# Sebastian Raschka 2014-2024
# mlxtend Machine Learning Library Extensions
#
# Function for processing names.
# Author: Sebastian Raschka <sebastianraschka.com>
#
# License: BSD 3 clause


import re
import string
import sys
import unicodedata

from packaging.version import Version
from pandas import __version__ as pandas_version

if sys.version_info <= (3, 0):
    raise ImportError(
        "Sorry, the text.names module is incompatible"
        " with Python 2.x,"
        " due to its unicode intricacies"
    )


def generalize_names(name, output_sep=" ", firstname_output_letters=1):
    """Generalize a person's first and last name.

    Description : Returns a person's name in the format
    `<last_name><separator><firstname letter(s)> (all lowercase)`

    Parameters
    ----------
    name : `str`
        Name of the player
    output_sep : `str` (default: ' ')
        String for separating last name and first name in the output.
    firstname_output_letters : `int`
        Number of letters in the abbreviated first name.

    Returns
    ----------
    gen_name : `str`
        The generalized name.

    Examples
    -----------
    For usage examples, please see
    https://rasbt.github.io/mlxtend/user_guide/text/generalize_names/

    """
    # set first and last name positions
    last, first = "last", "first"
    last_pos = -1

    name = name.lower()

    # fix primarily Dutch names
    exc = ["van der ", "de ", "van ", "von ", "di "]
    for e in exc:
        if name.startswith(e):
            repl = e.replace(" ", "")
            name = repl + name[len(e) - 1 :].strip()

    exc = [
        " van der ",
        " de ",
        " van ",
        " von ",
        " di ",
        ", van der ",
        ", de",
        ", van ",
        ", von ",
        ", di ",
    ]

    for e in exc:
        name = name.replace(e, " " + e.replace(" ", ""))

    if "," in name:
        last, first = first, last
        name = name.replace(",", "")
        last_pos = 1

    spl = name.split()
    if len(spl) > 2:
        name = "%s %s" % (spl[0], spl[last_pos])

    # remove accents
    if sys.version_info.major == 2:
        name = name.decode("utf-8")

    name = "".join(
        x
        for x in unicodedata.normalize("NFKD", name)
        if x in string.ascii_letters + " "
    )

    # get first and last name if applicable
    m = re.match(r"(?P<first>\w+)\W+(?P<last>\w+)", name)
    if m:
        output = "%s%s%s" % (
            m.group(last),
            output_sep,
            m.group(first)[:firstname_output_letters],
        )
    else:
        output = name

    gen_name = output.strip()
    return gen_name


def generalize_names_duplcheck(df, col_name):
    """Generalizes names and removes duplicates.

    Description : Applies mlxtend.text.generalize_names to a DataFrame
    with 1 first name letter by default
    and uses more first name letters if duplicates are detected.

    Parameters
    ----------
    df : `pandas.DataFrame`
        DataFrame that contains a column where
        generalize_names should be applied.
    col_name : `str`
        Name of the DataFrame column where `generalize_names`
        function should be applied to.

    Returns
    ----------
    df_new : `str`
        New DataFrame object where generalize_names function has
        been applied without duplicates.

    Examples
    -----------
    For usage examples, please see
    https://rasbt.github.io/mlxtend/user_guide/text/generalize_names_duplcheck/

    """
    df_new = df.copy()

    df_new.drop_duplicates(subset=[col_name], inplace=True)

    df_new[col_name] = df_new[col_name].apply(generalize_names)

    if Version(pandas_version) < Version("0.17"):
        dupl = list(
            df_new[df_new.duplicated(subset=col_name, keep="last")].index
        ) + list(df_new[df_new.duplicated(subset=col_name, keep="first")].index)
    else:
        dupl = list(
            df_new[df_new.duplicated(subset=col_name, keep="last")].index
        ) + list(df_new[df_new.duplicated(subset=col_name, keep="first")].index)

    firstname_letters = 2
    while len(dupl) > 0:
        for idx in dupl:
            df_new.loc[idx, col_name] = generalize_names(
                df.loc[idx, col_name], firstname_output_letters=firstname_letters
            )
        if Version(pandas_version) < Version("0.17"):
            dupl = list(
                df_new[df_new.duplicated(subset=col_name, keep="last")].index
            ) + list(df_new[df_new.duplicated(subset=col_name, keep="first")].index)
        else:
            dupl = list(
                df_new[df_new.duplicated(subset=col_name, keep="last")].index
            ) + list(df_new[df_new.duplicated(subset=col_name, keep="first")].index)
        firstname_letters += 1
    return df_new