"""
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THE SOFTWARE CODE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
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"""
from azureml.core.run import Run
import os
import argparse
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib


def train_model(run, data, alpha):
    run.log("alpha", alpha)
    run.parent.log("alpha", alpha)
    reg = Ridge(alpha=alpha)
    reg.fit(data["train"]["X"], data["train"]["y"])
    preds = reg.predict(data["test"]["X"])
    run.log("mse", mean_squared_error(
        preds, data["test"]["y"]), description="Mean squared error metric")
    run.parent.log("mse", mean_squared_error(
        preds, data["test"]["y"]), description="Mean squared error metric")
    return reg


def main():
    print("Running train.py")

    parser = argparse.ArgumentParser("train")
    parser.add_argument(
        "--build_id",
        type=str,
        help="The build ID of the build triggering this pipeline run",
    )
    parser.add_argument(
        "--model_name",
        type=str,
        help="Name of the Model",
        default="sklearn_regression_model.pkl",
    )
    parser.add_argument(
        "--alpha",
        type=float,
        default=0.5,
        help=("Ridge regression regularization strength hyperparameter; "
              "must be a positive float.")
    )

    args = parser.parse_args()

    print("Argument [build_id]: %s" % args.build_id)
    print("Argument [model_name]: %s" % args.model_name)
    print("Argument [alpha]: %s" % args.alpha)

    model_name = args.model_name
    build_id = args.build_id
    alpha = args.alpha

    run = Run.get_context()

    X, y = load_diabetes(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=0)
    data = {"train": {"X": X_train, "y": y_train},
            "test": {"X": X_test, "y": y_test}}

    reg = train_model(run, data, alpha)

    joblib.dump(value=reg, filename=model_name)

    # upload model file explicitly into artifacts for parent run
    run.parent.upload_file(name="./outputs/" + model_name,
                           path_or_stream=model_name)
    print("Uploaded the model {} to experiment {}".format(
        model_name, run.experiment.name))
    dirpath = os.getcwd()
    print(dirpath)
    print("Following files are uploaded ")
    print(run.parent.get_file_names())

    # Add properties to identify this specific training run
    run.tag("BuildId", value=build_id)
    run.tag("run_type", value="train")
    print(f"tags now present for run: {run.tags}")

    run.complete()


if __name__ == '__main__':
    main()
