# 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.
"""Superset wrapper around pyarrow.Table."""

import datetime
import logging
from typing import Any, Optional

import numpy as np
import pandas as pd
import pyarrow as pa
from numpy.typing import NDArray

from superset.db_engine_specs import BaseEngineSpec
from superset.superset_typing import DbapiDescription, DbapiResult, ResultSetColumnType
from superset.utils import core as utils, json
from superset.utils.core import GenericDataType

logger = logging.getLogger(__name__)


def dedup(l: list[str], suffix: str = "__", case_sensitive: bool = True) -> list[str]:  # noqa: E741
    """De-duplicates a list of string by suffixing a counter

    Always returns the same number of entries as provided, and always returns
    unique values. Case sensitive comparison by default.

    >>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))
    foo,bar,bar__1,bar__2,Bar
    >>> print(
        ','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False))
    )
    foo,bar,bar__1,bar__2,Bar__3
    """
    new_l: list[str] = []
    seen: dict[str, int] = {}
    for item in l:
        s_fixed_case = item if case_sensitive else item.lower()
        if s_fixed_case in seen:
            seen[s_fixed_case] += 1
            item += suffix + str(seen[s_fixed_case])
        else:
            seen[s_fixed_case] = 0
        new_l.append(item)
    return new_l


def stringify(obj: Any) -> str:
    return json.dumps(obj, default=json.json_iso_dttm_ser)


def stringify_values(array: NDArray[Any]) -> NDArray[Any]:
    result = np.copy(array)

    with np.nditer(result, flags=["refs_ok"], op_flags=[["readwrite"]]) as it:
        for obj in it:
            if na_obj := pd.isna(obj):
                # pandas <NA> type cannot be converted to string
                obj[na_obj] = None
            else:
                try:
                    # for simple string conversions
                    # this handles odd character types better
                    obj[...] = obj.astype(str)
                except ValueError:
                    obj[...] = stringify(obj)

    return result


def destringify(obj: str) -> Any:
    return json.loads(obj)


def convert_to_string(value: Any) -> str:
    """
    Used to ensure column names from the cursor description are strings.
    """
    if isinstance(value, str):
        return value

    if isinstance(value, bytes):
        return value.decode("utf-8")

    return str(value)


def normalize_cursor_description_names(
    cursor_description: DbapiDescription,
) -> list[str]:
    """
    Replace empty cursor.description names with synthetic names that do not
    collide with any explicit column names.
    """
    normalized_names: list[str] = []
    unavailable_names = {
        convert_to_string(col[0])
        for col in cursor_description
        if convert_to_string(col[0])
    }
    synthetic_index = 0

    for col in cursor_description:
        column_name = convert_to_string(col[0])
        if column_name:
            normalized_names.append(column_name)
            continue

        while True:
            synthetic_name = f"_col_{synthetic_index}"
            synthetic_index += 1
            if synthetic_name not in unavailable_names:
                unavailable_names.add(synthetic_name)
                normalized_names.append(synthetic_name)
                break

    return normalized_names


class SupersetResultSet:
    def __init__(  # pylint: disable=too-many-locals  # noqa: C901
        self,
        data: DbapiResult,
        cursor_description: DbapiDescription,
        db_engine_spec: type[BaseEngineSpec],
    ):
        self.db_engine_spec = db_engine_spec
        data = data or []
        column_names: list[str] = []
        pa_data: list[pa.Array] = []
        deduped_cursor_desc: list[tuple[Any, ...]] = []
        numpy_dtype: list[tuple[str, ...]] = []
        stringified_arr: NDArray[Any]

        if cursor_description:
            # get deduped list of column names
            # Some databases (e.g. SQL Server) return an empty string as the
            # column name for un-aliased expressions like SELECT COUNT(*).
            # An empty field name is illegal in NumPy structured arrays and in
            # PyArrow tables, so we substitute a synthetic name when needed.
            # Synthetic names are chosen to avoid colliding with any explicit
            # column names before deduplication runs.
            # See https://github.com/apache/superset/issues/23848
            column_names = dedup(normalize_cursor_description_names(cursor_description))

            # fix cursor descriptor with the deduped names
            deduped_cursor_desc = [
                tuple([column_name, *list(description)[1:]])  # noqa: C409
                for column_name, description in zip(
                    column_names, cursor_description, strict=False
                )
            ]

            # generate numpy structured array dtype
            numpy_dtype = [(column_name, "object") for column_name in column_names]

        # only do expensive recasting if datatype is not standard list of tuples
        if data and (not isinstance(data, list) or not isinstance(data[0], tuple)):
            data = [tuple(row) for row in data]
        array = np.array(data, dtype=numpy_dtype)

        for column in column_names:
            try:
                pa_data.append(pa.array(array[column].tolist()))
            except (
                pa.lib.ArrowInvalid,
                pa.lib.ArrowTypeError,
                pa.lib.ArrowNotImplementedError,
                ValueError,
                TypeError,  # this is super hackey,
                # https://issues.apache.org/jira/browse/ARROW-7855
            ):
                # attempt serialization of values as strings
                stringified_arr = stringify_values(array[column])
                pa_data.append(pa.array(stringified_arr.tolist()))

        if pa_data:  # pylint: disable=too-many-nested-blocks
            for i, column in enumerate(column_names):
                if pa.types.is_nested(pa_data[i].type):
                    # TODO: revisit nested column serialization once nested types
                    #  are added as a natively supported column type in Superset
                    #  (superset.utils.core.GenericDataType).
                    stringified_arr = stringify_values(array[column])
                    pa_data[i] = pa.array(stringified_arr.tolist())

                elif pa.types.is_temporal(pa_data[i].type):
                    # workaround for bug converting
                    # `psycopg2.tz.FixedOffsetTimezone` tzinfo values.
                    # related: https://issues.apache.org/jira/browse/ARROW-5248
                    sample = self.first_nonempty(array[column])
                    if sample and isinstance(sample, datetime.datetime):
                        try:
                            if sample.tzinfo:
                                tz = sample.tzinfo
                                series = pd.Series(array[column])
                                series = pd.to_datetime(series, utc=True)
                                pa_data[i] = pa.Array.from_pandas(
                                    series,
                                    type=pa.timestamp("ns", tz=tz),
                                )
                        except Exception as ex:  # pylint: disable=broad-except
                            logger.exception(ex)

        if not pa_data:
            column_names = []

        self.table = pa.Table.from_arrays(pa_data, names=column_names)
        self._type_dict: dict[str, Any] = {}
        try:
            # The driver may not be passing a cursor.description
            self._type_dict = {
                col: db_engine_spec.get_datatype(deduped_cursor_desc[i][1])
                for i, col in enumerate(column_names)
                if deduped_cursor_desc
            }
        except Exception as ex:  # pylint: disable=broad-except
            logger.exception(ex)

    @staticmethod
    def convert_pa_dtype(pa_dtype: pa.DataType) -> Optional[str]:
        if pa.types.is_boolean(pa_dtype):
            return "BOOL"
        if pa.types.is_integer(pa_dtype):
            return "INT"
        if pa.types.is_floating(pa_dtype):
            return "FLOAT"
        if pa.types.is_string(pa_dtype):
            return "STRING"
        if pa.types.is_temporal(pa_dtype):
            return "DATETIME"
        return None

    @staticmethod
    def convert_table_to_df(table: pa.Table) -> pd.DataFrame:
        try:
            return table.to_pandas(integer_object_nulls=True)
        except pa.lib.ArrowInvalid:
            return table.to_pandas(integer_object_nulls=True, timestamp_as_object=True)

    @staticmethod
    def first_nonempty(items: NDArray[Any]) -> Any:
        return next((i for i in items if i), None)

    def is_temporal(self, db_type_str: Optional[str]) -> bool:
        column_spec = self.db_engine_spec.get_column_spec(db_type_str)
        if column_spec is None:
            return False
        return column_spec.is_dttm

    def type_generic(
        self, db_type_str: Optional[str]
    ) -> Optional[utils.GenericDataType]:
        column_spec = self.db_engine_spec.get_column_spec(db_type_str)
        if column_spec is None:
            return None

        if column_spec.is_dttm:
            return GenericDataType.TEMPORAL

        return column_spec.generic_type

    def data_type(self, col_name: str, pa_dtype: pa.DataType) -> Optional[str]:
        """Given a pyarrow data type, Returns a generic database type"""
        if set_type := self._type_dict.get(col_name):
            return set_type

        if mapped_type := self.convert_pa_dtype(pa_dtype):
            return mapped_type

        return None

    def to_pandas_df(self) -> pd.DataFrame:
        return self.convert_table_to_df(self.table)

    @property
    def pa_table(self) -> pa.Table:
        return self.table

    @property
    def size(self) -> int:
        return self.table.num_rows

    @property
    def columns(self) -> list[ResultSetColumnType]:
        if not self.table.column_names:
            return []

        columns = []
        for col in self.table.schema:
            db_type_str = self.data_type(col.name, col.type)
            column: ResultSetColumnType = {
                "column_name": col.name,
                "name": col.name,
                "type": db_type_str,
                "type_generic": self.type_generic(db_type_str),
                "is_dttm": self.is_temporal(db_type_str) or False,
            }
            columns.append(column)
        return columns