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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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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
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import io
from typing import Any

import pandas as pd

from superset.utils.core import GenericDataType


def quote_formulas(df: pd.DataFrame) -> pd.DataFrame:
    """
    Make sure to quote any formulas for security reasons.
    """
    formula_prefixes = {"=", "+", "-", "@"}

    for col in df.select_dtypes(include="object").columns:
        df[col] = df[col].apply(
            lambda x: (
                f"'{x}"
                if isinstance(x, str) and len(x) and x[0] in formula_prefixes
                else x
            )
        )

    return df


def df_to_excel(df: pd.DataFrame, **kwargs: Any) -> Any:
    output = io.BytesIO()

    # make sure formulas are quoted, to prevent malicious injections
    df = quote_formulas(df)

    # pylint: disable=abstract-class-instantiated
    with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
        df.to_excel(writer, **kwargs)

    return output.getvalue()


def apply_column_types(
    df: pd.DataFrame, column_types: list[GenericDataType]
) -> pd.DataFrame:
    """
    Applies the column types to the dataframe to prepare for an excel export

    :param df: The dataframe to apply the column types to
    :param column_types: The types of the columns
    :return: The dataframe with the column types applied
    """
    for column, column_type in zip(df.columns, column_types, strict=False):
        if column_type == GenericDataType.NUMERIC:
            try:
                df[column] = pd.to_numeric(df[column])
                # if the number is too large, convert it to a string
                # Excel does not support numbers larger than 10^15
                df[column] = df[column].apply(
                    lambda x: str(x)
                    if isinstance(x, (int, float)) and abs(x) > 10**15
                    else x
                )
            except ValueError:
                df[column] = df[column].astype(str)
        elif pd.api.types.is_datetime64tz_dtype(df[column]):
            # timezones are not supported
            df[column] = df[column].astype(str)
    return df