def test_generate_reach_forecast_field_headers(): client = ReachPlanServiceClient( credentials=ga_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 = reach_plan_service.GenerateReachForecastRequest() request.customer_id = 'customer_id/value' # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.generate_reach_forecast), '__call__') as call: call.return_value = reach_plan_service.GenerateReachForecastResponse() client.generate_reach_forecast(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', 'customer_id=customer_id/value', ) in kw['metadata']
def test_generate_reach_forecast_flattened(): client = ReachPlanServiceClient( credentials=ga_credentials.AnonymousCredentials(), ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.generate_reach_forecast), '__call__') as call: # Designate an appropriate return value for the call. call.return_value = reach_plan_service.GenerateReachForecastResponse() # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.generate_reach_forecast( customer_id='customer_id_value', campaign_duration=reach_plan_service.CampaignDuration( duration_in_days=1708), planned_products=[ reach_plan_service.PlannedProduct( plannable_product_code='plannable_product_code_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].customer_id == 'customer_id_value' assert args[ 0].campaign_duration == reach_plan_service.CampaignDuration( duration_in_days=1708) assert args[0].planned_products == [ reach_plan_service.PlannedProduct( plannable_product_code='plannable_product_code_value') ]
def test_generate_reach_forecast( transport: str = 'grpc', request_type=reach_plan_service.GenerateReachForecastRequest): client = ReachPlanServiceClient( credentials=ga_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.generate_reach_forecast), '__call__') as call: # Designate an appropriate return value for the call. call.return_value = reach_plan_service.GenerateReachForecastResponse() response = client.generate_reach_forecast(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == reach_plan_service.GenerateReachForecastRequest() # Establish that the response is the type that we expect. assert isinstance(response, reach_plan_service.GenerateReachForecastResponse)
def test_generate_reach_forecast_flattened_error(): client = ReachPlanServiceClient( credentials=ga_credentials.AnonymousCredentials(), ) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.generate_reach_forecast( reach_plan_service.GenerateReachForecastRequest(), customer_id='customer_id_value', campaign_duration=reach_plan_service.CampaignDuration( duration_in_days=1708), planned_products=[ reach_plan_service.PlannedProduct( plannable_product_code='plannable_product_code_value') ], )