import argparse

parser = argparse.ArgumentParser()


parser.add_argument(
    '--verbose',
    '-v',
    help="Increase verbosity. Add multiple times to increase further.",
    action='count', default=0
)
parser.add_argument(
    '--remote-log-addr',
    help="Connect to a remote logger like cutelog. Specify the ip",
    type=str,
)
parser.add_argument(
    '--remote-log-port',
    help="Connect to a remote logger like cutelog. Specify the port",
    type=int,
    default=19996
)

# parser.add_argument('--foo')
inference_parser = parser.add_argument_group('inference server')
connection_parser = parser.add_argument_group('connection')

inference_parser.add_argument("--model_dir",
                    help="directory with the model to use for inference",
                    type=str, # TODO: make into Path
                    default='./experiments/pedestrians/models/models_04_Oct_2023_21_04_48_eth_vel_ar3')

inference_parser.add_argument("--conf",
                    help="path to json config file for hyperparameters, relative to model_dir",
                    type=str,
                    default='config.json')

# Model Parameters (hyperparameters)
inference_parser.add_argument("--offline_scene_graph",
                    help="whether to precompute the scene graphs offline, options are 'no' and 'yes'",
                    type=str,
                    default='yes')

inference_parser.add_argument("--dynamic_edges",
                    help="whether to use dynamic edges or not, options are 'no' and 'yes'",
                    type=str,
                    default='yes')

inference_parser.add_argument("--edge_state_combine_method",
                    help="the method to use for combining edges of the same type",
                    type=str,
                    default='sum')

inference_parser.add_argument("--edge_influence_combine_method",
                    help="the method to use for combining edge influences",
                    type=str,
                    default='attention')

inference_parser.add_argument('--edge_addition_filter',
                    nargs='+',
                    help="what scaling to use for edges as they're created",
                    type=float,
                    default=[0.25, 0.5, 0.75, 1.0]) # We don't automatically pad left with 0.0, if you want a sharp
                                                    # and short edge addition, then you need to have a 0.0 at the
                                                    # beginning, e.g. [0.0, 1.0].

inference_parser.add_argument('--edge_removal_filter',
                    nargs='+',
                    help="what scaling to use for edges as they're removed",
                    type=float,
                    default=[1.0, 0.0])  # We don't automatically pad right with 0.0, if you want a sharp drop off like
                                         # the default, then you need to have a 0.0 at the end.


inference_parser.add_argument('--incl_robot_node',
                    help="whether to include a robot node in the graph or simply model all agents",
                    action='store_true')

inference_parser.add_argument('--map_encoding',
                    help="Whether to use map encoding or not",
                    action='store_true')

inference_parser.add_argument('--no_edge_encoding',
                    help="Whether to use neighbors edge encoding",
                    action='store_true')


inference_parser.add_argument('--batch_size',
                    help='training batch size',
                    type=int,
                    default=256)

inference_parser.add_argument('--k_eval',
                    help='how many samples to take during evaluation',
                    type=int,
                    default=25)

# Data Parameters
inference_parser.add_argument("--eval_data_dict",
                    help="what file to load for evaluation data (WHEN NOT USING LIVE DATA)",
                    type=str,
                    default='./experiments/processed/eth_test.pkl')

inference_parser.add_argument("--output_dir",
                    help="what dir to save output (i.e., saved models, logs, etc) (WHEN NOT USING LIVE OUTPUT)",
                    type=str,
                    default='../experiments/pedestrians/OUT/test_inference')


# inference_parser.add_argument('--device',
#                     help='what device to perform training on',
#                     type=str,
#                     default='cuda:0')

inference_parser.add_argument("--eval_device",
                    help="what device to use during inference",
                    type=str,
                    default="cpu")


inference_parser.add_argument('--seed',
                    help='manual seed to use, default is 123',
                    type=int,
                    default=123)


# Internal connections.

connection_parser.add_argument('--zmq-trajectory-addr',
                    help='Manually specity communication addr for the trajectory messages',
                    type=str,
                    default="ipc:///tmp/feeds/traj")

connection_parser.add_argument('--zmq-camera-stream-addr',
                    help='Manually specity communication addr for the camera stream messages',
                    type=str,
                    default="ipc:///tmp/feeds/img")

connection_parser.add_argument('--zmq-prediction-addr',
                    help='Manually specity communication addr for the prediction messages',
                    type=str,
                    default="ipc:///tmp/feeds/preds")


connection_parser.add_argument('--ws-port',
                    help='Port to listen for incomming websocket connections. Also serves the testing html-page.',
                    type=int,
                    default=8888)

connection_parser.add_argument('--bypass-prediction',
                    help='For debugging purpose: websocket input immediately to output',
                    action='store_true')