from abc import ABC, abstractmethod
from typing import List, Tuple, Dict

import pandas as pd

from processing.steps.feature_extraction_step import NameCategory


class BaseNameFormatter(ABC):
    """
    Base class for name formatting transformations.
    Contains common logic for NER tagging and attribute computation.
    """

    def __init__(self, connectors: List[str] = None, additional_surnames: List[str] = None):
        self.connectors = connectors or ['wa', 'ya', 'ka', 'ba']
        self.additional_surnames = additional_surnames or [
            'jean', 'paul', 'marie', 'joseph', 'pierre', 'claude',
            'andre', 'michel', 'robert'
        ]

    @classmethod
    def parse_native_components(cls, native_str: str) -> List[str]:
        """Parse native name string into individual components"""
        if pd.isna(native_str) or not native_str:
            return []
        return native_str.strip().split()

    def create_ner_tags(self, text: str, native_parts: List[str], surname: str) -> List[Tuple[int, int, str]]:
        """Create NER entity tags for transformed text"""
        entities = []
        current_pos = 0
        words = text.split()

        for word in words:
            start_pos = current_pos
            end_pos = current_pos + len(word)

            # Determine tag based on word content
            if word in native_parts or any(connector in word for connector in self.connectors):
                tag = 'NATIVE'
            elif word == surname or word in self.additional_surnames:
                tag = 'SURNAME'
            else:
                # Check if it's a compound native word or new surname
                if any(part in word for part in native_parts):
                    tag = 'NATIVE'
                else:
                    tag = 'SURNAME'

            entities.append((start_pos, end_pos, tag))
            current_pos = end_pos + 1  # +1 for space

        return entities

    @classmethod
    def compute_derived_attributes(cls, name: str) -> Dict:
        """Compute all derived attributes for the transformed name"""
        words_count = len(name.split()) if name else 0
        length = len(name) if name else 0

        return {
            'words': words_count,
            'length': length,
            'identified_category': NameCategory.SIMPLE if words_count == 3 else NameCategory.COMPOSE,
        }

    @abstractmethod
    def transform(self, row: pd.Series) -> Dict:
        """Transform a row according to the specific format rules"""
        pass

    @property
    @abstractmethod
    def transformation_type(self) -> str:
        """Return the transformation type identifier"""
        pass