import json
import ast
import logging
import re
import traceback
import warnings
from datetime import datetime
from typing import Literal, overload

import numpy as np
import pandas as pd
from src.constants.delimiters import Delimiter
from src.constants.regex_patterns import (
    BACKTICK_PATTERN,
    PIPE_PATTERN,
    TRIPLE_BACKTICK_PATTERN,
)


def extract_text_from_delimiters(
    raw_output: str, delimiter: Delimiter, match_index: int = -1
) -> str:
    """
    Extracts a match enclosed in specified delimiters from the raw output.
    This function searches for text enclosed by a specified delimiter in the given raw output string.
    It returns the match specified by the `which_match` index. If no match is found, it returns "N/A".

    - raw_output (str): The raw output string to search within.
    - which_match (int, optional): The index of the match to return. Defaults to -1, which returns the last match. If the index is out of range, the last match is returned.
    - str: The extracted text or "N/A" if no match is found.

    Raises:
        - TypeError: If `raw_output` is not a string or `delimiter` is not a Delimiter enum value.
        - ValueError: If an unsupported delimiter is provided.

    Returns:
        - str: The extracted answer or "N/A" if no match is found.

    .. deprecated:: 2026.02
        Using Delimiter.PIPE with this function is deprecated and will be removed
        in a future version. Prompts should return JSON format instead of
        pipe-delimited strings. Use `json_utils.parse_json_list()` or
        `json_utils.parse_json_dict()` for parsing JSON responses.
    """
    if not isinstance(raw_output, str):
        raise TypeError(
            "Expected a string for raw_output, got {0}.".format(
                type(raw_output).__name__
            )
        )

    if not isinstance(delimiter, Delimiter):
        raise TypeError(
            "Expected a Delimiter enum value, got {0}.".format(type(delimiter).__name__)
        )

    # Define a pattern based on the delimiter type
    if delimiter == Delimiter.PIPE:
        warnings.warn(
            "Delimiter.PIPE is deprecated for LLM output parsing. "
            "Prompts should return JSON format instead. "
            "Use json_utils.parse_json_list() or json_utils.parse_json_dict() instead.",
            DeprecationWarning,
            stacklevel=2,
        )
        pattern = PIPE_PATTERN
    elif delimiter == Delimiter.BACKTICK:
        pattern = BACKTICK_PATTERN
    elif delimiter == Delimiter.TRIPLE_BACKTICK:
        pattern = TRIPLE_BACKTICK_PATTERN
    else:
        raise ValueError("Unsupported delimiter. Use one of the Delimiter enum values.")

    # Find all matches based on the pattern
    matches = re.findall(pattern=pattern, string=raw_output)

    if len(matches) == 0:
        return "N/A"

    # Adjust for negative indices
    if match_index < 0:
        match_index += len(matches)

    if not 0 <= match_index < len(matches):
        warnings.warn("Index out of range. Returning the last match by default.")
        match_index = -1

    return matches[match_index]


def page_key_sort(page_key: str) -> tuple[int, float | str]:
    """Sorts page keys, prioritizing numeric keys first.

    Args:
        page_key (str): The page key to sort.

    Returns:
        tuple[int, float|str]: A tuple where the first element is 0 for numeric keys and 1 for non-numeric keys, and the second element is the numeric value for numeric keys or the original string for non-numeric keys.

    Example:
        >>> page_key_sort("1")
        (0, 1.0)
        >>> page_key_sort("A")
        (1, "A")


    Example Usage with mixed types:
        >>> L = ["10", "2", "A", "1"]
        >>> sort(L, key=page_key_sort)
        >>> print(L)
        ['1', '2', '10', 'A']

    """
    try:
        return (0, float(page_key))  # Numeric pages first, in numerical order
    except ValueError:
        return (1, str(page_key))  # Non-numeric pages after, in alphabetical order


def json_parsing_search(response_text: str, field_list: list[str]) -> dict[str, str]:
    """
    Parses a JSON-like string to extract values for fields.

    This function attempts to clean and format the input string to resemble a valid JSON structure.
    It then searches for the specified fields within the string and extracts their corresponding values.

    Parameters:
        response_text (str): The JSON-like string to parse.
        field_list (list[str]): A list of field names to search for in the response_text.

    Returns:
        dict[str, str]: A dictionary where keys are field names and values are the extracted values from the response_text.
    """
    try:
        if response_text.split("{", 1)[1].strip()[0] == '"':
            response_text = "{" + response_text.split("{", 1)[1]
        else:
            response_text = "{" + response_text
    except:
        response_text = "{" + response_text

    if len(response_text.split("}", 1)) > 1:
        if response_text.rsplit("}", 1)[0].strip()[-1] == '"':
            response_text = response_text.rsplit("}", 1)[0] + "}"
        elif response_text.rsplit("}", 1)[0].strip()[-1] == "}":
            response_text = response_text.rsplit("}", 1)[0]
    else:
        response_text = response_text.rstrip(",") + "}"

    field_l = []
    answer_l = []
    position_dict = {}

    for f in field_list:
        location = response_text.find('"' + f + '"')
        if location != -1:
            position_dict[location] = f

    field_list = list(dict(sorted(position_dict.items())).values())

    for f in field_list:
        if f in response_text:
            field_l.append(f)
            value = response_text.split('"' + f + '"')[0]
            response_text = response_text.split('"' + f + '"')[1]
            if f != field_list[0] and f != field_list[-1]:
                answer_l.append(
                    value.strip("\n")
                    .strip('"')
                    .strip(":")
                    .strip(" ")
                    .strip("\n")
                    .strip(" ")
                    .strip(",")
                    .strip('"')
                )
            elif f == field_list[-1]:
                answer_l.append(
                    value.strip("\n")
                    .strip('"')
                    .strip(":")
                    .strip(" ")
                    .strip("\n")
                    .strip(" ")
                    .strip(",")
                    .strip('"')
                )
                answer_l.append(
                    response_text.strip("\n")
                    .strip('"')
                    .strip(":")
                    .strip(" ")
                    .strip("}")
                    .strip("\n")
                    .strip(" ")
                    .strip('"')
                )

    return dict(zip(field_l, answer_l))


def universal_json_load(s: str):
    """
    Extract and parse all highest-level valid JSON objects from a string.
    Returns the last valid top-level JSON object found (dict, list, etc).
    Raises ValueError if no valid JSON objects are found. Does not clean up syntax.

    .. deprecated:: 2026.02
        This function is deprecated and will be removed in a future version.
        Use `json_utils.parse_json_dict()` for dictionary outputs or
        `json_utils.parse_json_list()` for list outputs instead.
        The new parsers provide type-specific parsing with better error handling.
    """
    warnings.warn(
        "universal_json_load is deprecated and will be removed in a future version. "
        "Use json_utils.parse_json_dict() or json_utils.parse_json_list() instead.",
        DeprecationWarning,
        stacklevel=2,
    )

    if not isinstance(s, str):
        raise TypeError(f"Expected string input, got {type(s).__name__}")
    found = []
    i = 0
    while i < len(s):
        if s[i] in "{[":
            start = i
            stack = [s[i]]
            j = i + 1
            while j < len(s):
                # Skip over string literals to avoid counting brackets inside strings
                if s[j] == '"':
                    j += 1
                    while j < len(s):
                        if s[j] == "\\" and j + 1 < len(s):
                            j += 2  # Skip escaped character
                            continue
                        if s[j] == '"':
                            break
                        j += 1
                elif s[j] in "{[":
                    stack.append(s[j])
                elif s[j] in "}]":
                    if not stack:
                        break
                    open_bracket = stack.pop()
                    if (open_bracket == "{" and s[j] != "}") or (
                        open_bracket == "[" and s[j] != "]"
                    ):
                        break
                    if not stack:
                        candidate = s[start : j + 1]
                        # Only consider top-level objects (not nested)
                        # Check if start is not inside another object
                        # This is guaranteed by stack being empty only at top-level
                        try:
                            obj = json.loads(candidate)
                            found.append(obj)
                        except Exception:
                            pass
                        i = j  # Move past this top-level object
                        break
                j += 1
            else:
                pass
        i += 1
    if found:
        return found[-1]
    raise ValueError(f"No valid JSON objects found in string: {s[:100]}...")


reimbursement_strings = [  # These are for `method='keyword'`
    "%",
    "$",
    "percent",
    "billed charges",
    "compensation",
    "reimbursement",
    "fee schedule",
]
reimb_regex = r"(?<![$%])(?:\$\d+|\d+[$%])(?![$%])"


# TODO: check should this always be checking just page 1?  Bc in file_processing and table_utils it is but in preprocessing_funcs it's checking specific pages
def contains_reimbursement(text, page="1", method="keyword"):
    """
    Checks if the given text contains any reimbursement-related keywords or patterns.

    Args:
        text (str or dict): The text to check. If a dictionary is provided, it should have page numbers as keys.
        page (str, optional): The page number to check in the dictionary. Defaults to "1".
        method (str, optional): The method to use for checking. Options are "keyword"
         and "regex". Defaults to "keyword".

    Returns:
        bool: True if any reimbursement-related keyword or pattern is found, False otherwise.
    """
    if method == "keyword":
        if isinstance(text, dict):
            text_to_check = text.get(page, "").lower()
        elif isinstance(text, str):
            text_to_check = text.lower()
        else:
            logging.warning("contains_reimbursement - Invalid data type")
            return False
        return any(keyword in text_to_check for keyword in reimbursement_strings)
    elif method == "regex":
        return bool(re.search(reimb_regex, text))
    else:
        raise ValueError("Invalid method. Choose 'keyword' or 'regex'.")


def count_reimbursements_in_exhibit(exhibit_text: str) -> int:  # JUST by regex
    """Counts reimbursements in an exhibit text.  Reimbursements are detected by a regex
    search as defined by `reimb_regex`.

    Args:
        exhibit_text (str): Input exhibit text

    Returns:
        int: Number of reimbursements detected
    """
    return len(re.findall(reimb_regex, exhibit_text))


# Overloads for type checking
@overload
def is_empty(value: str, pd_mask: bool = True) -> bool: ...


@overload
def is_empty(value: list, pd_mask: bool = True) -> bool: ...


@overload
def is_empty(value: pd.Series, pd_mask: Literal[True] = True) -> pd.Series: ...


@overload
def is_empty(value: pd.Series, pd_mask: Literal[False]) -> bool: ...


def is_empty(value: str | list | pd.Series, pd_mask: bool = True) -> bool | pd.Series:
    """
    Checks if a value is considered empty or invalid.

    Args:
        value: The value to check.
        Can be a string, list, or pandas Series.
        pd_mask (bool): Only relevant for Series inputs.
            If True, returns a mask for empty values in a pandas Series.
            Otherwise, the function returns True iff the entire Series is empty.

    Returns:
        bool | pd.Series:
            - When the input is a string, returns True if the value is empty or invalid, False otherwise.
            - When the input is a list, returns True if the list is empty or all elements are empty/invalid.
            - When the input is a pandas Series:
                - If `pd_mask` is True, returns a boolean mask indicating which elements are empty or invalid.
                - If `pd_mask` is False, returns True iff all elements are empty/invalid, or if the entire Series is empty.
    """
    empty_values = [None, "", "N/A", "NA", "null", "none", "NaN", np.nan, "nan", "None"]

    if isinstance(value, list):  # Handle list inputs
        return not value or all(is_empty(v) for v in value)

    # if it's a pd.Series, return the mask (True for empty values)
    if isinstance(value, pd.Series):
        if pd_mask:
            return (
                value.isna()
                | (value.isin(empty_values))
                | (value.astype(str).str.strip() == "")
                | (
                    value.astype(str)
                    .str.lower()
                    .str.strip()
                    .isin(["n/a", "na", "null", "none", "nan"])
                )
            )
        else:
            # If the Series is empty, it's considered empty
            if value.empty:
                return True
            # Use the string-version check for each element in the Series
            return all(is_empty(str(v)) for v in value)

    if pd.isna(value):
        return True

    if isinstance(value, str):
        if not value or value.isspace():
            return True
        lower_stripped = value.lower().strip()
        if lower_stripped in [
            "n/a",
            "na",
            "null",
            "none",
            "nan",
            "no_identifiers_found",
        ]:
            return True

    return value in empty_values


def datetime_str():
    return datetime.now().strftime("[%Y-%m-%d %H:%M:%S]")


def extract_signature_page(text_dict: dict, filename: str) -> dict:
    """Extracts the signature page(s) from a contract text dictionary and returns it as a dictionary.
    First tries to match the textract marker "this page has N signature(s)", then falls back to looking
    for the word "signature" if no matches are found.  "this page has N signature(s)" is Textract's marker for signatures

    Args:
        text_dict (dict): A dictionary containing text data, organized by pages.
        filename (str): The name of the file being processed.

    Returns:
        dict: A dictionary containing the extracted signature page(s).
    """

    signature_pages = {}
    # This regex pattern matches the Textract marker for signatures
    # It will look for both numeric and a few textual representations of numbers (one-ten)
    textract_pattern = re.compile(
        r"this page has (?:\d+|one|two|three|four|five|six|seven|eight|nine|ten) signature",
        re.IGNORECASE,
    )

    keywords = [
        "signature and information",
        "signature authorization",
        "in witness whereof",
    ]

    # First pass: Try to match the full textract pattern
    for page_num, page_text in text_dict.items():
        if textract_pattern.search(page_text):
            signature_pages[page_num] = page_text

    # If no pages found with textract pattern, try with  keyword
    if not signature_pages:
        # Iterate through each page in the text dictionary and check for keywords
        for page_num, page_text in text_dict.items():
            lower_text = page_text.lower()
            if any(keyword in lower_text for keyword in keywords):
                signature_pages[page_num] = page_text

    # If no signature pages found, try checking for the word "signature page follows" and return the next page only if it exists
    if not signature_pages:
        for page_num, page_text in text_dict.items():
            if "signature page follows" in page_text.lower():
                next_page_num = str(int(page_num) + 1)
                if next_page_num in text_dict:
                    signature_pages[next_page_num] = text_dict[next_page_num]

    return signature_pages


def extract_effective_date_pages(text_dict: dict, filename: str) -> dict:
    """
    Extracts the effective date page(s) from a contract text dictionary and returns it as a dictionary.

    This function identifies pages that either contain the Textract marker
    "this page has N signature(s)" or the keyword 'effective date:'.
    Additionally, it ensures that the first page (page 1) is always included
    in the result, as it often contains effective date.

    Args:
        text_dict (dict): A dictionary containing text data, organized by pages.
        filename (str): The name of the file being processed.

    Returns:
        dict: A dictionary containing the extracted effective date page(s),
              where keys are page numbers and values are the corresponding page text.
    """

    effective_date_pages = {}

    # Regex pattern to match the Textract marker for signature pages which often has effective dates
    # This pattern accounts for both numeric and textual representations of numbers (one-ten).
    textract_pattern = re.compile(
        r"this page has (?:\d+|one|two|three|four|five|six|seven|eight|nine|ten) signature",
        re.IGNORECASE,
    )

    # Iterate through each page in the text dictionary.
    for page_num, page_text in text_dict.items():
        # page_num = int(float(page_num))
        # Check if the page contains the Textract marker or the keyword 'effective date:'.
        if textract_pattern.search(page_text) or "effective date:" in page_text.lower():
            effective_date_pages[page_num] = page_text
        # Always include the first page (page 1) as it often contains effective date.
        elif page_num == 1 or page_num == "1" or page_num == "1.0":
            effective_date_pages[page_num] = page_text

    return effective_date_pages


def normalize_state_to_abbreviation(state_value: str) -> str:
    """Convert state names or abbreviations to standardized two-letter abbreviations.

    Handles various formats of state names and abbreviations, including full names,
    common abbreviations, and variations. Returns the standardized two-letter abbreviation
    or the original value if no match is found.

    Args:
        state_value (str): State name or abbreviation from extraction

    Returns:
        str: Two-letter state abbreviation or original value if no match found
    """
    if not state_value or state_value in ["N/A", "UNKNOWN"]:
        return state_value

    # Dictionary mapping full state names to abbreviations
    state_mapping = {
        "alabama": "AL",
        "alaska": "AK",
        "arizona": "AZ",
        "arkansas": "AR",
        "california": "CA",
        "colorado": "CO",
        "connecticut": "CT",
        "delaware": "DE",
        "florida": "FL",
        "georgia": "GA",
        "hawaii": "HI",
        "idaho": "ID",
        "illinois": "IL",
        "indiana": "IN",
        "iowa": "IA",
        "kansas": "KS",
        "kentucky": "KY",
        "louisiana": "LA",
        "maine": "ME",
        "maryland": "MD",
        "massachusetts": "MA",
        "michigan": "MI",
        "minnesota": "MN",
        "mississippi": "MS",
        "missouri": "MO",
        "montana": "MT",
        "nebraska": "NE",
        "nevada": "NV",
        "new hampshire": "NH",
        "new jersey": "NJ",
        "new mexico": "NM",
        "new york": "NY",
        "north carolina": "NC",
        "north dakota": "ND",
        "ohio": "OH",
        "oklahoma": "OK",
        "oregon": "OR",
        "pennsylvania": "PA",
        "rhode island": "RI",
        "south carolina": "SC",
        "south dakota": "SD",
        "tennessee": "TN",
        "texas": "TX",
        "utah": "UT",
        "vermont": "VT",
        "virginia": "VA",
        "washington": "WA",
        "west virginia": "WV",
        "wisconsin": "WI",
        "wyoming": "WY",
        "district of columbia": "DC",
    }

    # Clean and normalize the input
    cleaned_state = state_value.strip().lower()

    # Check if it's already a valid 2-letter abbreviation
    if len(cleaned_state) == 2 and cleaned_state.upper() in state_mapping.values():
        return cleaned_state.upper()

    # Check if it matches a full state name
    if cleaned_state in state_mapping:
        return state_mapping[cleaned_state]

    # Handle common variations
    variations = {
        "calif": "CA",
        "cal": "CA",
        "fla": "FL",
        "florida": "FL",
        "ny": "NY",
        "n.y.": "NY",
        "tx": "TX",
        "tex": "TX",
        "penn": "PA",
        "pa": "PA",
        "mass": "MA",
        "massachusetts": "MA",
        "wash": "WA",
        "washington state": "WA",
        "d.c.": "DC",
        "dc": "DC",
        "washington dc": "DC",
    }

    if cleaned_state in variations:
        return variations[cleaned_state]

    # If no match found, return original value
    return state_value


def flatten_to_strings(items) -> list[str]:
    """Helper function to flatten and stringify any nested structure

    Args:
        items (Any): The input items; can be a list, tuple, or single value

    Returns:
        list[str]: A flattened list of stringified items
    """
    result = []
    if isinstance(items, (list, tuple)):
        for item in items:
            if isinstance(item, (list, tuple)):
                result.extend(flatten_to_strings(item))
            else:
                result.append(str(item).strip())
    else:
        result.append(str(items).strip())

    return result


def parse_state_field_to_list(raw_value):
    """
    Convert PROVIDER_STATE raw input into a clean list of state strings.

    Accepts:
      - list                        → returned as-is
      - JSON list string            → parsed to list
      - comma-separated string      → split to list
      - single string               → wrapped in list
      - other types                 → converted to string → wrapped in list

    Returns:
        list[str]: Clean list of state strings
    """
    if raw_value is None:
        return []

    # Case 1: Already a list
    if isinstance(raw_value, list):
        return raw_value

    # Case 2: String input
    if isinstance(raw_value, str):
        stripped = raw_value.strip()

        # JSON list string
        if stripped.startswith("[") and stripped.endswith("]"):
            try:
                parsed = json.loads(stripped)
                if isinstance(parsed, list):
                    return parsed
                else:
                    return [parsed]
            except Exception as e:
                logging.debug(
                    f"Failed JSON parse for state list: {e}. Falling back to comma split."
                )
                # fallback: treat as comma-separated

        # Comma-separated list
        if "," in stripped:
            return [s.strip() for s in stripped.split(",") if s.strip()]

        # Single state string
        return [stripped]

    # Case 3: Any other type (int, dict, etc.)
    try:
        return [str(raw_value).strip()]
    except Exception:
        logging.error(f"Could not convert value to string: {raw_value!r}")
        return []


def normalize_state_field(answers_dict: dict, field_name: str = "PROVIDER_STATE"):
    if field_name not in answers_dict or not answers_dict[field_name]:
        answers_dict[field_name] = []
        return answers_dict

    raw_value = answers_dict[field_name]

    # ---- Step 1: Parse into list ----
    state_list = parse_raw_state_value(raw_value)

    # ---- Step 2: Normalize each state ----
    normalized_states = []
    for state in state_list:
        try:
            normalized_states.append(normalize_state_to_abbreviation(state))
        except Exception as e:
            logging.warning(
                f"Failed to normalize state '{state}': {e}. Keeping original."
            )
            normalized_states.append(state)

    answers_dict[field_name] = normalized_states
    return answers_dict


def parse_raw_state_value(raw_value):
    """
    Parses various input formats into a flat list of state strings.
    Handles: strings, lists, nested lists, and various delimiters.
    """
    if not raw_value:
        return []

    # If it's a string that looks like a list, try to parse it
    if isinstance(raw_value, str):
        stripped = raw_value.strip()

        # Try to parse as Python literal (handles "[['NC', 'WA'], 'CA|UT']")
        if stripped.startswith("["):
            try:
                parsed = ast.literal_eval(stripped)
                return parse_raw_state_value(parsed)  # Recursive call with parsed list
            except (ValueError, SyntaxError):
                pass  # Not valid Python literal, continue with string parsing

        # Regular string splitting on delimiters (not whitespace - state names can have spaces like "West Virginia")
        states = re.split(r"[|,;]+", stripped)
        return [s.strip() for s in states if s.strip()]

    if isinstance(raw_value, list):
        result = []
        for item in raw_value:
            result.extend(parse_raw_state_value(item))  # Recursive call
        return result

    return [str(raw_value)]


def normalize_to_json_list(val):
    """
    Normalizes a value to a JSON list format.
    Handles: pipe-delimited strings, string representations of Python lists,
    actual lists with pipe-delimited items, and single values.
    """
    # Handle None and empty values first
    if is_empty(val):
        return val

    # Handle pandas NA/NaN - check for scalar first
    try:
        if pd.isna(val):
            return val
    except (ValueError, TypeError):
        # pd.isna fails on arrays/lists, which is fine - we'll handle them below
        pass

    if isinstance(val, str):
        # Check if it's a string representation of a Python list (e.g., "['item1', 'item2']")
        if val.startswith("[") and val.endswith("]"):
            try:
                # Try JSON parse first
                parsed = json.loads(val)
            except json.JSONDecodeError:
                # If JSON fails, try ast.literal_eval for Python list syntax
                try:
                    parsed = ast.literal_eval(val)
                except (ValueError, SyntaxError):
                    return val

            if isinstance(parsed, list):
                expanded = []
                for item in parsed:
                    if isinstance(item, str) and "|" in item:
                        expanded.extend(item.split("|"))
                    else:
                        expanded.append(item)
                result = json.dumps(expanded)
                return result
            return val

        # Pipe-delimited format - convert to JSON list
        if "|" in val:
            result = json.dumps(val.split("|"))
            return result

        # Single value - convert to JSON list
        result = json.dumps([val])
        return result

    if isinstance(val, list):
        # Check if list contains pipe-delimited strings and expand them
        expanded = []
        for item in val:
            if isinstance(item, str) and "|" in item:
                expanded.extend(item.split("|"))
            else:
                expanded.append(item)
        result = json.dumps(expanded)
        return result
    return val


def normalize_one_to_one_field_value(field_name: str, value) -> str | list:
    """
    Normalize a 1:1 field value to ensure single-value fields are strings, not lists.

    This function handles the common case where LLM returns a single-value field as
    a list with one element (e.g., ['The Value']). It extracts the string from such
    lists while preserving legitimate multi-value lists.

    Args:
        field_name (str): Name of the field being normalized (for logging/debugging)
        value: The field value to normalize. Can be:
            - str: Returned as-is
            - list with 1 element: Extract the string
            - list with >1 elements: Returned unchanged (may be legitimate multi-value)
            - None/empty: Returned as-is

    Returns:
        str | list: Normalized value:
            - If input is a list with exactly 1 element: returns that element as string
            - If input is already a string: returns as-is
            - If input is a list with >1 elements: returns list unchanged
            - If input is None or empty: returns as-is

    Examples:
        >>> normalize_one_to_one_field_value("CONTRACT_TITLE", ["Provider Agreement"])
        'Provider Agreement'
        >>> normalize_one_to_one_field_value("PAYER_NAME", "Aetna")
        'Aetna'
        >>> normalize_one_to_one_field_value("PROVIDER_NAME", ["Provider A", "Provider B"])
        ['Provider A', 'Provider B']  # Multi-value preserved
        >>> normalize_one_to_one_field_value("LOB", ["Medicare", "Medicaid"])
        ['Medicare', 'Medicaid']  # Multi-value preserved
    """
    # Handle None and empty values
    if value is None or (isinstance(value, str) and is_empty(value)):
        return value

    # If it's already a string, return as-is
    if isinstance(value, str):
        return value

    # If it's a list
    if isinstance(value, list):
        # List with exactly 1 element: extract the string
        if len(value) == 1:
            return str(value[0]) if value[0] is not None else "N/A"
        # List with >1 elements: preserve (may be legitimate multi-value)
        # This handles cases like PROVIDER_NAME with multiple providers,
        # or dynamic fields (LOB, PROGRAM, PRODUCT, NETWORK) when passed to 1:1
        elif len(value) > 1:
            return value
        # Empty list: return "N/A"
        else:
            return "N/A"

    # For any other type, convert to string
    return str(value) if value is not None else "N/A"


def normalize_one_to_one_answers_dict(
    answers_dict: dict[str, str | list],
) -> dict[str, str | list]:
    """
    Normalize all values in a 1:1 answers dictionary.

    Applies normalization to all fields in the dictionary, converting single-element
    lists to strings while preserving legitimate multi-value lists.

    Args:
        answers_dict (dict): Dictionary of field names to values (may be strings or lists)

    Returns:
        dict: Dictionary with normalized values (single-element lists converted to strings)

    Example:
        >>> answers = {
        ...     "CONTRACT_TITLE": ["Provider Agreement"],
        ...     "PAYER_NAME": "Aetna",
        ...     "PROVIDER_NAME": ["Provider A", "Provider B"]
        ... }
        >>> normalize_one_to_one_answers_dict(answers)
        {
            "CONTRACT_TITLE": "Provider Agreement",
            "PAYER_NAME": "Aetna",
            "PROVIDER_NAME": ["Provider A", "Provider B"]
        }
    """
    DO_NOT_NORMALIZE = ["PROV_INFO_JSON", "PAYER_STATE", "PROVIDER_STATE"]
    normalized = {}
    for field_name, value in answers_dict.items():
        if field_name not in DO_NOT_NORMALIZE:
            normalized[field_name] = normalize_one_to_one_field_value(field_name, value)
        else:
            normalized[field_name] = value

    return normalized