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#
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import random
from typing import Dict, Set

import pandas as pd
import pytest
from sqlalchemy import column, Float, String

from superset import db
from superset.connectors.sqla.models import SqlaTable, SqlMetric
from superset.models.slice import Slice
from superset.utils.core import get_example_default_schema
from superset.utils.database import get_example_database
from tests.integration_tests.dashboard_utils import create_slice, create_table_metadata
from tests.integration_tests.test_app import app

misc_dash_slices: Set[str] = set()


ENERGY_USAGE_TBL_NAME = "energy_usage"


@pytest.fixture(scope="session")
def load_energy_table_data():
    with app.app_context():
        database = get_example_database()
        df = _get_dataframe()
        df.to_sql(
            ENERGY_USAGE_TBL_NAME,
            database.get_sqla_engine(),
            if_exists="replace",
            chunksize=500,
            index=False,
            dtype={"source": String(255), "target": String(255), "value": Float()},
            method="multi",
            schema=get_example_default_schema(),
        )
    yield
    with app.app_context():
        engine = get_example_database().get_sqla_engine()
        engine.execute("DROP TABLE IF EXISTS energy_usage")


@pytest.fixture()
def load_energy_table_with_slice(load_energy_table_data):
    with app.app_context():
        _create_energy_table()
        yield
        _cleanup()


def _get_dataframe():
    data = _get_energy_data()
    return pd.DataFrame.from_dict(data)


def _create_energy_table():
    table = create_table_metadata(
        table_name=ENERGY_USAGE_TBL_NAME,
        database=get_example_database(),
        table_description="Energy consumption",
    )
    table.fetch_metadata()

    if not any(col.metric_name == "sum__value" for col in table.metrics):
        col = str(column("value").compile(db.engine))
        table.metrics.append(
            SqlMetric(metric_name="sum__value", expression=f"SUM({col})")
        )
    db.session.merge(table)
    db.session.commit()
    table.fetch_metadata()

    for slice_data in _get_energy_slices():
        _create_and_commit_energy_slice(
            table,
            slice_data["slice_title"],
            slice_data["viz_type"],
            slice_data["params"],
        )


def _create_and_commit_energy_slice(
    table: SqlaTable, title: str, viz_type: str, param: Dict[str, str]
):
    slice = create_slice(title, viz_type, table, param)
    existing_slice = (
        db.session.query(Slice).filter_by(slice_name=slice.slice_name).first()
    )
    if existing_slice:
        db.session.delete(existing_slice)
    db.session.add(slice)
    db.session.commit()
    return slice


def _cleanup() -> None:
    for slice_data in _get_energy_slices():
        slice = (
            db.session.query(Slice)
            .filter_by(slice_name=slice_data["slice_title"])
            .first()
        )
        db.session.delete(slice)

    metric = (
        db.session.query(SqlMetric).filter_by(metric_name="sum__value").one_or_none()
    )
    if metric:
        db.session.delete(metric)

    db.session.commit()


def _get_energy_data():
    data = []
    for i in range(85):
        data.append(
            {
                "source": f"energy_source{i}",
                "target": f"energy_target{i}",
                "value": random.uniform(0.1, 11.0),
            }
        )
    return data


def _get_energy_slices():
    return [
        {
            "slice_title": "Energy Sankey",
            "viz_type": "sankey",
            "params": {
                "collapsed_fieldsets": "",
                "groupby": ["source", "target"],
                "metric": "sum__value",
                "row_limit": "5000",
                "slice_name": "Energy Sankey",
                "viz_type": "sankey",
            },
        },
        {
            "slice_title": "Energy Force Layout",
            "viz_type": "graph_chart",
            "params": {
                "source": "source",
                "target": "target",
                "edgeLength": 400,
                "repulsion": 1000,
                "layout": "force",
                "metric": "sum__value",
                "row_limit": "5000",
                "slice_name": "Force",
                "viz_type": "graph_chart",
            },
        },
        {
            "slice_title": "Heatmap",
            "viz_type": "heatmap",
            "params": {
                "all_columns_x": "source",
                "all_columns_y": "target",
                "canvas_image_rendering": "pixelated",
                "collapsed_fieldsets": "",
                "linear_color_scheme": "blue_white_yellow",
                "metric": "sum__value",
                "normalize_across": "heatmap",
                "slice_name": "Heatmap",
                "viz_type": "heatmap",
                "xscale_interval": "1",
                "yscale_interval": "1",
            },
            "query_context": '{"datasource":{"id":12,"type":"table"},"force":false,"queries":[{"time_range":" : ","filters":[],"extras":{"time_grain_sqla":null,"having":"","having_druid":[],"where":""},"applied_time_extras":{},"columns":[],"metrics":[],"annotation_layers":[],"row_limit":5000,"timeseries_limit":0,"order_desc":true,"url_params":{},"custom_params":{},"custom_form_data":{}}],"result_format":"json","result_type":"full"}',
        },
    ]
