text: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you 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
  #
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  #
  # 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.
  
  # pylint: disable=invalid-name,unnecessary-comprehension
  """TVM testing utilities
  
  Organization
  ************
  
  This file contains functions expected to be called directly by a user
  while writing unit tests.  Integrations with the pytest framework
  are in plugin.py.
  
  Testing Markers
  ***************
  
  We use pytest markers to specify the requirements of test functions. Currently
  there is a single distinction that matters for our testing environment: does
  the test require a gpu. For tests that require just a gpu or just a cpu, we
  have the decorator :py:func:`requires_gpu` that enables the test when a gpu is
  available. To avoid running tests that don't require a gpu on gpu nodes, this
  decorator also sets the pytest marker `gpu` so we can use select the gpu subset
  of tests (using `pytest -m gpu`).
  
  Unfortunately, many tests are written like this:
  
  .. python::
  
      def test_something():
          for target in all_targets():
              do_something()
  
  The test uses both gpu and cpu targets, so the test needs to be run on both cpu
  and gpu nodes. But we still want to only run the cpu targets on the cpu testing
  node. The solution is to mark these tests with the gpu marker so they will be
  run on the gpu nodes. But we also modify all_targets (renamed to
  enabled_targets) so that it only returns gpu targets on gpu nodes and cpu
  targets on cpu nodes (using an environment variable).
  
  Instead of using the all_targets function, future tests that would like to
  test against a variety of targets should use the
  :py:func:`tvm.testing.parametrize_targets` functionality. This allows us
  greater control over which targets are run on which testing nodes.
  
  If in the future we want to add a new type of testing node (for example
  fpgas), we need to add a new marker in `tests/python/pytest.ini` and a new
  function in this module. Then targets using this node should be added to the
  `TVM_TEST_TARGETS` environment variable in the CI.
  
  """
  import inspect
  import copy
  import copyreg
  import ctypes
  import functools
  import itertools
  import logging
  import os
  import pickle
  import platform
  import shutil
  import sys
  import time
  
  from typing import Optional, Callable, Union, List
  
  import pytest
  import numpy as np
  
  import tvm
  import tvm.arith
  import tvm.tir
  import tvm.te
  import tvm._ffi
  
  from tvm.contrib import nvcc, cudnn
  import tvm.contrib.hexagon._ci_env_check as hexagon
  from tvm.error import TVMError
  
  
  SKIP_SLOW_TESTS = os.getenv("SKIP_SLOW_TESTS", "").lower() in {"true", "1", "yes"}
  
  
  def assert_allclose(actual, desired, rtol=1e-7, atol=1e-7):
      """Version of np.testing.assert_allclose with `atol` and `rtol` fields set
      in reasonable defaults.
  
      Arguments `actual` and `desired` are not interchangeable, since the function
      compares the `abs(actual-desired)` with `atol+rtol*abs(desired)`.  Since we
      often allow `desired` to be close to zero, we generally want non-zero `atol`.
      """
      actual = np.asanyarray(actual)
      desired = np.asanyarray(desired)
      np.testing.assert_allclose(actual.shape, desired.shape)
      np.testing.assert_allclose(actual, desired, rtol=rtol, atol=atol, verbose=True)
  
  
  def check_numerical_grads(
      function, input_values, grad_values, function_value=None, delta=1e-3, atol=1e-2, rtol=0.1
  ):
      """A helper function that checks that numerical gradients of a function are
      equal to gradients computed in some different way (analytical gradients).
  
      Numerical gradients are computed using finite difference approximation. To
      reduce the number of function evaluations, the number of points used is
      gradually increased if the error value is too high (up to 5 points).
  
      Parameters
      ----------
      function
          A function that takes inputs either as positional or as keyword
          arguments (either `function(*input_values)` or `function(**input_values)`
          should be correct) and returns a scalar result. Should accept numpy
          ndarrays.
  
      input_values : Dict[str, numpy.ndarray] or List[numpy.ndarray]
          A list of values or a dict assigning values to variables. Represents the
          point at which gradients should be computed.
  
      grad_values : Dict[str, numpy.ndarray] or List[numpy.ndarray]
          Gradients computed using a different method.
  
      function_value : float, optional
          Should be equal to `function(**input_values)`.
  
      delta : float, optional
          A small number used for numerical computation of partial derivatives.
          The default 1e-3 is a good choice for float32.
  
      atol : float, optional
          Absolute tolerance. Gets multiplied by `sqrt(n)` where n is the size of a
          gradient.
  
      rtol : float, optional
          Relative tolerance.
      """
      # If input_values is a list then function accepts positional arguments
      # In this case transform it to a function taking kwargs of the form {"0": ..., "1": ...}
      if not isinstance(input_values, dict):
          input_len = len(input_values)
          input_values = {str(idx): val for idx, val in enumerate(input_values)}
  
          def _function(_input_len=input_len, _orig_function=function, **kwargs):
              return _orig_function(*(kwargs[str(i)] for i in range(input_len)))
  
          function = _function
  
          grad_values = {str(idx): val for idx, val in enumerate(grad_values)}
  
      if function_value is None:
          function_value = function(**input_values)
  
      # a helper to modify j-th element of val by a_delta
      def modify(val, j, a_delta):
          val = val.copy()
          val.reshape(-1)[j] = val.reshape(-1)[j] + a_delta
          return val
  
      # numerically compute a partial derivative with respect to j-th element of the var `name`
      def derivative(x_name, j, a_delta):
          modified_values = {
              n: modify(val, j, a_delta) if n == x_name else val for n, val in input_values.items()
          }
          return (function(**modified_values) - function_value) / a_delta
  
      def compare_derivative(j, n_der, grad):
          der = grad.reshape(-1)[j]
          return np.abs(n_der - der) < atol + rtol * np.abs(n_der)
  
      for x_name, grad in grad_values.items():
          if grad.shape != input_values[x_name].shape:
              raise AssertionError(
                  "Gradient wrt '{}' has unexpected shape {}, expected {} ".format(
                      x_name, grad.shape, input_values[x_name].shape
                  )
              )
  
          ngrad = np.zeros_like(grad)
  
          wrong_positions = []
  
          # compute partial derivatives for each position in this variable
          for j in range(np.prod(grad.shape)):
              # forward difference approximation
              nder = derivative(x_name, j, delta)
  
              # if the derivative is not equal to the analytical one, try to use more
              # precise and expensive methods
              if not compare_derivative(j, nder, grad):
                  # central difference approximation
                  nder = (derivative(x_name, j, -delta) + nder) / 2
  
                  if not compare_derivative(j, nder, grad):
                      # central difference approximation using h = delta/2
                      cnder2 = (
                          derivative(x_name, j, delta / 2) + derivative(x_name, j, -delta / 2)
                      ) / 2
                      # five-point derivative
                      nder = (4 * cnder2 - nder) / 3
  
              # if the derivatives still don't match, add this position to the
              # list of wrong positions
              if not compare_derivative(j, nder, grad):
                  wrong_positions.append(np.unravel_index(j, grad.shape))
  
              ngrad.reshape(-1)[j] = nder
  
          wrong_percentage = int(100 * len(wrong_positions) / np.prod(grad.shape))
  
          dist = np.sqrt(np.sum((ngrad - grad) ** 2))
          grad_norm = np.sqrt(np.sum(ngrad**2))
  
          if not (np.isfinite(dist) and np.isfinite(grad_norm)):
              raise ValueError(
                  "NaN or infinity detected during numerical gradient checking wrt '{}'\n"
                  "analytical grad = {}\n numerical grad = {}\n".format(x_name, grad, ngrad)
              )
  
          # we multiply atol by this number to make it more universal for different sizes
          sqrt_n = np.sqrt(float(np.prod(grad.shape)))
  
          if dist > atol * sqrt_n + rtol * grad_norm:
              raise AssertionError(
                  "Analytical and numerical grads wrt '{}' differ too much\n"
                  "analytical grad = {}\n numerical grad = {}\n"
                  "{}% of elements differ, first 10 of wrong positions: {}\n"
                  "distance > atol*sqrt(n) + rtol*grad_norm\n"
                  "distance {} > {}*{} + {}*{}".format(
                      x_name,
                      grad,
                      ngrad,
                      wrong_percentage,
                      wrong_positions[:10],
                      dist,
                      atol,
                      sqrt_n,
                      rtol,
                      grad_norm,
                  )
              )
  
          max_diff = np.max(np.abs(ngrad - grad))
          avg_diff = np.mean(np.abs(ngrad - grad))
          logging.info(
              "Numerical grad test wrt '%s' of shape %s passes, "
              "dist = %f, max_diff = %f, avg_diff = %f",
              x_name,
              grad.shape,
              dist,
              max_diff,
              avg_diff,
          )
  
  
  def assert_prim_expr_equal(lhs, rhs):
      """Assert lhs and rhs equals to each iother.
  
      Parameters
      ----------
      lhs : tvm.tir.PrimExpr
          The left operand.
  
      rhs : tvm.tir.PrimExpr
          The left operand.
      """
      ana = tvm.arith.Analyzer()
      if not ana.can_prove_equal(lhs, rhs):
          raise ValueError("{} and {} are not equal".format(lhs, rhs))
  
  
  def check_bool_expr_is_true(bool_expr, vranges, cond=None):
      """Check that bool_expr holds given the condition cond
      for every value of free variables from vranges.
  
      for example, 2x > 4y solves to x > 2y given x in (0, 10) and y in (0, 10)
      here bool_expr is x > 2y, vranges is {x: (0, 10), y: (0, 10)}, cond is 2x > 4y
      We creates iterations to check,
      for x in range(10):
        for y in range(10):
          assert !(2x > 4y) || (x > 2y)
  
      Parameters
      ----------
      bool_expr : tvm.ir.PrimExpr
          Boolean expression to check
      vranges: Dict[tvm.tir.expr.Var, tvm.ir.Range]
          Free variables and their ranges
      cond: tvm.ir.PrimExpr
          extra conditions needs to be satisfied.
      """
      if cond is not None:
          bool_expr = tvm.te.any(tvm.tir.Not(cond), bool_expr)
  
      def _run_expr(expr, vranges):
          """Evaluate expr for every value of free variables
          given by vranges and return the tensor of results.
          """
  
          def _compute_body(*us):
              vmap = {v: u + r.min for (v, r), u in zip(vranges.items(), us)}
              return tvm.tir.stmt_functor.substitute(expr, vmap)
  
          A = tvm.te.compute([r.extent.value for v, r in vranges.items()], _compute_body)
          args = [tvm.nd.empty(A.shape, A.dtype)]
          sch = tvm.te.create_schedule(A.op)
          mod = tvm.build(sch, [A])
          mod(*args)
          return args[0].numpy()
  
      res = _run_expr(bool_expr, vranges)
      if not np.all(res):
          indices = list(np.argwhere(res == 0)[0])
          counterex = [(str(v), i + r.min) for (v, r), i in zip(vranges.items(), indices)]
          counterex = sorted(counterex, key=lambda x: x[0])
          counterex = ", ".join([v + " = " + str(i) for v, i in counterex])
          ana = tvm.arith.Analyzer()
          raise AssertionError(
              "Expression {}\nis not true on {}\n"
              "Counterexample: {}".format(ana.simplify(bool_expr), vranges, counterex)
          )
  
  
  def check_int_constraints_trans_consistency(constraints_trans, vranges=None):
      """Check IntConstraintsTransform is a bijective transformation.
  
      Parameters
      ----------
      constraints_trans : arith.IntConstraintsTransform
          Integer constraints transformation
      vranges: Dict[tvm.tir.Var, tvm.ir.Range]
          Free variables and their ranges
      """
      if vranges is None:
          vranges = {}
  
      def _check_forward(constraints1, constraints2, varmap, backvarmap):
          ana = tvm.arith.Analyzer()
          all_vranges = vranges.copy()
          all_vranges.update({v: r for v, r in constraints1.ranges.items()})
  
          # Check that the transformation is injective
          cond_on_vars = tvm.tir.const(1, "bool")
          for v in constraints1.variables:
              if v in varmap:
                  # variable mapping is consistent
                  v_back = ana.simplify(tvm.tir.stmt_functor.substitute(varmap[v], backvarmap))
                  cond_on_vars = tvm.te.all(cond_on_vars, v == v_back)
          # Also we have to check that the new relations are true when old relations are true
          cond_subst = tvm.tir.stmt_functor.substitute(
              tvm.te.all(tvm.tir.const(1, "bool"), *constraints2.relations), backvarmap
          )
          # We have to include relations from vranges too
          for v in constraints2.variables:
              if v in constraints2.ranges:
                  r = constraints2.ranges[v]
                  range_cond = tvm.te.all(v >= r.min, v < r.min + r.extent)
                  range_cond = tvm.tir.stmt_functor.substitute(range_cond, backvarmap)
                  cond_subst = tvm.te.all(cond_subst, range_cond)
          cond_subst = ana.simplify(cond_subst)
          check_bool_expr_is_true(
              tvm.te.all(cond_subst, cond_on_vars),
              all_vranges,
              cond=tvm.te.all(tvm.tir.const(1, "bool"), *constraints1.relations),
          )
  
      _check_forward(
          constraints_trans.src,
          constraints_trans.dst,
          constraints_trans.src_to_dst,
          constraints_trans.dst_to_src,
      )
      _check_forward(
          constraints_trans.dst,
          constraints_trans.src,
          constraints_trans.dst_to_src,
          constraints_trans.src_to_dst,
      )
  
  
  def _get_targets(target_names=None):
      if target_names is None:
          target_names = _tvm_test_targets()
  
      if not target_names:
          target_names = DEFAULT_TEST_TARGETS
  
      targets = []
      for target in target_names:
          target_kind = target.split()[0]
  
          if target_kind == "cuda" and "cudnn" in tvm.target.Target(target).attrs.get("libs", []):
              is_enabled = tvm.support.libinfo()["USE_CUDNN"].lower() in ["on", "true", "1"]
              is_runnable = is_enabled and cudnn.exists()
          elif target_kind == "hexagon":
              is_enabled = tvm.support.libinfo()["USE_HEXAGON"].lower() in ["on", "true", "1"]
              # If Hexagon has compile-time support, we can always fall back
              is_runnable = is_enabled and "ANDROID_SERIAL_NUMBER" in os.environ
          else:
              is_enabled = tvm.runtime.enabled(target_kind)
              is_runnable = is_enabled and tvm.device(target_kind).exist
  
          targets.append(
              {
                  "target": target,
                  "target_kind": target_kind,
                  "is_enabled": is_enabled,
                  "is_runnable": is_runnable,
              }
          )
  
      if all(not t["is_runnable"] for t in targets):
          if tvm.runtime.enabled("llvm"):
              logging.warning(
                  "None of the following targets are supported by this build of TVM: %s."
                  " Try setting TVM_TEST_TARGETS to a supported target. Defaulting to llvm.",
                  target_str,
              )
              return _get_targets(["llvm"])
  
          raise TVMError(
              "None of the following targets are supported by this build of TVM: %s."
              " Try setting TVM_TEST_TARGETS to a supported target."
              " Cannot default to llvm, as it is not enabled." % target_str
          )
  
      return targets
  
  
  DEFAULT_TEST_TARGETS = [
      "llvm",
      "cuda",
      "nvptx",
      "vulkan -from_device=0",
      "opencl",
      "opencl -device=mali,aocl_sw_emu",
      "opencl -device=intel_graphics",
      "metal",
      "rocm",
      "hexagon",
  ]
  
  
  def device_enabled(target):
      """Check if a target should be used when testing.
  
      It is recommended that you use :py:func:`tvm.testing.parametrize_targets`
      instead of manually checking if a target is enabled.
  
      This allows the user to control which devices they are testing against. In
      tests, this should be used to check if a device should be used when said
      device is an optional part of the test.
  
      Parameters
      ----------
      target : str
          Target string to check against
  
      Returns
      -------
      bool
          Whether or not the device associated with this target is enabled.
  
      Example
      -------
      >>> @tvm.testing.uses_gpu
      >>> def test_mytest():
      >>>     for target in ["cuda", "llvm"]:
      >>>         if device_enabled(target):
      >>>             test_body...
  
      Here, `test_body` will only be reached by with `target="cuda"` on gpu test
      nodes and `target="llvm"` on cpu test nodes.
      """
      assert isinstance(target, str), "device_enabled requires a target as a string"
      # only check if device name is found, sometime there are extra flags
      target_kind = target.split(" ")[0]
      return any(target_kind == t["target_kind"] for t in _get_targets() if t["is_runnable"])
  
  
  def enabled_targets():
      """Get all enabled targets with associated devices.
  
      In most cases, you should use :py:func:`tvm.testing.parametrize_targets` instead of
      this function.
  
      In this context, enabled means that TVM was built with support for
      this target, the target name appears in the TVM_TEST_TARGETS
      environment variable, and a suitable device for running this
      target exists.  If TVM_TEST_TARGETS is not set, it defaults to
      variable DEFAULT_TEST_TARGETS in this module.
  
      If you use this function in a test, you **must** decorate the test with
      :py:func:`tvm.testing.uses_gpu` (otherwise it will never be run on the gpu).
  
      Returns
      -------
      targets: list
          A list of pairs of all enabled devices and the associated context
  
      """
      return [(t["target"], tvm.device(t["target"])) for t in _get_targets() if t["is_runnable"]]
  
  
  class Feature:
  
      """A feature that may be required to run a test.
  
      Parameters
      ----------
      name: str
  
          The short name of the feature.  Should match the name in the
          requires_* decorator.  This is applied as a mark to all tests
          using this feature, and can be used in pytests ``-m``
          argument.
  
      long_name: Optional[str]
  
          The long name of the feature, to be used in error messages.
  
          If None, defaults to the short name.
  
      cmake_flag: Optional[str]
  
          The flag that must be enabled in the config.cmake in order to
          use this feature.
  
          If None, no flag is required to use this feature.
  
      target_kind_enabled: Optional[str]
  
          The target kind that must be enabled to run tests using this
          feature.  If present, the target_kind must appear in the
          TVM_TEST_TARGETS environment variable, or in
          tvm.testing.DEFAULT_TEST_TARGETS if TVM_TEST_TARGETS is
          undefined.
  
          If None, this feature does not require a specific target to be
          enabled.
  
      compile_time_check: Optional[Callable[[], Union[bool,str]]]
  
          A check that returns True if the feature can be used at
          compile-time.  (e.g. Validating the version number of the nvcc
          compiler.)  If the feature does not have support to perform
          compile-time tests, the check should returns False to display
          a generic error message, or a string to display a more
          specific error message.
  
          If None, no additional check is performed.
  
      target_kind_hardware: Optional[str]
  
          The target kind that must have available hardware in order to
          run tests using this feature.  This is checked using
          tvm.device(target_kind_hardware).exist.  If a feature requires
          a different check, this should be implemented using
          run_time_check.
  
          If None, this feature does not require a specific
          tvm.device to exist.
  
      run_time_check: Optional[Callable[[], Union[bool,str]]]
  
          A check that returns True if the feature can be used at
          run-time.  (e.g. Validating the compute version supported by a
          GPU.)  If the feature does not have support to perform
          run-time tests, the check should returns False to display a
          generic error message, or a string to display a more specific
          error message.
  
          If None, no additional check is performed.
  
      parent_features: Optional[Union[str,List[str]]]
  
          The short name of a feature or features that are required in
          order to use this feature.  (e.g. Using cuDNN requires using
          CUDA) This feature should inherit all checks of the parent
          feature, with the exception of the `target_kind_enabled`
          checks.
  
          If None, this feature does not require any other parent
          features.
  
      """
  
      _all_features = {}
  
      def __init__(
          self,
          name: str,
          long_name: Optional[str] = None,
          cmake_flag: Optional[str] = None,
          target_kind_enabled: Optional[str] = None,
          compile_time_check: Optional[Callable[[], Union[bool, str]]] = None,
          target_kind_hardware: Optional[str] = None,
          run_time_check: Optional[Callable[[], Union[bool, str]]] = None,
          parent_features: Optional[Union[str, List[str]]] = None,
      ):
          self.name = name
          self.long_name = long_name or name
          self.cmake_flag = cmake_flag
          self.target_kind_enabled = target_kind_enabled
          self.compile_time_check = compile_time_check
          self.target_kind_hardware = target_kind_hardware
          self.run_time_check = run_time_check
  
          if parent_features is None:
              self.parent_features = []
          elif isinstance(parent_features, str):
              self.parent_features = [parent_features]
          else:
              self.parent_features = parent_features
  
          self._all_features[self.name] = self
  
      def _register_marker(self, config):
          config.addinivalue_line("markers", f"{self.name}: Mark a test as using {self.long_name}")
  
      def _uses_marks(self):
          for parent in self.parent_features:
              yield from self._all_features[parent]._uses_marks()
  
          yield getattr(pytest.mark, self.name)
  
      def _compile_only_marks(self):
          for parent in self.parent_features:
              yield from self._all_features[parent]._compile_only_marks()
  
          if self.compile_time_check is not None:
              res = self.compile_time_check()
              if isinstance(res, str):
                  yield pytest.mark.skipif(True, reason=res)
              else:
                  yield pytest.mark.skipif(
                      not res, reason=f"Compile-time support for {self.long_name} not present"
                  )
  
          if self.target_kind_enabled is not None:
              target_kind = self.target_kind_enabled.split()[0]
              yield pytest.mark.skipif(
                  all(enabled.split()[0] != target_kind for enabled in _tvm_test_targets()),
                  reason=(
                      f"{self.target_kind_enabled} tests disabled "
                      f"by TVM_TEST_TARGETS environment variable"
                  ),
              )
  
          if self.cmake_flag is not None:
              yield pytest.mark.skipif(
                  not _cmake_flag_enabled(self.cmake_flag),
                  reason=(
                      f"{self.long_name} support not enabled.  "
                      f"Set {self.cmake_flag} in config.cmake to enable."
                  ),
              )
  
      def _run_only_marks(self):
          for parent in self.parent_features:
              yield from self._all_features[parent]._run_only_marks()
  
          if self.run_time_check is not None:
              res = self.run_time_check()
              if isinstance(res, str):
                  yield pytest.mark.skipif(True, reason=res)
              else:
                  yield pytest.mark.skipif(
                      not res, reason=f"Run-time support for {self.long_name} not present"
                  )
  
          if self.target_kind_hardware is not None:
              yield pytest.mark.skipif(
                  not tvm.device(self.target_kind_hardware).exist,
                  reason=f"No device exists for target {self.target_kind_hardware}",
              )
  
      def marks(self, support_required="compile-and-run"):
          """Return a list of marks to be used
  
          Parameters
          ----------
  
          support_required: str
  
              Allowed values: "compile-and-run" (default),
              "compile-only", or "optional".
  
              See Feature.__call__ for details.
          """
          if support_required not in ["compile-and-run", "compile-only", "optional"]:
              raise ValueError(f"Unknown feature support type: {support_required}")
  
          if support_required == "compile-and-run":
              marks = itertools.chain(
                  self._run_only_marks(), self._compile_only_marks(), self._uses_marks()
              )
          elif support_required == "compile-only":
              marks = itertools.chain(self._compile_only_marks(), self._uses_marks())
          elif support_required == "optional":
              marks = self._uses_marks()
          else:
              raise ValueError(f"Unknown feature support type: {support_required}")
  
          return list(marks)
  
      def __call__(self, func=None, *, support_required="compile-and-run"):
          """Mark a pytest function as requiring this feature
  
          Can be used either as a bare decorator, or as a decorator with
          arguments.
  
          Parameters
          ----------
  
          func: Callable
  
              The pytest test function to be marked
  
          support_required: str
  
              Allowed values: "compile-and-run" (default),
              "compile-only", or "optional".
  
              If "compile-and-run", the test case is marked as using the
              feature, and is skipped if the environment lacks either
              compile-time or run-time support for the feature.
  
              If "compile-only", the test case is marked as using the
              feature, and is skipped if the environment lacks
              compile-time support.
  
              If "optional", the test case is marked as using the
              feature, but isn't skipped.  This is kept for backwards
              compatibility for tests that use `enabled_targets()`, and
              should be avoided in new test code.  Instead, prefer
              parametrizing over the target using the `target` fixture.
  
          Examples
          --------
  
          .. code-block:: python
  
            @feature
            def test_compile_and_run():
                ...
  
            @feature(compile_only=True)
            def test_compile_only():
                ...
  
          """
  
          if support_required not in ["compile-and-run", "compile-only", "optional"]:
              raise ValueError(f"Unknown feature support type: {support_required}")
  
          def wrapper(func):
              for mark in self.marks(support_required=support_required):
                  func = mark(func)
              return func
  
          if func is None:
              return wrapper
  
          return wrapper(func)
  
      @classmethod
      def require(cls, name, support_required="compile-and-run"):
          """Returns a decorator that marks a test as requiring a feature
  
          Parameters
          ----------
  
          name: str
  
              The name of the feature that is used by the test
  
          support_required: str
  
              Allowed values: "compile-and-run" (default),
              "compile-only", or "optional".
  
              See Feature.__call__ for details.
  
          Examples
          --------
  
          .. code-block:: python
  
            @Feature.require("cuda")
            def test_compile_and_run():
                ...
  
            @Feature.require("cuda", compile_only=True)
            def test_compile_only():
                ...
          """
          return cls._all_features[name](support_required=support_required)
  
  
  def _any_gpu_exists():
      return (
          tvm.cuda().exist
          or tvm.rocm().exist
          or tvm.opencl().exist
          or tvm.metal().exist
          or tvm.vulkan().exist
      )
  
  
  # Mark a test as requiring llvm to run
  requires_llvm = Feature(
      "llvm", "LLVM", cmake_flag="USE_LLVM", target_kind_enabled="llvm", target_kind_hardware="llvm"
  )
  
  # Mark a test as requiring a GPU to run.
  requires_gpu = Feature("gpu", run_time_check=_any_gpu_exists)
  
  # Mark to differentiate tests that use the GPU in some capacity.
  #
  # These tests will be run on CPU-only test nodes and on test nodes with GPUs.
  # To mark a test that must have a GPU present to run, use
  # :py:func:`tvm.testing.requires_gpu`.
  uses_gpu = requires_gpu(support_required="optional")
  
  # Mark a test as requiring the x86 Architecture to run.
  requires_x86 = Feature(
      "x86", "x86 Architecture", run_time_check=lambda: platform.machine() == "x86_64"
  )
  
  # Mark a test as requiring the CUDA runtime.
  requires_cuda = Feature(
      "cuda",
      "CUDA",
      cmake_flag="USE_CUDA",
      target_kind_enabled="cuda",
      target_kind_hardware="cuda",
      parent_features="gpu",
  )
  
  # Mark a test as requiring a tensorcore to run
  requires_tensorcore = Feature(
      "tensorcore",
      "NVIDIA Tensor Core",
      run_time_check=lambda: tvm.cuda().exist and nvcc.have_tensorcore(tvm.cuda().compute_version),
      parent_features="cuda",
  )
  
  # Mark a test as requiring the cuDNN library.
  requires_cudnn = Feature("cudnn", "cuDNN", cmake_flag="USE_CUDNN", parent_features="cuda")
  
  # Mark a test as requiring the cuBLAS library.
  requires_cublas = Feature("cublas", "cuBLAS", cmake_flag="USE_CUBLAS", parent_features="cuda")
  
  # Mark a test as requiring the NVPTX compilation on the CUDA runtime
  requires_nvptx = Feature(
      "nvptx",
      "NVPTX",
      target_kind_enabled="nvptx",
      target_kind_hardware="nvptx",
      parent_features=["llvm", "cuda"],
  )
  
  # Mark a test as requiring the CUDA Graph Feature
  requires_cudagraph = Feature(
      "cudagraph",
      "CUDA Graph",
      target_kind_enabled="cuda",
      compile_time_check=nvcc.have_cudagraph,
      parent_features="cuda",
  )
  
  # Mark a test as requiring the OpenCL runtime
  requires_opencl = Feature(
      "opencl",
      "OpenCL",
      cmake_flag="USE_OPENCL",
      target_kind_enabled="opencl",
      target_kind_hardware="opencl",
      parent_features="gpu",
  )
  
  # Mark a test as requiring the rocm runtime
  requires_rocm = Feature(
      "rocm",
      "ROCm",
      cmake_flag="USE_ROCM",
      target_kind_enabled="rocm",
      target_kind_hardware="rocm",
      parent_features="gpu",
  )
  
  # Mark a test as requiring the metal runtime
  requires_metal = Feature(
      "metal",
      "Metal",
      cmake_flag="USE_METAL",
      target_kind_enabled="metal",
      target_kind_hardware="metal",
      parent_features="gpu",
  )
  
  # Mark a test as requiring the vulkan runtime
  requires_vulkan = Feature(
      "vulkan",
      "Vulkan",
      cmake_flag="USE_VULKAN",
      target_kind_enabled="vulkan",
      target_kind_hardware="vulkan",
      parent_features="gpu",
  )
  
  # Mark a test as requiring microTVM to run
  requires_micro = Feature("micro", "MicroTVM", cmake_flag="USE_MICRO")
  
  # Mark a test as requiring CUTLASS to run
  requires_cutlass = Feature("cutlass", "CUTLASS", cmake_flag="USE_CUTLASS")
  
  # Mark a test as requiring rpc to run
  requires_rpc = Feature("rpc", "RPC", cmake_flag="USE_RPC")
  
  # Mark a test as requiring Arm(R) Ethos(TM)-N to run
  requires_ethosn = Feature("ethosn", "Arm(R) Ethos(TM)-N", cmake_flag="USE_ETHOSN")
  
  # Mark a test as requiring Hexagon to run
  requires_hexagon = Feature(
      "hexagon",
      "Hexagon",
      cmake_flag="USE_HEXAGON",
      target_kind_enabled="hexagon",
      compile_time_check=hexagon._compile_time_check,
      run_time_check=hexagon._run_time_check,
      parent_features="llvm",
  )
  
  # Mark a test as requiring the CMSIS NN library
  requires_cmsisnn = Feature("cmsisnn", "CMSIS NN", cmake_flag="USE_CMSISNN")
  
  # Mark a test as requiring the corstone300 FVP
  requires_corstone300 = Feature(
      "corstone300",
      "Corstone-300",
      compile_time_check=lambda: (
          (shutil.which("arm-none-eabi-gcc") is None) or "ARM embedded toolchain unavailable"
      ),
      parent_features="cmsisnn",
  )
  
  # Mark a test as requiring Vitis AI to run
  requires_vitis_ai = Feature("vitis_ai", "Vitis AI", cmake_flag="USE_VITIS_AI")
  
  
  def _cmake_flag_enabled(flag):
      flag = tvm.support.libinfo()[flag]
  
      # Because many of the flags can be library flags, we check if the
      # flag is not disabled, rather than checking if it is enabled.
      return flag.lower() not in ["off", "false", "0"]
  
  
  def _tvm_test_targets():
      target_str = os.environ.get("TVM_TEST_TARGETS", "").strip()
      if target_str:
          # Use dict instead of set for de-duplication so that the
          # targets stay in the order specified.
          return list({t.strip(): None for t in target_str.split(";") if t.strip()})
  
      return DEFAULT_TEST_TARGETS
  
  
  def _compose(args, decs):
      """Helper to apply multiple markers"""
      if len(args) > 0:
          f = args[0]
          for d in reversed(decs):
              f = d(f)
          return f
      return decs
  
  
  def slow(fn):
      @functools.wraps(fn)
      def wrapper(*args, **kwargs):
          if SKIP_SLOW_TESTS:
              pytest.skip("Skipping slow test since RUN_SLOW_TESTS environment variables is 'true'")
          else:
              fn(*args, **kwargs)
  
      return wrapper
  
  
  def requires_nvcc_version(major_version, minor_version=0, release_version=0):
      """Mark a test as requiring at least a specific version of nvcc.
  
      Unit test marked with this decorator will run only if the
      installed version of NVCC is at least `(major_version,
      minor_version, release_version)`.
  
      This also marks the test as requiring a cuda support.
  
      Parameters
      ----------
      major_version: int
  
          The major version of the (major,minor,release) version tuple.
  
      minor_version: int
  
          The minor version of the (major,minor,release) version tuple.
  
      release_version: int
  
          The release version of the (major,minor,release) version tuple.
  
      """
  
      try:
          nvcc_version = nvcc.get_cuda_version()
      except RuntimeError:
          nvcc_version = (0, 0, 0)
  
      min_version = (major_version, minor_version, release_version)
      version_str = ".".join(str(v) for v in min_version)
      requires = [
          pytest.mark.skipif(nvcc_version < min_version, reason=f"Requires NVCC >= {version_str}"),
          *requires_cuda.marks(),
      ]
  
      def inner(func):
          return _compose([func], requires)
  
      return inner
  
  
  def skip_if_32bit(reason):
      def decorator(*args):
          if "32bit" in platform.architecture()[0]:
              return _compose(args, [pytest.mark.skip(reason=reason)])
  
          return _compose(args, [])
  
      return decorator
  
  
  def requires_package(*packages):
      """Mark a test as requiring python packages to run.
  
      If the packages listed are not available, tests marked with
      `requires_package` will appear in the pytest results as being skipped.
      This is equivalent to using ``foo = pytest.importorskip('foo')`` inside
      the test body.
  
      Parameters
      ----------
      packages : List[str]
  
          The python packages that should be available for the test to
          run.
  
      Returns
      -------
      mark: pytest mark
  
          The pytest mark to be applied to unit tests that require this
  
      """
  
      def has_package(package):
          try:
              __import__(package)
              return True
          except ImportError:
              return False
  
      marks = [
          pytest.mark.skipif(not has_package(package), reason=f"Cannot import '{package}'")
          for package in packages
      ]
  
      def wrapper(func):
          for mark in marks:
              func = mark(func)
          return func
  
      return wrapper
  
  
  def parametrize_targets(*args):
  
      """Parametrize a test over a specific set of targets.
  
      Use this decorator when you want your test to be run over a
      specific set of targets and devices.  It is intended for use where
      a test is applicable only to a specific target, and is
      inapplicable to any others (e.g. verifying target-specific
      assembly code matches known assembly code).  In most
      circumstances, :py:func:`tvm.testing.exclude_targets` or
      :py:func:`tvm.testing.known_failing_targets` should be used
      instead.
  
      If used as a decorator without arguments, the test will be
      parametrized over all targets in
      :py:func:`tvm.testing.enabled_targets`.  This behavior is
      automatically enabled for any target that accepts arguments of
      ``target`` or ``dev``, so the explicit use of the bare decorator
      is no longer needed, and is maintained for backwards
      compatibility.
  
      Parameters
      ----------
      f : function
          Function to parametrize. Must be of the form `def test_xxxxxxxxx(target, dev)`:,
          where `xxxxxxxxx` is any name.
      targets : list[str], optional
          Set of targets to run against. If not supplied,
          :py:func:`tvm.testing.enabled_targets` will be used.
  
      Example
      -------
      >>> @tvm.testing.parametrize_targets("llvm", "cuda")
      >>> def test_mytest(target, dev):
      >>>     ...  # do something
      """
  
      # Backwards compatibility, when used as a decorator with no
      # arguments implicitly parametrizes over "target".  The
      # parametrization is now handled by _auto_parametrize_target, so
      # this use case can just return the decorated function.
      if len(args) == 1 and callable(args[0]):
          return args[0]
  
      return pytest.mark.parametrize("target", list(args), scope="session")
  
  
  def exclude_targets(*args):
      """Exclude a test from running on a particular target.
  
      Use this decorator when you want your test to be run over a
      variety of targets and devices (including cpu and gpu devices),
      but want to exclude some particular target or targets.  For
      example, a test may wish to be run against all targets in
      tvm.testing.enabled_targets(), except for a particular target that
      does not support the capabilities.
  
      Applies pytest.mark.skipif to the targets given.
  
      Parameters
      ----------
      f : function
          Function to parametrize. Must be of the form `def test_xxxxxxxxx(target, dev)`:,
          where `xxxxxxxxx` is any name.
      targets : list[str]
          Set of targets to exclude.
  
      Example
      -------
      >>> @tvm.testing.exclude_targets("cuda")
      >>> def test_mytest(target, dev):
      >>>     ...  # do something
  
      Or
  
      >>> @tvm.testing.exclude_targets("llvm", "cuda")
      >>> def test_mytest(target, dev):
      >>>     ...  # do something
  
      """
  
      def wraps(func):
          func.tvm_excluded_targets = args
          return func
  
      return wraps
  
  
  def known_failing_targets(*args):
      """Skip a test that is known to fail on a particular target.
  
      Use this decorator when you want your test to be run over a
      variety of targets and devices (including cpu and gpu devices),
      but know that it fails for some targets.  For example, a newly
      implemented runtime may not support all features being tested, and
      should be excluded.
  
      Applies pytest.mark.xfail to the targets given.
  
      Parameters
      ----------
      f : function
          Function to parametrize. Must be of the form `def test_xxxxxxxxx(target, dev)`:,
          where `xxxxxxxxx` is any name.
      targets : list[str]
          Set of targets to skip.
  
      Example
      -------
      >>> @tvm.testing.known_failing_targets("cuda")
      >>> def test_mytest(target, dev):
      >>>     ...  # do something
  
      Or
  
      >>> @tvm.testing.known_failing_targets("llvm", "cuda")
      >>> def test_mytest(target, dev):
      >>>     ...  # do something
  
      """
  
      def wraps(func):
          func.tvm_known_failing_targets = args
          return func
  
      return wraps
  
  
  def parameter(*values, ids=None, by_dict=None):
      """Convenience function to define pytest parametrized fixtures.
  
      Declaring a variable using ``tvm.testing.parameter`` will define a
      parametrized pytest fixture that can be used by test
      functions. This is intended for cases that have no setup cost,
      such as strings, integers, tuples, etc.  For cases that have a
      significant setup cost, please use :py:func:`tvm.testing.fixture`
      instead.
  
      If a test function accepts multiple parameters defined using
      ``tvm.testing.parameter``, then the test will be run using every
      combination of those parameters.
  
      The parameter definition applies to all tests in a module.  If a
      specific test should have different values for the parameter, that
      test should be marked with ``@pytest.mark.parametrize``.
  
      Parameters
      ----------
      values : Any
  
         A list of parameter values.  A unit test that accepts this
         parameter as an argument will be run once for each parameter
         given.
  
      ids : List[str], optional
  
         A list of names for the parameters.  If None, pytest will
         generate a name from the value.  These generated names may not
         be readable/useful for composite types such as tuples.
  
      by_dict : Dict[str, Any]
  
         A mapping from parameter name to parameter value, to set both the
         values and ids.
  
      Returns
      -------
      function
         A function output from pytest.fixture.
  
      Example
      -------
      >>> size = tvm.testing.parameter(1, 10, 100)
      >>> def test_using_size(size):
      >>>     ... # Test code here
  
      Or
  
      >>> shape = tvm.testing.parameter((5,10), (512,1024), ids=['small','large'])
      >>> def test_using_size(shape):
      >>>     ... # Test code here
  
      Or
  
      >>> shape = tvm.testing.parameter(by_dict={'small': (5,10), 'large': (512,1024)})
      >>> def test_using_size(shape):
      >>>     ... # Test code here
  
      """
  
      if by_dict is not None:
          if values or ids:
              raise RuntimeError(
                  "Use of the by_dict parameter cannot be used alongside positional arguments"
              )
  
          ids, values = zip(*by_dict.items())
  
      # Optional cls parameter in case a parameter is defined inside a
      # class scope.
      @pytest.fixture(params=values, ids=ids)
      def as_fixture(*_cls, request):
          return request.param
  
      return as_fixture
  
  
  _parametrize_group = 0
  
  
  def parameters(*value_sets, ids=None):
      """Convenience function to define pytest parametrized fixtures.
  
      Declaring a variable using tvm.testing.parameters will define a
      parametrized pytest fixture that can be used by test
      functions. Like :py:func:`tvm.testing.parameter`, this is intended
      for cases that have no setup cost, such as strings, integers,
      tuples, etc.  For cases that have a significant setup cost, please
      use :py:func:`tvm.testing.fixture` instead.
  
      Unlike :py:func:`tvm.testing.parameter`, if a test function
      accepts multiple parameters defined using a single call to
      ``tvm.testing.parameters``, then the test will only be run once
      for each set of parameters, not for all combinations of
      parameters.
  
      These parameter definitions apply to all tests in a module.  If a
      specific test should have different values for some parameters,
      that test should be marked with ``@pytest.mark.parametrize``.
  
      Parameters
      ----------
      values : List[tuple]
  
         A list of parameter value sets.  Each set of values represents
         a single combination of values to be tested.  A unit test that
         accepts parameters defined will be run once for every set of
         parameters in the list.
  
      ids : List[str], optional
  
         A list of names for the parameter sets.  If None, pytest will
         generate a name from each parameter set.  These generated names may
         not be readable/useful for composite types such as tuples.
  
      Returns
      -------
      List[function]
         Function outputs from pytest.fixture.  These should be unpacked
         into individual named parameters.
  
      Example
      -------
      >>> size, dtype = tvm.testing.parameters( (16,'float32'), (512,'float16') )
      >>> def test_feature_x(size, dtype):
      >>>     # Test code here
      >>>     assert( (size,dtype) in [(16,'float32'), (512,'float16')])
  
      """
      global _parametrize_group
      parametrize_group = _parametrize_group
      _parametrize_group += 1
  
      outputs = []
      for param_values in zip(*value_sets):
  
          # Optional cls parameter in case a parameter is defined inside a
          # class scope.
          def fixture_func(*_cls, request):
              return request.param
  
          fixture_func.parametrize_group = parametrize_group
          fixture_func.parametrize_values = param_values
          fixture_func.parametrize_ids = ids
          outputs.append(pytest.fixture(fixture_func))
  
      return outputs
  
  
  def fixture(func=None, *, cache_return_value=False):
      """Convenience function to define pytest fixtures.
  
      This should be used as a decorator to mark functions that set up
      state before a function.  The return value of that fixture
      function is then accessible by test functions as that accept it as
      a parameter.
  
      Fixture functions can accept parameters defined with
      :py:func:`tvm.testing.parameter`.
  
      By default, the setup will be performed once for each unit test
      that uses a fixture, to ensure that unit tests are independent.
      If the setup is expensive to perform, then the
      cache_return_value=True argument can be passed to cache the setup.
      The fixture function will be run only once (or once per parameter,
      if used with tvm.testing.parameter), and the same return value
      will be passed to all tests that use it.  If the environment
      variable TVM_TEST_DISABLE_CACHE is set to a non-zero value, it
      will disable this feature and no caching will be performed.
  
      Example
      -------
      >>> @tvm.testing.fixture
      >>> def cheap_setup():
      >>>     return 5 # Setup code here.
      >>>
      >>> def test_feature_x(target, dev, cheap_setup)
      >>>     assert(cheap_setup == 5) # Run test here
  
      Or
  
      >>> size = tvm.testing.parameter(1, 10, 100)
      >>>
      >>> @tvm.testing.fixture
      >>> def cheap_setup(size):
      >>>     return 5*size # Setup code here, based on size.
      >>>
      >>> def test_feature_x(cheap_setup):
      >>>     assert(cheap_setup in [5, 50, 500])
  
      Or
  
      >>> @tvm.testing.fixture(cache_return_value=True)
      >>> def expensive_setup():
      >>>     time.sleep(10) # Setup code here
      >>>     return 5
      >>>
      >>> def test_feature_x(target, dev, expensive_setup):
      >>>     assert(expensive_setup == 5)
  
      """
  
      force_disable_cache = bool(int(os.environ.get("TVM_TEST_DISABLE_CACHE", "0")))
      cache_return_value = cache_return_value and not force_disable_cache
  
      # Deliberately at function scope, so that caching can track how
      # many times the fixture has been used.  If used, the cache gets
      # cleared after the fixture is no longer needed.
      scope = "function"
  
      def wraps(func):
          if cache_return_value:
              func = _fixture_cache(func)
          func = pytest.fixture(func, scope=scope)
          return func
  
      if func is None:
          return wraps
  
      return wraps(func)
  
  
  class _DeepCopyAllowedClasses(dict):
      def __init__(self, allowed_class_list):
          self.allowed_class_list = allowed_class_list
          super().__init__()
  
      def get(self, key, *args, **kwargs):
          """Overrides behavior of copy.deepcopy to avoid implicit copy.
  
          By default, copy.deepcopy uses a dict of id->object to track
          all objects that it has seen, which is passed as the second
          argument to all recursive calls.  This class is intended to be
          passed in instead, and inspects the type of all objects being
          copied.
  
          Where copy.deepcopy does a best-effort attempt at copying an
          object, for unit tests we would rather have all objects either
          be copied correctly, or to throw an error.  Classes that
          define an explicit method to perform a copy are allowed, as
          are any explicitly listed classes.  Classes that would fall
          back to using object.__reduce__, and are not explicitly listed
          as safe, will throw an exception.
  
          """
          obj = ctypes.cast(key, ctypes.py_object).value
          cls = type(obj)
          if (
              cls in copy._deepcopy_dispatch
              or issubclass(cls, type)
              or getattr(obj, "__deepcopy__", None)
              or copyreg.dispatch_table.get(cls)
              or cls.__reduce__ is not object.__reduce__
              or cls.__reduce_ex__ is not object.__reduce_ex__
              or cls in self.allowed_class_list
          ):
              return super().get(key, *args, **kwargs)
  
          rfc_url = (
              "https://github.com/apache/tvm-rfcs/blob/main/rfcs/0007-parametrized-unit-tests.md"
          )
          raise TypeError(
              (
                  f"Cannot copy fixture of type {cls.__name__}.  TVM fixture caching "
                  "is limited to objects that explicitly provide the ability "
                  "to be copied (e.g. through __deepcopy__, __getstate__, or __setstate__),"
                  "and forbids the use of the default `object.__reduce__` and "
                  "`object.__reduce_ex__`.  For third-party classes that are "
                  "safe to use with copy.deepcopy, please add the class to "
                  "the arguments of _DeepCopyAllowedClasses in tvm.testing._fixture_cache.\n"
                  "\n"
                  f"For discussion on this restriction, please see {rfc_url}."
              )
          )
  
  
  def _fixture_cache(func):
      cache = {}
  
      # Can't use += on a bound method's property.  Therefore, this is a
      # list rather than a variable so that it can be accessed from the
      # pytest_collection_modifyitems().
      num_tests_use_this_fixture = [0]
  
      num_times_fixture_used = 0
  
      # Using functools.lru_cache would require the function arguments
      # to be hashable, which wouldn't allow caching fixtures that
      # depend on numpy arrays.  For example, a fixture that takes a
      # numpy array as input, then calculates uses a slow method to
      # compute a known correct output for that input.  Therefore,
      # including a fallback for serializable types.
      def get_cache_key(*args, **kwargs):
          try:
              hash((args, kwargs))
              return (args, kwargs)
          except TypeError as e:
              pass
  
          try:
              return pickle.dumps((args, kwargs))
          except TypeError as e:
              raise TypeError(
                  "TVM caching of fixtures requires arguments to the fixture "
                  "to be either hashable or serializable"
              ) from e
  
      @functools.wraps(func)
      def wrapper(*args, **kwargs):
          if num_tests_use_this_fixture[0] == 0:
              raise RuntimeError(
                  "Fixture use count is 0.  "
                  "This can occur if tvm.testing.plugin isn't registered.  "
                  "If using outside of the TVM test directory, "
                  "please add `pytest_plugins = ['tvm.testing.plugin']` to your conftest.py"
              )
  
          try:
              cache_key = get_cache_key(*args, **kwargs)
  
              try:
                  cached_value = cache[cache_key]
              except KeyError:
                  cached_value = cache[cache_key] = func(*args, **kwargs)
  
              yield copy.deepcopy(
                  cached_value,
                  # allowed_class_list should be a list of classes that
                  # are safe to copy using copy.deepcopy, but do not
                  # implement __deepcopy__, __reduce__, or
                  # __reduce_ex__.
                  _DeepCopyAllowedClasses(allowed_class_list=[]),
              )
  
          finally:
              # Clear the cache once all tests that use a particular fixture
              # have completed.
              nonlocal num_times_fixture_used
              num_times_fixture_used += 1
              if num_times_fixture_used >= num_tests_use_this_fixture[0]:
                  cache.clear()
  
      # Set in the pytest_collection_modifyitems(), by _count_num_fixture_uses
      wrapper.num_tests_use_this_fixture = num_tests_use_this_fixture
  
      return wrapper
  
  
  def identity_after(x, sleep):
      """Testing function to return identity after sleep
  
      Parameters
      ----------
      x : int
          The input value.
  
      sleep : float
          The amount of time to sleep
  
      Returns
      -------
      x : object
          The original value
      """
      if sleep:
          time.sleep(sleep)
      return x
  
  
  def terminate_self():
      """Testing function to terminate the process."""
      sys.exit(-1)
  
  
  def is_ampere_or_newer():
      """Check if the target environment has an NVIDIA Ampere GPU or newer."""
      arch = tvm.contrib.nvcc.get_target_compute_version()
      major, _ = tvm.contrib.nvcc.parse_compute_version(arch)
      return major >= 8
  
  
  def main():
      test_file = inspect.getsourcefile(sys._getframe(1))
      sys.exit(pytest.main([test_file] + sys.argv[1:]))
  