import os
import shutil
from azureml.core import ScriptRunConfig


def run_remote_inference(test_experiment, compute_target, train_run,
                         test_dataset, target_column_name, inference_folder='./forecast'):
    # Create local directory to copy the model.pkl and forecsting_script.py files into.
    # These files will be uploaded to and executed on the compute instance.
    os.makedirs(inference_folder, exist_ok=True)
    shutil.copy('forecasting_script.py', inference_folder)

    train_run.download_file('outputs/model.pkl',
                            os.path.join(inference_folder, 'model.pkl'))

    inference_env = train_run.get_environment()

    config = ScriptRunConfig(source_directory=inference_folder,
                             script='forecasting_script.py',
                             arguments=['--target_column_name',
                                        target_column_name,
                                        '--test_dataset',
                                        test_dataset.as_named_input(test_dataset.name)],
                             compute_target=compute_target,
                             environment=inference_env)

    run = test_experiment.submit(config,
                                 tags={'training_run_id':
                                       train_run.id,
                                       'run_algorithm':
                                       train_run.properties['run_algorithm'],
                                       'valid_score':
                                       train_run.properties['score'],
                                       'primary_metric':
                                       train_run.properties['primary_metric']})

    run.log("run_algorithm", run.tags['run_algorithm'])
    return run