def get_context_generator(
override_opt: Optional[Dict[str, Any]] = None,
task: Optional[str] = 'blended_skill_talk',
**kwargs,
) -> ContextGenerator:
"""
Return an object to return BlendedSkillTalk-style context info (personas, etc.).
"""
argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
if override_opt is not None:
argparser.set_params(**override_opt)
opt = argparser.parse_args([])
task_module = load_task_module(task)
context_generator_class = getattr(task_module, 'ContextGenerator', None)
context_generator = context_generator_class(opt, datatype='test', seed=0, **kwargs)
# We pull from the test set so that the model can't regurgitate
# memorized conversations
return context_generator
def _create_task_agents(opt: Opt):
"""
Create task agent(s) for the given task name.
It does this by calling the create_agent function in agents.py of the given task. If
create_agents function does not exist, it just looks for the teacher (agent) class
defined by the task name directly. (This saves the task creator bothering to define
the create_agents function when it is not needed.)
"""
my_module = load_task_module(opt['task'])
try:
# Tries to call the create_agent function in agents.py
task_agents = my_module.create_agents(opt) # type: ignore
except AttributeError:
# Create_agent not found, so try to create the teacher directly.
return create_task_agent_from_taskname(opt)
if type(task_agents) != list:
task_agents = [task_agents]
return task_agents
def _create_task_agents(opt: Opt):
"""
Create task agent(s) for the given task name.
It does this by calling the create_agent function in agents.py of the given task. If
create_agents function does not exist, it just looks for the teacher (agent) class
defined by the task name directly. (This saves the task creator bothering to define
the create_agents function when it is not needed.)
"""
if opt.get('interactive_task', False) or opt.get('selfchat_task', False):
# do not need task agents in interactive or self chat settings
return []
try:
# Tries to call the create_agent function in agents.py
my_module = load_task_module(opt['task'])
task_agents = my_module.create_agents(opt) # type: ignore
except (ModuleNotFoundError, AttributeError):
# Create_agent not found, so try to create the teacher directly.
return create_task_agent_from_taskname(opt)
if type(task_agents) != list:
task_agents = [task_agents]
return task_agents
def test_load_task(self):
task_module = load_task_module(OPTIONS['task'])
self.assertEqual(task_module, c2agents)