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# to you 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
#
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# KIND, either express or implied.  See the License for the
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import logging
from typing import Any, Dict, List, Optional

from flask import current_app
from sqlalchemy.exc import SQLAlchemyError

from superset.connectors.sqla.models import SqlaTable, SqlMetric, TableColumn
from superset.dao.base import BaseDAO
from superset.extensions import db
from superset.models.core import Database
from superset.models.dashboard import Dashboard
from superset.models.slice import Slice
from superset.views.base import DatasourceFilter

logger = logging.getLogger(__name__)


class DatasetDAO(BaseDAO):
    model_cls = SqlaTable
    base_filter = DatasourceFilter

    @staticmethod
    def get_owner_by_id(owner_id: int) -> Optional[object]:
        return (
            db.session.query(current_app.appbuilder.sm.user_model)
            .filter_by(id=owner_id)
            .one_or_none()
        )

    @staticmethod
    def get_database_by_id(database_id: int) -> Optional[Database]:
        try:
            return db.session.query(Database).filter_by(id=database_id).one_or_none()
        except SQLAlchemyError as ex:  # pragma: no cover
            logger.error("Could not get database by id: %s", str(ex))
            return None

    @staticmethod
    def get_related_objects(database_id: int) -> Dict[str, Any]:
        charts = (
            db.session.query(Slice)
            .filter(
                Slice.datasource_id == database_id, Slice.datasource_type == "table"
            )
            .all()
        )
        chart_ids = [chart.id for chart in charts]

        dashboards = (
            (
                db.session.query(Dashboard)
                .join(Dashboard.slices)
                .filter(Slice.id.in_(chart_ids))
            )
            .distinct()
            .all()
        )
        return dict(charts=charts, dashboards=dashboards)

    @staticmethod
    def validate_table_exists(database: Database, table_name: str, schema: str) -> bool:
        try:
            database.get_table(table_name, schema=schema)
            return True
        except SQLAlchemyError as ex:  # pragma: no cover
            logger.error("Got an error %s validating table: %s", str(ex), table_name)
            return False

    @staticmethod
    def validate_uniqueness(database_id: int, name: str) -> bool:
        dataset_query = db.session.query(SqlaTable).filter(
            SqlaTable.table_name == name, SqlaTable.database_id == database_id
        )
        return not db.session.query(dataset_query.exists()).scalar()

    @staticmethod
    def validate_update_uniqueness(
        database_id: int, dataset_id: int, name: str
    ) -> bool:
        dataset_query = db.session.query(SqlaTable).filter(
            SqlaTable.table_name == name,
            SqlaTable.database_id == database_id,
            SqlaTable.id != dataset_id,
        )
        return not db.session.query(dataset_query.exists()).scalar()

    @staticmethod
    def validate_columns_exist(dataset_id: int, columns_ids: List[int]) -> bool:
        dataset_query = (
            db.session.query(TableColumn.id).filter(
                TableColumn.table_id == dataset_id, TableColumn.id.in_(columns_ids)
            )
        ).all()
        return len(columns_ids) == len(dataset_query)

    @staticmethod
    def validate_columns_uniqueness(dataset_id: int, columns_names: List[str]) -> bool:
        dataset_query = (
            db.session.query(TableColumn.id).filter(
                TableColumn.table_id == dataset_id,
                TableColumn.column_name.in_(columns_names),
            )
        ).all()
        return len(dataset_query) == 0

    @staticmethod
    def validate_metrics_exist(dataset_id: int, metrics_ids: List[int]) -> bool:
        dataset_query = (
            db.session.query(SqlMetric.id).filter(
                SqlMetric.table_id == dataset_id, SqlMetric.id.in_(metrics_ids)
            )
        ).all()
        return len(metrics_ids) == len(dataset_query)

    @staticmethod
    def validate_metrics_uniqueness(dataset_id: int, metrics_names: List[str]) -> bool:
        dataset_query = (
            db.session.query(SqlMetric.id).filter(
                SqlMetric.table_id == dataset_id,
                SqlMetric.metric_name.in_(metrics_names),
            )
        ).all()
        return len(dataset_query) == 0

    @classmethod
    def update(
        cls, model: SqlaTable, properties: Dict[str, Any], commit: bool = True
    ) -> Optional[SqlaTable]:
        """
        Updates a Dataset model on the metadata DB
        """
        if "columns" in properties:
            new_columns = list()
            for column in properties.get("columns", []):
                if column.get("id"):
                    column_obj = db.session.query(TableColumn).get(column.get("id"))
                    column_obj = DatasetDAO.update_column(
                        column_obj, column, commit=commit
                    )
                else:
                    column_obj = DatasetDAO.create_column(column, commit=commit)
                new_columns.append(column_obj)
            properties["columns"] = new_columns

        if "metrics" in properties:
            new_metrics = list()
            for metric in properties.get("metrics", []):
                if metric.get("id"):
                    metric_obj = db.session.query(SqlMetric).get(metric.get("id"))
                    metric_obj = DatasetDAO.update_metric(
                        metric_obj, metric, commit=commit
                    )
                else:
                    metric_obj = DatasetDAO.create_metric(metric, commit=commit)
                new_metrics.append(metric_obj)
            properties["metrics"] = new_metrics

        return super().update(model, properties, commit=commit)

    @classmethod
    def update_column(
        cls, model: TableColumn, properties: Dict[str, Any], commit: bool = True
    ) -> Optional[TableColumn]:
        return DatasetColumnDAO.update(model, properties, commit=commit)

    @classmethod
    def create_column(
        cls, properties: Dict[str, Any], commit: bool = True
    ) -> Optional[TableColumn]:
        """
        Creates a Dataset model on the metadata DB
        """
        return DatasetColumnDAO.create(properties, commit=commit)

    @classmethod
    def update_metric(
        cls, model: SqlMetric, properties: Dict[str, Any], commit: bool = True
    ) -> Optional[SqlMetric]:
        return DatasetMetricDAO.update(model, properties, commit=commit)

    @classmethod
    def create_metric(
        cls, properties: Dict[str, Any], commit: bool = True
    ) -> Optional[SqlMetric]:
        """
        Creates a Dataset model on the metadata DB
        """
        return DatasetMetricDAO.create(properties, commit=commit)


class DatasetColumnDAO(BaseDAO):
    model_cls = TableColumn


class DatasetMetricDAO(BaseDAO):
    model_cls = SqlMetric