def test_download_eval_file(self):
WHOIS.get_registrar_certification_list.return_value = self._get_registrar_certs(
)
FILE_MANAGER.info.return_value = FileInfo(
id=2,
name='test.html',
path='2015/12/9/1',
mimetype='text/html',
filetype=6,
crdate='2015-12-09 16:16:28.598757',
size=5)
content = "<html><body>The content.</body></html>"
FILE_MANAGER.load.return_value.download.return_value = content
response = self.client.get(
reverse("webwhois:download_evaluation_file",
kwargs={"handle": "REG-MOJEID"}))
self.assertEqual(response.content, content.encode())
self.assertEqual(response['Content-Type'], 'text/html')
self.assertEqual(response['Content-Disposition'],
'attachment; filename="test.html"')
self.assertEqual(WHOIS.mock_calls,
[call.get_registrar_certification_list()])
self.assertEqual(FILE_MANAGER.mock_calls, [
call.info(2),
call.load(2),
call.load().download(5),
call.load().finalize_download()
])
def test_givenSeq2SeqModelRetrained_whenLoadRetrainedWeights_thenLoadProperly(
self, torch_nn_mock, torch_mock):
all_layers_params_mock = MagicMock()
all_layers_params_mock.__getitem__(
).__len__.return_value = self.decoder_output_size
torch_mock.load.return_value = all_layers_params_mock
seq2seq_model = Seq2SeqModel(
self.a_cpu_device,
input_size=self.encoder_input_size_dim,
encoder_hidden_size=self.encoder_hidden_size,
encoder_num_layers=self.encoder_num_layers,
decoder_hidden_size=self.decoder_hidden_size,
decoder_num_layers=self.decoder_num_layers,
output_size=self.decoder_output_size,
verbose=True,
)
seq2seq_model._load_weights(self.a_fake_retrain_path)
torch_mock.assert_has_calls([
call.load(self.a_fake_retrain_path, map_location=self.a_cpu_device)
])
torch_nn_mock.assert_called()
torch_nn_mock.asser_has_calls([call(all_layers_params_mock)])
def test_givenRetrainedWeights_whenInstantiatingAFastTextSeq2SeqModel_thenShouldUseRetrainedWeights(
self, load_state_dict_mock, torch_mock):
all_layers_params = MagicMock()
torch_mock.load.return_value = all_layers_params
self.seq2seq_model = FastTextSeq2SeqModel(self.a_torch_device,
self.verbose,
path_to_retrained_model=self.a_path_to_retrained_model)
torch_load_call = [call.load(self.a_path_to_retrained_model, map_location=self.a_torch_device)]
torch_mock.assert_has_calls(torch_load_call)
load_state_dict_call = [call(all_layers_params)]
load_state_dict_mock.assert_has_calls(load_state_dict_call)
def it_adds_a_load_method(utilities, mixed_instance):
mixed_instance.load()
expect(utilities.mock_calls) == [call.load(mixed_instance)]
def it_adds_a_load_method(utilities, mixed_instance):
mixed_instance.load()
expect(utilities.mock_calls) == [
call.load(mixed_instance)
]