doczyai-pipelines/fieldExtraction/src/investment/postprocessing_funcs.py

import ast
import hashlib
import logging
import os
import re
from datetime import datetime
from functools import cache

import pandas as pd
import src.prompts.prompt_templates as prompt_templates
import src.utils.llm_utils as llm_utils
from constants.delimiters import Delimiter
from src.prompts.fieldset import FieldSet
from src.prompts.prompt_templates import invoke_derived_term_date
from src.utils import string_utils

# Determine the base directory for the project
BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# Path to the mappings folder
MAPPINGS_DIR = os.path.join(BASE_DIR, "crosswalk", "mappings")


def rename_columns(df):
    """
    Use the provided mapping to rename columns in the data

    Args:
        df (pd.DataFrame): The input DataFrame with columns to be renamed.
    Returns:
        pd.DataFrame: The DataFrame with renamed columns.
    """
    # Check if the DataFrame is empty, return it as is if it is
    if df.empty:
        return df

    # If not empty, proceed with column renaming
    rename_map = {
        "PROCEDURE_CD": "CPT4_PROC_CD",
        "PROCEDURE_CD_DESC": "CPT4_PROC_CD_DESC",
    }
    # Rename columns using the mapping
    df = df.rename(columns=rename_map)
    return df


def normalize_indicator_field(value: str) -> str:
    """
    Normalize the indicator field value to 'Y' or 'N'.

    Args:
        value (str): The input value from an _IND field.
    Returns:
        str: 'Y' if the value is 'Y', otherwise 'N'.
    """
    if isinstance(value, str) and value.strip().upper() == "Y":
        return "Y"
    return "N"


def format_rate_fields_with_commas(value: str) -> str:
    """
    Format the rate with commas and two decimal places.

    Args:
        value (str): A string representing a numeric value.
    Returns:
        str: The formatted rate string.
    """
    if string_utils.is_empty(value):
        return ""

    try:
        numeric_value = float(value)
        return f"{numeric_value:,.2f}"
    except ValueError:
        return ""


def remove_hyphens(value: str) -> str:
    """
    Remove hyphens from the input string.

    Args:
        value (str): The input string.
    Returns:
        str: The cleaned string with hyphens removed.
    """
    if not isinstance(value, str):
        return ""
    return value.replace("-", "")


def flatten_singleton_string_list(list_str: str) -> str:
    """Take in a list within a string, and if it has a single element, return that element.
    If it has multiple elements, return the list as a string.

    Args:
        list_str (str): A string representation of a list.

    Returns:
        str: The single element if the list has one element, otherwise the list as a string.
    """
    if string_utils.is_empty(list_str):
        return ""
    try:
        # Convert the string to a list using ast.literal_eval
        list_obj = ast.literal_eval(list_str)
        # Check if list_obj is actually a list
        if not isinstance(list_obj, list):
            return list_str
        # If the list has one element, return that element
        if len(list_obj) == 1:
            return list_obj[0]
        # Otherwise, return the list as a string
        return ", ".join(list_obj)
    except (ValueError, SyntaxError):
        return list_str


def validate_and_reformat_date(date_str: str) -> str:
    """
    Validate if a string is in YYYY/MM/DD format or reformat it to YYYY/MM/DD if possible.

    Args:
        date_str (str): The input date string.

    Returns:
        str: The reformatted date string if valid or reformatted, otherwise the original string.
    """
    if not isinstance(date_str, str):
        return date_str

    try:
        # Check if the date is already in YYYY/MM/DD format
        datetime.strptime(date_str, "%Y/%m/%d")
        return date_str
    except ValueError:
        # Attempt to reformat the date from known formats
        known_formats = ["%Y-%m-%d", "%m/%d/%Y", "%d-%b-%Y", "%d/%m/%Y"]
        for fmt in known_formats:
            try:
                return datetime.strptime(date_str, fmt).strftime("%Y/%m/%d")
            except ValueError:
                continue
    return date_str


@cache  # memoize repeated calls to this function
def date_postprocessing(date: str) -> str:
    """Processes dates using `investment_prompts.date_fix_prompt`.

    Args:
        date (str): Input date string to be processed.

    Returns:
        str: Processed date; either a date in YYYY/MM/DD format, a duration (e.g. "3 years"), or "N/A".
    """
    if string_utils.is_empty(date):
        return "N/A"

    prompt = prompt_templates.DATE_FIX_PROMPT(date)
    response = llm_utils.invoke_claude(prompt, "haiku_latest", "date_fix")
    return string_utils.extract_text_from_delimiters(response, Delimiter.PIPE)


def date_postprocess(df, field_json_path):
    """
    Postprocess the date fields in the DataFrame.
    """
    # Date field postprocessing using FieldSet definition
    date_fields = FieldSet(file_path=field_json_path).filter(format="date")
    for field in date_fields.fields:
        if field.field_name in df.columns:
            df[field.field_name] = list(map(date_postprocessing, df[field.field_name]))

    # Derived termination date
    df["AARETE_DERIVED_TERMINATION_DT"] = list(
        map(
            invoke_derived_term_date,
            df["AARETE_DERIVED_EFFECTIVE_DT"],
            df["TERMINATION_DT"],
        )
    )

    return df


def normalize_auto_renewal_term(text: str) -> str:
    """
    Normalize the auto-renewal term to a standard format.

    Args:
        text (str): The input auto-renewal term.

    Returns:
        str: The normalized auto-renewal term.
    """
    if not text or not isinstance(text, str):
        return ""

    # Convert to lowercase and strip whitespace
    text = text.lower().strip()

    # Remove numbers enclosed in parentheses (e.g., "(1) ", "(12) ")
    text = re.sub(r"\(\d+\) ", "", text)

    # Define replacements
    replacements = {
        r"\bone\b": "1",
        r"\btwo\b": "2",
        r"\bthree\b": "3",
        r"\btwelve\b": "12",
        r"year to year": "1 year",
        r"12 months": "1 year",
        r"\btwelve months\b": "1 year",
        r"\bone year\b": "1 year",
        r"\bone-year\b": "1 year",
        r"\b1 year\b": "1 year",
        r"\bmonth to month\b": "1 month",
    }

    # Apply replacements
    for pattern, replacement in replacements.items():
        text = re.sub(pattern, replacement, text)

    # Remove any remaining parentheses or extra spaces
    text = text.strip().replace("(", "").replace(")", "")

    text = text.replace("-", " ")
    if text == "year":
        text = "1 year"

    return text


def normalize_cpt_fields(value):
    """
    Normalize code values to a list format.

    Args:
        value (Any): Input value which could be a list, string, or other types.

    Returns:
        list: A normalized list of string codes.
    """
    if not value or (isinstance(value, float) and pd.isna(value)):  # Handle NaN values
        return ""  # Empty string

    # First normalize to a list
    if isinstance(value, (list, tuple)):  # If already a list or tuple
        result = [str(v).strip() for v in value]
    elif isinstance(value, str):  # If it's a string
        value = value.strip()
        if value.startswith("[") and value.endswith(
            "]"
        ):  # String representation of a list
            try:
                # Parse it first
                parsed_list = ast.literal_eval(value)
                result = []
                # Process each item in the parsed list
                for item in parsed_list:
                    item_str = str(item).strip()
                    # If the item contains commas, split it into multiple items
                    if "," in item_str:
                        result.extend([str(v).strip() for v in item_str.split(",")])
                    else:
                        result.append(item_str)
            except (ValueError, SyntaxError):
                result = [value]  # If parsing fails, treat it as a single value
        elif "," in value:  # If it's a comma-separated string
            # Split by comma and create a list
            result = [str(v).strip() for v in value.split(",")]
        elif "-" in value:
            # Range format
            result = [value]
        else:  # Single value
            result = [value]
    elif isinstance(value, (float, int)):  # If it's a number
        result = [str(int(value)).strip()]
    else:
        result = [str(value).strip()]  # Default case for other types

    return str(result)  # Return the list directly


def generate_reimb_ids(df: pd.DataFrame) -> pd.DataFrame:
    """Generates REIMB_ID and REIMB_LESSER_OF_ID for each row in the DataFrame based on
        the FILE_NAME and other fields.
    The REIMB_ID is constructed using the following format:
    {FILE_NAME}_exh_pg_{EXHIBIT_PAGE}_{index}_{hash_value}

    the REIMB_LESSER_OF_ID is just the {hash_value} itself.

    Where:
    - FILE_NAME is the name of the file (without extension).
    - EXHIBIT_PAGE is the page number of the exhibit.
    - index is the index of the row in the group.
    - hash_value is the first 8 characters of the MD5 hash of a concatenated string of relevant fields.
    The relevant fields are SERVICE_TERM, REIMB_TERM, PROGRAM, PRODUCT, NETWORK, and LOB.

    Args:
        df (pd.DataFrame): Input DataFrame containing the columns FILE_NAME, EXHIBIT_PAGE, SERVICE_TERM, REIMB_TERM, PROGRAM, PRODUCT, NETWORK, and LOB.
        The FILE_NAME column should contain the name of the file (with or without extension).
        The EXHIBIT_PAGE column should contain the page number of the exhibit.

    Returns:
        pd.DataFrame: DataFrame with generated REIMB_IDs and REIMB_LESSER_OF_IDs for each row.
    """
    # First sort to ensure consistent ordering
    df = (
        df.sort_values(by=["FILE_NAME", "EXHIBIT_PAGE"])
        if "EXHIBIT_PAGE" in df.columns
        else df.sort_values(by=["FILE_NAME"])
    )

    # Create a temporary copy to avoid SettingWithCopyWarning
    df_temp = df.copy()

    # Process each file+exhibit_page group to reset counter for each exhibit page
    group_cols = (
        ["FILE_NAME", "EXHIBIT_PAGE"] if "EXHIBIT_PAGE" in df.columns else ["FILE_NAME"]
    )
    for group_key, indices in df.groupby(group_cols).groups.items():
        if isinstance(group_key, tuple):
            filename, exhibit_page = group_key
            exhibit_page = str(exhibit_page).zfill(3)
        else:
            filename = group_key
            exhibit_page = "000"
        clean_filename = str(filename).split("/")[-1].replace(".", "_")

        for i, idx in enumerate(indices):
            row = df.loc[idx]

            # Get fields to include in hash
            service_term = str(row.get("SERVICE_TERM", ""))
            reimb_term = str(row.get("REIMB_TERM", ""))
            program = str(row.get("PROGRAM", ""))
            product = str(row.get("PRODUCT", ""))
            network = str(row.get("NETWORK", ""))
            lob = str(row.get("LOB", ""))

            # Create hash string
            hash_string = f"{clean_filename}_{service_term}_{reimb_term}_{program}_{product}_{network}_{lob}"
            hash_value = hashlib.md5(hash_string.encode()).hexdigest()[:8]

            # Generate REIMB_ID and REIMB_LESSER_OF_ID
            df_temp.at[idx, "REIMB_ID"] = (
                f"{clean_filename}_exh_pg_{exhibit_page}_{i+1:03d}_{hash_value}"
            )
            df_temp.at[idx, "REIMB_LESSER_OF_ID"] = hash_value

    return df_temp


def reorder_columns(df: pd.DataFrame, column_order: list[str]) -> pd.DataFrame:
    """
    Reorders the columns of the DataFrame based on the given column order.
    Steps:
    1. Adds any missing columns from `column_order` to the DataFrame at once, filled with empty strings.
    2. Reorders the columns of the DataFrame to match `column_order`.
    3. Appends any columns in the DataFrame that are not in `column_order` to the end.

    Args:
        df (pd.DataFrame): The input DataFrame.
        column_order (list[str]): The desired column order.

    Returns:
        pd.DataFrame: The reordered DataFrame.
    """
    # Create a copy to avoid fragmentation
    df_copy = df.copy()

    # Find missing columns from column_order
    missing_columns = [col for col in column_order if col not in df_copy.columns]

    # Add all missing columns at once using a dictionary
    if missing_columns:
        # Create a dictionary of empty columns
        empty_cols = {col: [""] * len(df_copy) for col in missing_columns}
        # Add all missing columns at once with pd.concat
        df_copy = pd.concat(
            [df_copy, pd.DataFrame(empty_cols, index=df_copy.index)], axis=1
        )

    # Reorder columns to match column_order
    ordered_columns = [col for col in column_order if col in df_copy.columns]
    # Add columns in df that are not in column_order to the end
    remaining_columns = [col for col in df_copy.columns if col not in column_order]
    # Combine the ordered columns and remaining columns
    final_column_order = ordered_columns + remaining_columns

    # Return the DataFrame with reordered columns
    return df_copy[final_column_order]


def auto_renewal(df: pd.DataFrame) -> pd.DataFrame:
    # Normalize the 'AUTO_RENEWAL_TERM' column
    if "AUTO_RENEWAL_TERM" in df.columns:
        df["AUTO_RENEWAL_TERM"] = df["AUTO_RENEWAL_TERM"].apply(
            normalize_auto_renewal_term
        )
    return df


def update_termination_date_for_conditions(df: pd.DataFrame) -> pd.DataFrame:
    """
    Updates the 'AARETE_DERIVED_TERMINATION_DT' column based on specific conditions
    involving 'TERMINATION_DT', 'AUTO_RENEWAL_IND', and 'AARETE_DERIVED_TERMINATION_DT'.

    Args:
        df (pd.DataFrame): The input DataFrame.

    Returns:
        pd.DataFrame: The updated DataFrame with modified 'AARETE_DERIVED_TERMINATION_DT' values.
    """

    if (
        "TERMINATION_DT" in df.columns
        and "AUTO_RENEWAL_IND" in df.columns
        and "AARETE_DERIVED_TERMINATION_DT" in df.columns
    ):
        # Replace Y with 9999/12/31
        df.loc[df["AUTO_RENEWAL_IND"] == "Y", "AARETE_DERIVED_TERMINATION_DT"] = (
            "9999/12/31"
        )

        # Replace "N" and "year to year" with 9999/12/31
        df.loc[
            (df["TERMINATION_DT"] == "year to year")
            & (df["AUTO_RENEWAL_IND"] == "N")
            & (df["AARETE_DERIVED_TERMINATION_DT"] == "N/A"),
            "AARETE_DERIVED_TERMINATION_DT",
        ] = "9999/12/31"

    return df


def standardize_reimb_method_and_fee_schedule(
    df: pd.DataFrame, VALID_UNIT_OF_MEASURE: list
) -> pd.DataFrame:
    """
    Standardizes the 'AARETE_DERIVED_REIMB_METHOD' column and updates the
    'AARETE_DERIVED_FEE_SCHEDULE' column based on specific values.

    Args:
        df (pd.DataFrame): The input DataFrame.

    Returns:
        pd.DataFrame: The updated DataFrame with standardized columns.
    """
    if (
        "AARETE_DERIVED_REIMB_METHOD" in df.columns
        and "AARETE_DERIVED_FEE_SCHEDULE" in df.columns
    ):
        # Standardize 'AARETE_DERIVED_REIMB_METHOD' values
        # Copy AWP, ASP, WAC to AARETE_DERIVED_FEE_SCHEDULE column for the corresponding row
        medrx_fee_schedule_values = ["AWP", "ASP", "WAC"]
        df.loc[
            df["AARETE_DERIVED_REIMB_METHOD"].isin(medrx_fee_schedule_values),
            "AARETE_DERIVED_FEE_SCHEDULE",
        ] = df["AARETE_DERIVED_REIMB_METHOD"]

        # Convert AARETE_DERIVED_REIMB_METHOD values [AWP, ASP, WAC] to 'MedRx'
        df["AARETE_DERIVED_REIMB_METHOD"] = df["AARETE_DERIVED_REIMB_METHOD"].replace(
            {"AWP": "MedRx", "ASP": "MedRx", "WAC": "MedRx"}
        )

    if "AARETE_DERIVED_REIMB_METHOD" in df.columns:

        # Remove rows where 'AARETE_DERIVED_REIMB_METHOD' is 'DOFR' but 'REIMB_TERM' does not contain 'DOFR'
        mask = (df["AARETE_DERIVED_REIMB_METHOD"] == "DOFR") & (
            ~df["REIMB_TERM"].str.contains("DOFR", na=False)
        )
        df = df[~mask]

    # standardize the 'UNIT_OF_MEASURE' column
    if "UNIT_OF_MEASURE" in df.columns:
        df["UNIT_OF_MEASURE"] = df["UNIT_OF_MEASURE"].str.replace(
            "Per Encounter", "Per Visit"
        )

        # remove cases of Per Member Per Month (PMPM)
        df = df[~df["UNIT_OF_MEASURE"].isin(["Per Member Per Month (PMPM)"])]

        # Enhanced logic: Set UNIT_OF_MEASURE as 'Per Unit' for Flat Rate reimbursement method with empty UNIT_OF_MEASURE
        # This catches all edge cases including NaN, None, empty strings, etc.
        if "AARETE_DERIVED_REIMB_METHOD" in df.columns:
            flat_rate_mask = df["AARETE_DERIVED_REIMB_METHOD"] == "Flat Rate"
            empty_uom_mask = (
                df["UNIT_OF_MEASURE"].isna()
                | df["UNIT_OF_MEASURE"].eq("")
                | df["UNIT_OF_MEASURE"].apply(string_utils.is_empty)
            )
            df.loc[flat_rate_mask & empty_uom_mask, "UNIT_OF_MEASURE"] = "Per Unit"

        # Ensure UNIT_OF_MEASURE is one of the valid values
        df["UNIT_OF_MEASURE"] = df["UNIT_OF_MEASURE"].apply(
            lambda x: x if x in VALID_UNIT_OF_MEASURE else ""
        )

    return df


def process_patient_age_range(df):
    """
    Process the 'PATIENT_AGE_RANGE' column into 'PATIENT_AGE_MIN' and 'PATIENT_AGE_MAX'.
    If a single number is provided, both min and max are set to that number.
    Remove PATIENT_AGE_RANGE column.

    Args:
        df (pd.DataFrame): The input DataFrame.

    Returns:
        pd.DataFrame: The updated DataFrame with 'PATIENT_AGE_MIN' and 'PATIENT_AGE_MAX' columns.
    """
    if "PATIENT_AGE_RANGE" in df.columns:
        try:
            # Create temporary columns for processing
            df["PATIENT_AGE_MIN"] = df["PATIENT_AGE_RANGE"]
            df["PATIENT_AGE_MAX"] = df["PATIENT_AGE_RANGE"]

            # Handle hyphenated ranges
            mask = df["PATIENT_AGE_RANGE"].str.contains("-", na=False)
            if mask.any():
                temp_split = df.loc[mask, "PATIENT_AGE_RANGE"].str.split(
                    "-", expand=True
                )
                df.loc[mask, "PATIENT_AGE_MIN"] = temp_split[0]
                df.loc[mask, "PATIENT_AGE_MAX"] = temp_split[1]

        except Exception as e:
            # If any error occurs, keep the original values
            pass

        # Remove the original PATIENT_AGE_RANGE column
        df.drop(columns=["PATIENT_AGE_RANGE"], inplace=True, errors="ignore")

    return df


def add_aarete_derived_amendment_num(df: pd.DataFrame) -> pd.DataFrame:
    """
    Add the 'AARETE_DERIVED_AMENDMENT_NUM' column to the DataFrame.

    Args:
        df (pd.DataFrame): The input DataFrame.

    Returns:
        pd.DataFrame: The updated DataFrame with the 'AARETE_DERIVED_AMENDMENT_NUM' column.
    """

    if "CONTRACT_AMENDMENT_NUM" in df.columns:
        df["AARETE_DERIVED_AMENDMENT_NUM"] = (
            df["CONTRACT_AMENDMENT_NUM"]
            .str.split("-")
            .str[1]
            .fillna(df["CONTRACT_AMENDMENT_NUM"])
        )

    return df


def add_aarete_derived_product(df):
    """
    Add AARETE_DERIVED_PRODUCT column to df, based on PRODUCT column.

    The new column should be Title Case of the PRODUCT column value.
    """
    if "PRODUCT" in df.columns:
        df["AARETE_DERIVED_PRODUCT"] = df["PRODUCT"].str.title()
    return df


def update_lob_for_duals(answer_dicts):
    final_answer_dicts = []
    # Iterate through each row in the dataframe
    for answer_dict in answer_dicts:
        # Get SERVICE_TERM value
        service_term = answer_dict.get("SERVICE_TERM", "").lower()
        # Check if the SERVICE_TERM contains "Medicare" and "Medicaid" - case insensitive
        # AND check that there's no explicit PROGRAM or PRODUCT that would override this logic
        if (
            "medicare" in service_term
            and "medicaid" in service_term
            and string_utils.is_empty(answer_dict.get("AARETE_DERIVED_PROGRAM"))
            and string_utils.is_empty(answer_dict.get("AARETE_DERIVED_PRODUCT"))
        ):
            prompt = prompt_templates.DUAL_LOB_CHECK(service_term)
            logging.debug(f"Prompt for dual LOB check: {prompt}")
            claude_answer_raw = llm_utils.invoke_claude(
                prompt, model_id="legacy_sonnet", filename="", max_tokens=200
            )
            logging.debug(f"Claude response for dual LOB check: {claude_answer_raw}")
            claude_answer_extracted = string_utils.extract_text_from_delimiters(
                claude_answer_raw, Delimiter.PIPE
            )
            if (
                claude_answer_extracted.lower() == "neither"
                or claude_answer_extracted.lower() == "n/a"
            ):  # If neither, take original AARETE_DERIVED_LOB
                pass
            else:
                answer_dict["AARETE_DERIVED_LOB"] = claude_answer_extracted
        final_answer_dicts.append(answer_dict)
    return final_answer_dicts


def fill_empty_reimb_pct_rate(df):
    """
    Fill 'REIMB_PCT_RATE' based on reimbursement method:
    - Set 'REIMB_PCT_RATE' to '100' for rows where:
      - 'REIMB_PCT_RATE', 'REIMB_FEE_RATE', and 'REIMB_CONVERSION_FACTOR' are all empty.
    - Set 'REIMB_PCT_RATE' to 'N/A' for rows where:
      - 'AARETE_DERIVED_REIMB_METHOD' contains specific grouper.
    """
    required_cols = [
        "REIMB_PCT_RATE",
        "REIMB_FEE_RATE",
        "REIMB_CONVERSION_FACTOR",
        "AARETE_DERIVED_REIMB_METHOD",
    ]

    if all(col in df.columns for col in required_cols):
        # Fill with '100' when all three rate fields are empty
        pct_empty = string_utils.is_empty(df["REIMB_PCT_RATE"])
        fee_empty = string_utils.is_empty(df["REIMB_FEE_RATE"])
        conversion_empty = string_utils.is_empty(df["REIMB_CONVERSION_FACTOR"])

        fill_mask = pct_empty & fee_empty & conversion_empty
        df.loc[fill_mask, "REIMB_PCT_RATE"] = "100"

        # Set to N/A for specific reimbursement methods
        na_methods = ["Per Diem", "Flat Rate", "Per Visit", "Case Rate"]

        na_mask = df["AARETE_DERIVED_REIMB_METHOD"].isin(na_methods)
        df.loc[na_mask, "REIMB_PCT_RATE"] = "N/A"

    return df


def remove_redundant_reimb_info(df):
    """
    Checks if "REIMB_" fields are the same as their corresponding 1:1 fields.
    If they are the same, replace the "REIMB_" values with ""

    Args:
        df (pd.DataFrame): The input DataFrame.

    Returns:
        pd.DataFrame: The updated DataFrame with redundant reimbursement field values removed.
    """
    COLUMN_PAIRS = [
        ("REIMB_EFFECTIVE_DT", "AARETE_DERIVED_EFFECTIVE_DT"),
        ("REIMB_TERMINATION_DT", "AARETE_DERIVED_TERMINATION_DT"),
        ("REIMB_PROV_NAME", "PROV_GROUP_NAME_FULL"),
    ]
    for column_tuple in COLUMN_PAIRS:
        reimb_col = column_tuple[0]
        one_to_one_col = column_tuple[1]
        if all(col in df.columns for col in [reimb_col, one_to_one_col]):
            mask = df[reimb_col] == df[one_to_one_col]
            df.loc[mask, reimb_col] = ""
    return df


def deduplicate_provider_columns(df: pd.DataFrame) -> pd.DataFrame:
    """Deduplicate provider fields in the DataFrame.

    This function removes PROV_GROUP_* values from the PROV_OTHER_* columns if they are already present.
    In addition, it deduplicates PROV_OTHER_* values.

    E.g. If PROV_GROUP_TIN is "123456789" and PROV_OTHER_TIN is "123456789 | 987654321 | 987654321",
    then PROV_OTHER_TIN will be updated to "987654321".


    Args:
        df (pd.DataFrame): The input DataFrame.

    Returns:
        pd.DataFrame: The updated DataFrame with deduplicated provider fields.
    """
    provider_columns = [
        "PROV_GROUP_TIN",
        "PROV_GROUP_NPI",
        "PROV_GROUP_NAME_FULL",
        "PROV_OTHER_TIN",
        "PROV_OTHER_NPI",
        "PROV_OTHER_NAME_FULL",
    ]
    other_columns = ["PROV_OTHER_TIN", "PROV_OTHER_NPI", "PROV_OTHER_NAME_FULL"]

    # Check if all provider columns exist in the DataFrame
    if all(col in df.columns for col in provider_columns):
        # Process each row
        for index, row in df.iterrows():
            # Get GROUP values for this row
            group_tin = str(row.get("PROV_GROUP_TIN", "")).strip()
            group_npi = str(row.get("PROV_GROUP_NPI", "")).strip()
            group_name = str(row.get("PROV_GROUP_NAME_FULL", "")).strip()

            # Simple mapping of OTHER columns to their corresponding GROUP values
            column_mappings = {
                "PROV_OTHER_TIN": group_tin,
                "PROV_OTHER_NPI": group_npi,
                "PROV_OTHER_NAME_FULL": group_name,
            }

            # Process each OTHER column
            for other_col, group_val in column_mappings.items():
                other_value = str(row.get(other_col, "")).strip()

                if (
                    other_value
                    and not string_utils.is_empty(other_value)
                    and other_value != "UNKNOWN"
                ):
                    # Split by pipe and strip whitespace
                    other_list = [
                        v.strip() for v in other_value.split("|") if v.strip()
                    ]

                    # Remove corresponding GROUP value if present
                    if (
                        group_val
                        and not string_utils.is_empty(group_val)
                        and group_val != "UNKNOWN"
                    ):
                        other_list = [v for v in other_list if v != group_val]

                    # Remove duplicates while preserving order
                    seen = set()
                    deduped_list = []
                    for item in other_list:
                        if (
                            item not in seen
                            and not string_utils.is_empty(item)
                            and item != "UNKNOWN"
                        ):
                            seen.add(item)
                            deduped_list.append(item)

                    # Update the DataFrame
                    df.at[index, other_col] = (
                        " | ".join(deduped_list) if deduped_list else ""
                    )
    return df


def update_grouper_base_rate_and_grouper_pct_rate(df):
    """
    Update GROUPER_PCT_RATE and GROUPER_BASE_RATE columns based on grouper conditions.

    Creates new columns and populates them when:
    - AARETE_DERIVED_REIMB_METHOD is 'Grouper', OR
    - GROUPER_TYPE is not empty (using string_utils.is_empty) AND not empty array/empty array string, OR
    - GROUPER_CD is not empty (using string_utils.is_empty) AND not empty array/empty array string

    Args:
        df: DataFrame to update

    Returns:
        DataFrame with updated GROUPER_PCT_RATE and GROUPER_BASE_RATE columns
    """
    # Required columns for the operation
    required_cols = [
        "AARETE_DERIVED_REIMB_METHOD",
        "REIMB_PCT_RATE",
        "REIMB_CONVERSION_FACTOR",
        "GROUPER_TYPE" "GROUPER_CD",
    ]

    # Check if all required columns exist
    if not all(col in df.columns for col in required_cols):
        # Initialize empty columns and return if required columns are missing
        df["GROUPER_PCT_RATE"] = ""
        df["GROUPER_BASE_RATE"] = ""
        return df

    # Create grouper condition mask
    grouper_mask = (df["AARETE_DERIVED_REIMB_METHOD"] == "Grouper") & (
        (~string_utils.is_empty(df["GROUPER_TYPE"]) & df["GROUPER_TYPE"].ne("[]"))
        | (~string_utils.is_empty(df["GROUPER_CD"]) & df["GROUPER_CD"].ne("[]"))
    )

    # Initialize new columns with empty strings
    df["GROUPER_PCT_RATE"] = ""
    df["GROUPER_BASE_RATE"] = ""

    # Update values where grouper condition is met
    df.loc[grouper_mask, "GROUPER_PCT_RATE"] = df.loc[grouper_mask, "REIMB_PCT_RATE"]
    df.loc[grouper_mask, "GROUPER_BASE_RATE"] = df.loc[
        grouper_mask, "REIMB_CONVERSION_FACTOR"
    ]

    return df