text: # Copyright (c) 2016-present, Facebook, Inc.
  #
  # 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
  #
  #     http://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.
  ##############################################################################
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from caffe2.python import core, workspace
  from caffe2.python.core import CreatePythonOperator
  import caffe2.python.hypothesis_test_util as hu
  from hypothesis import given
  import hypothesis.strategies as st
  import numpy as np
  import unittest
  
  try:
      import numba
      HAS_NUMBA = True
  except ImportError:
      HAS_NUMBA = False
  
  
  class PythonOpTest(hu.HypothesisTestCase):
      @unittest.skipIf(not HAS_NUMBA, "")
      @given(x=hu.tensor(),
             n=st.integers(min_value=1, max_value=20),
             w=st.integers(min_value=1, max_value=20))
      def test_multithreaded_evaluation_numba_nogil(self, x, n, w):
          @numba.jit(nopython=True, nogil=True)
          def g(input_, output):
              output[...] = input_
  
          def f(inputs, outputs):
              outputs[0].reshape(inputs[0].shape)
              g(inputs[0].data, outputs[0].data)
  
          ops = [CreatePythonOperator(f, ["x"], [str(i)]) for i in range(n)]
          net = core.Net("net")
          net.Proto().op.extend(ops)
          net.Proto().type = "dag"
          net.Proto().num_workers = w
          iters = 100
          plan = core.Plan("plan")
          plan.AddStep(core.ExecutionStep("test-step", net, iters))
          workspace.FeedBlob("x", x)
          workspace.RunPlan(plan.Proto().SerializeToString())
          for i in range(n):
              y = workspace.FetchBlob(str(i))
              np.testing.assert_almost_equal(x, y)
  
  if __name__ == "__main__":
      import unittest
      unittest.main()
  