# Copyright 2019 The Kubeflow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import NamedTuple


def automl_create_model_for_tables(
    gcp_project_id: str,
    gcp_region: str,
    display_name: str,
    dataset_id: str,
    target_column_path: str = None,
    input_feature_column_paths: list = None,
    optimization_objective: str = 'MAXIMIZE_AU_PRC',
    train_budget_milli_node_hours: int = 1000,
) -> NamedTuple('Outputs', [('model_path', str), ('model_id', str), ('model_page_url', 'URI'),]):
    from google.cloud import automl
    client = automl.AutoMlClient()

    location_path = client.location_path(gcp_project_id, gcp_region)
    model_dict = {
        'display_name': display_name,
        'dataset_id': dataset_id,
        'tables_model_metadata': {
            'target_column_spec': automl.types.ColumnSpec(name=target_column_path),
            'input_feature_column_specs': [automl.types.ColumnSpec(name=path) for path in input_feature_column_paths] if input_feature_column_paths else None,
            'optimization_objective': optimization_objective,
            'train_budget_milli_node_hours': train_budget_milli_node_hours,
        },
    }

    create_model_response = client.create_model(location_path, model_dict)
    print('Create model operation: {}'.format(create_model_response.operation))
    result = create_model_response.result()
    print(result)
    model_name = result.name
    model_id = model_name.rsplit('/', 1)[-1]
    model_url = 'https://console.cloud.google.com/automl-tables/locations/{region}/datasets/{dataset_id};modelId={model_id};task=basic/train?project={project_id}'.format(
        project_id=gcp_project_id,
        region=gcp_region,
        dataset_id=dataset_id,
        model_id=model_id,
    )

    return (model_name, model_id, model_url)


if __name__ == '__main__':
    import kfp
    kfp.components.func_to_container_op(
        automl_create_model_for_tables,
        output_component_file='component.yaml',
        base_image='python:3.7',
        packages_to_install=['google-cloud-automl==0.4.0']
    )