Source code for bokeh.core.serialization

#-----------------------------------------------------------------------------
# Copyright (c) Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
""" Serialization and deserialization utilities. """

#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import annotations

import logging # isort:skip
log = logging.getLogger(__name__)

#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------

# Standard library imports
import base64
import datetime as dt
import gzip
import sys
from array import array as TypedArray
from math import isinf, isnan
from types import SimpleNamespace
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    ClassVar,
    Generic,
    Literal,
    NoReturn,
    Sequence,
    TypeAlias,
    TypedDict,
    TypeVar,
    cast,
)

# External imports
import numpy as np

# Bokeh imports
from ..settings import settings
from ..util.dataclasses import (
    Unspecified,
    dataclass,
    entries,
    is_dataclass,
)
from ..util.dependencies import uses_pandas
from ..util.serialization import (
    array_encoding_disabled,
    convert_datetime_type,
    convert_timedelta_type,
    is_datetime_type,
    is_timedelta_type,
    make_id,
    transform_array,
    transform_series,
)
from ..util.warnings import BokehUserWarning, warn
from .types import ID

if TYPE_CHECKING:
    import numpy.typing as npt
    from typing_extensions import NotRequired

    from ..core.has_props import Setter
    from ..model import Model

#-----------------------------------------------------------------------------
# Globals and constants
#-----------------------------------------------------------------------------

__all__ = (
    "Buffer",
    "DeserializationError",
    "Deserializer",
    "Serializable",
    "SerializationError",
    "Serializer",
)

_MAX_SAFE_INT = 2**53 - 1

#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------

AnyRep: TypeAlias = Any

class Ref(TypedDict):
    id: ID

class RefRep(TypedDict):
    type: Literal["ref"]
    id: ID

class SymbolRep(TypedDict):
    type: Literal["symbol"]
    name: str

class NumberRep(TypedDict):
    type: Literal["number"]
    value: Literal["nan", "-inf", "+inf"] | float

class ArrayRep(TypedDict):
    type: Literal["array"]
    entries: NotRequired[list[AnyRep]]

ArrayRepLike: TypeAlias = ArrayRep | list[AnyRep]

class SetRep(TypedDict):
    type: Literal["set"]
    entries: NotRequired[list[AnyRep]]

class MapRep(TypedDict):
    type: Literal["map"]
    entries: NotRequired[list[tuple[AnyRep, AnyRep]]]

class BytesRep(TypedDict):
    type: Literal["bytes"]
    data: Buffer | Ref | str

class SliceRep(TypedDict):
    type: Literal["slice"]
    start: int | None
    stop: int | None
    step: int | None

class ObjectRep(TypedDict):
    type: Literal["object"]
    name: str
    attributes: NotRequired[dict[str, AnyRep]]

class ObjectRefRep(TypedDict):
    type: Literal["object"]
    name: str
    id: ID
    attributes: NotRequired[dict[str, AnyRep]]

ModelRep = ObjectRefRep

ByteOrder: TypeAlias = Literal["little", "big"]

DataType: TypeAlias = Literal["uint8", "int8", "uint16", "int16", "uint32", "int32", "float32", "float64"] # "uint64", "int64"
NDDataType: TypeAlias = Literal["bool"] | DataType | Literal["object"]

class TypedArrayRep(TypedDict):
    type: Literal["typed_array"]
    array: BytesRep
    order: ByteOrder
    dtype: DataType

class NDArrayRep(TypedDict):
    type: Literal["ndarray"]
    array: BytesRep | ArrayRepLike
    order: ByteOrder
    dtype: NDDataType
    shape: list[int]


[docs]
@dataclass
class Buffer:
    id: ID
    data: bytes | memoryview

    @property
    def ref(self) -> Ref:
        return Ref(id=self.id)

    def to_bytes(self) -> bytes:
        return self.data.tobytes() if isinstance(self.data, memoryview) else self.data

    def to_compressed_bytes(self) -> bytes:
        level = settings.compression_level()
        # we do not want the result to be different depending on mtime, since that is
        # irrelevant and also makes things harder to test, but Python 3.11 and 3.12 have
        # bug where using mtime=0 results in the Gzip header OS field varies by platforam
        # instead of getting set to a fixed value of 255. So, for now use mtime=1 instead.
        return gzip.compress(self.to_bytes(), mtime=1, compresslevel=level)

    def to_base64(self) -> str:
        return base64.b64encode(self.to_compressed_bytes()).decode("utf-8")


T = TypeVar("T")


[docs]
@dataclass
class Serialized(Generic[T]):
    content: T
    buffers: list[Buffer] | None = None


Encoder: TypeAlias = Callable[[Any, "Serializer"], AnyRep]
Decoder: TypeAlias = Callable[[AnyRep, "Deserializer"], Any]


[docs]
class SerializationError(ValueError):
    pass



[docs]
class Serializable:
    """ A mixin for making a type serializable. """

    def to_serializable(self, serializer: Serializer) -> AnyRep:
        """ Converts this object to a serializable representation. """
        raise NotImplementedError()


ObjID = int


[docs]
class Serializer:
    """ Convert built-in and custom types into serializable representations.
        Not all built-in types are supported (e.g., decimal.Decimal due to
        lacking support for fixed point arithmetic in JavaScript).
    """
    _encoders: ClassVar[dict[type[Any], Encoder]] = {}

    @classmethod
    def register(cls, type: type[Any], encoder: Encoder) -> None:
        assert type not in cls._encoders, f"'{type} is already registered"
        cls._encoders[type] = encoder

    _references: dict[ObjID, Ref]
    _deferred: bool
    _circular: dict[ObjID, Any]
    _buffers: list[Buffer]

    def __init__(self, *, references: set[Model] = set(), deferred: bool = True) -> None:
        self._references = {id(obj): obj.ref for obj in references}
        self._deferred = deferred
        self._circular = {}
        self._buffers = []

    def has_ref(self, obj: Any) -> bool:
        return id(obj) in self._references

    def add_ref(self, obj: Any, ref: Ref) -> None:
        assert id(obj) not in self._references
        self._references[id(obj)] = ref

    def get_ref(self, obj: Any) -> Ref | None:
        return self._references.get(id(obj))

    @property
    def buffers(self) -> list[Buffer]:
        return list(self._buffers)

    def serialize(self, obj: Any) -> Serialized[Any]:
        return Serialized(self.encode(obj), self.buffers)

    def encode(self, obj: Any) -> AnyRep:
        ref = self.get_ref(obj)
        if ref is not None:
            return ref

        ident = id(obj)
        if ident in self._circular:
            self.error("circular reference")

        self._circular[ident] = obj
        try:
            return self._encode(obj)
        finally:
            del self._circular[ident]

    def encode_struct(self, **fields: Any) -> dict[str, AnyRep]:
        return {key: self.encode(val) for key, val in fields.items() if val is not Unspecified}

    def _encode(self, obj: Any) -> AnyRep:
        if isinstance(obj, Serializable):
            return obj.to_serializable(self)
        elif (encoder := self._encoders.get(type(obj))) is not None:
            return encoder(obj, self)
        elif obj is None:
            return None
        elif isinstance(obj, bool):
            return self._encode_bool(obj)
        elif isinstance(obj, str):
            return self._encode_str(obj)
        elif isinstance(obj, int):
            return self._encode_int(obj)
        elif isinstance(obj, float):
            return self._encode_float(obj)
        elif isinstance(obj, tuple):
            return self._encode_tuple(obj)
        elif isinstance(obj, list):
            return self._encode_list(obj)
        elif isinstance(obj, set):
            return self._encode_set(obj)
        elif isinstance(obj, dict):
            return self._encode_dict(obj)
        elif isinstance(obj, SimpleNamespace):
            return self._encode_struct(obj)
        elif isinstance(obj, bytes):
            return self._encode_bytes(obj)
        elif isinstance(obj, slice):
            return self._encode_slice(obj)
        elif isinstance(obj, TypedArray):
            return self._encode_typed_array(obj)
        elif isinstance(obj, np.ndarray):
            if obj.shape != ():
                return self._encode_ndarray(obj)
            else:
                return self._encode(obj.item())
        elif is_dataclass(obj):
            return self._encode_dataclass(obj)
        else:
            return self._encode_other(obj)

    def _encode_bool(self, obj: bool) -> AnyRep:
        return obj

    def _encode_str(self, obj: str) -> AnyRep:
        return obj

    def _encode_int(self, obj: int) -> AnyRep:
        if -_MAX_SAFE_INT < obj <= _MAX_SAFE_INT:
            return obj
        else:
            warn("out of range integer may result in loss of precision", BokehUserWarning)
            return self._encode_float(float(obj))

    def _encode_float(self, obj: float) -> NumberRep | float:
        if isnan(obj):
            return NumberRep(type="number", value="nan")
        elif isinf(obj):
            return NumberRep(type="number", value="-inf" if obj < 0 else "+inf")
        else:
            return obj

    def _encode_tuple(self, obj: tuple[Any, ...]) -> ArrayRepLike:
        return self._encode_list(list(obj))

    def _encode_list(self, obj: list[Any]) -> ArrayRepLike:
        return [self.encode(item) for item in obj]

    def _encode_set(self, obj: set[Any]) -> SetRep:
        if len(obj) == 0:
            return SetRep(type="set")
        else:
            return SetRep(
                type="set",
                entries=[self.encode(entry) for entry in obj],
            )

    def _encode_dict(self, obj: dict[Any, Any]) -> MapRep:
        if len(obj) == 0:
            result = MapRep(type="map")
        else:
            result = MapRep(
                type="map",
                entries=[(self.encode(key), self.encode(val)) for key, val in obj.items()],
            )

        return result

    def _encode_struct(self, obj: SimpleNamespace) -> MapRep:
        return self._encode_dict(obj.__dict__)

    def _encode_dataclass(self, obj: Any) -> ObjectRep:
        cls = type(obj)

        module = cls.__module__
        name = cls.__qualname__.replace("<locals>.", "")

        rep = ObjectRep(
            type="object",
            name=f"{module}.{name}",
        )

        attributes = list(entries(obj))
        if attributes:
            rep["attributes"] = {key: self.encode(val) for key, val in attributes}

        return rep

    def _encode_bytes(self, obj: bytes | memoryview) -> BytesRep:
        buffer = Buffer(make_id(), obj)

        data: Buffer | str
        if self._deferred:
            self._buffers.append(buffer)
            data = buffer
        else:
            data = buffer.to_base64()

        return BytesRep(type="bytes", data=data)

    def _encode_slice(self, obj: slice) -> SliceRep:
        return SliceRep(
            type="slice",
            start=self.encode(obj.start),
            stop=self.encode(obj.stop),
            step=self.encode(obj.step),
        )

    def _encode_typed_array(self, obj: TypedArray[Any]) -> TypedArrayRep:
        array = self._encode_bytes(memoryview(obj))

        typecode = obj.typecode
        itemsize = obj.itemsize

        def dtype() -> DataType:
            match typecode:
                case "f":
                    return "float32"
                case "d":
                    return "float64"
                case "B" | "H" | "I" | "L" | "Q":
                    match obj.itemsize:
                        case 1: return "uint8"
                        case 2: return "uint16"
                        case 4: return "uint32"
                        #case 8: return "uint64"
                case "b" | "h" | "i" | "l" | "q":
                    match obj.itemsize:
                        case 1: return "int8"
                        case 2: return "int16"
                        case 4: return "int32"
                        #case 8: return "int64"
            self.error(f"can't serialize array with items of type '{typecode}@{itemsize}'")

        return TypedArrayRep(
            type="typed_array",
            array=array,
            order=sys.byteorder,
            dtype=dtype(),
        )

    def _encode_ndarray(self, obj: npt.NDArray[Any]) -> NDArrayRep:
        array = transform_array(obj)

        data: ArrayRepLike | BytesRep
        dtype: NDDataType
        if array_encoding_disabled(array):
            data = self._encode_list(array.flatten().tolist())
            dtype = "object"
        else:
            data = self._encode_bytes(array.data)
            dtype = cast(NDDataType, array.dtype.name)

        return NDArrayRep(
            type="ndarray",
            array=data,
            shape=list(array.shape),
            dtype=dtype,
            order=sys.byteorder,
        )

    def _encode_other(self, obj: Any) -> AnyRep:
        # date/time values that get serialized as milliseconds
        if is_datetime_type(obj):
            return convert_datetime_type(obj)

        if is_timedelta_type(obj):
            return convert_timedelta_type(obj)

        if isinstance(obj, dt.date):
            return obj.isoformat()

        # NumPy scalars
        if np.issubdtype(type(obj), np.floating):
            return self._encode_float(float(obj))
        if np.issubdtype(type(obj), np.integer):
            return self._encode_int(int(obj))
        if np.issubdtype(type(obj), np.bool_):
            return self._encode_bool(bool(obj))

        # avoid importing pandas here unless it is actually in use
        if uses_pandas(obj):
            import pandas as pd
            if isinstance(obj, pd.Series | pd.Index | pd.api.extensions.ExtensionArray):
                return self._encode_ndarray(transform_series(obj))
            elif obj is pd.NA:
                return None

        # handle array libraries that support conversion to a numpy array (e.g. polars, PyTorch)
        if hasattr(obj, "__array__") and isinstance(arr := obj.__array__(), np.ndarray):
            return self._encode_ndarray(arr)

        self.error(f"can't serialize {type(obj)}")

    def error(self, message: str) -> NoReturn:
        raise SerializationError(message)



[docs]
class DeserializationError(ValueError):
    pass


class UnknownReferenceError(DeserializationError):

    def __init__(self, id: ID) -> None:
        super().__init__(f"can't resolve reference '{id}'")
        self.id = id


[docs]
class Deserializer:
    """ Convert from serializable representations to built-in and custom types. """

    _decoders: ClassVar[dict[str, Decoder]] = {}

    @classmethod
    def register(cls, type: str, decoder: Decoder) -> None:
        assert type not in cls._decoders, f"'{type} is already registered"
        cls._decoders[type] = decoder

    _references: dict[ID, Model]
    _setter: Setter | None

    _decoding: bool
    _buffers: dict[ID, Buffer]

    def __init__(self, references: Sequence[Model] | None = None, *, setter: Setter | None = None):
        self._references = {obj.id: obj for obj in references or []}
        self._setter = setter
        self._decoding = False
        self._buffers = {}

    def has_ref(self, obj: Model) -> bool:
        return obj.id in self._references

    def deserialize(self, obj: Any | Serialized[Any]) -> Any:
        if isinstance(obj, Serialized):
            return self.decode(obj.content, obj.buffers)
        else:
            return self.decode(obj)

    def decode(self, obj: AnyRep, buffers: list[Buffer] | None = None) -> Any:
        if buffers is not None:
            for buffer in buffers:
                self._buffers[buffer.id] = buffer

        if self._decoding:
            return self._decode(obj)

        self._decoding = True

        try:
            return self._decode(obj)
        finally:
            self._buffers.clear()
            self._decoding = False

    def _decode(self, obj: AnyRep) -> Any:
        if isinstance(obj, dict):
            if "type" in obj:
                match obj["type"]:
                    case type if type in self._decoders:
                        return self._decoders[type](obj, self)
                    case "ref":
                        return self._decode_ref(cast(Ref, obj))
                    case "symbol":
                        return self._decode_symbol(cast(SymbolRep, obj))
                    case "number":
                        return self._decode_number(cast(NumberRep, obj))
                    case "array":
                        return self._decode_array(cast(ArrayRep, obj))
                    case "set":
                        return self._decode_set(cast(SetRep, obj))
                    case "map":
                        return self._decode_map(cast(MapRep, obj))
                    case "bytes":
                        return self._decode_bytes(cast(BytesRep, obj))
                    case "slice":
                        return self._decode_slice(cast(SliceRep, obj))
                    case "typed_array":
                        return self._decode_typed_array(cast(TypedArrayRep, obj))
                    case "ndarray":
                        return self._decode_ndarray(cast(NDArrayRep, obj))
                    case "object":
                        if "id" in obj:
                            return self._decode_object_ref(cast(ObjectRefRep, obj))
                        else:
                            return self._decode_object(cast(ObjectRep, obj))
                    case type:
                        self.error(f"unable to decode an object of type '{type}'")
            elif "id" in obj:
                return self._decode_ref(cast(Ref, obj))
            else:
                return {key: self._decode(val) for key, val in obj.items()}
        elif isinstance(obj, list):
            return [self._decode(entry) for entry in obj]
        else:
            return obj

    def _decode_ref(self, obj: Ref) -> Model:
        id = obj["id"]
        instance = self._references.get(id)
        if instance is not None:
            return instance
        else:
            self.error(UnknownReferenceError(id))

    def _decode_symbol(self, obj: SymbolRep) -> float:
        name = obj["name"]
        self.error(f"can't resolve named symbol '{name}'") # TODO: implement symbol resolution

    def _decode_number(self, obj: NumberRep) -> float:
        value = obj["value"]
        return float(value) if isinstance(value, str) else value

    def _decode_array(self, obj: ArrayRep) -> list[Any]:
        entries = obj.get("entries", [])
        return [ self._decode(entry) for entry in entries ]

    def _decode_set(self, obj: SetRep) -> set[Any]:
        entries = obj.get("entries", [])
        return { self._decode(entry) for entry in entries }

    def _decode_map(self, obj: MapRep) -> dict[Any, Any]:
        entries = obj.get("entries", [])
        return { self._decode(key): self._decode(val) for key, val in entries }

    def _decode_bytes(self, obj: BytesRep) -> bytes | memoryview[int]:
        data = obj["data"]

        if isinstance(data, str):
            return gzip.decompress(base64.b64decode(data))
        elif isinstance(data, Buffer):
            buffer = data # in case of decode(encode(obj))
        else:
            id = data["id"]

            if id in self._buffers:
                buffer = self._buffers[id]
            else:
                self.error(f"can't resolve buffer '{id}'")

        return buffer.data

    def _decode_slice(self, obj: SliceRep) -> slice:
        start = self._decode(obj["start"])
        stop = self._decode(obj["stop"])
        step = self._decode(obj["step"])
        return slice(start, stop, step)

    def _decode_typed_array(self, obj: TypedArrayRep) -> TypedArray[Any]:
        array = obj["array"]
        order = obj["order"]
        dtype = obj["dtype"]

        data = self._decode(array)

        dtype_to_typecode = dict(
            uint8="B",
            int8="b",
            uint16="H",
            int16="h",
            uint32="I",
            int32="i",
            #uint64="Q",
            #int64="q",
            float32="f",
            float64="d",
        )

        typecode = dtype_to_typecode.get(dtype)
        if typecode is None:
            self.error(f"unsupported dtype '{dtype}'")

        typed_array: TypedArray[Any] = TypedArray(typecode, data)
        if order != sys.byteorder:
            typed_array.byteswap()

        return typed_array

    def _decode_ndarray(self, obj: NDArrayRep) -> npt.NDArray[Any]:
        array = obj["array"]
        order = obj["order"]
        dtype = obj["dtype"]
        shape = obj["shape"]

        decoded = self._decode(array)

        ndarray: npt.NDArray[Any]
        if isinstance(decoded, bytes):
            ndarray = np.copy(np.frombuffer(decoded, dtype=dtype))

            if order != sys.byteorder:
                ndarray.byteswap(inplace=True)
        else:
            ndarray = np.array(decoded, dtype=dtype)

        if len(shape) > 1:
            ndarray = ndarray.reshape(shape)

        return ndarray

    def _decode_object(self, obj: ObjectRep) -> object:
        raise NotImplementedError()

    def _decode_object_ref(self, obj: ObjectRefRep) -> Model:
        id = obj["id"]
        instance = self._references.get(id)
        if instance is not None:
            warn(f"reference already known '{id}'", BokehUserWarning)
            return instance

        name = obj["name"]
        attributes = obj.get("attributes")

        cls = self._resolve_type(name)
        instance = cls.__new__(cls, id=id)

        if instance is None:
            self.error(f"can't instantiate {name}(id={id})")

        self._references[instance.id] = instance

        # We want to avoid any Model specific initialization that happens with
        # Slider(...) when reconstituting from JSON, but we do need to perform
        # general HasProps machinery that sets properties, so call it explicitly
        if not instance._initialized:
            from .has_props import HasProps
            HasProps.__init__(instance)

        if attributes is not None:
            decoded_attributes = {key: self._decode(val) for key, val in attributes.items()}
            for key, val in decoded_attributes.items():
                instance.set_from_json(key, val, setter=self._setter)

        return instance

    def _resolve_type(self, type: str) -> type[Model]:
        from ..model import Model
        cls = Model.model_class_reverse_map.get(type)
        if cls is not None:
            if issubclass(cls, Model):
                return cls
            else:
                self.error(f"object of type '{type}' is not a subclass of 'Model'")
        else:
            if type == "Figure":
                from ..plotting import figure
                return figure # XXX: helps with push_session(); this needs a better resolution scheme
            else:
                self.error(f"can't resolve type '{type}'")

    def error(self, error: str | DeserializationError) -> NoReturn:
        if isinstance(error, str):
            raise DeserializationError(error)
        else:
            raise error


#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------

#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------

#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------