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')
],
)