_name="0_b+1", make_args=lambda b: (0, b + 1, None, None)),
        dict(testcase_name="0_5b_2", make_args=lambda b: (0, 5 * b, 2, None)),
        dict(testcase_name="0_5b+1_2", make_args=lambda b: (0, 5 * b + 1, 2, None)),
        dict(testcase_name="b_5b+2_2", make_args=lambda b: (b, 5 * b + 2, 2, None)),
        dict(testcase_name="0_b-1_2", make_args=lambda b: (0, b - 1, 2, None)),
        dict(testcase_name="0_b-2_2", make_args=lambda b: (0, b - 2, 2, None)),
        dict(testcase_name="0_-b_2", make_args=lambda b: (0, -b, 2, None)),
        dict(testcase_name="0_1-b_2", make_args=lambda b: (0, 1 - b, 2, None)),
        dict(testcase_name="0_b-3_2", make_args=lambda b: (0, b - 3, 2, None)),
        # Cannot tell if size >= 0
        # Negative step
        dict(testcase_name="b_0_-1", make_args=lambda b: (b, 0, -1, None)),
        dict(testcase_name="b_1_-2", make_args=lambda b: (b, 1, -2, None)),
        dict(testcase_name="b_-1_-1", make_args=lambda b: (b, -1, -1, None)),
        dict(testcase_name="5b+1_0_-2",
             make_args=lambda b: (5 * b + 1, 0, -2, None)),
        dict(testcase_name="5b+2_0_-2",
             make_args=lambda b: (5 * b + 2, 0, -2, None)),
        dict(testcase_name="b-3_0_-2", make_args=lambda b: (b - 3, 0, -2, None)),
        # Cannot tell if size >= 0
        # Symbolic step
        dict(testcase_name="0_10_b", make_args=lambda b: (0, 10, b)),
        dict(testcase_name="0_0_b", make_args=lambda b: (0, 0, b)),
        dict(testcase_name="10_0_-b", make_args=lambda b: (10, 0, -b)),
        dict(testcase_name="b_1_-b", make_args=lambda b: (b, 1, -b)),
        # Float return type
        dict(testcase_name="0_b_1_f32", make_args=lambda b: (0, b, 1, np.float32))
      ]
    ],
    # 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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ..."]).both_enable_and_disable_xla(),
    PolyHarness("jnp.argsort", "",
                lambda op: jnp.argsort(op),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                polymorphic_shapes=["b, ..."]),
    [
        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)],
                    polymorphic_shapes=["b, ..."])
        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)],
                    polymorphic_shapes=["b, ...", "b, ..."])
        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)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("jnp.bincount", "length=poly",
                lambda x: jnp.bincount(x % 4, length=x.shape[0]),
                arg_descriptors=[RandArg((12,), np.int32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("broadcast_to", "",
                lambda x: jnp.broadcast_to(x, [x.shape[0], x.shape[0], 4]),
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b1, _, b2"]),
    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)],
                polymorphic_shapes=["b1, _, b2"]),
    PolyHarness("clamp", "",
                lax.clamp,
                arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((3, 4, 5), _f32),
                                 RandArg((3, 4, 5), _f32)],
                polymorphic_shapes=["b, ...", "b, ...", "b, ..."]),
    PolyHarness("collapse", "",
                lambda x: lax.collapse(x, 1, 4),
                arg_descriptors=[RandArg((3, 4, 5, 6, 7), _f32)],
                polymorphic_shapes=["b0, b1, _, b3, ..."]),
    PolyHarness("concatenate", "",
                lambda x: jnp.concatenate([x, x], axis=0),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                polymorphic_shapes=["b0, b1, _"]),
    PolyHarness("concatenate", "grad",
                jax.grad(lambda x: jnp.sum(jnp.concatenate([x, jnp.sin(x)], axis=0))),
                arg_descriptors=[RandArg((3, 4, 5), _f32)],
                polymorphic_shapes=["b0, b1, _"]),

    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)],
                polymorphic_shapes=["_, b, _", 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)],
                polymorphic_shapes=["_, b, _", 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)],
                polymorphic_shapes=["_, b, _", None]).both_enable_and_disable_xla(),
    PolyHarness("conv_general_dilated", "1d_stride=2_zero_output",
              lambda lhs, rhs: lax.conv_general_dilated(
                lhs, rhs,
                window_strides=(2,),
                padding="VALID",
                rhs_dilation=None,
                dimension_numbers=lax.ConvDimensionNumbers(lhs_spec=(0, 2, 1),
                                                           rhs_spec=(2, 1, 0),
                                                           out_spec=(0, 2, 1))
              ).shape[1],  # should be 0 in JAX native
              arg_descriptors=[RandArg((1, 4, 16), _f32),
                               RandArg((8, 16, 16), _f32)],
              polymorphic_shapes=["_, b, _",
                                  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)],
                polymorphic_shapes=["b, _, _", None],
                tol=5e-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)],
                polymorphic_shapes=["_, b, _", None],
                tol=5e-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)],
                polymorphic_shapes=["b, ...", None]).both_enable_and_disable_xla(),
    [
      [
        PolyHarness(cum_name, "reduce_axis_poly",
                    lambda x: cum_func(x, axis=0),
                    arg_descriptors=[RandArg((3, 5), _f32)],
                    polymorphic_shapes=["b, ..."]),
        PolyHarness(cum_name, "reduce_axis_static",
                    lambda x: cum_func(x, axis=1),
                    arg_descriptors=[RandArg((3, 5), _f32)],
                    polymorphic_shapes=["b, ..."])
      ]
      for cum_name, cum_func in [
          ("cumlogsumexp", lax_control_flow.cumlogsumexp),
          ("cummax", lax_control_flow.cummax),
          ("cummin", lax_control_flow.cummin),
          ("cumsum", lax_control_flow.cumsum),
          ("cumprod", lax_control_flow.cumprod)
      ]
    ],
    PolyHarness("delta", "0",
                lambda x: lax_internal._delta(_f32, x.shape, axes=(0, 1)) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ...", "b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ...", 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)],
                polymorphic_shapes=["b, ...", None]).both_enable_and_disable_xla(),
    PolyHarness("dynamic_slice", "idx=tuple_int_start_oob_large",
                # x:shape: (b, 4)
                lambda x: lax.dynamic_slice(x, (1, 1), (x.shape[0], 2)),
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]).both_enable_and_disable_xla(),
    PolyHarness("dynamic_slice", "idx=tuple_int_start_oob_small",
              # x:shape: (b, 4)
              lambda x: lax.dynamic_slice(x, (-1, 1), (x.shape[0] - 1, 2)),
              arg_descriptors=[RandArg((3, 4), _f32)],
              polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ...", 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)],
                polymorphic_shapes=["b, _", None]).both_enable_and_disable_xla(),
    [
      PolyHarness("eig", f"shape={jtu.format_shape_dtype_string((3, 5, 5), dtype)}_poly={poly}_{left=}_{right=}",
                  lambda x, left, right: lax.linalg.eig(x, compute_left_eigenvectors=left, compute_right_eigenvectors=right),
                  arg_descriptors=[RandArg((3, 5, 5), dtype),
                                   StaticArg(left), StaticArg(right)],
                  polymorphic_shapes=[poly],
                  # In non-native serialization, we cannot check exact match,
                  # we ought to check the invariants of the result.
                  check_result=config.jax2tf_default_native_serialization.value)
      for dtype in {np.float32, np.float64, np.complex64, np.complex128} & jtu.supported_dtypes()
      for poly in ["b, ...", "b, w, w"]
      for left in ([True, False] if dtype == np.float32 else [True])
      for right in ([True, False] if dtype == np.float32 else [False])
    ],
    PolyHarness("einsum", "0",
                lambda x: jnp.einsum("...i->...", x),
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("einsum", "0_alt",
                lambda x: jnp.einsum(x, (..., 1), [...]),
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ...", "b, ..."]),
    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)],
                polymorphic_shapes=["b, ...", "b, ..."]),
    PolyHarness("einsum", "2",
                lambda x, y: jnp.einsum("...ij,jk->...ik", x, y),
                arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((5, 6), _f32)],
                polymorphic_shapes=["b, ...", 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)],
                polymorphic_shapes=["b, ...", 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)],
                polymorphic_shapes=["_, b", "b, ..."]),
    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)],
                polymorphic_shapes=["_, b", "b, ..."]),
    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)],
                polymorphic_shapes=["_, b", "b, ..."]),
    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)],
                polymorphic_shapes=["_, b", "b, ..."]),
    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)],
                polymorphic_shapes=["b, ...", 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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    [
        PolyHarness("fft", f"{fft_type=}_{nr_fft_lengths=}",
            lambda x, fft_type, nr_fft_lengths: lax.fft_p.bind(
                x, fft_type=fft_type,
                fft_lengths=tuple(
                    x.shape[-nr_fft_lengths:] if fft_type != xla_client.FftType.IRFFT else
                    [(x.shape[-1] - 1) * 2])),
            arg_descriptors=[
                RandArg((3, 4, 5, 6),
                        np.float32 if fft_type == xla_client.FftType.RFFT else np.complex64),
                StaticArg(fft_type),
                StaticArg(nr_fft_lengths)],
            # All axes but the last one are dynamic. This means that the test
            # with nr_fft_lengths==1 will not have dynamic fft_lengths.
            polymorphic_shapes=["b0, b1, b2, ..."],
            tol=1e-4)

         for fft_type in (xla_client.FftType.FFT, xla_client.FftType.IFFT,
                         xla_client.FftType.RFFT, xla_client.FftType.IRFFT)
         for nr_fft_lengths in (1, 2)
    ],
    PolyHarness("full", "",
                lambda x: lax.full((x.shape[0], 2), 3.) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("gather", "1d",
                lambda operand, start_indices, x: lax.gather(
                    operand,
                    start_indices,
                    dimension_numbers=lax.GatherDimensionNumbers(
                        offset_dims=(1,),
                        collapsed_slice_dims=(),
                        start_index_map=(0,)),
                    slice_sizes=x.shape,
                    mode="promise_in_bounds"),
                arg_descriptors=[
                  RandArg((10,), np.float32),
                  np.random.randint(0, high=10, size=(3, 1),
                                    dtype=np.int32),
                  np.zeros((10,), dtype=jnp.int32),
                ],
                polymorphic_shapes=["(t, )", "(3, 1)", "(t)"]),
    # 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)],
                polymorphic_shapes=[None, "b0, ..."]).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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ...", "b, ..."]).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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b + 2, ..."]).both_enable_and_disable_xla(),
    PolyHarness("getitem", "op=poly_idx=slice-ct-2",
                lambda a: a[:, :2],
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["b, ..."]).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)],
                polymorphic_shapes=["_, b1, b2, ..."]),
    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)],
                polymorphic_shapes=["_, b1, b2, ..."]),
    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)],
                polymorphic_shapes=["_, b1, b2, ..."]),
    PolyHarness("index_in_dim", "0",
                lambda x: lax.index_in_dim(x, -1, axis=0, keepdims=False),
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("index_in_dim", "idx=neg",
                lambda x: lax.index_in_dim(x, -1, axis=0, keepdims=False),
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."],
                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)],
                polymorphic_shapes=["b0, b1, ..."],
                expect_error=expect_error_associative_scan),
    PolyHarness("iota", "",
                lambda x: x + lax.iota(_f32, x.shape[0]),
                arg_descriptors=[RandArg((3,), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("matmul", "0",
                jnp.matmul,
                arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 4, 5), _f32)],
                polymorphic_shapes=["b, ...", "b, ..."],
                tol=1e-5),
    PolyHarness("matmul", "1",
                jnp.matmul,
                arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((4, 5), _f32)],
                polymorphic_shapes=["b, ...", 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)],
                    polymorphic_shapes=["b, ..."])
        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)],
                    polymorphic_shapes=["b, ...", "b, ..."])
        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)],
                polymorphic_shapes=["b, ..."],
                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)],
                polymorphic_shapes=["b, ..."],
                expect_error=expect_error_associative_scan),
    PolyHarness("one_hot", "poly_num_classes",
                lambda x, y: jax.nn.one_hot(x, y.shape[0]),
                arg_descriptors=[np.arange(16, dtype=_f32), RandArg((16,), _f32)],
                polymorphic_shapes=[None, "b0, ..."]),
    PolyHarness("one_hot", "all_poly",
                lambda x, y: jax.nn.one_hot(x, y.shape[0]),
                arg_descriptors=[np.arange(16, dtype=_f32), RandArg((16,), _f32)],
                polymorphic_shapes=["b, ...", "b, ..."]),
    PolyHarness("ones", "",
                lambda x: jnp.ones(x.shape, dtype=_f32) + x,
                arg_descriptors=[RandArg((3, 2, 4), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("pad", "",
                lax.pad,
                arg_descriptors=[RandArg((3, 2, 5), _f32), np.float32(5.),
                                 StaticArg(((0, 0, 0), (0, 0, 0), (1, 1, 1)))],
                polymorphic_shapes=["b, ...", 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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("percentile", "axis=None",
                lambda x: jnp.percentile(x, 50, axis=None),
                arg_descriptors=[RandArg((3, 5), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("nanquantile", "axis=None",
                lambda x: jnp.nanquantile(x, .5, axis=None),
                arg_descriptors=[RandArg((3, 5), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("percentile", "axis=0",
                lambda x: jnp.percentile(x, 50, axis=0),
                arg_descriptors=[RandArg((3, 5), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("nanquantile", "axis=0",
                lambda x: jnp.nanquantile(x, .5, axis=0),
                arg_descriptors=[RandArg((3, 5), _f32)],
                polymorphic_shapes=["b, ..."]),
    [
      PolyHarness(
          "qr", f"shape={jtu.format_shape_dtype_string(shape, dtype)}_poly={poly}_{full_matrices=}",
          lambda x, full_matrices: lax.linalg.qr(x, full_matrices=full_matrices),
          arg_descriptors=[RandArg(shape, dtype), StaticArg(full_matrices)],
          polymorphic_shapes=[poly],
          tol=(None if config.jax2tf_default_native_serialization.value else 1e-5))
      for dtype in {np.float32, np.float64, np.complex64, np.complex128} & jtu.supported_dtypes()
      # m and n must be static for now
      for shape, poly, full_matrices in [
          ((2, 0, 4), "b, ...", False),  # m = 0
          ((2, 4, 0), "b, ...", False),  # n = 0
          ((2, 3, 4, 4), "b1, b2, ...", False),  # m == n
          ((2, 3, 4, 4), "b1, b2, ...", True),
          ((2, 3, 4, 5), "b1, b2, ...", False),  # m < n
          ((2, 3, 4, 5), "b1, b2, ...", True),
          ((2, 3, 8, 4), "b1, b2, ...", False),  # m > n
          ((2, 3, 8, 4), "b1, b2, ...", True),
      ]
    ],
    [
      # The random primitive tests, with threefry (both partitionable and
      # non-partitionable), and unsafe_rbg.
      [
        PolyHarness("random_gamma", f"{flags_name}",
                    lambda key, a: jax.random.gamma(key, a),
                    arg_descriptors=[RandArg((3, key_size), np.uint32), RandArg((3, 4, 5), _f32)],
                    polymorphic_shapes=["b, ...", "b, w, ..."], tol=1E-5,
                    override_jax_config_flags=override_jax_config_flags),  # type: ignore
        # The known dimensions product must be even.
        PolyHarness("random_categorical", f"axis=0_{flags_name}",
                    lambda key, a: jax.random.categorical(key, a, axis=0),
                    arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 8), _f32)],
                    polymorphic_shapes=[None, "b0, ..."],
                    override_jax_config_flags=override_jax_config_flags),  # type: ignore
        PolyHarness("random_categorical", f"axis=1_{flags_name}",
                    lambda key, a: jax.random.categorical(key, a, axis=1),
                    arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 5, 8), _f32)],
                    polymorphic_shapes=[None, "b0, b1, ..."],
                    override_jax_config_flags=override_jax_config_flags),  # type: ignore
        PolyHarness("random_categorical", f"axis=1_then_reshape_{flags_name}",
                    lambda key, a: jax.random.categorical(key, a, axis=1).reshape(-1),
                    arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 5, 8), _f32)],
                    polymorphic_shapes=[None, "b0, b1, ..."],
                    override_jax_config_flags=override_jax_config_flags),  # type: ignore
        PolyHarness("random_categorical", f"0_dim_{flags_name}",  # One axis has 0 size
                    lambda key, a: jax.random.categorical(key, a, axis=1),
                    arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 5, 0), _f32)],
                    polymorphic_shapes=[None, "b0, b1, ..."],
                    override_jax_config_flags=override_jax_config_flags),  # type: ignore
        PolyHarness("random_split", f"{flags_name}",
                    lambda key, a: jax.random.key_data(jax.random.split(key, 2 * a.shape[0])),
                    arg_descriptors=[RandArg((key_size,), np.uint32),
                                     RandArg((3, 4), _f32)],
                    polymorphic_shapes=[None, "b0, ..."],
                    override_jax_config_flags=override_jax_config_flags),  # type: ignore
        # Works when the known dimensions are known to be even or odd.
        PolyHarness("random_uniform", f"even_1_{flags_name}",
                    lambda key, a: jax.random.uniform(key, a.shape, dtype=_f32),
                    arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 4, 5), _f32)],
                    polymorphic_shapes=[None, "b0, ..."],
                    override_jax_config_flags=override_jax_config_flags),  # type: ignore
        PolyHarness("random_uniform", f"even_2_{flags_name}",
                    lambda key, a: jax.random.uniform(key, (2 * a.shape[0], a.shape[1]),
                                                      dtype=_f32),
                    arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 4), _f32)],
                    polymorphic_shapes=[None, "b0, b1, ..."],
                    override_jax_config_flags=override_jax_config_flags),  # type: ignore
        PolyHarness("random_uniform", f"error_not_even_{flags_name}",
                    lambda key, a: jax.random.uniform(key, a.shape, dtype=_f32),
                    arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 5), _f32)],
                    polymorphic_shapes=[None, "b0, ..."],
                    expect_error=(
                        (core.InconclusiveDimensionOperation,
                         "the product of the known dimensions must be even") if flags_name == "threefry_non_partitionable" else (None, None)),
                    override_jax_config_flags=override_jax_config_flags)  # type: ignore
      ]
        for key_size, flags_name, override_jax_config_flags in [
          (2, "threefry_non_partitionable",
           dict(jax_default_prng_impl="threefry2x32", jax_threefry_partitionable=False)),
          (2, "threefry_partitionable",
           dict(jax_default_prng_impl="threefry2x32", jax_threefry_partitionable=True)),
          (4, "unsafe_rbg",
           dict(jax_default_prng_impl="unsafe_rbg"))
        ]
    ],
    # For reduce_window we have a variant with one reduction axis of
    # non-static shape, and one with additionally the dimension window
    # non-static.
    PolyHarness("reduce_window", "min_window_size=static",
                # x: f32[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)],
                polymorphic_shapes=["b, ..."]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "min_window_size=dynamic",
                # x: f32[b, 8]
                lambda x: lax.reduce_window(x, np.array(1., _f32), lax.min,
                                            (2, x.shape[0]), (1, 1), "VALID"),
                arg_descriptors=[RandArg((3, 8), _f32)],
                polymorphic_shapes=["b, ..."]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "min_plus_max_window_size=static",
                # x: f32[b, 8]
                lambda x: (
                    # Test that we don't get confusion for the reducer name.
                    lax.reduce_window(x, np.array(1., _f32), lax.min,
                                      (2, 2), (1, 1), "VALID") +
                    lax.reduce_window(x, np.array(1., _f32), lax.max,
                                      (2, 2), (1, 1), "VALID")),
                arg_descriptors=[RandArg((3, 8), _f32)],
                polymorphic_shapes=["b, ..."]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "min_plus_max_window_size=dynamic",
                # x: f32[b, 8]
                lambda x: (
                    # Test that we don't get confusion for the reducer name.
                    lax.reduce_window(x, np.array(1., _f32), lax.min,
                                      (2, x.shape[0]), (1, 1), "VALID") +
                    lax.reduce_window(x, np.array(1., _f32), lax.max,
                                      (2, x.shape[0]), (1, 1), "VALID")),
                arg_descriptors=[RandArg((3, 8), _f32)],
                polymorphic_shapes=["b, ..."]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "add_monoid_base_window_size=static",
                # x: f32[b, 8]
                lambda x: lax.reduce_window(x, np.array(0., _f32), lax.add,
                                            (2, 2), (1, 1), "VALID"),
                arg_descriptors=[RandArg((3, 8), _f32)],
                polymorphic_shapes=["b, ..."]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "add_monoid_base_window_size=dynamic",
                # x: f32[b, 8]
                lambda x: lax.reduce_window(x, np.array(0., _f32), lax.add,
                                            (2, x.shape[0]), (1, 1), "VALID"),
                arg_descriptors=[RandArg((3, 8), _f32)],
                polymorphic_shapes=["b, ..."]).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_monoid_strides_window_size=static",
                # x: f32[1, 16*b, 1]
                lambda x: lax.reduce_window(
                    jnp.reshape(x, (1, -1, 1)),
                    np.array(0., _f32), lax.add, (1, 4, 1), (1, 2, 1), "SAME"),
                arg_descriptors=[RandArg((1, 128, 16), _f32)],
                polymorphic_shapes=["_, b1, ..."]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "add_generic_window_size=static",
                # x: f32[1, 16*b, 1]
                # Use an initial value of 1. to trigger the generic reduction path
                lambda x: lax.reduce_window(
                    jnp.reshape(x, (1, -1, 1)),
                    np.array(1., _f32), lax.add, (1, 4, 1), (1, 2, 1), "SAME"),
                arg_descriptors=[RandArg((1, 128, 16), _f32)],
                polymorphic_shapes=["_, b1, ..."]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "variadic_generic_window_size=static",
              # x: f32[b, 8]  y: f32[b, 8]
              lambda x, y: lax.reduce_window(
                (x, y), (np.array(1., _f32), np.array(2, _i32)),
                lambda xy0, xy1: (lax.add(xy0[0], xy1[0]),
                                  lax.sub(xy0[1], xy1[1])),
                (2, 2), (1, 1), "VALID"),
              arg_descriptors=[RandArg((3, 8), _f32), RandArg((3, 8), _i32)],
              polymorphic_shapes=["b, ...", "b, ..."]).both_enable_and_disable_xla(),
    PolyHarness("reduce_window", "variadic_generic_window_size=dynamic",
              # x: f32[b, 8]  y: f32[b, 8]
              lambda x, y: lax.reduce_window(
                (x, y), (np.array(1., _f32), np.array(2, _i32)),
                lambda xy0, xy1: (lax.add(xy0[0], xy1[0]),
                                  lax.sub(xy0[1], xy1[1])),
                (2, x.shape[0]), (1, 1), "VALID"),
              arg_descriptors=[RandArg((3, 8), _f32), RandArg((3, 8), _i32)],
              polymorphic_shapes=["b, ...", "b, ..."]).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)],
    #               polymorphic_shapes=[None, "b0, ..."]),
    [
        PolyHarness("reduce", reduce_op.__name__,
                    lambda x: reduce_op(x, axis=-1, keepdims=True),  # type: ignore
                    arg_descriptors=[RandArg((3, 5), _f32)],
                    polymorphic_shapes=["b, ..."])
        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)],
                polymorphic_shapes=["b, ..."]),
    # 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)],
                polymorphic_shapes=["b, ..."]),
    # 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)],
                polymorphic_shapes=["b, ..."]),
    # 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)],
                polymorphic_shapes=[None, "b0, ..."]),
    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)],
                polymorphic_shapes=["b, ..."],
                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)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("reshape", "1",
                lambda x: x.reshape([x.shape[0], -1]),
                arg_descriptors=[RandArg((3, 2, 3), _f32)],
                polymorphic_shapes=["b0, b1, ..."]),
    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)],
                polymorphic_shapes=["b0, _, b2, b3, ..."]),
    PolyHarness("reshape", "3",
                lambda x: jnp.reshape(x, [2, -1]),
                arg_descriptors=[RandArg((3, 4, 5, 6, 7), _f32)],
                polymorphic_shapes=["b0, _, b2, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("reshape", "error",
                lambda x: x.reshape([x.shape[0], -1, 3]),
                arg_descriptors=[RandArg((3, 2, 4), _f32)],
                polymorphic_shapes=["b, ..."],
                input_signature=[tf.TensorSpec([None, 2, 4], _f32)],
                skip_jax_run=True,
                expect_error=(core.InconclusiveDimensionOperation,
                              re.escape(
                                "Cannot divide evenly the sizes of shapes (b, 2, 4) and (b, -1, 3)"))),
    PolyHarness("roll", "axis=0",
                lambda x: jnp.roll(x, 2, axis=0),
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("roll", "axis=None",
                lambda x: jnp.roll(x, 2),
                arg_descriptors=[RandArg((3, 4), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("scatter_add", "",
                partial(lax.scatter_add, indices_are_sorted=False, unique_indices=True),
                arg_descriptors=[RandArg((7, 4), _f32),  # op: [b, 4]
                                 np.array([[1], [2]], np.int32),  # indices: [2, 1]
                                 RandArg((7, 2), _f32),  # updates: [b, 2]
                                 StaticArg(lax.ScatterDimensionNumbers((0,), (1,), (1,)))],
                polymorphic_shapes=["b, ...", None, "b, ..."]),
    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),  # op: [b, 4]
                                 np.array([[1], [2]], np.int32),  # indices: [2, 1]
                                 RandArg((7, 2), _f32),  # updates: [b, 2]
                                 StaticArg(lax.ScatterDimensionNumbers((0,), (1,), (1,)))],
                polymorphic_shapes=["b, ...", None, "b, ..."]),
    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),  # op: [b, 4]
                                 # indices: [b, 2]
                                 np.array([[1, 2], [-2, 0], [6, 4], [7, -1], [1, 0], [3, 0], [0, 5]], np.int32),
                                 RandArg((7, 1), _f32),  # updates: [b, 1]
                                 StaticArg(lax.ScatterDimensionNumbers((1,), (0,), (0, 1,)))],
                polymorphic_shapes=["b, ...", "b, ...", "b, ..."]),
    PolyHarness("scatter_grad", "",
                lambda *args: jax.grad(
                    lambda *args:
                        jnp.sum(lax.scatter(  # type: ignore
                          *args,
                          indices_are_sorted=False,
                          unique_indices=False,
                          ))
                )(*args),
                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,))),
                               ],
                polymorphic_shapes=["b, ...", None, "b, ..."]),
    PolyHarness("scatter_grad", "poly_indices",
                lambda *args: jax.grad(
                  lambda *args:
                  jnp.sum(lax.scatter(  # type: ignore
                    *args,
                    indices_are_sorted=False,
                    unique_indices=False))
                )(*args),
                arg_descriptors=[RandArg((7, 4), _f32),  # op: [b, 4]
                                 # indices: [b, 2]
                                 np.array(
                                   [[1, 2], [-2, 0], [6, 4], [7, -1], [1, 0],
                                    [3, 0], [0, 5]], np.int32),
                                 RandArg((7, 1), _f32),  # updates: [b, 1]
                                 StaticArg(lax.ScatterDimensionNumbers((1,), (0,), (0, 1))),
                                 ],
                polymorphic_shapes=["b, ...", "b, ...", "b, ..."]),
    [
      PolyHarness("schur",
                  f"shape={jtu.format_shape_dtype_string(shape, dtype)}_{poly=}_{compute_schur_vectors=}",
                  lambda a, compute_schur_vectors: lax.linalg.schur(
                    a, compute_schur_vectors=compute_schur_vectors),
                  arg_descriptors=[RandArg(shape, dtype),
                                   StaticArg(compute_schur_vectors)],
                  polymorphic_shapes=[poly],
                  # In non-native serialization, we cannot check exact match,
                  # we ought to check the invariants of the result.
                  check_result=config.jax2tf_default_native_serialization.value)
      for dtype in {np.float32, np.float64, np.complex64, np.complex128} & jtu.supported_dtypes()
      for compute_schur_vectors in [True, False]
      for (shape, poly) in [
        ((3, 3), "w, w"),
        ((3, 4, 4), "b, w, w"),
      ]
    ],
    PolyHarness("select", "0",
                # x.shape = (b, 3)
                lambda x: lax.select(x > 5., x, x),
                arg_descriptors=[RandArg((7, 3), _f32)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ...", 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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    # 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)],
    #             polymorphic_shapes=["b, ..."]),
    PolyHarness("squeeze", "axis=empty",
                jnp.squeeze,
                arg_descriptors=[RandArg((5,), _f32), StaticArg(())],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("squeeze", "axis=None",
                jnp.squeeze,
                arg_descriptors=[RandArg((5,), _f32), StaticArg(None)],
                polymorphic_shapes=["b, ..."],
                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,))],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("squeeze", "axis=1_2",
                jnp.squeeze,
                arg_descriptors=[RandArg((4, 1, 1), _f32), StaticArg((1, 2))],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("squeeze", "error",
                jnp.squeeze,
                arg_descriptors=[RandArg((3, 33), _f32), StaticArg(-1)],
                polymorphic_shapes=["b0, b1"],
                input_signature=[tf.TensorSpec([None, None], _f32)],
                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)],
                polymorphic_shapes=["b, ...", 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)],
                polymorphic_shapes=["b, ...", "b, ..."]),
    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)],
                polymorphic_shapes=["b, ...", "b, ..."]),
    PolyHarness("tile", "0",
                lambda x: jnp.tile(x, (1, 2)),
                arg_descriptors=[RandArg((4, 3), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("tile", "1",
                # The repetitions are polys
                lambda x: jnp.tile(x, (1, x.shape[0])),
                arg_descriptors=[RandArg((4, 2), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("lax_top_k", "",
                lambda x: jax.lax.top_k(x, x.shape[-1] - 1),
                arg_descriptors=[RandArg((16,), _f32)],
                polymorphic_shapes=["b, ..."]),
    PolyHarness("tri", "N=poly_M=None",
                lambda x: jnp.tri(x.shape[0]) + x,
                arg_descriptors=[RandArg((3, 1), _f32)],
                polymorphic_shapes=["b, ..."]),
    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)],
                polymorphic_shapes=["b, ..."]),
    [
      PolyHarness("triangular_solve",
                  f"shape={jtu.format_shape_dtype_string(a_shape, dtype)}_{left_side=}_{a_poly=}_{b_poly=}",
                  lambda a, b, left_side: lax.linalg.triangular_solve(
                    jnp.tril(a) + 5 * jnp.eye(a.shape[-1], dtype=a.dtype),
                    b, left_side=left_side,
                    lower=True, transpose_a=False, conjugate_a=False,
                    unit_diagonal=False),
                  arg_descriptors=[RandArg(a_shape, dtype),
                                   RandArg(b_shape, dtype),
                                   StaticArg(left_side)],
                  polymorphic_shapes=[a_poly, b_poly],
                  # In non-native serialization, we cannot check exact match,
                  # we ought to check the invariants of the result.
                  check_result=config.jax2tf_default_native_serialization.value)
      for dtype in {np.float32, np.float64, np.complex64, np.complex128} & jtu.supported_dtypes()
      for (left_side, a_shape, b_shape, a_poly, b_poly) in [
          (True, (3, 4, 4), (3, 4, 5), "b, ...", "b, ..."),
          (True, (3, 4, 4), (3, 4, 5), "b, k, k", "b, k, m"),
          (False, (3, 4, 4), (3, 5, 4), "b, ...", "b, ..."),
          (False, (3, 4, 4), (3, 5, 4), "b, k, k", "b, m, k"),
          # We use custom calls on CPU if not batched
          (True, (4, 4), (4, 5), "k, k", "k, m"),
          (False, (4, 4), (5, 4), "k, k", "m, k"),
      ]
    ],
    [
        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)],
                    polymorphic_shapes=["b, ..."])
        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)],
                    polymorphic_shapes=["b, ...", "b, ..."])
        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)],
                polymorphic_shapes=["b, ...", None, "b, ..."]),
]

def _get_jax2tf_limitations(
    device, h: test_harnesses.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 = test_harnesses.all_harnesses
  res = []

  # Index by group
  harness_groups: dict[
    str, Sequence[test_harnesses.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
    harness_groups[h.group_name].append(h)

  selected_harnesses = []
  for _, 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])
    (_, max_count), = c.most_common(1)
    # Pick the first alphabetically among those with max_count, to ensure
    # that we generate deterministic tests.
    dtypes_with_max_count = (dtype for dtype, count in c.items()
                             if count == max_count)
    dtype, *_ = sorted(dtypes_with_max_count, key=str)
    selected_harnesses.extend([h for h in hlist if h.dtype == dtype])

  batch_size = 3
  for h in selected_harnesses:
    if h.group_name in [
        "tridiagonal_solve",  # batching not implemented in JAX
    ]:
      continue

    def make_batched_arg_descriptor(
        ad: test_harnesses.ArgDescriptor) -> test_harnesses.ArgDescriptor | None:
      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

    limitations = [
        l for l in _get_jax2tf_limitations(device, h)
        if not l.skip_comparison and (l.custom_assert or l.tol is not 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,
                               polymorphic_shapes=["b, ..."] * len(new_args),
                               limitations=limitations)
    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(_flatten_harnesses(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.
  @test_harnesses.parameterized(
      _flatten_harnesses(_POLY_SHAPE_TEST_HARNESSES),
      #one_containing="",
  )
  def test_harness(self, harness: PolyHarness):
    if harness.expect_error == expect_error_associative_scan and (
        not config.jax2tf_default_native_serialization.value
        or jtu.test_device_matches(["tpu"])
    ):
      harness.expect_error = (None, None)

    # Exclude some harnesses that are known to fail for native serialization
    # FOR NATIVE SERIALIZATION
    if config.jax2tf_default_native_serialization.value:
      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 = {
          "householder_product:gpu",
          "vmap_geqrf:gpu",  # used for linalg.qr
          "vmap_lu:gpu",
          # custom_linear_solve works as long as lu works.
          "vmap_custom_linear_solve:gpu",
          "vmap_qr:gpu", "qr:gpu",
          "vmap_svd: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")

      if harness.group_name == "schur" and not jtu.test_device_matches(["cpu"]):
        raise unittest.SkipTest("schur decomposition is only implemented on CPU.")

      if "fft_fft_type" in harness.fullname:
        if "nr_fft_lengths_2" in harness.fullname:
          raise unittest.SkipTest("native serialization with shape polymorphism not implemented for fft with non-constant fft_lengths on GPU and TPU")

      if harness.group_name == "vmap_eigh" and jtu.test_device_matches(["gpu"]):
        # For eigh on GPU with shape polymorphism under native serialization,
        # we use a different lowering for small matrices. See README.md.
        shape = harness.original_harness.params["shape"]
        if 0 < shape[-1] <= 32:
          harness.check_result = False

      if harness.group_name == "vmap_tan":
        # Tan (b/274462307) require support for custom call mhlo.tan.
        raise unittest.SkipTest(
            "native lowering with shape polymorphism requires additional StableHLO feature support")

      if (jtu.test_device_matches(["cpu", "gpu"]) and
          harness.fullname in [
              "cumsum_reduce_axis_poly", "cumprod_reduce_axis_poly",
              "cummin_reduce_axis_poly", "cummax_reduce_axis_poly",
              "cumlogsumexp_reduce_axis_poly",
              "jnp_insert_insert_constant", "jnp_insert_insert_poly",
              "jnp_nonzero_size_constant", "jnp_nonzero_size_poly"]):
        # Need associative scan reductions on CPU and GPU. On TPU we use the
        # reduce_window HLO, but on CPU and GPU (with axis size >= 32) we use
        # a recursive associative scan that we cannot express with shape
        # polymorphism.
        raise unittest.SkipTest(
            "native serialization with shape polymorphism not implemented for window_reductions on CPU and GPU")

    # FOR GRAPH SERIALIZATION
    if not config.jax2tf_default_native_serialization.value:
      if ("random_gamma_threefry_non_partitionable" in harness.fullname and
          jtu.test_device_matches(["cpu"])):
        harness.tol = 1e-6

      if harness.group_name == "vmap_cumsum":
        # For cumsum we use a different implementation than JAX native
        # See README.md for associative scan reductions
        harness.tol = 1e-5

      if "vmap_" in harness.group_name:
        # For non-native serialization, it seems that we cannot just use
        # the custom_asserts; we get too many errors.
        if [l for l in harness.limitations if l.custom_assert]:
          harness.check_result = False

      if "vmap_integer_pow" in harness.group_name:
        # For non-native serialization the overflow behavior is different.
        harness.check_result = False

      if "average_axis_None_weights_Some" in harness.fullname:
        harness.tol = 1e-5

      if harness.group_name == "schur":
        raise unittest.SkipTest("jax2tf graph serialization does not support schur.")

      if harness.group_name == "eig" and "left_True_right_True" in harness.fullname:
        raise unittest.SkipTest("jax2tf graph serialization does not support both left and right.")

      if harness.group_name == "vmap_eigh":
        self.skipTest("b/312378994: error in TF eager execution for tf.linalg.eigh")

      if "conv_general_dilated_1d_stride_2_zero_output_enable_xla_False" in harness.fullname:
        raise unittest.SkipTest("incomplete support for conv_general_dilated in enable_xla=False")

      if harness.group_name == "reduce_window" and "variadic" in harness.fullname:
        raise unittest.SkipTest("jax2tf graph serialization does not support variadic reduce_window.")

      if (harness.group_name == "reduce_window" and
          not harness.enable_xla and
          "window_size_dynamic" in harness.fullname and
          any(n in harness.fullname
              for n in ["min_plus_max", "add_monoid_base", "min_window"])):
        raise unittest.SkipTest(
            "jax2tf graph serialization with enable_xla=False does not support "
            "dynamic tf.nn.pool")

      if "reduce_window_add_generic" in harness.fullname and not harness.enable_xla:
        raise unittest.SkipTest("TODO(b/287733072): wrong result for enable_xla_False")

    # FOR BOTH NATIVE AND GRAPH SERIALIZATION
    if harness.group_name == "vmap_conv_general_dilated":
      # https://github.com/openxla/stablehlo/issues/1268
      raise unittest.SkipTest("Need more dynamism for DynamicConvOp")

    if harness.group_name == "eig" and not jtu.test_device_matches(["cpu"]):
      raise unittest.SkipTest("JAX implements eig only on CPU.")

    prev_jax_config_flags = {
      fname: getattr(jax.config, fname)
      for fname, fvalue in harness.override_jax_config_flags.items()
    }
    try:
      for fname, fvalue in harness.override_jax_config_flags.items():
        jax.config.update(fname, fvalue)
      harness.run_test(self)
    finally:
      for fname, _ in harness.override_jax_config_flags.items():
        jax.config.update(fname, prev_jax_config_flags[fname])


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