# 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.
# isort:skip_file
from datetime import datetime

import tests.test_app
from superset.dataframe import df_to_records
from superset.db_engine_specs import BaseEngineSpec
from superset.result_set import dedup, SupersetResultSet

from .base_tests import SupersetTestCase


class TestSupersetResultSet(SupersetTestCase):
    def test_dedup(self):
        self.assertEqual(dedup(["foo", "bar"]), ["foo", "bar"])
        self.assertEqual(
            dedup(["foo", "bar", "foo", "bar", "Foo"]),
            ["foo", "bar", "foo__1", "bar__1", "Foo"],
        )
        self.assertEqual(
            dedup(["foo", "bar", "bar", "bar", "Bar"]),
            ["foo", "bar", "bar__1", "bar__2", "Bar"],
        )
        self.assertEqual(
            dedup(["foo", "bar", "bar", "bar", "Bar"], case_sensitive=False),
            ["foo", "bar", "bar__1", "bar__2", "Bar__3"],
        )

    def test_get_columns_basic(self):
        data = [("a1", "b1", "c1"), ("a2", "b2", "c2")]
        cursor_descr = (("a", "string"), ("b", "string"), ("c", "string"))
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(
            results.columns,
            [
                {"is_date": False, "type": "STRING", "name": "a"},
                {"is_date": False, "type": "STRING", "name": "b"},
                {"is_date": False, "type": "STRING", "name": "c"},
            ],
        )

    def test_get_columns_with_int(self):
        data = [("a1", 1), ("a2", 2)]
        cursor_descr = (("a", "string"), ("b", "int"))
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(
            results.columns,
            [
                {"is_date": False, "type": "STRING", "name": "a"},
                {"is_date": False, "type": "INT", "name": "b"},
            ],
        )

    def test_get_columns_type_inference(self):
        data = [
            (1.2, 1, "foo", datetime(2018, 10, 19, 23, 39, 16, 660000), True),
            (3.14, 2, "bar", datetime(2019, 10, 19, 23, 39, 16, 660000), False),
        ]
        cursor_descr = (("a", None), ("b", None), ("c", None), ("d", None), ("e", None))
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(
            results.columns,
            [
                {"is_date": False, "type": "FLOAT", "name": "a"},
                {"is_date": False, "type": "INT", "name": "b"},
                {"is_date": False, "type": "STRING", "name": "c"},
                {"is_date": True, "type": "DATETIME", "name": "d"},
                {"is_date": False, "type": "BOOL", "name": "e"},
            ],
        )

    def test_is_date(self):
        data = [("a", 1), ("a", 2)]
        cursor_descr = (("a", "string"), ("a", "string"))
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.is_temporal("DATE"), True)
        self.assertEqual(results.is_temporal("DATETIME"), True)
        self.assertEqual(results.is_temporal("TIME"), True)
        self.assertEqual(results.is_temporal("TIMESTAMP"), True)
        self.assertEqual(results.is_temporal("STRING"), False)
        self.assertEqual(results.is_temporal(""), False)
        self.assertEqual(results.is_temporal(None), False)

    def test_dedup_with_data(self):
        data = [("a", 1), ("a", 2)]
        cursor_descr = (("a", "string"), ("a", "string"))
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        column_names = [col["name"] for col in results.columns]
        self.assertListEqual(column_names, ["a", "a__1"])

    def test_int64_with_missing_data(self):
        data = [(None,), (1239162456494753670,), (None,), (None,), (None,), (None,)]
        cursor_descr = [("user_id", "bigint", None, None, None, None, True)]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.columns[0]["type"], "BIGINT")

    def test_data_as_list_of_lists(self):
        data = [[1, "a"], [2, "b"]]
        cursor_descr = [
            ("user_id", "INT", None, None, None, None, True),
            ("username", "STRING", None, None, None, None, True),
        ]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        df = results.to_pandas_df()
        self.assertEqual(
            df_to_records(df),
            [{"user_id": 1, "username": "a"}, {"user_id": 2, "username": "b"}],
        )

    def test_nullable_bool(self):
        data = [(None,), (True,), (None,), (None,), (None,), (None,)]
        cursor_descr = [("is_test", "bool", None, None, None, None, True)]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.columns[0]["type"], "BOOL")
        df = results.to_pandas_df()
        self.assertEqual(
            df_to_records(df),
            [
                {"is_test": None},
                {"is_test": True},
                {"is_test": None},
                {"is_test": None},
                {"is_test": None},
                {"is_test": None},
            ],
        )

    def test_nested_types(self):
        data = [
            (
                4,
                [{"table_name": "unicode_test", "database_id": 1}],
                [1, 2, 3],
                {"chart_name": "scatter"},
            ),
            (
                3,
                [{"table_name": "birth_names", "database_id": 1}],
                [4, 5, 6],
                {"chart_name": "plot"},
            ),
        ]
        cursor_descr = [("id",), ("dict_arr",), ("num_arr",), ("map_col",)]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.columns[0]["type"], "INT")
        self.assertEqual(results.columns[1]["type"], "STRING")
        self.assertEqual(results.columns[2]["type"], "STRING")
        self.assertEqual(results.columns[3]["type"], "STRING")
        df = results.to_pandas_df()
        self.assertEqual(
            df_to_records(df),
            [
                {
                    "id": 4,
                    "dict_arr": '[{"table_name": "unicode_test", "database_id": 1}]',
                    "num_arr": "[1, 2, 3]",
                    "map_col": '{"chart_name": "scatter"}',
                },
                {
                    "id": 3,
                    "dict_arr": '[{"table_name": "birth_names", "database_id": 1}]',
                    "num_arr": "[4, 5, 6]",
                    "map_col": '{"chart_name": "plot"}',
                },
            ],
        )

    def test_single_column_multidim_nested_types(self):
        data = [
            (
                [
                    "test",
                    [
                        [
                            "foo",
                            123456,
                            [
                                [["test"], 3432546, 7657658766],
                                [["fake"], 656756765, 324324324324],
                            ],
                        ]
                    ],
                    ["test2", 43, 765765765],
                    None,
                    None,
                ],
            )
        ]
        cursor_descr = [("metadata",)]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.columns[0]["type"], "STRING")
        df = results.to_pandas_df()
        self.assertEqual(
            df_to_records(df),
            [
                {
                    "metadata": '["test", [["foo", 123456, [[["test"], 3432546, 7657658766], [["fake"], 656756765, 324324324324]]]], ["test2", 43, 765765765], null, null]'
                }
            ],
        )

    def test_nested_list_types(self):
        data = [([{"TestKey": [123456, "foo"]}],)]
        cursor_descr = [("metadata",)]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.columns[0]["type"], "STRING")
        df = results.to_pandas_df()
        self.assertEqual(
            df_to_records(df), [{"metadata": '[{"TestKey": [123456, "foo"]}]'}]
        )

    def test_empty_datetime(self):
        data = [(None,)]
        cursor_descr = [("ds", "timestamp", None, None, None, None, True)]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.columns[0]["type"], "TIMESTAMP")

    def test_no_type_coercion(self):
        data = [("a", 1), ("b", 2)]
        cursor_descr = [
            ("one", "varchar", None, None, None, None, True),
            ("two", "int", None, None, None, None, True),
        ]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.columns[0]["type"], "VARCHAR")
        self.assertEqual(results.columns[1]["type"], "INT")

    def test_empty_data(self):
        data = []
        cursor_descr = [
            ("emptyone", "varchar", None, None, None, None, True),
            ("emptytwo", "int", None, None, None, None, True),
        ]
        results = SupersetResultSet(data, cursor_descr, BaseEngineSpec)
        self.assertEqual(results.columns, [])