import logging
from logging import Logger
from typing import List, Optional

import pandas as pd
from pandas.api.types import is_object_dtype  # , is_integer_dtype, is_string_dtype
from upgini.resource_bundle import bundle
from upgini.utils.warning_counter import WarningCounter


class FeaturesValidator:
    def __init__(self, logger: Optional[Logger] = None):
        if logger is not None:
            self.logger = logger
        else:
            self.logger = logging.getLogger()
            self.logger.setLevel("FATAL")

    def validate(self, df: pd.DataFrame, features: List[str], warning_counter: WarningCounter) -> List[str]:
        one_hot_encoded_features = []
        empty_or_constant_features = []
        high_cardinality_features = []
        # count = len(df)

        for f in features:
            column = df[f]
            if is_object_dtype(column):
                column = column.astype("string")
            value_counts = column.value_counts(dropna=False, normalize=True)
            most_frequent_percent = value_counts.iloc[0]
            if most_frequent_percent >= 0.99:
                if set(value_counts.index.to_list()) == {0, 1}:
                    one_hot_encoded_features.append(f)
                else:
                    empty_or_constant_features.append(f)
                continue

            # if (is_string_dtype(column) or is_integer_dtype(column)) and column.nunique() / count >= 0.9:
            #     high_cardinality_features.append(f)
            #     continue

        if one_hot_encoded_features:
            msg = bundle.get("one_hot_encoded_features").format(one_hot_encoded_features)
            print(msg)
            self.logger.warning(msg)
            warning_counter.increment()

        if empty_or_constant_features:
            msg = bundle.get("empty_or_contant_features").format(empty_or_constant_features)
            print(msg)
            self.logger.warning(msg)

        if high_cardinality_features:
            msg = bundle.get("high_cardinality_features").format(high_cardinality_features)
            print(msg)
            self.logger.warning(msg)

        return empty_or_constant_features + high_cardinality_features
