def test_credentials_transport_error(): # It is an error to provide credentials and a transport instance. transport = transports.PredictionServiceGrpcTransport( credentials=credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), transport=transport, ) # It is an error to provide a credentials file and a transport instance. transport = transports.PredictionServiceGrpcTransport( credentials=credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = PredictionServiceClient( client_options={"credentials_file": "credentials.json"}, transport=transport, ) # It is an error to provide scopes and a transport instance. transport = transports.PredictionServiceGrpcTransport( credentials=credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = PredictionServiceClient( client_options={"scopes": ["1", "2"]}, transport=transport, )
def test_batch_predict(transport: str = "grpc", request_type=prediction_service.BatchPredictRequest): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client._transport.batch_predict), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = operations_pb2.Operation(name="operations/spam") response = client.batch_predict(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == prediction_service.BatchPredictRequest() # Establish that the response is the type that we expect. assert isinstance(response, future.Future)
def test_batch_predict_field_headers(): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), ) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = prediction_service.BatchPredictRequest() request.name = "name/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client._transport.batch_predict), "__call__") as call: call.return_value = operations_pb2.Operation(name="operations/op") client.batch_predict(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ( "x-goog-request-params", "name=name/value", ) in kw["metadata"]
def test_predict_flattened(): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client._transport.predict), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = prediction_service.PredictResponse() # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.predict( name="name_value", payload=data_items.ExamplePayload(image=data_items.Image( image_bytes=b"image_bytes_blob")), params={"key_value": "value_value"}, ) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0].name == "name_value" assert args[0].payload == data_items.ExamplePayload( image=data_items.Image(image_bytes=b"image_bytes_blob")) assert args[0].params == {"key_value": "value_value"}
def test_predict_flattened_error(): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), ) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.predict( prediction_service.PredictRequest(), name="name_value", payload=data_items.ExamplePayload(image=data_items.Image( image_bytes=b"image_bytes_blob")), params={"key_value": "value_value"}, )
def test_prediction_service_host_with_port(): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), client_options=client_options.ClientOptions( api_endpoint="automl.googleapis.com:8000"), ) assert client._transport._host == "automl.googleapis.com:8000"
def test_transport_grpc_default(): # A client should use the gRPC transport by default. client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), ) assert isinstance( client._transport, transports.PredictionServiceGrpcTransport, )
def test_batch_predict_flattened_error(): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), ) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.batch_predict( prediction_service.BatchPredictRequest(), name="name_value", input_config=io.BatchPredictInputConfig(gcs_source=io.GcsSource( input_uris=["input_uris_value"])), output_config=io.BatchPredictOutputConfig( gcs_destination=io.GcsDestination( output_uri_prefix="output_uri_prefix_value")), params={"key_value": "value_value"}, )
def test_prediction_service_auth_adc(): # If no credentials are provided, we should use ADC credentials. with mock.patch.object(auth, "default") as adc: adc.return_value = (credentials.AnonymousCredentials(), None) PredictionServiceClient() adc.assert_called_once_with( scopes=("https://www.googleapis.com/auth/cloud-platform", ), quota_project_id=None, )
def test_client_withDEFAULT_CLIENT_INFO(): client_info = gapic_v1.client_info.ClientInfo() with mock.patch.object(transports.PredictionServiceTransport, "_prep_wrapped_messages") as prep: client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), client_info=client_info, ) prep.assert_called_once_with(client_info) with mock.patch.object(transports.PredictionServiceTransport, "_prep_wrapped_messages") as prep: transport_class = PredictionServiceClient.get_transport_class() transport = transport_class( credentials=credentials.AnonymousCredentials(), client_info=client_info, ) prep.assert_called_once_with(client_info)
def test_batch_predict_flattened(): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client._transport.batch_predict), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = operations_pb2.Operation(name="operations/op") # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.batch_predict( name="name_value", input_config=io.BatchPredictInputConfig(gcs_source=io.GcsSource( input_uris=["input_uris_value"])), output_config=io.BatchPredictOutputConfig( gcs_destination=io.GcsDestination( output_uri_prefix="output_uri_prefix_value")), params={"key_value": "value_value"}, ) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0].name == "name_value" assert args[0].input_config == io.BatchPredictInputConfig( gcs_source=io.GcsSource(input_uris=["input_uris_value"])) assert args[0].output_config == io.BatchPredictOutputConfig( gcs_destination=io.GcsDestination( output_uri_prefix="output_uri_prefix_value")) assert args[0].params == {"key_value": "value_value"}
def test_prediction_service_grpc_lro_client(): client = PredictionServiceClient( credentials=credentials.AnonymousCredentials(), transport="grpc", ) transport = client._transport # Ensure that we have a api-core operations client. assert isinstance( transport.operations_client, operations_v1.OperationsClient, ) # Ensure that subsequent calls to the property send the exact same object. assert transport.operations_client is transport.operations_client
def test_prediction_service_client_client_options_from_dict(): with mock.patch( "google.cloud.automl_v1.services.prediction_service.transports.PredictionServiceGrpcTransport.__init__" ) as grpc_transport: grpc_transport.return_value = None client = PredictionServiceClient( client_options={"api_endpoint": "squid.clam.whelk"}) grpc_transport.assert_called_once_with( credentials=None, credentials_file=None, host="squid.clam.whelk", scopes=None, ssl_channel_credentials=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, )
def test__get_default_mtls_endpoint(): api_endpoint = "example.googleapis.com" api_mtls_endpoint = "example.mtls.googleapis.com" sandbox_endpoint = "example.sandbox.googleapis.com" sandbox_mtls_endpoint = "example.mtls.sandbox.googleapis.com" non_googleapi = "api.example.com" assert PredictionServiceClient._get_default_mtls_endpoint(None) is None assert (PredictionServiceClient._get_default_mtls_endpoint(api_endpoint) == api_mtls_endpoint) assert (PredictionServiceClient._get_default_mtls_endpoint( api_mtls_endpoint) == api_mtls_endpoint) assert (PredictionServiceClient._get_default_mtls_endpoint( sandbox_endpoint) == sandbox_mtls_endpoint) assert (PredictionServiceClient._get_default_mtls_endpoint( sandbox_mtls_endpoint) == sandbox_mtls_endpoint) assert (PredictionServiceClient._get_default_mtls_endpoint(non_googleapi) == non_googleapi)
def test_transport_instance(): # A client may be instantiated with a custom transport instance. transport = transports.PredictionServiceGrpcTransport( credentials=credentials.AnonymousCredentials(), ) client = PredictionServiceClient(transport=transport) assert client._transport is transport
def test_prediction_service_client_get_transport_class(): transport = PredictionServiceClient.get_transport_class() assert transport == transports.PredictionServiceGrpcTransport transport = PredictionServiceClient.get_transport_class("grpc") assert transport == transports.PredictionServiceGrpcTransport