How to use the parlai.core.params.ParlaiParser function in parlai

def test_qangaroo(self):
        from parlai.core.params import ParlaiParser
        from parlai.tasks.qangaroo.agents import DefaultTeacher

        opt = ParlaiParser().parse_args(args=self.args)
        opt['datatype'] = 'train'
        teacher = DefaultTeacher(opt)
        reply = teacher.act()
        check(opt, reply)

        shutil.rmtree(self.TMP_PATH)

def test_file_inference(self):
        """
        Test --inference with older model files.
        """
        testing_utils.download_unittest_models()
        with testing_utils.capture_output():
            pp = ParlaiParser(True, True)
            opt = pp.parse_args(
                ['--model-file', 'zoo:unittest/transformer_generator2/model']
            )
            agent = create_agent(opt, True)
            self.assertEqual(agent.opt['inference'], 'greedy')

        with testing_utils.capture_output():
            pp = ParlaiParser(True, True)
            opt = pp.parse_args(
                [
                    '--model-file',
                    'zoo:unittest/transformer_generator2/model',
                    '--beam-size',
                    '5',
                ],
                print_args=False,
            )
            agent = create_agent(opt, True)
            self.assertEqual(agent.opt['inference'], 'beam')

def setup_args(parser=None):
    if parser is None:
        parser = ParlaiParser(True, True, 'Evaluate a model')
    parser.add_pytorch_datateacher_args()
    # Get command line arguments
    parser.add_argument(
        '-rf',
        '--report-filename',
        type=str,
        default='',
        help='Saves a json file of the evaluation report either as an '
        'extension to the model-file (if begins with a ".") or a whole '
        'file path. Set to the empty string to not save at all.',
    )
    parser.add_argument(
        '--save-world-logs',
        type='bool',
        default=False,
        help='Saves a jsonl file containing all of the task examples and '

# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
from parlai.core.params import ParlaiParser
from core import manage_hit

argparser = ParlaiParser(False, False)
argparser.add_parlai_data_path()
argparser.add_mturk_args()

opt = argparser.parse_args()

task_module_name = 'parlai.mturk.tasks.' + opt['task']
Agent = __import__(task_module_name+'.agents', fromlist=['']).default_agent_class
task_config = __import__(task_module_name+'.task_config', fromlist=['']).task_config

print("Creating HIT tasks for "+task_module_name+" ...")

manage_hit.create_hits(
	opt=opt,
	task_config=task_config,
	task_module_name=task_module_name,
	bot=Agent(opt=opt),

def setup_args(parser=None):
    if parser is None:
        parser = ParlaiParser(True, True, 'compute statistics from model predictions')
    parser.add_pytorch_datateacher_args()
    DictionaryAgent.add_cmdline_args(parser)
    # Get command line arguments
    parser.add_argument('-ne', '--num-examples', type=int, default=-1)
    parser.add_argument('-ltim', '--log-every-n-secs', type=float, default=2)
    parser.add_argument(
        '-ed',
        '--external-dict',
        type=str,
        default=None,
        help='External dictionary for stat computation',
    )
    parser.add_argument(
        '-fb',
        '--freq-bins',
        type=str,

def main():
    completed_workers = []
    argparser = ParlaiParser(False, False)
    argparser.add_parlai_data_path()
    argparser.add_mturk_args()
    opt = argparser.parse_args()
    opt['task'] = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
    opt.update(task_config)

    mturk_agent_id = 'Worker'
    mturk_manager = MTurkManager(opt=opt, mturk_agent_ids=[mturk_agent_id])
    mturk_manager.setup_server()
    qual_name = 'ParlAIExcludeQual{}t{}'.format(
        random.randint(10000, 99999), random.randint(10000, 99999)
    )
    qual_desc = (
        'Qualification for a worker not correctly completing the '
        'first iteration of a task. Used to filter to different task pools.'
    )

def interactive(opt, print_parser=None):
    if print_parser is not None:
        if print_parser is True and isinstance(opt, ParlaiParser):
            print_parser = opt
        elif print_parser is False:
            print_parser = None
    if isinstance(opt, ParlaiParser):
        print('[ Deprecated Warning: interactive should be passed opt not Parser ]')
        opt = opt.parse_args()
    opt['task'] = 'parlai.agents.local_human.local_human:LocalHumanAgent'
    # Set the task to dialog, since that's the type we want its outputs to be
    print("Warning: hardcoding history_size=2")
    opt['override'] = {
        'no_cuda': True,
        'subtasks': ['dialog', 'sentiment'],
        'interactive': True,
        'prev_response_filter': True,
        'person_tokens': True,
        'history_size': 2,

def interactive(opt, print_parser=None):
    if print_parser is not None:
        if print_parser is True and isinstance(opt, ParlaiParser):
            print_parser = opt
        elif print_parser is False:
            print_parser = None
    if isinstance(opt, ParlaiParser):
        print('[ Deprecated Warning: interactive should be passed opt not Parser ]')
        opt = opt.parse_args()
    opt['task'] = 'parlai.agents.local_human.local_human:LocalHumanAgent'
    # Create model and assign it to the specified task
    agent = create_agent(opt, requireModelExists=True)
    world = create_task(opt, agent)
    if print_parser:
        # Show arguments after loading model
        print_parser.opt = agent.opt
        print_parser.print_args()

    # Create ConvAI2 data so we can assign personas.
    convai2_opt = opt.copy()
    convai2_opt['task'] = 'convai2:both'
    convai2_agent = RepeatLabelAgent(convai2_opt)
    convai2_world = create_task(convai2_opt, convai2_agent)

def setup_args(parser=None):
    if parser is None:
        parser = ParlaiParser(True, True, 'Interactive chat with a model')
    parser.add_argument('-d', '--display-examples', type='bool', default=False)
    parser.add_argument(
        '--display-prettify',
        type='bool',
        default=False,
        help='Set to use a prettytable when displaying '
        'examples with text candidates',
    )
    parser.add_argument(
        '--display-ignore-fields',
        type=str,
        default='label_candidates,text_candidates',
        help='Do not display these fields',
    )
    parser.set_defaults(model_file='models:convai2/kvmemnn/model')
    LocalHumanAgent.add_cmdline_args(parser)

def setup_args(parser=None):
    if parser is None:
        parser = ParlaiParser(True, True, 'Lint for ParlAI tasks')
    # Get command line arguments
    parser.add_argument('-ltim', '--log-every-n-secs', type=float, default=2)
    parser.add_argument('-d', '--display-examples', type='bool', default=False)
    parser.set_defaults(datatype='train:stream')
    return parser