reflex_copy/reflex/utils/serializers.py

"""Serializers used to convert Var types to JSON strings."""

from __future__ import annotations

import contextlib
import dataclasses
import decimal
import functools
import inspect
import json
import warnings
from collections.abc import Callable, Sequence
from datetime import date, datetime, time, timedelta
from enum import Enum
from pathlib import Path
from typing import Any, Literal, TypeVar, get_type_hints, overload
from uuid import UUID

from pydantic import BaseModel as BaseModelV2
from pydantic.v1 import BaseModel as BaseModelV1

from reflex.base import Base
from reflex.constants.colors import Color, format_color
from reflex.utils import console, types

# Mapping from type to a serializer.
# The serializer should convert the type to a JSON object.
SerializedType = str | bool | int | float | list | dict | None


Serializer = Callable[[Any], SerializedType]


SERIALIZERS: dict[type, Serializer] = {}
SERIALIZER_TYPES: dict[type, type] = {}

SERIALIZED_FUNCTION = TypeVar("SERIALIZED_FUNCTION", bound=Serializer)


@overload
def serializer(
    fn: None = None,
    to: type[SerializedType] | None = None,
    overwrite: bool | None = None,
) -> Callable[[SERIALIZED_FUNCTION], SERIALIZED_FUNCTION]: ...


@overload
def serializer(
    fn: SERIALIZED_FUNCTION,
    to: type[SerializedType] | None = None,
    overwrite: bool | None = None,
) -> SERIALIZED_FUNCTION: ...


def serializer(
    fn: SERIALIZED_FUNCTION | None = None,
    to: Any = None,
    overwrite: bool | None = None,
) -> SERIALIZED_FUNCTION | Callable[[SERIALIZED_FUNCTION], SERIALIZED_FUNCTION]:
    """Decorator to add a serializer for a given type.

    Args:
        fn: The function to decorate.
        to: The type returned by the serializer. If this is `str`, then any Var created from this type will be treated as a string.
        overwrite: Whether to overwrite the existing serializer.

    Returns:
        The decorated function.
    """

    def wrapper(fn: SERIALIZED_FUNCTION) -> SERIALIZED_FUNCTION:
        # Check the type hints to get the type of the argument.
        type_hints = get_type_hints(fn)
        args = [arg for arg in type_hints if arg != "return"]

        # Make sure the function takes a single argument.
        if len(args) != 1:
            raise ValueError("Serializer must take a single argument.")

        # Get the type of the argument.
        type_ = type_hints[args[0]]

        # Make sure the type is not already registered.
        registered_fn = SERIALIZERS.get(type_)
        if registered_fn is not None and registered_fn != fn and overwrite is not True:
            message = f"Overwriting serializer for type {type_} from {registered_fn.__module__}:{registered_fn.__qualname__} to {fn.__module__}:{fn.__qualname__}."
            if overwrite is False:
                raise ValueError(message)
            caller_frame = next(
                filter(
                    lambda frame: frame.filename != __file__,
                    inspect.getouterframes(inspect.currentframe()),
                ),
                None,
            )
            file_info = (
                f"(at {caller_frame.filename}:{caller_frame.lineno})"
                if caller_frame
                else ""
            )
            console.warn(
                f"{message} Call rx.serializer with `overwrite=True` if this is intentional. {file_info}"
            )

        to_type = to or type_hints.get("return")

        # Apply type transformation if requested
        if to_type:
            SERIALIZER_TYPES[type_] = to_type
            get_serializer_type.cache_clear()

        # Register the serializer.
        SERIALIZERS[type_] = fn
        get_serializer.cache_clear()

        # Return the function.
        return fn

    if fn is not None:
        return wrapper(fn)
    return wrapper


@overload
def serialize(
    value: Any, get_type: Literal[True]
) -> tuple[SerializedType | None, types.GenericType | None]: ...


@overload
def serialize(value: Any, get_type: Literal[False]) -> SerializedType | None: ...


@overload
def serialize(value: Any) -> SerializedType | None: ...


def serialize(
    value: Any, get_type: bool = False
) -> SerializedType | None | tuple[SerializedType | None, types.GenericType | None]:
    """Serialize the value to a JSON string.

    Args:
        value: The value to serialize.
        get_type: Whether to return the type of the serialized value.

    Returns:
        The serialized value, or None if a serializer is not found.
    """
    # Get the serializer for the type.
    serializer = get_serializer(type(value))

    # If there is no serializer, return None.
    if serializer is None:
        if dataclasses.is_dataclass(value) and not isinstance(value, type):
            return {k.name: getattr(value, k.name) for k in dataclasses.fields(value)}

        if get_type:
            return None, None
        return None

    # Serialize the value.
    serialized = serializer(value)

    # Return the serialized value and the type.
    if get_type:
        return serialized, get_serializer_type(type(value))
    else:
        return serialized


@functools.lru_cache
def get_serializer(type_: type) -> Serializer | None:
    """Get the serializer for the type.

    Args:
        type_: The type to get the serializer for.

    Returns:
        The serializer for the type, or None if there is no serializer.
    """
    # First, check if the type is registered.
    serializer = SERIALIZERS.get(type_)
    if serializer is not None:
        return serializer

    # If the type is not registered, check if it is a subclass of a registered type.
    for registered_type, serializer in reversed(SERIALIZERS.items()):
        if types._issubclass(type_, registered_type):
            return serializer

    # If there is no serializer, return None.
    return None


@functools.lru_cache
def get_serializer_type(type_: type) -> type | None:
    """Get the converted type for the type after serializing.

    Args:
        type_: The type to get the serializer type for.

    Returns:
        The serialized type for the type, or None if there is no type conversion registered.
    """
    # First, check if the type is registered.
    serializer = SERIALIZER_TYPES.get(type_)
    if serializer is not None:
        return serializer

    # If the type is not registered, check if it is a subclass of a registered type.
    for registered_type, serializer in reversed(SERIALIZER_TYPES.items()):
        if types._issubclass(type_, registered_type):
            return serializer

    # If there is no serializer, return None.
    return None


def has_serializer(type_: type, into_type: type | None = None) -> bool:
    """Check if there is a serializer for the type.

    Args:
        type_: The type to check.
        into_type: The type to serialize into.

    Returns:
        Whether there is a serializer for the type.
    """
    serializer_for_type = get_serializer(type_)
    return serializer_for_type is not None and (
        into_type is None or get_serializer_type(type_) == into_type
    )


def can_serialize(type_: type, into_type: type | None = None) -> bool:
    """Check if there is a serializer for the type.

    Args:
        type_: The type to check.
        into_type: The type to serialize into.

    Returns:
        Whether there is a serializer for the type.
    """
    return has_serializer(type_, into_type) or (
        isinstance(type_, type)
        and dataclasses.is_dataclass(type_)
        and (into_type is None or into_type is dict)
    )


@serializer(to=str)
def serialize_type(value: type) -> str:
    """Serialize a python type.

    Args:
        value: the type to serialize.

    Returns:
        The serialized type.
    """
    return value.__name__


@serializer(to=dict)
def serialize_base(value: Base) -> dict:
    """Serialize a Base instance.

    Args:
        value : The Base to serialize.

    Returns:
        The serialized Base.
    """
    from reflex.vars.base import Var

    return {
        k: v for k, v in value.dict().items() if isinstance(v, Var) or not callable(v)
    }


@serializer(to=dict)
def serialize_base_model_v1(model: BaseModelV1) -> dict:
    """Serialize a pydantic v1 BaseModel instance.

    Args:
        model: The BaseModel to serialize.

    Returns:
        The serialized BaseModel.
    """
    return model.dict()


if BaseModelV1 is not BaseModelV2:

    @serializer(to=dict)
    def serialize_base_model_v2(model: BaseModelV2) -> dict:
        """Serialize a pydantic v2 BaseModel instance.

        Args:
            model: The BaseModel to serialize.

        Returns:
            The serialized BaseModel.
        """
        return model.model_dump()


@serializer
def serialize_set(value: set) -> list:
    """Serialize a set to a JSON serializable list.

    Args:
        value: The set to serialize.

    Returns:
        The serialized list.
    """
    return list(value)


@serializer
def serialize_sequence(value: Sequence) -> list:
    """Serialize a sequence to a JSON serializable list.

    Args:
        value: The sequence to serialize.

    Returns:
        The serialized list.
    """
    return list(value)


@serializer(to=str)
def serialize_datetime(dt: date | datetime | time | timedelta) -> str:
    """Serialize a datetime to a JSON string.

    Args:
        dt: The datetime to serialize.

    Returns:
        The serialized datetime.
    """
    return str(dt)


@serializer(to=str)
def serialize_path(path: Path) -> str:
    """Serialize a pathlib.Path to a JSON string.

    Args:
        path: The path to serialize.

    Returns:
        The serialized path.
    """
    return str(path.as_posix())


@serializer
def serialize_enum(en: Enum) -> str:
    """Serialize a enum to a JSON string.

    Args:
        en: The enum to serialize.

    Returns:
        The serialized enum.
    """
    return en.value


@serializer(to=str)
def serialize_uuid(uuid: UUID) -> str:
    """Serialize a UUID to a JSON string.

    Args:
        uuid: The UUID to serialize.

    Returns:
        The serialized UUID.
    """
    return str(uuid)


@serializer(to=float)
def serialize_decimal(value: decimal.Decimal) -> float:
    """Serialize a Decimal to a float.

    Args:
        value: The Decimal to serialize.

    Returns:
        The serialized Decimal as a float.
    """
    return float(value)


@serializer(to=str)
def serialize_color(color: Color) -> str:
    """Serialize a color.

    Args:
        color: The color to serialize.

    Returns:
        The serialized color.
    """
    return format_color(color.color, color.shade, color.alpha)


with contextlib.suppress(ImportError):
    from pandas import DataFrame

    def format_dataframe_values(df: DataFrame) -> list[list[Any]]:
        """Format dataframe values to a list of lists.

        Args:
            df: The dataframe to format.

        Returns:
            The dataframe as a list of lists.
        """
        return [
            [str(d) if isinstance(d, (list, tuple)) else d for d in data]
            for data in list(df.values.tolist())
        ]

    @serializer
    def serialize_dataframe(df: DataFrame) -> dict:
        """Serialize a pandas dataframe.

        Args:
            df: The dataframe to serialize.

        Returns:
            The serialized dataframe.
        """
        return {
            "columns": df.columns.tolist(),
            "data": format_dataframe_values(df),
        }


with contextlib.suppress(ImportError):
    from plotly.graph_objects import Figure, layout
    from plotly.io import to_json

    @serializer
    def serialize_figure(figure: Figure) -> dict:
        """Serialize a plotly figure.

        Args:
            figure: The figure to serialize.

        Returns:
            The serialized figure.
        """
        return json.loads(str(to_json(figure)))

    @serializer
    def serialize_template(template: layout.Template) -> dict:
        """Serialize a plotly template.

        Args:
            template: The template to serialize.

        Returns:
            The serialized template.
        """
        return {
            "data": json.loads(str(to_json(template.data))),
            "layout": json.loads(str(to_json(template.layout))),
        }


with contextlib.suppress(ImportError):
    import base64
    import io

    from PIL.Image import MIME
    from PIL.Image import Image as Img

    @serializer
    def serialize_image(image: Img) -> str:
        """Serialize a plotly figure.

        Args:
            image: The image to serialize.

        Returns:
            The serialized image.
        """
        buff = io.BytesIO()
        image_format = getattr(image, "format", None) or "PNG"
        image.save(buff, format=image_format)
        image_bytes = buff.getvalue()
        base64_image = base64.b64encode(image_bytes).decode("utf-8")
        try:
            # Newer method to get the mime type, but does not always work.
            mime_type = image.get_format_mimetype()  # pyright: ignore [reportAttributeAccessIssue]
        except AttributeError:
            try:
                # Fallback method
                mime_type = MIME[image_format]
            except KeyError:
                # Unknown mime_type: warn and return image/png and hope the browser can sort it out.
                warnings.warn(  # noqa: B028
                    f"Unknown mime type for {image} {image_format}. Defaulting to image/png"
                )
                mime_type = "image/png"

        return f"data:{mime_type};base64,{base64_image}"