# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# 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
#
# 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.
import json
import textwrap
from typing import Dict, List, Tuple, Union

import pandas as pd
from sqlalchemy import DateTime, String
from sqlalchemy.sql import column

from superset import db, security_manager
from superset.connectors.base.models import BaseDatasource
from superset.connectors.sqla.models import SqlMetric, TableColumn
from superset.exceptions import NoDataException
from superset.models.core import Database
from superset.models.dashboard import Dashboard
from superset.models.slice import Slice
from superset.utils.core import get_example_database

from .helpers import (
    config,
    get_example_data,
    get_slice_json,
    merge_slice,
    misc_dash_slices,
    TBL,
    update_slice_ids,
)

admin = security_manager.find_user("admin")
if admin is None:
    raise NoDataException(
        "Admin user does not exist. "
        "Please, check if test users are properly loaded "
        "(`superset load_test_users`)."
    )


def gen_filter(
    subject: str, comparator: str, operator: str = "=="
) -> Dict[str, Union[bool, str]]:
    return {
        "clause": "WHERE",
        "comparator": comparator,
        "expressionType": "SIMPLE",
        "operator": operator,
        "subject": subject,
    }


def load_data(tbl_name: str, database: Database, sample: bool = False) -> None:
    pdf = pd.read_json(get_example_data("birth_names.json.gz"))
    # TODO(bkyryliuk): move load examples data into the pytest fixture
    if database.backend == "presto":
        pdf.ds = pd.to_datetime(pdf.ds, unit="ms")
        pdf.ds = pdf.ds.dt.strftime("%Y-%m-%d %H:%M%:%S")
    else:
        pdf.ds = pd.to_datetime(pdf.ds, unit="ms")
    pdf = pdf.head(100) if sample else pdf

    pdf.to_sql(
        tbl_name,
        database.get_sqla_engine(),
        if_exists="replace",
        chunksize=500,
        dtype={
            # TODO(bkyryliuk): use TIMESTAMP type for presto
            "ds": DateTime if database.backend != "presto" else String(255),
            "gender": String(16),
            "state": String(10),
            "name": String(255),
        },
        method="multi",
        index=False,
    )
    print("Done loading table!")
    print("-" * 80)


def load_birth_names(
    only_metadata: bool = False, force: bool = False, sample: bool = False
) -> None:
    """Loading birth name dataset from a zip file in the repo"""
    # pylint: disable=too-many-locals
    tbl_name = "birth_names"
    database = get_example_database()
    table_exists = database.has_table_by_name(tbl_name)

    if not only_metadata and (not table_exists or force):
        load_data(tbl_name, database, sample=sample)

    obj = db.session.query(TBL).filter_by(table_name=tbl_name).first()
    if not obj:
        print(f"Creating table [{tbl_name}] reference")
        obj = TBL(table_name=tbl_name)
        db.session.add(obj)
    obj.main_dttm_col = "ds"
    obj.database = database
    obj.filter_select_enabled = True
    obj.fetch_metadata()

    if not any(col.column_name == "num_california" for col in obj.columns):
        col_state = str(column("state").compile(db.engine))
        col_num = str(column("num").compile(db.engine))
        obj.columns.append(
            TableColumn(
                column_name="num_california",
                expression=f"CASE WHEN {col_state} = 'CA' THEN {col_num} ELSE 0 END",
            )
        )

    if not any(col.metric_name == "sum__num" for col in obj.metrics):
        col = str(column("num").compile(db.engine))
        obj.metrics.append(SqlMetric(metric_name="sum__num", expression=f"SUM({col})"))

    db.session.commit()

    slices, _ = create_slices(obj)
    create_dashboard(slices)


def create_slices(tbl: BaseDatasource) -> Tuple[List[Slice], List[Slice]]:
    metrics = [
        {
            "expressionType": "SIMPLE",
            "column": {"column_name": "num", "type": "BIGINT"},
            "aggregate": "SUM",
            "label": "Births",
            "optionName": "metric_11",
        }
    ]
    metric = "sum__num"

    defaults = {
        "compare_lag": "10",
        "compare_suffix": "o10Y",
        "limit": "25",
        "time_range": "No filter",
        "time_range_endpoints": ["inclusive", "exclusive"],
        "granularity_sqla": "ds",
        "groupby": [],
        "row_limit": config["ROW_LIMIT"],
        "since": "100 years ago",
        "until": "now",
        "viz_type": "table",
        "markup_type": "markdown",
    }

    slice_props = dict(
        datasource_id=tbl.id, datasource_type="table", owners=[admin], created_by=admin
    )

    print("Creating some slices")
    slices = [
        Slice(
            **slice_props,
            slice_name="Participants",
            viz_type="big_number",
            params=get_slice_json(
                defaults,
                viz_type="big_number",
                granularity_sqla="ds",
                compare_lag="5",
                compare_suffix="over 5Y",
                metric=metric,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Genders",
            viz_type="pie",
            params=get_slice_json(
                defaults, viz_type="pie", groupby=["gender"], metric=metric
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Trends",
            viz_type="line",
            params=get_slice_json(
                defaults,
                viz_type="line",
                groupby=["name"],
                granularity_sqla="ds",
                rich_tooltip=True,
                show_legend=True,
                metrics=metrics,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Genders by State",
            viz_type="dist_bar",
            params=get_slice_json(
                defaults,
                adhoc_filters=[
                    {
                        "clause": "WHERE",
                        "expressionType": "SIMPLE",
                        "filterOptionName": "2745eae5",
                        "comparator": ["other"],
                        "operator": "NOT IN",
                        "subject": "state",
                    }
                ],
                viz_type="dist_bar",
                metrics=[
                    {
                        "expressionType": "SIMPLE",
                        "column": {"column_name": "sum_boys", "type": "BIGINT(20)"},
                        "aggregate": "SUM",
                        "label": "Boys",
                        "optionName": "metric_11",
                    },
                    {
                        "expressionType": "SIMPLE",
                        "column": {"column_name": "sum_girls", "type": "BIGINT(20)"},
                        "aggregate": "SUM",
                        "label": "Girls",
                        "optionName": "metric_12",
                    },
                ],
                groupby=["state"],
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Girls",
            viz_type="table",
            params=get_slice_json(
                defaults,
                groupby=["name"],
                adhoc_filters=[gen_filter("gender", "girl")],
                row_limit=50,
                timeseries_limit_metric="sum__num",
                metrics=metrics,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Girl Name Cloud",
            viz_type="word_cloud",
            params=get_slice_json(
                defaults,
                viz_type="word_cloud",
                size_from="10",
                series="name",
                size_to="70",
                rotation="square",
                limit="100",
                adhoc_filters=[gen_filter("gender", "girl")],
                metric=metric,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Boys",
            viz_type="table",
            params=get_slice_json(
                defaults,
                groupby=["name"],
                adhoc_filters=[gen_filter("gender", "boy")],
                row_limit=50,
                metrics=metrics,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Boy Name Cloud",
            viz_type="word_cloud",
            params=get_slice_json(
                defaults,
                viz_type="word_cloud",
                size_from="10",
                series="name",
                size_to="70",
                rotation="square",
                limit="100",
                adhoc_filters=[gen_filter("gender", "boy")],
                metric=metric,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Top 10 Girl Name Share",
            viz_type="area",
            params=get_slice_json(
                defaults,
                adhoc_filters=[gen_filter("gender", "girl")],
                comparison_type="values",
                groupby=["name"],
                limit=10,
                stacked_style="expand",
                time_grain_sqla="P1D",
                viz_type="area",
                x_axis_forma="smart_date",
                metrics=metrics,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Top 10 Boy Name Share",
            viz_type="area",
            params=get_slice_json(
                defaults,
                adhoc_filters=[gen_filter("gender", "boy")],
                comparison_type="values",
                groupby=["name"],
                limit=10,
                stacked_style="expand",
                time_grain_sqla="P1D",
                viz_type="area",
                x_axis_forma="smart_date",
                metrics=metrics,
            ),
        ),
    ]
    misc_slices = [
        Slice(
            **slice_props,
            slice_name="Average and Sum Trends",
            viz_type="dual_line",
            params=get_slice_json(
                defaults,
                viz_type="dual_line",
                metric={
                    "expressionType": "SIMPLE",
                    "column": {"column_name": "num", "type": "BIGINT(20)"},
                    "aggregate": "AVG",
                    "label": "AVG(num)",
                    "optionName": "metric_vgops097wej_g8uff99zhk7",
                },
                metric_2="sum__num",
                granularity_sqla="ds",
                metrics=metrics,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Num Births Trend",
            viz_type="line",
            params=get_slice_json(defaults, viz_type="line", metrics=metrics),
        ),
        Slice(
            **slice_props,
            slice_name="Daily Totals",
            viz_type="table",
            params=get_slice_json(
                defaults,
                groupby=["ds"],
                since="40 years ago",
                until="now",
                viz_type="table",
                metrics=metrics,
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Number of California Births",
            viz_type="big_number_total",
            params=get_slice_json(
                defaults,
                metric={
                    "expressionType": "SIMPLE",
                    "column": {
                        "column_name": "num_california",
                        "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
                    },
                    "aggregate": "SUM",
                    "label": "SUM(num_california)",
                },
                viz_type="big_number_total",
                granularity_sqla="ds",
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Top 10 California Names Timeseries",
            viz_type="line",
            params=get_slice_json(
                defaults,
                metrics=[
                    {
                        "expressionType": "SIMPLE",
                        "column": {
                            "column_name": "num_california",
                            "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
                        },
                        "aggregate": "SUM",
                        "label": "SUM(num_california)",
                    }
                ],
                viz_type="line",
                granularity_sqla="ds",
                groupby=["name"],
                timeseries_limit_metric={
                    "expressionType": "SIMPLE",
                    "column": {
                        "column_name": "num_california",
                        "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
                    },
                    "aggregate": "SUM",
                    "label": "SUM(num_california)",
                },
                limit="10",
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Names Sorted by Num in California",
            viz_type="table",
            params=get_slice_json(
                defaults,
                metrics=metrics,
                groupby=["name"],
                row_limit=50,
                timeseries_limit_metric={
                    "expressionType": "SIMPLE",
                    "column": {
                        "column_name": "num_california",
                        "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
                    },
                    "aggregate": "SUM",
                    "label": "SUM(num_california)",
                },
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Number of Girls",
            viz_type="big_number_total",
            params=get_slice_json(
                defaults,
                metric=metric,
                viz_type="big_number_total",
                granularity_sqla="ds",
                adhoc_filters=[gen_filter("gender", "girl")],
                subheader="total female participants",
            ),
        ),
        Slice(
            **slice_props,
            slice_name="Pivot Table",
            viz_type="pivot_table",
            params=get_slice_json(
                defaults,
                viz_type="pivot_table",
                groupby=["name"],
                columns=["state"],
                metrics=metrics,
            ),
        ),
    ]
    for slc in slices:
        merge_slice(slc)

    for slc in misc_slices:
        merge_slice(slc)
        misc_dash_slices.add(slc.slice_name)

    return slices, misc_slices


def create_dashboard(slices: List[Slice]) -> None:
    print("Creating a dashboard")

    dash = db.session.query(Dashboard).filter_by(slug="births").first()
    if not dash:
        dash = Dashboard()
        dash.owners = [admin]
        dash.created_by = admin
        db.session.add(dash)

    dash.published = True
    dash.json_metadata = textwrap.dedent(
        """\
    {
        "label_colors": {
            "Girls": "#FF69B4",
            "Boys": "#ADD8E6",
            "girl": "#FF69B4",
            "boy": "#ADD8E6"
        }
    }"""
    )
    pos = json.loads(
        textwrap.dedent(
            # pylint: disable=line-too-long
            """\
        {
          "CHART-6GdlekVise": {
            "children": [],
            "id": "CHART-6GdlekVise",
            "meta": {
              "chartId": 5547,
              "height": 50,
              "sliceName": "Top 10 Girl Name Share",
              "width": 5
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-eh0w37bWbR"
            ],
            "type": "CHART"
          },
          "CHART-6n9jxb30JG": {
            "children": [],
            "id": "CHART-6n9jxb30JG",
            "meta": {
              "chartId": 5540,
              "height": 36,
              "sliceName": "Genders by State",
              "width": 5
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW--EyBZQlDi"
            ],
            "type": "CHART"
          },
          "CHART-Jj9qh1ol-N": {
            "children": [],
            "id": "CHART-Jj9qh1ol-N",
            "meta": {
              "chartId": 5545,
              "height": 50,
              "sliceName": "Boy Name Cloud",
              "width": 4
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-kzWtcvo8R1"
            ],
            "type": "CHART"
          },
          "CHART-ODvantb_bF": {
            "children": [],
            "id": "CHART-ODvantb_bF",
            "meta": {
              "chartId": 5548,
              "height": 50,
              "sliceName": "Top 10 Boy Name Share",
              "width": 5
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-kzWtcvo8R1"
            ],
            "type": "CHART"
          },
          "CHART-PAXUUqwmX9": {
            "children": [],
            "id": "CHART-PAXUUqwmX9",
            "meta": {
              "chartId": 5538,
              "height": 34,
              "sliceName": "Genders",
              "width": 3
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-2n0XgiHDgs"
            ],
            "type": "CHART"
          },
          "CHART-_T6n_K9iQN": {
            "children": [],
            "id": "CHART-_T6n_K9iQN",
            "meta": {
              "chartId": 5539,
              "height": 36,
              "sliceName": "Trends",
              "width": 7
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW--EyBZQlDi"
            ],
            "type": "CHART"
          },
          "CHART-eNY0tcE_ic": {
            "children": [],
            "id": "CHART-eNY0tcE_ic",
            "meta": {
              "chartId": 5537,
              "height": 34,
              "sliceName": "Participants",
              "width": 3
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-2n0XgiHDgs"
            ],
            "type": "CHART"
          },
          "CHART-g075mMgyYb": {
            "children": [],
            "id": "CHART-g075mMgyYb",
            "meta": {
              "chartId": 5541,
              "height": 50,
              "sliceName": "Girls",
              "width": 3
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-eh0w37bWbR"
            ],
            "type": "CHART"
          },
          "CHART-n-zGGE6S1y": {
            "children": [],
            "id": "CHART-n-zGGE6S1y",
            "meta": {
              "chartId": 5542,
              "height": 50,
              "sliceName": "Girl Name Cloud",
              "width": 4
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-eh0w37bWbR"
            ],
            "type": "CHART"
          },
          "CHART-vJIPjmcbD3": {
            "children": [],
            "id": "CHART-vJIPjmcbD3",
            "meta": {
              "chartId": 5543,
              "height": 50,
              "sliceName": "Boys",
              "width": 3
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-kzWtcvo8R1"
            ],
            "type": "CHART"
          },
          "DASHBOARD_VERSION_KEY": "v2",
          "GRID_ID": {
            "children": [
              "ROW-2n0XgiHDgs",
              "ROW--EyBZQlDi",
              "ROW-eh0w37bWbR",
              "ROW-kzWtcvo8R1"
            ],
            "id": "GRID_ID",
            "parents": [
              "ROOT_ID"
            ],
            "type": "GRID"
          },
          "HEADER_ID": {
            "id": "HEADER_ID",
            "meta": {
              "text": "Births"
            },
            "type": "HEADER"
          },
          "MARKDOWN-zaflB60tbC": {
            "children": [],
            "id": "MARKDOWN-zaflB60tbC",
            "meta": {
              "code": "<div style=\\"text-align:center\\">  <h1>Birth Names Dashboard</h1>  <img src=\\"/static/assets/images/babies.png\\" style=\\"width:50%;\\"></div>",
              "height": 34,
              "width": 6
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID",
              "ROW-2n0XgiHDgs"
            ],
            "type": "MARKDOWN"
          },
          "ROOT_ID": {
            "children": [
              "GRID_ID"
            ],
            "id": "ROOT_ID",
            "type": "ROOT"
          },
          "ROW--EyBZQlDi": {
            "children": [
              "CHART-_T6n_K9iQN",
              "CHART-6n9jxb30JG"
            ],
            "id": "ROW--EyBZQlDi",
            "meta": {
              "background": "BACKGROUND_TRANSPARENT"
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID"
            ],
            "type": "ROW"
          },
          "ROW-2n0XgiHDgs": {
            "children": [
              "CHART-eNY0tcE_ic",
              "MARKDOWN-zaflB60tbC",
              "CHART-PAXUUqwmX9"
            ],
            "id": "ROW-2n0XgiHDgs",
            "meta": {
              "background": "BACKGROUND_TRANSPARENT"
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID"
            ],
            "type": "ROW"
          },
          "ROW-eh0w37bWbR": {
            "children": [
              "CHART-g075mMgyYb",
              "CHART-n-zGGE6S1y",
              "CHART-6GdlekVise"
            ],
            "id": "ROW-eh0w37bWbR",
            "meta": {
              "background": "BACKGROUND_TRANSPARENT"
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID"
            ],
            "type": "ROW"
          },
          "ROW-kzWtcvo8R1": {
            "children": [
              "CHART-vJIPjmcbD3",
              "CHART-Jj9qh1ol-N",
              "CHART-ODvantb_bF"
            ],
            "id": "ROW-kzWtcvo8R1",
            "meta": {
              "background": "BACKGROUND_TRANSPARENT"
            },
            "parents": [
              "ROOT_ID",
              "GRID_ID"
            ],
            "type": "ROW"
          }
        }
        """  # pylint: enable=line-too-long
        )
    )
    # dashboard v2 doesn't allow add markup slice
    dash.slices = [slc for slc in slices if slc.viz_type != "markup"]
    update_slice_ids(pos, dash.slices)
    dash.dashboard_title = "USA Births Names"
    dash.position_json = json.dumps(pos, indent=4)
    dash.slug = "births"
    db.session.commit()