"""
Data Preprocessors Module

Provides text preprocessing and data cleaning utilities.
"""

import re
from typing import List, Dict, Any


class TextPreprocessor:
    """Text preprocessing utilities."""

    @staticmethod
    def clean_text(text: str) -> str:
        """
        Clean and normalize text.

        Args:
            text: Input text

        Returns:
            Cleaned text
        """
        # Remove extra whitespace
        text = re.sub(r'\s+', ' ', text)
        # Strip leading/trailing whitespace
        text = text.strip()
        return text

    @staticmethod
    def remove_special_chars(text: str, keep_chars: str = " .,!?-") -> str:
        """
        Remove special characters.

        Args:
            text: Input text
            keep_chars: Characters to keep

        Returns:
            Text with special chars removed
        """
        pattern = f"[^a-zA-Z0-9{re.escape(keep_chars)}]"
        return re.sub(pattern, '', text)


class DataCleaner:
    """Data cleaning utilities."""

    @staticmethod
    def remove_duplicates(data: List[Dict[str, Any]], key: str = "instruction") -> List[Dict[str, Any]]:
        """
        Remove duplicate examples.

        Args:
            data: List of data examples
            key: Key to check for duplicates

        Returns:
            Deduplicated data
        """
        seen = set()
        unique_data = []

        for example in data:
            value = example.get(key, "")
            if value and value not in seen:
                seen.add(value)
                unique_data.append(example)

        return unique_data

    @staticmethod
    def filter_by_length(
        data: List[Dict[str, Any]],
        min_length: int = 10,
        max_length: int = 10000,
        key: str = "output"
    ) -> List[Dict[str, Any]]:
        """
        Filter examples by length.

        Args:
            data: List of data examples
            min_length: Minimum text length
            max_length: Maximum text length
            key: Key to check length

        Returns:
            Filtered data
        """
        filtered = []
        for example in data:
            text = example.get(key, "")
            if min_length <= len(text) <= max_length:
                filtered.append(example)

        return filtered