text: # Copyright © 2020 Arm Ltd. All rights reserved.
  # SPDX-License-Identifier: MIT
  import os
  
  import pytest
  import pyarmnn as ann
  import numpy as np
  from typing import List
  
  
  @pytest.fixture()
  def parser(shared_data_folder):
      """
      Parse and setup the test network to be used for the tests below
      """
  
      # create onnx parser
      parser = ann.IOnnxParser()
  
      # path to model
      path_to_model = os.path.join(shared_data_folder, 'mock_model.onnx')
  
      # parse onnx binary & create network
      parser.CreateNetworkFromBinaryFile(path_to_model)
  
      yield parser
  
  
  def test_onnx_parser_swig_destroy():
      assert ann.IOnnxParser.__swig_destroy__, "There is a swig python destructor defined"
      assert ann.IOnnxParser.__swig_destroy__.__name__ == "delete_IOnnxParser"
  
  
  def test_check_onnx_parser_swig_ownership(parser):
      # Check to see that SWIG has ownership for parser. This instructs SWIG to take
      # ownership of the return value. This allows the value to be automatically
      # garbage-collected when it is no longer in use
      assert parser.thisown
  
  
  def test_onnx_parser_get_network_input_binding_info(parser):
      input_binding_info = parser.GetNetworkInputBindingInfo("input")
  
      tensor = input_binding_info[1]
      assert tensor.GetDataType() == 1
      assert tensor.GetNumDimensions() == 4
      assert tensor.GetNumElements() == 784
      assert tensor.GetQuantizationOffset() == 0
      assert tensor.GetQuantizationScale() == 0
  
  
  def test_onnx_parser_get_network_output_binding_info(parser):
      output_binding_info = parser.GetNetworkOutputBindingInfo("output")
  
      tensor = output_binding_info[1]
      assert tensor.GetDataType() == 1
      assert tensor.GetNumDimensions() == 4
      assert tensor.GetNumElements() == 10
      assert tensor.GetQuantizationOffset() == 0
      assert tensor.GetQuantizationScale() == 0
  
  
  def test_onnx_filenotfound_exception(shared_data_folder):
      parser = ann.IOnnxParser()
  
      # path to model
      path_to_model = os.path.join(shared_data_folder, 'some_unknown_model.onnx')
  
      # parse onnx binary & create network
  
      with pytest.raises(RuntimeError) as err:
          parser.CreateNetworkFromBinaryFile(path_to_model)
  
      # Only check for part of the exception since the exception returns
      # absolute path which will change on different machines.
      assert 'Invalid (null) filename' in str(err.value)
  
  
  def test_onnx_parser_end_to_end(shared_data_folder):
      parser = ann.IOnnxParser = ann.IOnnxParser()
  
      network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mock_model.onnx'))
  
      # load test image data stored in input_onnx.npy
      input_binding_info = parser.GetNetworkInputBindingInfo("input")
      input_tensor_data = np.load(os.path.join(shared_data_folder, 'onnx_parser/input_onnx.npy')).astype(np.float32)
  
      options = ann.CreationOptions()
      runtime = ann.IRuntime(options)
  
      preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
      opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
  
      assert 0 == len(messages)
  
      net_id, messages = runtime.LoadNetwork(opt_network)
  
      assert "" == messages
  
      input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
      output_tensors = ann.make_output_tensors([parser.GetNetworkOutputBindingInfo("output")])
  
      runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
  
      output = ann.workload_tensors_to_ndarray(output_tensors)
  
      # Load golden output file for result comparison.
      golden_output = np.load(os.path.join(shared_data_folder, 'onnx_parser/golden_output_onnx.npy'))
  
      # Check that output matches golden output to 4 decimal places (there are slight rounding differences after this)
      np.testing.assert_almost_equal(output[0], golden_output, decimal=4)
  