Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.


TASK: we have develop advanced tokenization , we are going gobal model more robustness, scalable, reusable, batch : int  


data :df.dataframe  Mutiple columns (we don't know how many columns) may be str or may be list(str) or  may be dic(str , list(str)

we develop advanced tokenization , padding , max_length, properly encode , decode , batch_wise encode, batch_wise decode,

special tokenids 


using transformers module AutoTokenizer



sample or simple reference code :
from typing import Any, Dict, List, Union
import pandas as pd
from transformers import AutoTokenizer
from transformers.tokenization_utils_base import BatchEncoding


def preprocess_dataframe(df: pd.DataFrame) -> List[Any]:
    """
    Preprocesses a pandas DataFrame by extracting and flattening its columns.

    Args:
        df (pd.DataFrame): The input DataFrame with columns containing str, List[str], or Dict[str, List[str]].

    Returns:
        List[Any]: A list containing preprocessed data ready for tokenization.
    """
    preprocessed_data = []
    for column in df.columns:
        try:
            column_data = df[column].dropna().tolist()
            for item in column_data:
                if isinstance(item, str):
                    preprocessed_data.append(item)
                elif isinstance(item, list):
                    preprocessed_data.extend(item)
                elif isinstance(item, dict):
                    for value in item.values():
                        if isinstance(value, list):
                            preprocessed_data.extend(value)
                        else:
                            preprocessed_data.append(str(value))
                else:
                    preprocessed_data.append(str(item))
        except Exception as e:
            print(f"Error processing column '{column}': {e}")
    return preprocessed_data


def encode_data(
    tokenizer: AutoTokenizer,
    data: List[str],
    max_length: int,
    padding: Union[bool, str],
    truncation: bool,
) -> BatchEncoding:
    """
    Encodes textual data using the provided tokenizer.

    Args:
        tokenizer (AutoTokenizer): The tokenizer to use for encoding.
        data (List[str]): The textual data to encode.
        max_length (int): The maximum sequence length.
        padding (Union[bool, str]): Padding strategy ('max_length', 'longest', True, False).
        truncation (bool): Whether to truncate sequences longer than max_length.

    Returns:
        BatchEncoding: The encoded data.
    """
    try:
        encoded = tokenizer.batch_encode_plus(
            data,
            max_length=max_length,
            padding=padding,
            truncation=truncation,
            return_tensors='pt',
            return_special_tokens_mask=True,
        )
        return encoded
    except Exception as e:
        print(f"Error during encoding: {e}")
        raise


def decode_data(tokenizer: AutoTokenizer, encoded_data: BatchEncoding) -> List[str]:
    """
    Decodes token IDs back to text using the provided tokenizer.

    Args:
        tokenizer (AutoTokenizer): The tokenizer to use for decoding.
        encoded_data (BatchEncoding): The encoded data.

    Returns:
        List[str]: The decoded textual data.
    """
    try:
        decoded = tokenizer.batch_decode(
            encoded_data['input_ids'],
            skip_special_tokens=True,
            clean_up_tokenization_spaces=True,
        )
        return decoded
    except Exception as e:
        print(f"Error during decoding: {e}")
        raise


def process_dataframe(
    df: pd.DataFrame,
    tokenizer_name: str,
    max_length: int = 128,
    padding: Union[bool, str] = 'max_length',
    truncation: bool = True,
) -> Dict[str, Any]:
    """
    Processes a DataFrame by tokenizing its contents.

    Args:
        df (pd.DataFrame): The input DataFrame.
        tokenizer_name (str): The name or path of the tokenizer to use.
        max_length (int, optional): The maximum sequence length. Defaults to 128.
        padding (Union[bool, str], optional): Padding strategy. Defaults to 'max_length'.
        truncation (bool, optional): Whether to truncate sequences. Defaults to True.

    Returns:
        Dict[str, Any]: A dictionary containing encoded data and other relevant information.
    """
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
    preprocessed_data = preprocess_dataframe(df)
    encoded_data = encode_data(
        tokenizer=tokenizer,
        data=preprocessed_data,
        max_length=max_length,
        padding=padding,
        truncation=truncation,
    )
    decoded_data = decode_data(tokenizer, encoded_data)
    return {
        'encoded_data': encoded_data,
        'decoded_data': decoded_data,
        'tokenizer': tokenizer,
    }