# What is this?
## Helper utilities for token counting
import base64
import io
import struct
from typing import (
    Any,
    Callable,
    List,
    Literal,
    Mapping,
    Optional,
    Tuple,
    Union,
    cast,
)

import tiktoken

import litellm
from litellm import verbose_logger
from litellm.constants import (
    DEFAULT_IMAGE_HEIGHT,
    DEFAULT_IMAGE_TOKEN_COUNT,
    DEFAULT_IMAGE_WIDTH,
    MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES,
    MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES,
    MAX_TILE_HEIGHT,
    MAX_TILE_WIDTH,
)
from litellm.litellm_core_utils.default_encoding import encoding as default_encoding
from litellm.llms.custom_httpx.http_handler import _get_httpx_client
from litellm.types.llms.anthropic import (
    AnthropicMessagesToolResultParam,
    AnthropicMessagesToolUseParam,
)
from litellm.types.llms.openai import (
    AllMessageValues,
    ChatCompletionNamedToolChoiceParam,
    ChatCompletionToolParam,
    OpenAIMessageContent,
)
from litellm.types.utils import Message, SelectTokenizerResponse


def get_modified_max_tokens(
    model: str,
    base_model: str,
    messages: Optional[List[AllMessageValues]],
    user_max_tokens: Optional[int],
    buffer_perc: Optional[float],
    buffer_num: Optional[float],
) -> Optional[int]:
    """
    Params:

    Returns the user's max output tokens, adjusted for:
    - the size of input - for models where input + output can't exceed X
    - model max output tokens - for models where there is a separate output token limit
    """
    try:
        if user_max_tokens is None:
            return None

        ## MODEL INFO
        _model_info = litellm.get_model_info(model=model)

        max_output_tokens = litellm.get_max_tokens(
            model=base_model
        )  # assume min context window is 4k tokens

        ## UNKNOWN MAX OUTPUT TOKENS - return user defined amount
        if max_output_tokens is None:
            return user_max_tokens

        input_tokens = litellm.token_counter(model=base_model, messages=messages)

        # token buffer
        if buffer_perc is None:
            buffer_perc = 0.1
        if buffer_num is None:
            buffer_num = 10
        token_buffer = max(
            buffer_perc * input_tokens, buffer_num
        )  # give at least a 10 token buffer. token counting can be imprecise.

        input_tokens += int(token_buffer)
        verbose_logger.debug(
            f"max_output_tokens: {max_output_tokens}, user_max_tokens: {user_max_tokens}"
        )
        ## CASE 1: model input + output can't exceed X - happens when max input = max output, e.g. gpt-3.5-turbo
        if _model_info["max_input_tokens"] == max_output_tokens:
            verbose_logger.debug(
                f"input_tokens: {input_tokens}, max_output_tokens: {max_output_tokens}"
            )
            if input_tokens > max_output_tokens:
                pass  # allow call to fail normally - don't set max_tokens to negative.
            elif (
                user_max_tokens + input_tokens > max_output_tokens
            ):  # we can still modify to keep it positive but below the limit
                verbose_logger.debug(
                    f"MODIFYING MAX TOKENS - user_max_tokens={user_max_tokens}, input_tokens={input_tokens}, max_output_tokens={max_output_tokens}"
                )
                user_max_tokens = int(max_output_tokens - input_tokens)
        ## CASE 2: user_max_tokens> model max output tokens
        elif user_max_tokens > max_output_tokens:
            user_max_tokens = max_output_tokens

        verbose_logger.debug(
            f"litellm.litellm_core_utils.token_counter.py::get_modified_max_tokens() - user_max_tokens: {user_max_tokens}"
        )

        return user_max_tokens
    except Exception as e:
        verbose_logger.debug(
            "litellm.litellm_core_utils.token_counter.py::get_modified_max_tokens() - Error while checking max token limit: {}\nmodel={}, base_model={}".format(
                str(e), model, base_model
            )
        )
        return user_max_tokens


def resize_image_high_res(
    width: int,
    height: int,
) -> Tuple[int, int]:
    # Maximum dimensions for high res mode
    max_short_side = MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES
    max_long_side = MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES

    # Return early if no resizing is needed
    if (
        width <= MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES
        and height <= MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES
    ):
        return width, height

    # Determine the longer and shorter sides
    longer_side = max(width, height)
    shorter_side = min(width, height)

    # Calculate the aspect ratio
    aspect_ratio = longer_side / shorter_side

    # Resize based on the short side being 768px
    if width <= height:  # Portrait or square
        resized_width = max_short_side
        resized_height = int(resized_width * aspect_ratio)
        # if the long side exceeds the limit after resizing, adjust both sides accordingly
        if resized_height > max_long_side:
            resized_height = max_long_side
            resized_width = int(resized_height / aspect_ratio)
    else:  # Landscape
        resized_height = max_short_side
        resized_width = int(resized_height * aspect_ratio)
        # if the long side exceeds the limit after resizing, adjust both sides accordingly
        if resized_width > max_long_side:
            resized_width = max_long_side
            resized_height = int(resized_width / aspect_ratio)

    return resized_width, resized_height


# Test the function with the given example
def calculate_tiles_needed(
    resized_width,
    resized_height,
    tile_width=MAX_TILE_WIDTH,
    tile_height=MAX_TILE_HEIGHT,
):
    tiles_across = (resized_width + tile_width - 1) // tile_width
    tiles_down = (resized_height + tile_height - 1) // tile_height
    total_tiles = tiles_across * tiles_down
    return total_tiles


def get_image_type(image_data: bytes) -> Union[str, None]:
    """take an image (really only the first ~100 bytes max are needed)
    and return 'png' 'gif' 'jpeg' 'webp' 'heic' or None. method added to
    allow deprecation of imghdr in 3.13"""

    if image_data[0:8] == b"\x89\x50\x4e\x47\x0d\x0a\x1a\x0a":
        return "png"

    if image_data[0:4] == b"GIF8" and image_data[5:6] == b"a":
        return "gif"

    if image_data[0:3] == b"\xff\xd8\xff":
        return "jpeg"

    if image_data[4:8] == b"ftyp":
        return "heic"

    if image_data[0:4] == b"RIFF" and image_data[8:12] == b"WEBP":
        return "webp"

    return None


def get_image_dimensions(
    data: str,
) -> Tuple[int, int]:
    """
    Async Function to get the dimensions of an image from a URL or base64 encoded string.

    Args:
        data (str): The URL or base64 encoded string of the image.

    Returns:
        Tuple[int, int]: The width and height of the image.
    """
    img_data = None
    try:
        # Try to open as URL
        client = _get_httpx_client()
        response = client.get(data)
        img_data = response.read()
    except Exception:
        # If not URL, assume it's base64
        _header, encoded = data.split(",", 1)
        img_data = base64.b64decode(encoded)

    img_type = get_image_type(img_data)

    if img_type == "png":
        w, h = struct.unpack(">LL", img_data[16:24])
        return w, h
    elif img_type == "gif":
        w, h = struct.unpack("<HH", img_data[6:10])
        return w, h
    elif img_type == "jpeg":
        with io.BytesIO(img_data) as fhandle:
            fhandle.seek(0)
            size = 2
            ftype = 0
            while not 0xC0 <= ftype <= 0xCF or ftype in (0xC4, 0xC8, 0xCC):
                fhandle.seek(size, 1)
                byte = fhandle.read(1)
                while ord(byte) == 0xFF:
                    byte = fhandle.read(1)
                ftype = ord(byte)
                size = struct.unpack(">H", fhandle.read(2))[0] - 2
            fhandle.seek(1, 1)
            h, w = struct.unpack(">HH", fhandle.read(4))
        return w, h
    elif img_type == "webp":
        # For WebP, the dimensions are stored at different offsets depending on the format
        # Check for VP8X (extended format)
        if img_data[12:16] == b"VP8X":
            w = struct.unpack("<I", img_data[24:27] + b"\x00")[0] + 1
            h = struct.unpack("<I", img_data[27:30] + b"\x00")[0] + 1
            return w, h
        # Check for VP8 (lossy format)
        elif img_data[12:16] == b"VP8 ":
            w = struct.unpack("<H", img_data[26:28])[0] & 0x3FFF
            h = struct.unpack("<H", img_data[28:30])[0] & 0x3FFF
            return w, h
        # Check for VP8L (lossless format)
        elif img_data[12:16] == b"VP8L":
            bits = struct.unpack("<I", img_data[21:25])[0]
            w = (bits & 0x3FFF) + 1
            h = ((bits >> 14) & 0x3FFF) + 1
            return w, h

    # return sensible default image dimensions if unable to get dimensions
    return DEFAULT_IMAGE_WIDTH, DEFAULT_IMAGE_HEIGHT


def calculate_img_tokens(
    data,
    mode: Literal["low", "high", "auto"] = "auto",
    base_tokens: int = 85,  # openai default - https://openai.com/pricing
    use_default_image_token_count: bool = False,
):
    """
    Calculate the number of tokens for an image.

    Args:
        data (str): The URL or base64 encoded string of the image.
        mode (Literal["low", "high", "auto"]): The mode to use for calculating the number of tokens.
        base_tokens (int): The base number of tokens for an image.
        use_default_image_token_count (bool): When True, will NOT make a GET request to the image URL and instead return the default image dimensions.

    Returns:
        int: The number of tokens for the image.
    """
    if use_default_image_token_count:
        verbose_logger.debug(
            "Using default image token count: {}".format(DEFAULT_IMAGE_TOKEN_COUNT)
        )
        return DEFAULT_IMAGE_TOKEN_COUNT
    if mode == "low" or mode == "auto":
        return base_tokens
    elif mode == "high":
        # Run the async function using the helper
        width, height = get_image_dimensions(
            data=data,
        )
        resized_width, resized_height = resize_image_high_res(
            width=width, height=height
        )
        tiles_needed_high_res = calculate_tiles_needed(
            resized_width=resized_width, resized_height=resized_height
        )
        tile_tokens = (base_tokens * 2) * tiles_needed_high_res
        total_tokens = base_tokens + tile_tokens
        return total_tokens


TokenCounterFunction = Callable[[str], int]
"""
Type for a function that counts tokens in a string.
"""


class _MessageCountParams:
    """
    A class to hold the parameters for counting tokens in messages.
    """

    def __init__(
        self,
        model: str,
        custom_tokenizer: Optional[Union[dict, SelectTokenizerResponse]],
    ):
        from litellm.utils import print_verbose

        actual_model = _fix_model_name(model)
        if actual_model == "gpt-3.5-turbo-0301":
            self.tokens_per_message = (
                4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
            )
            self.tokens_per_name = -1  # if there's a name, the role is omitted
        elif actual_model in litellm.open_ai_chat_completion_models:
            self.tokens_per_message = 3
            self.tokens_per_name = 1
        elif actual_model in litellm.azure_llms:
            self.tokens_per_message = 3
            self.tokens_per_name = 1
        else:
            print_verbose(
                f"Warning: unknown model {model}. Using default token params."
            )
            self.tokens_per_message = 3
            self.tokens_per_name = 1
        self.count_function = _get_count_function(model, custom_tokenizer)


def token_counter(
    model="",
    custom_tokenizer: Optional[Union[dict, SelectTokenizerResponse]] = None,
    text: Optional[Union[str, List[str]]] = None,
    messages: Optional[List[Union[AllMessageValues, Message]]] = None,
    count_response_tokens: Optional[bool] = False,
    tools: Optional[List[ChatCompletionToolParam]] = None,
    tool_choice: Optional[ChatCompletionNamedToolChoiceParam] = None,
    use_default_image_token_count: Optional[bool] = False,
    default_token_count: Optional[int] = None,
) -> int:
    """
    Count the number of tokens in a given text using a specified model.

    Args:
    model (str): The name of the model to use for tokenization. Default is an empty string.
    custom_tokenizer (Optional[dict]): A custom tokenizer created with the `create_pretrained_tokenizer` or `create_tokenizer` method. Must be a dictionary with a string value for `type` and Tokenizer for `tokenizer`. Default is None.
    text (str): The raw text string to be passed to the model. Default is None.
    messages (Optional[List[AllMessageValues]]): Alternative to passing in text. A list of dictionaries representing messages with "role" and "content" keys. Default is None.
    count_response_tokens (Optional[bool]): set to True to indicate we are processing a stream response.
    tools (Optional[List[ChatCompletionToolParam]]): The available tools. Default is None.
    tool_choice (Optional[ChatCompletionNamedToolChoiceParam]): The tool choice. Default is None.
    use_default_image_token_count (Optional[bool]): When True, will NOT make a GET request to the image URL and instead return the default image dimensions. Default is False.
    default_token_count (Optional[int]): The default number of tokens to return for a message block, if an error occurs. Default is None.

    Returns:
    int: The number of tokens in the text.
    """
    from litellm.utils import convert_list_message_to_dict

    #########################################################
    # Flag to disable token counter
    # We've gotten reports of this consuming CPU cycles,
    # exposing this flag to allow users to disable
    # it to confirm if this is indeed the issue
    #########################################################
    if litellm.disable_token_counter is True:
        return 0

    verbose_logger.debug(
        f"messages in token_counter: {messages}, text in token_counter: {text}"
    )
    if text is not None and messages is not None:
        raise ValueError("text and messages cannot both be set")
    if use_default_image_token_count is None:
        use_default_image_token_count = False

    if text is not None:
        if tools or tool_choice:
            raise ValueError("tools or tool_choice cannot be set if using text")
        if isinstance(text, List):
            text_to_count = "".join(t for t in text if isinstance(t, str))
        elif isinstance(text, str):
            text_to_count = text
        count_function = _get_count_function(model, custom_tokenizer)
        num_tokens = count_function(text_to_count)

    elif messages is not None:
        new_messages = cast(
            List[AllMessageValues], convert_list_message_to_dict(messages)
        )
        params = _MessageCountParams(model, custom_tokenizer)
        num_tokens = _count_messages(
            params, new_messages, use_default_image_token_count, default_token_count
        )
        if count_response_tokens is False:
            includes_system_message = any(
                [message.get("role", None) == "system" for message in new_messages]
            )
            num_tokens += _count_extra(
                params.count_function, tools, tool_choice, includes_system_message
            )

    else:
        raise ValueError("Either text or messages must be provided")

    return num_tokens


def _count_messages(
    params: _MessageCountParams,
    messages: List[AllMessageValues],
    use_default_image_token_count: bool,
    default_token_count: Optional[int],
) -> int:
    """
    Count the number of tokens in a list of messages.

    Args:
        params (_MessageCountParams): The parameters for counting tokens.
        messages (List[AllMessageValues]): The list of messages to count tokens in.
        use_default_image_token_count (bool): When True, will NOT make a GET request to the image URL and instead return the default image dimensions.
        default_token_count (Optional[int]): The default number of tokens to return for a message block, if an error occurs.
    """
    num_tokens = 0
    if len(messages) == 0:
        return num_tokens
    for message in messages:
        num_tokens += params.tokens_per_message
        for key, value in message.items():
            if value is None:
                pass
            elif key == "tool_calls":
                if isinstance(value, List):
                    for tool_call in value:
                        if "function" in tool_call:
                            function_arguments = tool_call["function"].get(
                                "arguments", []
                            )
                            num_tokens += params.count_function(str(function_arguments))
                        else:
                            raise ValueError(
                                f"Unsupported tool call {tool_call} must contain a function key"
                            )
                else:
                    raise ValueError(
                        f"Unsupported type {type(value)} for key tool_calls in message {message}"
                    )
            elif isinstance(value, str):
                num_tokens += params.count_function(value)
                if key == "name":
                    num_tokens += params.tokens_per_name
            elif key == "content" and isinstance(value, List):
                num_tokens += _count_content_list(
                    params.count_function,
                    value,
                    use_default_image_token_count,
                    default_token_count,
                )
            else:
                # Skip unsupported keys instead of raising an error
                continue
    return num_tokens


def _count_extra(
    count_function: TokenCounterFunction,
    tools: Optional[List[ChatCompletionToolParam]],
    tool_choice: Optional[ChatCompletionNamedToolChoiceParam],
    includes_system_message: bool,
) -> int:
    """Count extra tokens for function definitions and tool choices.
    Args:
        count_function (TokenCounterFunction): The function to count tokens.
        tools (Optional[List[ChatCompletionToolParam]]): The available tools.
        tool_choice (Optional[ChatCompletionNamedToolChoiceParam]): The tool choice.
        includes_system_message (bool): Whether the messages include a system message.
    """

    num_tokens = 3  # every reply is primed with <|start|>assistant<|message|>

    if tools:
        num_tokens += count_function(_format_function_definitions(tools))
        num_tokens += 9  # Additional tokens for function definition of tools
    # If there's a system message and tools are present, subtract four tokens
    if tools and includes_system_message:
        num_tokens -= 4
    # If tool_choice is 'none', add one token.
    # If it's an object, add 4 + the number of tokens in the function name.
    # If it's undefined or 'auto', don't add anything.
    if tool_choice == "none":
        num_tokens += 1
    elif isinstance(tool_choice, dict):
        num_tokens += 7
        num_tokens += count_function(str(tool_choice["function"]["name"]))

    return num_tokens


def _get_count_function(
    model: Optional[str],
    custom_tokenizer: Optional[Union[dict, SelectTokenizerResponse]] = None,
) -> TokenCounterFunction:
    """
    Get the function to count tokens based on the model and custom tokenizer."""
    from litellm.utils import _select_tokenizer, print_verbose

    if model is not None or custom_tokenizer is not None:
        tokenizer_json = custom_tokenizer or _select_tokenizer(model)  # type: ignore
        if tokenizer_json["type"] == "huggingface_tokenizer":

            def count_tokens(text: str) -> int:
                enc = tokenizer_json["tokenizer"].encode(text)
                return len(enc.ids)

        elif tokenizer_json["type"] == "openai_tokenizer":
            model_to_use = _fix_model_name(model)  # type: ignore
            try:
                if "gpt-4o" in model_to_use:
                    encoding = tiktoken.get_encoding("o200k_base")
                else:
                    encoding = tiktoken.encoding_for_model(model_to_use)
            except KeyError:
                print_verbose("Warning: model not found. Using cl100k_base encoding.")
                encoding = tiktoken.get_encoding("cl100k_base")

            def count_tokens(text: str) -> int:
                return len(encoding.encode(text, disallowed_special=()))

        else:
            raise ValueError("Unsupported tokenizer type")
    else:

        def count_tokens(text: str) -> int:
            return len(default_encoding.encode(text, disallowed_special=()))

    return count_tokens


def _fix_model_name(model: str) -> str:
    """We normalize some model names to others"""
    if model in litellm.azure_llms:
        # azure llms use gpt-35-turbo instead of gpt-3.5-turbo 🙃
        return model.replace("-35", "-3.5")
    elif model in litellm.open_ai_chat_completion_models:
        return model  # type: ignore
    else:
        return "gpt-3.5-turbo"


def _count_image_tokens(
    image_url: Any,
    use_default_image_token_count: bool,
) -> int:
    """
    Count tokens for an image_url content block.

    Args:
        image_url: The image URL data - can be a string URL or dict with 'url' and 'detail'
        use_default_image_token_count: Whether to use default image token counts

    Returns:
        int: Number of tokens for the image

    Raises:
        ValueError: If image_url is invalid type or detail value is invalid
    """
    if isinstance(image_url, dict):
        detail = image_url.get("detail", "auto")
        if detail not in ["low", "high", "auto"]:
            raise ValueError(
                f"Invalid detail value: {detail}. Expected 'low', 'high', or 'auto'."
            )
        url = image_url.get("url")
        if not url:
            raise ValueError("Missing required key 'url' in image_url dict.")
        return calculate_img_tokens(
            data=url,
            mode=detail,  # type: ignore
            use_default_image_token_count=use_default_image_token_count,
        )
    elif isinstance(image_url, str):
        if not image_url.strip():
            raise ValueError("Empty image_url string is not valid.")
        return calculate_img_tokens(
            data=image_url,
            mode="auto",
            use_default_image_token_count=use_default_image_token_count,
        )
    else:
        raise ValueError(
            f"Invalid image_url type: {type(image_url).__name__}. "
            "Expected str or dict with 'url' field."
        )


def _validate_anthropic_content(content: Mapping[str, Any]) -> type:
    """
    Validate and determine which Anthropic TypedDict applies.

    Returns the corresponding TypedDict class if recognized, otherwise raises.
    """
    content_type = content.get("type")
    if not content_type:
        raise ValueError("Anthropic content missing required field: 'type'")

    mapping = {
        "tool_use": AnthropicMessagesToolUseParam,
        "tool_result": AnthropicMessagesToolResultParam,
    }

    expected_cls = mapping.get(content_type)
    if expected_cls is None:
        raise ValueError(f"Unknown Anthropic content type: '{content_type}'")

    missing = [
        k for k in getattr(expected_cls, "__required_keys__", set()) if k not in content
    ]
    if missing:
        raise ValueError(
            f"Missing required fields in {content_type} block: {', '.join(missing)}"
        )

    return expected_cls


def _count_anthropic_content(
    content: Mapping[str, Any],
    count_function: TokenCounterFunction,
    use_default_image_token_count: bool,
    default_token_count: Optional[int],
) -> int:
    """
    Count tokens in Anthropic-specific content blocks (tool_use, tool_result, etc.).

    Uses TypedDict definitions from litellm.types.llms.anthropic to determine
    what fields to count and how to handle nested structures.

    Dynamically infers which fields to count based on the TypedDict definition,
    avoiding hardcoded field names.
    """
    typeddict_cls = _validate_anthropic_content(content)
    type_hints = getattr(typeddict_cls, "__annotations__", {})
    tokens = 0

    # Fields to skip (metadata/identifiers that don't contribute to prompt tokens)
    skip_fields = {"type", "id", "tool_use_id", "cache_control", "is_error"}

    # Iterate over all fields defined in the TypedDict
    for field_name, field_type in type_hints.items():
        if field_name in skip_fields:
            continue

        field_value = content.get(field_name)
        if field_value is None:
            continue
        try:
            if isinstance(field_value, str):
                tokens += count_function(field_value)
            elif isinstance(field_value, list):
                tokens += _count_content_list(
                    count_function,
                    field_value,  # type: ignore
                    use_default_image_token_count,
                    default_token_count,
                )
            elif isinstance(field_value, dict):
                tokens += count_function(str(field_value))
        except Exception as e:
            if default_token_count is not None:
                return default_token_count
            raise ValueError(f"Error counting field '{field_name}': {e}")
    return tokens


def _count_content_list(
    count_function: TokenCounterFunction,
    content_list: OpenAIMessageContent,
    use_default_image_token_count: bool,
    default_token_count: Optional[int],
) -> int:
    """
    Recursively count tokens from a list of content blocks.
    """
    try:
        num_tokens = 0
        for c in content_list:
            if isinstance(c, str):
                num_tokens += count_function(c)
            elif c["type"] == "text":
                num_tokens += count_function(c.get("text", ""))
            elif c["type"] == "image_url":
                image_url = c.get("image_url")
                num_tokens += _count_image_tokens(
                    image_url, use_default_image_token_count
                )
            elif c["type"] in ("tool_use", "tool_result"):
                num_tokens += _count_anthropic_content(
                    c,
                    count_function,
                    use_default_image_token_count,
                    default_token_count,
                )
            else:
                raise ValueError(
                    f"Invalid content item type: {type(c).__name__}. "
                    f"Expected str or dict with 'type' field. "
                    f"Value: {c!r}"
                )
        return num_tokens
    except Exception as e:
        if default_token_count is not None:
            return default_token_count
        raise ValueError(
            f"Error getting number of tokens from content list: {e}, "
            f"default_token_count={default_token_count}"
        )


def _format_function_definitions(tools):
    """Formats tool definitions in the format that OpenAI appears to use.
    Based on https://github.com/forestwanglin/openai-java/blob/main/jtokkit/src/main/java/xyz/felh/openai/jtokkit/utils/TikTokenUtils.java
    """
    lines = []
    lines.append("namespace functions {")
    lines.append("")
    for tool in tools:
        function = tool.get("function")
        if function_description := function.get("description"):
            lines.append(f"// {function_description}")
        function_name = function.get("name")
        parameters = function.get("parameters", {})
        properties = parameters.get("properties")
        if properties and properties.keys():
            lines.append(f"type {function_name} = (_: {{")
            lines.append(_format_object_parameters(parameters, 0))
            lines.append("}) => any;")
        else:
            lines.append(f"type {function_name} = () => any;")
        lines.append("")
    lines.append("} // namespace functions")
    return "\n".join(lines)


def _format_object_parameters(parameters, indent):
    properties = parameters.get("properties")
    if not properties:
        return ""
    required_params = parameters.get("required", [])
    lines = []
    for key, props in properties.items():
        description = props.get("description")
        if description:
            lines.append(f"// {description}")
        question = "?"
        if required_params and key in required_params:
            question = ""
        lines.append(f"{key}{question}: {_format_type(props, indent)},")
    return "\n".join([" " * max(0, indent) + line for line in lines])


def _format_type(props, indent):
    type = props.get("type")
    if type == "string":
        if "enum" in props:
            return " | ".join([f'"{item}"' for item in props["enum"]])
        return "string"
    elif type == "array":
        # items is required, OpenAI throws an error if it's missing
        return f"{_format_type(props['items'], indent)}[]"
    elif type == "object":
        return f"{{\n{_format_object_parameters(props, indent + 2)}\n}}"
    elif type in ["integer", "number"]:
        if "enum" in props:
            return " | ".join([f'"{item}"' for item in props["enum"]])
        return "number"
    elif type == "boolean":
        return "boolean"
    elif type == "null":
        return "null"
    else:
        # This is a guess, as an empty string doesn't yield the expected token count
        return "any"
