reflex_copy/reflex/vars/base.py

"""Collection of base classes."""

from __future__ import annotations

import contextlib
import dataclasses
import datetime
import dis
import functools
import inspect
import json
import random
import re
import string
import sys
import warnings
from types import CodeType, FunctionType
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    FrozenSet,
    Generic,
    Iterable,
    List,
    Literal,
    NoReturn,
    Optional,
    Set,
    Tuple,
    Type,
    TypeVar,
    Union,
    cast,
    get_args,
    overload,
)

from typing_extensions import ParamSpec, TypeGuard, deprecated, get_type_hints, override

from reflex import constants
from reflex.base import Base
from reflex.utils import console, imports, serializers, types
from reflex.utils.exceptions import (
    VarAttributeError,
    VarDependencyError,
    VarTypeError,
    VarValueError,
)
from reflex.utils.format import format_state_name
from reflex.utils.imports import (
    ImmutableParsedImportDict,
    ImportDict,
    ImportVar,
    ParsedImportDict,
    parse_imports,
)
from reflex.utils.types import GenericType, Self, get_origin

if TYPE_CHECKING:
    from reflex.state import BaseState

    from .function import FunctionVar, ToFunctionOperation
    from .number import (
        BooleanVar,
        NumberVar,
        ToBooleanVarOperation,
        ToNumberVarOperation,
    )
    from .object import ObjectVar, ToObjectOperation
    from .sequence import ArrayVar, StringVar, ToArrayOperation, ToStringOperation


VAR_TYPE = TypeVar("VAR_TYPE", covariant=True)
OTHER_VAR_TYPE = TypeVar("OTHER_VAR_TYPE")

warnings.filterwarnings("ignore", message="fields may not start with an underscore")


@dataclasses.dataclass(
    eq=False,
    frozen=True,
)
class Var(Generic[VAR_TYPE]):
    """Base class for immutable vars."""

    # The name of the var.
    _js_expr: str = dataclasses.field()

    # The type of the var.
    _var_type: types.GenericType = dataclasses.field(default=Any)

    # Extra metadata associated with the Var
    _var_data: Optional[VarData] = dataclasses.field(default=None)

    def __str__(self) -> str:
        """String representation of the var. Guaranteed to be a valid Javascript expression.

        Returns:
            The name of the var.
        """
        return self._js_expr

    @property
    def _var_is_local(self) -> bool:
        """Whether this is a local javascript variable.

        Returns:
            False
        """
        return False

    @property
    @deprecated("Use `_js_expr` instead.")
    def _var_name(self) -> str:
        """The name of the var.

        Returns:
            The name of the var.
        """
        return self._js_expr

    @property
    def _var_field_name(self) -> str:
        """The name of the field.

        Returns:
            The name of the field.
        """
        var_data = self._get_all_var_data()
        field_name = var_data.field_name if var_data else None
        return field_name or self._js_expr

    @property
    @deprecated("Use `_js_expr` instead.")
    def _var_name_unwrapped(self) -> str:
        """The name of the var without extra curly braces.

        Returns:
            The name of the var.
        """
        return self._js_expr

    @property
    def _var_is_string(self) -> bool:
        """Whether the var is a string literal.

        Returns:
            False
        """
        return False

    def __post_init__(self):
        """Post-initialize the var."""
        # Decode any inline Var markup and apply it to the instance
        _var_data, _js_expr = _decode_var_immutable(self._js_expr)

        if _var_data or _js_expr != self._js_expr:
            self.__init__(
                _js_expr=_js_expr,
                _var_type=self._var_type,
                _var_data=VarData.merge(self._var_data, _var_data),
            )

    def __hash__(self) -> int:
        """Define a hash function for the var.

        Returns:
            The hash of the var.
        """
        return hash((self._js_expr, self._var_type, self._var_data))

    def _get_all_var_data(self) -> VarData | None:
        """Get all VarData associated with the Var.

        Returns:
            The VarData of the components and all of its children.
        """
        return self._var_data

    def equals(self, other: Var) -> bool:
        """Check if two vars are equal.

        Args:
            other: The other var to compare.

        Returns:
            Whether the vars are equal.
        """
        return (
            self._js_expr == other._js_expr
            and self._var_type == other._var_type
            and self._get_all_var_data() == other._get_all_var_data()
        )

    @overload
    def _replace(
        self, _var_type: Type[OTHER_VAR_TYPE], merge_var_data=None, **kwargs: Any
    ) -> Var[OTHER_VAR_TYPE]: ...

    @overload
    def _replace(
        self, _var_type: GenericType | None = None, merge_var_data=None, **kwargs: Any
    ) -> Self: ...

    def _replace(
        self, _var_type: GenericType | None = None, merge_var_data=None, **kwargs: Any
    ) -> Self | Var:
        """Make a copy of this Var with updated fields.

        Args:
            merge_var_data: VarData to merge into the existing VarData.
            **kwargs: Var fields to update.

        Returns:
            A new Var with the updated fields overwriting the corresponding fields in this Var.

        Raises:
            TypeError: If _var_is_local, _var_is_string, or _var_full_name_needs_state_prefix is not None.
        """
        if kwargs.get("_var_is_local", False) is not False:
            raise TypeError("The _var_is_local argument is not supported for Var.")

        if kwargs.get("_var_is_string", False) is not False:
            raise TypeError("The _var_is_string argument is not supported for Var.")

        if kwargs.get("_var_full_name_needs_state_prefix", False) is not False:
            raise TypeError(
                "The _var_full_name_needs_state_prefix argument is not supported for Var."
            )

        value_with_replaced = dataclasses.replace(
            self,
            _var_type=_var_type or self._var_type,
            _var_data=VarData.merge(
                kwargs.get("_var_data", self._var_data), merge_var_data
            ),
            **kwargs,
        )

        if (js_expr := kwargs.get("_js_expr", None)) is not None:
            object.__setattr__(value_with_replaced, "_js_expr", js_expr)

        return value_with_replaced

    @classmethod
    def create(
        cls,
        value: Any,
        _var_is_local: bool | None = None,
        _var_is_string: bool | None = None,
        _var_data: VarData | None = None,
    ) -> Var:
        """Create a var from a value.

        Args:
            value: The value to create the var from.
            _var_is_local: Whether the var is local. Deprecated.
            _var_is_string: Whether the var is a string literal. Deprecated.
            _var_data: Additional hooks and imports associated with the Var.

        Returns:
            The var.
        """
        if _var_is_local is not None:
            console.deprecate(
                feature_name="_var_is_local",
                reason="The _var_is_local argument is not supported for Var."
                "If you want to create a Var from a raw Javascript expression, use the constructor directly",
                deprecation_version="0.6.0",
                removal_version="0.7.0",
            )
        if _var_is_string is not None:
            console.deprecate(
                feature_name="_var_is_string",
                reason="The _var_is_string argument is not supported for Var."
                "If you want to create a Var from a raw Javascript expression, use the constructor directly",
                deprecation_version="0.6.0",
                removal_version="0.7.0",
            )

        # If the value is already a var, do nothing.
        if isinstance(value, Var):
            return value

        # Try to pull the imports and hooks from contained values.
        if not isinstance(value, str):
            return LiteralVar.create(value)

        if _var_is_string is False or _var_is_local is True:
            return cls(
                _js_expr=value,
                _var_data=_var_data,
            )

        return LiteralVar.create(value, _var_data=_var_data)

    @classmethod
    @deprecated("Use `.create()` instead.")
    def create_safe(
        cls,
        *args: Any,
        **kwargs: Any,
    ) -> Var:
        """Create a var from a value.

        Args:
            *args: The arguments to create the var from.
            **kwargs: The keyword arguments to create the var from.

        Returns:
            The var.
        """
        return cls.create(*args, **kwargs)

    def __format__(self, format_spec: str) -> str:
        """Format the var into a Javascript equivalent to an f-string.

        Args:
            format_spec: The format specifier (Ignored for now).

        Returns:
            The formatted var.
        """
        hashed_var = hash(self)

        _global_vars[hashed_var] = self

        # Encode the _var_data into the formatted output for tracking purposes.
        return f"{constants.REFLEX_VAR_OPENING_TAG}{hashed_var}{constants.REFLEX_VAR_CLOSING_TAG}{self._js_expr}"

    @overload
    def to(self, output: Type[StringVar]) -> ToStringOperation: ...

    @overload
    def to(self, output: Type[str]) -> ToStringOperation: ...

    @overload
    def to(self, output: Type[BooleanVar]) -> ToBooleanVarOperation: ...

    @overload
    def to(
        self, output: Type[NumberVar], var_type: type[int] | type[float] = float
    ) -> ToNumberVarOperation: ...

    @overload
    def to(
        self,
        output: Type[ArrayVar],
        var_type: type[list] | type[tuple] | type[set] = list,
    ) -> ToArrayOperation: ...

    @overload
    def to(
        self, output: Type[ObjectVar], var_type: types.GenericType = dict
    ) -> ToObjectOperation: ...

    @overload
    def to(
        self, output: Type[FunctionVar], var_type: Type[Callable] = Callable
    ) -> ToFunctionOperation: ...

    @overload
    def to(
        self,
        output: Type[OUTPUT] | types.GenericType,
        var_type: types.GenericType | None = None,
    ) -> OUTPUT: ...

    def to(
        self,
        output: Type[OUTPUT] | types.GenericType,
        var_type: types.GenericType | None = None,
    ) -> Var:
        """Convert the var to a different type.

        Args:
            output: The output type.
            var_type: The type of the var.

        Raises:
            TypeError: If the var_type is not a supported type for the output.

        Returns:
            The converted var.
        """
        from reflex.event import (
            EventChain,
            EventChainVar,
            EventSpec,
            EventVar,
            ToEventChainVarOperation,
            ToEventVarOperation,
        )

        from .function import FunctionVar, ToFunctionOperation
        from .number import (
            BooleanVar,
            NumberVar,
            ToBooleanVarOperation,
            ToNumberVarOperation,
        )
        from .object import ObjectVar, ToObjectOperation
        from .sequence import ArrayVar, StringVar, ToArrayOperation, ToStringOperation

        base_type = var_type
        if types.is_optional(base_type):
            base_type = types.get_args(base_type)[0]

        fixed_type = get_origin(base_type) or base_type

        fixed_output_type = get_origin(output) or output

        # If the first argument is a python type, we map it to the corresponding Var type.
        if fixed_output_type is dict:
            return self.to(ObjectVar, output)
        if fixed_output_type in (list, tuple, set):
            return self.to(ArrayVar, output)
        if fixed_output_type in (int, float):
            return self.to(NumberVar, output)
        if fixed_output_type is str:
            return self.to(StringVar, output)
        if fixed_output_type is bool:
            return self.to(BooleanVar, output)
        if fixed_output_type is None:
            return ToNoneOperation.create(self)
        if fixed_output_type is EventSpec:
            return self.to(EventVar, output)
        if fixed_output_type is EventChain:
            return self.to(EventChainVar, output)
        if issubclass(fixed_output_type, Base):
            return self.to(ObjectVar, output)
        if dataclasses.is_dataclass(fixed_output_type) and not issubclass(
            fixed_output_type, Var
        ):
            return self.to(ObjectVar, output)

        if issubclass(output, BooleanVar):
            return ToBooleanVarOperation.create(self)

        if issubclass(output, NumberVar):
            if fixed_type is not None:
                if fixed_type is Union:
                    inner_types = get_args(base_type)
                    if not all(issubclass(t, (int, float)) for t in inner_types):
                        raise TypeError(
                            f"Unsupported type {var_type} for NumberVar. Must be int or float."
                        )

                elif not issubclass(fixed_type, (int, float)):
                    raise TypeError(
                        f"Unsupported type {var_type} for NumberVar. Must be int or float."
                    )
            return ToNumberVarOperation.create(self, var_type or float)

        if issubclass(output, ArrayVar):
            if fixed_type is not None and not issubclass(
                fixed_type, (list, tuple, set)
            ):
                raise TypeError(
                    f"Unsupported type {var_type} for ArrayVar. Must be list, tuple, or set."
                )
            return ToArrayOperation.create(self, var_type or list)

        if issubclass(output, StringVar):
            return ToStringOperation.create(self, var_type or str)

        if issubclass(output, EventVar):
            return ToEventVarOperation.create(self, var_type or EventSpec)

        if issubclass(output, EventChainVar):
            return ToEventChainVarOperation.create(self, var_type or EventChain)

        if issubclass(output, (ObjectVar, Base)):
            return ToObjectOperation.create(self, var_type or dict)

        if issubclass(output, FunctionVar):
            # if fixed_type is not None and not issubclass(fixed_type, Callable):
            #     raise TypeError(
            #         f"Unsupported type {var_type} for FunctionVar. Must be Callable."
            #     )
            return ToFunctionOperation.create(self, var_type or Callable)

        if issubclass(output, NoneVar):
            return ToNoneOperation.create(self)

        if dataclasses.is_dataclass(output):
            return ToObjectOperation.create(self, var_type or dict)

        # If we can't determine the first argument, we just replace the _var_type.
        if not issubclass(output, Var) or var_type is None:
            return dataclasses.replace(
                self,
                _var_type=output,
            )

        # We couldn't determine the output type to be any other Var type, so we replace the _var_type.
        if var_type is not None:
            return dataclasses.replace(
                self,
                _var_type=var_type,
            )

        return self

    def guess_type(self) -> Var:
        """Guesses the type of the variable based on its `_var_type` attribute.

        Returns:
            Var: The guessed type of the variable.

        Raises:
            TypeError: If the type is not supported for guessing.
        """
        from reflex.event import EventChain, EventChainVar, EventSpec, EventVar

        from .number import BooleanVar, NumberVar
        from .object import ObjectVar
        from .sequence import ArrayVar, StringVar

        var_type = self._var_type
        if var_type is None:
            return self.to(None)
        if types.is_optional(var_type):
            var_type = types.get_args(var_type)[0]

        if var_type is Any:
            return self

        fixed_type = get_origin(var_type) or var_type

        if fixed_type is Union:
            inner_types = get_args(var_type)

            if all(
                inspect.isclass(t) and issubclass(t, (int, float)) for t in inner_types
            ):
                return self.to(NumberVar, self._var_type)

            if all(
                inspect.isclass(t)
                and (issubclass(t, Base) or dataclasses.is_dataclass(t))
                for t in inner_types
            ):
                return self.to(ObjectVar, self._var_type)

            return self

        if fixed_type is Literal:
            args = get_args(var_type)
            fixed_type = unionize(*(type(arg) for arg in args))

        if not inspect.isclass(fixed_type):
            raise TypeError(f"Unsupported type {var_type} for guess_type.")

        if issubclass(fixed_type, bool):
            return self.to(BooleanVar, self._var_type)
        if issubclass(fixed_type, (int, float)):
            return self.to(NumberVar, self._var_type)
        if issubclass(fixed_type, dict):
            return self.to(ObjectVar, self._var_type)
        if issubclass(fixed_type, (list, tuple, set)):
            return self.to(ArrayVar, self._var_type)
        if issubclass(fixed_type, str):
            return self.to(StringVar, self._var_type)
        if issubclass(fixed_type, EventSpec):
            return self.to(EventVar, self._var_type)
        if issubclass(fixed_type, EventChain):
            return self.to(EventChainVar, self._var_type)
        if issubclass(fixed_type, Base):
            return self.to(ObjectVar, self._var_type)
        if dataclasses.is_dataclass(fixed_type):
            return self.to(ObjectVar, self._var_type)
        return self

    def get_default_value(self) -> Any:
        """Get the default value of the var.

        Returns:
            The default value of the var.

        Raises:
            ImportError: If the var is a dataframe and pandas is not installed.
        """
        if types.is_optional(self._var_type):
            return None

        type_ = (
            get_origin(self._var_type)
            if types.is_generic_alias(self._var_type)
            else self._var_type
        )
        if type_ is Literal:
            args = get_args(self._var_type)
            return args[0] if args else None
        if issubclass(type_, str):
            return ""
        if issubclass(type_, types.get_args(Union[int, float])):
            return 0
        if issubclass(type_, bool):
            return False
        if issubclass(type_, list):
            return []
        if issubclass(type_, dict):
            return {}
        if issubclass(type_, tuple):
            return ()
        if types.is_dataframe(type_):
            try:
                import pandas as pd

                return pd.DataFrame()
            except ImportError as e:
                raise ImportError(
                    "Please install pandas to use dataframes in your app."
                ) from e
        return set() if issubclass(type_, set) else None

    def get_setter_name(self, include_state: bool = True) -> str:
        """Get the name of the var's generated setter function.

        Args:
            include_state: Whether to include the state name in the setter name.

        Returns:
            The name of the setter function.
        """
        setter = constants.SETTER_PREFIX + self._var_field_name
        var_data = self._get_all_var_data()
        if var_data is None:
            return setter
        if not include_state or var_data.state == "":
            return setter
        return ".".join((var_data.state, setter))

    def get_setter(self) -> Callable[[BaseState, Any], None]:
        """Get the var's setter function.

        Returns:
            A function that that creates a setter for the var.
        """
        actual_name = self._var_field_name

        def setter(state: BaseState, value: Any):
            """Get the setter for the var.

            Args:
                state: The state within which we add the setter function.
                value: The value to set.
            """
            if self._var_type in [int, float]:
                try:
                    value = self._var_type(value)
                    setattr(state, actual_name, value)
                except ValueError:
                    console.debug(
                        f"{type(state).__name__}.{self._js_expr}: Failed conversion of {value} to '{self._var_type.__name__}'. Value not set.",
                    )
            else:
                setattr(state, actual_name, value)

        setter.__qualname__ = self.get_setter_name()

        return setter

    def _var_set_state(self, state: type[BaseState] | str):
        """Set the state of the var.

        Args:
            state: The state to set.

        Returns:
            The var with the state set.
        """
        formatted_state_name = (
            state
            if isinstance(state, str)
            else format_state_name(state.get_full_name())
        )

        return StateOperation.create(
            formatted_state_name,
            self,
            _var_data=VarData.merge(
                VarData.from_state(state, self._js_expr), self._var_data
            ),
        ).guess_type()

    def __eq__(self, other: Var | Any) -> BooleanVar:
        """Check if the current variable is equal to the given variable.

        Args:
            other (Var | Any): The variable to compare with.

        Returns:
            BooleanVar: A BooleanVar object representing the result of the equality check.
        """
        from .number import equal_operation

        return equal_operation(self, other)

    def __ne__(self, other: Var | Any) -> BooleanVar:
        """Check if the current object is not equal to the given object.

        Parameters:
            other (Var | Any): The object to compare with.

        Returns:
            BooleanVar: A BooleanVar object representing the result of the comparison.
        """
        from .number import equal_operation

        return ~equal_operation(self, other)

    def bool(self) -> BooleanVar:
        """Convert the var to a boolean.

        Returns:
            The boolean var.
        """
        from .number import boolify

        return boolify(self)

    def __and__(self, other: Var | Any) -> Var:
        """Perform a logical AND operation on the current instance and another variable.

        Args:
            other: The variable to perform the logical AND operation with.

        Returns:
            A `BooleanVar` object representing the result of the logical AND operation.
        """
        return and_operation(self, other)

    def __rand__(self, other: Var | Any) -> Var:
        """Perform a logical AND operation on the current instance and another variable.

        Args:
            other: The variable to perform the logical AND operation with.

        Returns:
            A `BooleanVar` object representing the result of the logical AND operation.
        """
        return and_operation(other, self)

    def __or__(self, other: Var | Any) -> Var:
        """Perform a logical OR operation on the current instance and another variable.

        Args:
            other: The variable to perform the logical OR operation with.

        Returns:
            A `BooleanVar` object representing the result of the logical OR operation.
        """
        return or_operation(self, other)

    def __ror__(self, other: Var | Any) -> Var:
        """Perform a logical OR operation on the current instance and another variable.

        Args:
            other: The variable to perform the logical OR operation with.

        Returns:
            A `BooleanVar` object representing the result of the logical OR operation.
        """
        return or_operation(other, self)

    def __invert__(self) -> BooleanVar:
        """Perform a logical NOT operation on the current instance.

        Returns:
            A `BooleanVar` object representing the result of the logical NOT operation.
        """
        return ~self.bool()

    def to_string(self):
        """Convert the var to a string.

        Returns:
            The string var.
        """
        from .function import JSON_STRINGIFY
        from .sequence import StringVar

        return JSON_STRINGIFY.call(self).to(StringVar)

    def as_ref(self) -> Var:
        """Get a reference to the var.

        Returns:
            The reference to the var.
        """
        from .object import ObjectVar

        refs = Var(
            _js_expr="refs",
            _var_data=VarData(
                imports={
                    f"/{constants.Dirs.STATE_PATH}": [imports.ImportVar(tag="refs")]
                }
            ),
        ).to(ObjectVar, Dict[str, str])
        return refs[LiteralVar.create(str(self))]

    @deprecated("Use `.js_type()` instead.")
    def _type(self) -> StringVar:
        """Returns the type of the object.

        This method uses the `typeof` function from the `FunctionStringVar` class
        to determine the type of the object.

        Returns:
            StringVar: A string variable representing the type of the object.
        """
        return self.js_type()

    def js_type(self) -> StringVar:
        """Returns the javascript type of the object.

        This method uses the `typeof` function from the `FunctionStringVar` class
        to determine the type of the object.

        Returns:
            StringVar: A string variable representing the type of the object.
        """
        from .function import FunctionStringVar
        from .sequence import StringVar

        type_of = FunctionStringVar("typeof")
        return type_of.call(self).to(StringVar)

    def without_data(self):
        """Create a copy of the var without the data.

        Returns:
            The var without the data.
        """
        return dataclasses.replace(self, _var_data=None)

    def contains(self, value: Any = None, field: Any = None):
        """Get an attribute of the var.

        Args:
            value: The value to check for.
            field: The field to check for.

        Raises:
            TypeError: If the var does not support contains check.
        """
        raise TypeError(
            f"Var of type {self._var_type} does not support contains check."
        )

    def __get__(self, instance: Any, owner: Any):
        """Get the var.

        Args:
            instance: The instance to get the var from.
            owner: The owner of the var.

        Returns:
            The var.
        """
        return self

    def reverse(self):
        """Reverse the var.

        Raises:
            TypeError: If the var does not support reverse.
        """
        raise TypeError("Cannot reverse non-list var.")

    def __getattr__(self, name: str):
        """Get an attribute of the var.

        Args:
            name: The name of the attribute.

        Returns:
            The attribute.

        Raises:
            VarAttributeError: If the attribute does not exist.
            TypeError: If the var type is Any.
        """
        if name.startswith("_"):
            return self.__getattribute__(name)

        if self._var_type is Any:
            raise TypeError(
                f"You must provide an annotation for the state var `{str(self)}`. Annotation cannot be `{self._var_type}`."
            )

        if name in REPLACED_NAMES:
            raise VarAttributeError(
                f"Field {name!r} was renamed to {REPLACED_NAMES[name]!r}"
            )

        raise VarAttributeError(
            f"The State var has no attribute '{name}' or may have been annotated wrongly.",
        )

    def _decode(self) -> Any:
        """Decode Var as a python value.

        Note that Var with state set cannot be decoded python-side and will be
        returned as full_name.

        Returns:
            The decoded value or the Var name.
        """
        if isinstance(self, LiteralVar):
            return self._var_value  # type: ignore
        try:
            return json.loads(str(self))
        except ValueError:
            try:
                return json.loads(self.json())
            except (ValueError, NotImplementedError):
                return str(self)

    @property
    def _var_state(self) -> str:
        """Compat method for getting the state.

        Returns:
            The state name associated with the var.
        """
        var_data = self._get_all_var_data()
        return var_data.state if var_data else ""

    @overload
    @classmethod
    def range(cls, stop: int | NumberVar, /) -> ArrayVar[List[int]]: ...

    @overload
    @classmethod
    def range(
        cls,
        start: int | NumberVar,
        end: int | NumberVar,
        step: int | NumberVar = 1,
        /,
    ) -> ArrayVar[List[int]]: ...

    @classmethod
    def range(
        cls,
        first_endpoint: int | NumberVar,
        second_endpoint: int | NumberVar | None = None,
        step: int | NumberVar | None = None,
    ) -> ArrayVar[List[int]]:
        """Create a range of numbers.

        Args:
            first_endpoint: The end of the range if second_endpoint is not provided, otherwise the start of the range.
            second_endpoint: The end of the range.
            step: The step of the range.

        Returns:
            The range of numbers.
        """
        from .sequence import ArrayVar

        return ArrayVar.range(first_endpoint, second_endpoint, step)

    def __bool__(self) -> bool:
        """Raise exception if using Var in a boolean context.

        Raises:
            VarTypeError: when attempting to bool-ify the Var.
        """
        raise VarTypeError(
            f"Cannot convert Var {str(self)!r} to bool for use with `if`, `and`, `or`, and `not`. "
            "Instead use `rx.cond` and bitwise operators `&` (and), `|` (or), `~` (invert)."
        )

    def __iter__(self) -> Any:
        """Raise exception if using Var in an iterable context.

        Raises:
            VarTypeError: when attempting to iterate over the Var.
        """
        raise VarTypeError(
            f"Cannot iterate over Var {str(self)!r}. Instead use `rx.foreach`."
        )

    def __contains__(self, _: Any) -> Var:
        """Override the 'in' operator to alert the user that it is not supported.

        Raises:
            VarTypeError: the operation is not supported
        """
        raise VarTypeError(
            "'in' operator not supported for Var types, use Var.contains() instead."
        )

    def json(self) -> str:
        """Serialize the var to a JSON string.

        Raises:
            NotImplementedError: If the method is not implemented.
        """
        raise NotImplementedError("Var subclasses must implement the json method.")


OUTPUT = TypeVar("OUTPUT", bound=Var)


class LiteralVar(Var):
    """Base class for immutable literal vars."""

    @classmethod
    def create(
        cls,
        value: Any,
        _var_data: VarData | None = None,
    ) -> Var:
        """Create a var from a value.

        Args:
            value: The value to create the var from.
            _var_data: Additional hooks and imports associated with the Var.

        Returns:
            The var.

        Raises:
            TypeError: If the value is not a supported type for LiteralVar.
        """
        from .number import LiteralBooleanVar, LiteralNumberVar
        from .object import LiteralObjectVar
        from .sequence import LiteralArrayVar, LiteralStringVar

        if isinstance(value, Var):
            if _var_data is None:
                return value
            return value._replace(merge_var_data=_var_data)

        if isinstance(value, str):
            return LiteralStringVar.create(value, _var_data=_var_data)

        if isinstance(value, bool):
            return LiteralBooleanVar.create(value, _var_data=_var_data)

        if isinstance(value, (int, float)):
            return LiteralNumberVar.create(value, _var_data=_var_data)

        if isinstance(value, dict):
            return LiteralObjectVar.create(value, _var_data=_var_data)

        if isinstance(value, (list, tuple, set)):
            return LiteralArrayVar.create(value, _var_data=_var_data)

        if value is None:
            return LiteralNoneVar.create(_var_data=_var_data)

        from reflex.event import (
            EventChain,
            EventHandler,
            EventSpec,
            LiteralEventChainVar,
            LiteralEventVar,
        )
        from reflex.utils.format import get_event_handler_parts

        from .object import LiteralObjectVar

        if isinstance(value, EventSpec):
            return LiteralEventVar.create(value, _var_data=_var_data)

        if isinstance(value, EventChain):
            return LiteralEventChainVar.create(value, _var_data=_var_data)

        if isinstance(value, EventHandler):
            return Var(_js_expr=".".join(filter(None, get_event_handler_parts(value))))

        serialized_value = serializers.serialize(value)
        if serialized_value is not None:
            if isinstance(serialized_value, dict):
                return LiteralObjectVar.create(
                    serialized_value,
                    _var_type=type(value),
                    _var_data=_var_data,
                )
            if isinstance(serialized_value, str):
                return LiteralStringVar.create(
                    serialized_value, _var_type=type(value), _var_data=_var_data
                )
            return LiteralVar.create(serialized_value, _var_data=_var_data)

        if isinstance(value, Base):
            # get the fields of the pydantic class
            fields = value.__fields__.keys()
            one_level_dict = {field: getattr(value, field) for field in fields}

            return LiteralObjectVar.create(
                {
                    field: value
                    for field, value in one_level_dict.items()
                    if not callable(value)
                },
                _var_type=type(value),
                _var_data=_var_data,
            )

        if dataclasses.is_dataclass(value) and not isinstance(value, type):
            return LiteralObjectVar.create(
                {
                    k: (None if callable(v) else v)
                    for k, v in dataclasses.asdict(value).items()
                },
                _var_type=type(value),
                _var_data=_var_data,
            )

        raise TypeError(
            f"Unsupported type {type(value)} for LiteralVar. Tried to create a LiteralVar from {value}."
        )

    def __post_init__(self):
        """Post-initialize the var."""

    def json(self) -> str:
        """Serialize the var to a JSON string.

        Raises:
            NotImplementedError: If the method is not implemented.
        """
        raise NotImplementedError(
            "LiteralVar subclasses must implement the json method."
        )


@serializers.serializer
def serialize_literal(value: LiteralVar):
    """Serialize a Literal type.

    Args:
        value: The Literal to serialize.

    Returns:
        The serialized Literal.
    """
    return value._var_value


P = ParamSpec("P")
T = TypeVar("T")


# NoReturn is used to match CustomVarOperationReturn with no type hint.
@overload
def var_operation(
    func: Callable[P, CustomVarOperationReturn[NoReturn]],
) -> Callable[P, Var]: ...


@overload
def var_operation(
    func: Callable[P, CustomVarOperationReturn[bool]],
) -> Callable[P, BooleanVar]: ...


NUMBER_T = TypeVar("NUMBER_T", int, float, Union[int, float])


@overload
def var_operation(
    func: Callable[P, CustomVarOperationReturn[NUMBER_T]],
) -> Callable[P, NumberVar[NUMBER_T]]: ...


@overload
def var_operation(
    func: Callable[P, CustomVarOperationReturn[str]],
) -> Callable[P, StringVar]: ...


LIST_T = TypeVar("LIST_T", bound=Union[List[Any], Tuple, Set])


@overload
def var_operation(
    func: Callable[P, CustomVarOperationReturn[LIST_T]],
) -> Callable[P, ArrayVar[LIST_T]]: ...


OBJECT_TYPE = TypeVar("OBJECT_TYPE", bound=Dict)


@overload
def var_operation(
    func: Callable[P, CustomVarOperationReturn[OBJECT_TYPE]],
) -> Callable[P, ObjectVar[OBJECT_TYPE]]: ...


def var_operation(
    func: Callable[P, CustomVarOperationReturn[T]],
) -> Callable[P, Var[T]]:
    """Decorator for creating a var operation.

    Example:
    ```python
    @var_operation
    def add(a: NumberVar, b: NumberVar):
        return custom_var_operation(f"{a} + {b}")
    ```

    Args:
        func: The function to decorate.

    Returns:
        The decorated function.
    """

    @functools.wraps(func)
    def wrapper(*args: P.args, **kwargs: P.kwargs) -> Var[T]:
        func_args = list(inspect.signature(func).parameters)
        args_vars = {
            func_args[i]: (LiteralVar.create(arg) if not isinstance(arg, Var) else arg)
            for i, arg in enumerate(args)
        }
        kwargs_vars = {
            key: LiteralVar.create(value) if not isinstance(value, Var) else value
            for key, value in kwargs.items()
        }

        return CustomVarOperation.create(
            args=tuple(list(args_vars.items()) + list(kwargs_vars.items())),
            return_var=func(*args_vars.values(), **kwargs_vars),  # type: ignore
        ).guess_type()

    return wrapper


def unionize(*args: Type) -> Type:
    """Unionize the types.

    Args:
        args: The types to unionize.

    Returns:
        The unionized types.
    """
    if not args:
        return Any
    if len(args) == 1:
        return args[0]
    # We are bisecting the args list here to avoid hitting the recursion limit
    # In Python versions >= 3.11, we can simply do `return Union[*args]`
    midpoint = len(args) // 2
    first_half, second_half = args[:midpoint], args[midpoint:]
    return Union[unionize(*first_half), unionize(*second_half)]


def figure_out_type(value: Any) -> types.GenericType:
    """Figure out the type of the value.

    Args:
        value: The value to figure out the type of.

    Returns:
        The type of the value.
    """
    if isinstance(value, list):
        return List[unionize(*(figure_out_type(v) for v in value))]
    if isinstance(value, set):
        return Set[unionize(*(figure_out_type(v) for v in value))]
    if isinstance(value, tuple):
        return Tuple[unionize(*(figure_out_type(v) for v in value)), ...]
    if isinstance(value, dict):
        return Dict[
            unionize(*(figure_out_type(k) for k in value)),
            unionize(*(figure_out_type(v) for v in value.values())),
        ]
    if isinstance(value, Var):
        return value._var_type
    return type(value)


class cached_property_no_lock(functools.cached_property):
    """A special version of functools.cached_property that does not use a lock."""

    def __init__(self, func):
        """Initialize the cached_property_no_lock.

        Args:
            func: The function to cache.
        """
        super().__init__(func)
        self.lock = contextlib.nullcontext()


class CachedVarOperation:
    """Base class for cached var operations to lower boilerplate code."""

    def __post_init__(self):
        """Post-initialize the CachedVarOperation."""
        object.__delattr__(self, "_js_expr")

    def __getattr__(self, name: str) -> Any:
        """Get an attribute of the var.

        Args:
            name: The name of the attribute.

        Returns:
            The attribute.
        """
        if name == "_js_expr":
            return self._cached_var_name

        parent_classes = inspect.getmro(self.__class__)

        next_class = parent_classes[parent_classes.index(CachedVarOperation) + 1]

        return next_class.__getattr__(self, name)  # type: ignore

    def _get_all_var_data(self) -> VarData | None:
        """Get all VarData associated with the Var.

        Returns:
            The VarData of the components and all of its children.
        """
        return self._cached_get_all_var_data

    @cached_property_no_lock
    def _cached_get_all_var_data(self) -> VarData | None:
        """Get the cached VarData.

        Returns:
            The cached VarData.
        """
        return VarData.merge(
            *map(
                lambda value: (
                    value._get_all_var_data() if isinstance(value, Var) else None
                ),
                map(
                    lambda field: getattr(self, field.name),
                    dataclasses.fields(self),  # type: ignore
                ),
            ),
            self._var_data,
        )

    def __hash__(self) -> int:
        """Calculate the hash of the object.

        Returns:
            The hash of the object.
        """
        return hash(
            (
                self.__class__.__name__,
                *[
                    getattr(self, field.name)
                    for field in dataclasses.fields(self)  # type: ignore
                    if field.name not in ["_js_expr", "_var_data", "_var_type"]
                ],
            )
        )


def and_operation(a: Var | Any, b: Var | Any) -> Var:
    """Perform a logical AND operation on two variables.

    Args:
        a: The first variable.
        b: The second variable.

    Returns:
        The result of the logical AND operation.
    """
    return _and_operation(a, b)  # type: ignore


@var_operation
def _and_operation(a: Var, b: Var):
    """Perform a logical AND operation on two variables.

    Args:
        a: The first variable.
        b: The second variable.

    Returns:
        The result of the logical AND operation.
    """
    return var_operation_return(
        js_expression=f"({a} && {b})",
        var_type=unionize(a._var_type, b._var_type),
    )


def or_operation(a: Var | Any, b: Var | Any) -> Var:
    """Perform a logical OR operation on two variables.

    Args:
        a: The first variable.
        b: The second variable.

    Returns:
        The result of the logical OR operation.
    """
    return _or_operation(a, b)  # type: ignore


@var_operation
def _or_operation(a: Var, b: Var):
    """Perform a logical OR operation on two variables.

    Args:
        a: The first variable.
        b: The second variable.

    Returns:
        The result of the logical OR operation.
    """
    return var_operation_return(
        js_expression=f"({a} || {b})",
        var_type=unionize(a._var_type, b._var_type),
    )


@dataclasses.dataclass(
    eq=False,
    frozen=True,
    **{"slots": True} if sys.version_info >= (3, 10) else {},
)
class CallableVar(Var):
    """Decorate a Var-returning function to act as both a Var and a function.

    This is used as a compatibility shim for replacing Var objects in the
    API with functions that return a family of Var.
    """

    fn: Callable[..., Var] = dataclasses.field(
        default_factory=lambda: lambda: Var(_js_expr="undefined")
    )
    original_var: Var = dataclasses.field(
        default_factory=lambda: Var(_js_expr="undefined")
    )

    def __init__(self, fn: Callable[..., Var]):
        """Initialize a CallableVar.

        Args:
            fn: The function to decorate (must return Var)
        """
        original_var = fn()
        super(CallableVar, self).__init__(
            _js_expr=original_var._js_expr,
            _var_type=original_var._var_type,
            _var_data=VarData.merge(original_var._get_all_var_data()),
        )
        object.__setattr__(self, "fn", fn)
        object.__setattr__(self, "original_var", original_var)

    def __call__(self, *args, **kwargs) -> Var:
        """Call the decorated function.

        Args:
            *args: The args to pass to the function.
            **kwargs: The kwargs to pass to the function.

        Returns:
            The Var returned from calling the function.
        """
        return self.fn(*args, **kwargs)

    def __hash__(self) -> int:
        """Calculate the hash of the object.

        Returns:
            The hash of the object.
        """
        return hash((self.__class__.__name__, self.original_var))


RETURN_TYPE = TypeVar("RETURN_TYPE")

DICT_KEY = TypeVar("DICT_KEY")
DICT_VAL = TypeVar("DICT_VAL")

LIST_INSIDE = TypeVar("LIST_INSIDE")


class FakeComputedVarBaseClass(property):
    """A fake base class for ComputedVar to avoid inheriting from property."""

    __pydantic_run_validation__ = False


def is_computed_var(obj: Any) -> TypeGuard[ComputedVar]:
    """Check if the object is a ComputedVar.

    Args:
        obj: The object to check.

    Returns:
        Whether the object is a ComputedVar.
    """
    return isinstance(obj, FakeComputedVarBaseClass)


@dataclasses.dataclass(
    eq=False,
    frozen=True,
    **{"slots": True} if sys.version_info >= (3, 10) else {},
)
class ComputedVar(Var[RETURN_TYPE]):
    """A field with computed getters."""

    # Whether to track dependencies and cache computed values
    _cache: bool = dataclasses.field(default=False)

    # Whether the computed var is a backend var
    _backend: bool = dataclasses.field(default=False)

    # The initial value of the computed var
    _initial_value: RETURN_TYPE | types.Unset = dataclasses.field(default=types.Unset())

    # Explicit var dependencies to track
    _static_deps: set[str] = dataclasses.field(default_factory=set)

    # Whether var dependencies should be auto-determined
    _auto_deps: bool = dataclasses.field(default=True)

    # Interval at which the computed var should be updated
    _update_interval: Optional[datetime.timedelta] = dataclasses.field(default=None)

    _fget: Callable[[BaseState], RETURN_TYPE] = dataclasses.field(
        default_factory=lambda: lambda _: None
    )  # type: ignore

    def __init__(
        self,
        fget: Callable[[BASE_STATE], RETURN_TYPE],
        initial_value: RETURN_TYPE | types.Unset = types.Unset(),
        cache: bool = False,
        deps: Optional[List[Union[str, Var]]] = None,
        auto_deps: bool = True,
        interval: Optional[Union[int, datetime.timedelta]] = None,
        backend: bool | None = None,
        **kwargs,
    ):
        """Initialize a ComputedVar.

        Args:
            fget: The getter function.
            initial_value: The initial value of the computed var.
            cache: Whether to cache the computed value.
            deps: Explicit var dependencies to track.
            auto_deps: Whether var dependencies should be auto-determined.
            interval: Interval at which the computed var should be updated.
            backend: Whether the computed var is a backend var.
            **kwargs: additional attributes to set on the instance

        Raises:
            TypeError: If the computed var dependencies are not Var instances or var names.
        """
        hint = kwargs.pop("return_type", None) or get_type_hints(fget).get(
            "return", Any
        )

        kwargs["_js_expr"] = kwargs.pop("_js_expr", fget.__name__)
        kwargs["_var_type"] = kwargs.pop("_var_type", hint)

        Var.__init__(
            self,
            _js_expr=kwargs.pop("_js_expr"),
            _var_type=kwargs.pop("_var_type"),
            _var_data=kwargs.pop("_var_data", None),
        )

        if backend is None:
            backend = fget.__name__.startswith("_")

        object.__setattr__(self, "_backend", backend)
        object.__setattr__(self, "_initial_value", initial_value)
        object.__setattr__(self, "_cache", cache)

        if isinstance(interval, int):
            interval = datetime.timedelta(seconds=interval)

        object.__setattr__(self, "_update_interval", interval)

        if deps is None:
            deps = []
        else:
            for dep in deps:
                if isinstance(dep, Var):
                    continue
                if isinstance(dep, str) and dep != "":
                    continue
                raise TypeError(
                    "ComputedVar dependencies must be Var instances or var names (non-empty strings)."
                )
        object.__setattr__(
            self,
            "_static_deps",
            {dep._js_expr if isinstance(dep, Var) else dep for dep in deps},
        )
        object.__setattr__(self, "_auto_deps", auto_deps)

        object.__setattr__(self, "_fget", fget)

    @override
    def _replace(self, merge_var_data=None, **kwargs: Any) -> Self:
        """Replace the attributes of the ComputedVar.

        Args:
            merge_var_data: VarData to merge into the existing VarData.
            **kwargs: Var fields to update.

        Returns:
            The new ComputedVar instance.

        Raises:
            TypeError: If kwargs contains keys that are not allowed.
        """
        field_values = dict(
            fget=kwargs.pop("fget", self._fget),
            initial_value=kwargs.pop("initial_value", self._initial_value),
            cache=kwargs.pop("cache", self._cache),
            deps=kwargs.pop("deps", self._static_deps),
            auto_deps=kwargs.pop("auto_deps", self._auto_deps),
            interval=kwargs.pop("interval", self._update_interval),
            backend=kwargs.pop("backend", self._backend),
            _js_expr=kwargs.pop("_js_expr", self._js_expr),
            _var_type=kwargs.pop("_var_type", self._var_type),
            _var_data=kwargs.pop(
                "_var_data", VarData.merge(self._var_data, merge_var_data)
            ),
        )

        if kwargs:
            unexpected_kwargs = ", ".join(kwargs.keys())
            raise TypeError(f"Unexpected keyword arguments: {unexpected_kwargs}")

        return type(self)(**field_values)

    @property
    def _cache_attr(self) -> str:
        """Get the attribute used to cache the value on the instance.

        Returns:
            An attribute name.
        """
        return f"__cached_{self._js_expr}"

    @property
    def _last_updated_attr(self) -> str:
        """Get the attribute used to store the last updated timestamp.

        Returns:
            An attribute name.
        """
        return f"__last_updated_{self._js_expr}"

    def needs_update(self, instance: BaseState) -> bool:
        """Check if the computed var needs to be updated.

        Args:
            instance: The state instance that the computed var is attached to.

        Returns:
            True if the computed var needs to be updated, False otherwise.
        """
        if self._update_interval is None:
            return False
        last_updated = getattr(instance, self._last_updated_attr, None)
        if last_updated is None:
            return True
        return datetime.datetime.now() - last_updated > self._update_interval

    @overload
    def __get__(
        self: ComputedVar[int] | ComputedVar[float],
        instance: None,
        owner: Type,
    ) -> NumberVar: ...

    @overload
    def __get__(
        self: ComputedVar[str],
        instance: None,
        owner: Type,
    ) -> StringVar: ...

    @overload
    def __get__(
        self: ComputedVar[dict[DICT_KEY, DICT_VAL]],
        instance: None,
        owner: Type,
    ) -> ObjectVar[dict[DICT_KEY, DICT_VAL]]: ...

    @overload
    def __get__(
        self: ComputedVar[list[LIST_INSIDE]],
        instance: None,
        owner: Type,
    ) -> ArrayVar[list[LIST_INSIDE]]: ...

    @overload
    def __get__(
        self: ComputedVar[set[LIST_INSIDE]],
        instance: None,
        owner: Type,
    ) -> ArrayVar[set[LIST_INSIDE]]: ...

    @overload
    def __get__(
        self: ComputedVar[tuple[LIST_INSIDE, ...]],
        instance: None,
        owner: Type,
    ) -> ArrayVar[tuple[LIST_INSIDE, ...]]: ...

    @overload
    def __get__(self, instance: None, owner: Type) -> ComputedVar[RETURN_TYPE]: ...

    @overload
    def __get__(self, instance: BaseState, owner: Type) -> RETURN_TYPE: ...

    def __get__(self, instance: BaseState | None, owner):
        """Get the ComputedVar value.

        If the value is already cached on the instance, return the cached value.

        Args:
            instance: the instance of the class accessing this computed var.
            owner: the class that this descriptor is attached to.

        Returns:
            The value of the var for the given instance.
        """
        if instance is None:
            state_where_defined = owner
            while self._js_expr in state_where_defined.inherited_vars:
                state_where_defined = state_where_defined.get_parent_state()

            field_name = (
                format_state_name(state_where_defined.get_full_name())
                + "."
                + self._js_expr
            )

            return dispatch(
                field_name,
                var_data=VarData.from_state(state_where_defined, self._js_expr),
                result_var_type=self._var_type,
                existing_var=self,
            )

        if not self._cache:
            return self.fget(instance)

        # handle caching
        if not hasattr(instance, self._cache_attr) or self.needs_update(instance):
            # Set cache attr on state instance.
            setattr(instance, self._cache_attr, self.fget(instance))
            # Ensure the computed var gets serialized to redis.
            instance._was_touched = True
            # Set the last updated timestamp on the state instance.
            setattr(instance, self._last_updated_attr, datetime.datetime.now())
        return getattr(instance, self._cache_attr)

    def _deps(
        self,
        objclass: Type,
        obj: FunctionType | CodeType | None = None,
        self_name: Optional[str] = None,
    ) -> set[str]:
        """Determine var dependencies of this ComputedVar.

        Save references to attributes accessed on "self".  Recursively called
        when the function makes a method call on "self" or define comprehensions
        or nested functions that may reference "self".

        Args:
            objclass: the class obj this ComputedVar is attached to.
            obj: the object to disassemble (defaults to the fget function).
            self_name: if specified, look for this name in LOAD_FAST and LOAD_DEREF instructions.

        Returns:
            A set of variable names accessed by the given obj.

        Raises:
            VarValueError: if the function references the get_state, parent_state, or substates attributes
                (cannot track deps in a related state, only implicitly via parent state).
        """
        if not self._auto_deps:
            return self._static_deps
        d = self._static_deps.copy()
        if obj is None:
            fget = self._fget
            if fget is not None:
                obj = cast(FunctionType, fget)
            else:
                return set()
        with contextlib.suppress(AttributeError):
            # unbox functools.partial
            obj = cast(FunctionType, obj.func)  # type: ignore
        with contextlib.suppress(AttributeError):
            # unbox EventHandler
            obj = cast(FunctionType, obj.fn)  # type: ignore

        if self_name is None and isinstance(obj, FunctionType):
            try:
                # the first argument to the function is the name of "self" arg
                self_name = obj.__code__.co_varnames[0]
            except (AttributeError, IndexError):
                self_name = None
        if self_name is None:
            # cannot reference attributes on self if method takes no args
            return set()

        invalid_names = ["get_state", "parent_state", "substates", "get_substate"]
        self_is_top_of_stack = False
        for instruction in dis.get_instructions(obj):
            if (
                instruction.opname in ("LOAD_FAST", "LOAD_DEREF")
                and instruction.argval == self_name
            ):
                # bytecode loaded the class instance to the top of stack, next load instruction
                # is referencing an attribute on self
                self_is_top_of_stack = True
                continue
            if self_is_top_of_stack and instruction.opname in (
                "LOAD_ATTR",
                "LOAD_METHOD",
            ):
                try:
                    ref_obj = getattr(objclass, instruction.argval)
                except Exception:
                    ref_obj = None
                if instruction.argval in invalid_names:
                    raise VarValueError(
                        f"Cached var {str(self)} cannot access arbitrary state via `{instruction.argval}`."
                    )
                if callable(ref_obj):
                    # recurse into callable attributes
                    d.update(
                        self._deps(
                            objclass=objclass,
                            obj=ref_obj,
                        )
                    )
                # recurse into property fget functions
                elif isinstance(ref_obj, property) and not isinstance(
                    ref_obj, ComputedVar
                ):
                    d.update(
                        self._deps(
                            objclass=objclass,
                            obj=ref_obj.fget,  # type: ignore
                        )
                    )
                elif (
                    instruction.argval in objclass.backend_vars
                    or instruction.argval in objclass.vars
                ):
                    # var access
                    d.add(instruction.argval)
            elif instruction.opname == "LOAD_CONST" and isinstance(
                instruction.argval, CodeType
            ):
                # recurse into nested functions / comprehensions, which can reference
                # instance attributes from the outer scope
                d.update(
                    self._deps(
                        objclass=objclass,
                        obj=instruction.argval,
                        self_name=self_name,
                    )
                )
            self_is_top_of_stack = False
        return d

    def mark_dirty(self, instance) -> None:
        """Mark this ComputedVar as dirty.

        Args:
            instance: the state instance that needs to recompute the value.
        """
        with contextlib.suppress(AttributeError):
            delattr(instance, self._cache_attr)

    def _determine_var_type(self) -> Type:
        """Get the type of the var.

        Returns:
            The type of the var.
        """
        hints = get_type_hints(self._fget)
        if "return" in hints:
            return hints["return"]
        return Any

    @property
    def __class__(self) -> Type:
        """Get the class of the var.

        Returns:
            The class of the var.
        """
        return FakeComputedVarBaseClass

    @property
    def fget(self) -> Callable[[BaseState], RETURN_TYPE]:
        """Get the getter function.

        Returns:
            The getter function.
        """
        return self._fget


class DynamicRouteVar(ComputedVar[Union[str, List[str]]]):
    """A ComputedVar that represents a dynamic route."""

    pass


if TYPE_CHECKING:
    BASE_STATE = TypeVar("BASE_STATE", bound=BaseState)


@overload
def computed_var(
    fget: None = None,
    initial_value: Any | types.Unset = types.Unset(),
    cache: bool = False,
    deps: Optional[List[Union[str, Var]]] = None,
    auto_deps: bool = True,
    interval: Optional[Union[datetime.timedelta, int]] = None,
    backend: bool | None = None,
    **kwargs,
) -> Callable[[Callable[[BASE_STATE], RETURN_TYPE]], ComputedVar[RETURN_TYPE]]: ...


@overload
def computed_var(
    fget: Callable[[BASE_STATE], RETURN_TYPE],
    initial_value: RETURN_TYPE | types.Unset = types.Unset(),
    cache: bool = False,
    deps: Optional[List[Union[str, Var]]] = None,
    auto_deps: bool = True,
    interval: Optional[Union[datetime.timedelta, int]] = None,
    backend: bool | None = None,
    **kwargs,
) -> ComputedVar[RETURN_TYPE]: ...


def computed_var(
    fget: Callable[[BASE_STATE], Any] | None = None,
    initial_value: Any | types.Unset = types.Unset(),
    cache: bool = False,
    deps: Optional[List[Union[str, Var]]] = None,
    auto_deps: bool = True,
    interval: Optional[Union[datetime.timedelta, int]] = None,
    backend: bool | None = None,
    **kwargs,
) -> ComputedVar | Callable[[Callable[[BASE_STATE], Any]], ComputedVar]:
    """A ComputedVar decorator with or without kwargs.

    Args:
        fget: The getter function.
        initial_value: The initial value of the computed var.
        cache: Whether to cache the computed value.
        deps: Explicit var dependencies to track.
        auto_deps: Whether var dependencies should be auto-determined.
        interval: Interval at which the computed var should be updated.
        backend: Whether the computed var is a backend var.
        **kwargs: additional attributes to set on the instance

    Returns:
        A ComputedVar instance.

    Raises:
        ValueError: If caching is disabled and an update interval is set.
        VarDependencyError: If user supplies dependencies without caching.
    """
    if cache is False and interval is not None:
        raise ValueError("Cannot set update interval without caching.")

    if cache is False and (deps is not None or auto_deps is False):
        raise VarDependencyError("Cannot track dependencies without caching.")

    if fget is not None:
        return ComputedVar(fget, cache=cache)

    def wrapper(fget: Callable[[BASE_STATE], Any]) -> ComputedVar:
        return ComputedVar(
            fget,
            initial_value=initial_value,
            cache=cache,
            deps=deps,
            auto_deps=auto_deps,
            interval=interval,
            backend=backend,
            **kwargs,
        )

    return wrapper


RETURN = TypeVar("RETURN")


class CustomVarOperationReturn(Var[RETURN]):
    """Base class for custom var operations."""

    @classmethod
    def create(
        cls,
        js_expression: str,
        _var_type: Type[RETURN] | None = None,
        _var_data: VarData | None = None,
    ) -> CustomVarOperationReturn[RETURN]:
        """Create a CustomVarOperation.

        Args:
            js_expression: The JavaScript expression to evaluate.
            _var_type: The type of the var.
            _var_data: Additional hooks and imports associated with the Var.

        Returns:
            The CustomVarOperation.
        """
        return CustomVarOperationReturn(
            _js_expr=js_expression,
            _var_type=_var_type or Any,
            _var_data=_var_data,
        )


def var_operation_return(
    js_expression: str,
    var_type: Type[RETURN] | None = None,
    var_data: VarData | None = None,
) -> CustomVarOperationReturn[RETURN]:
    """Shortcut for creating a CustomVarOperationReturn.

    Args:
        js_expression: The JavaScript expression to evaluate.
        var_type: The type of the var.
        var_data: Additional hooks and imports associated with the Var.

    Returns:
        The CustomVarOperationReturn.
    """
    return CustomVarOperationReturn.create(
        js_expression,
        var_type,
        var_data,
    )


@dataclasses.dataclass(
    eq=False,
    frozen=True,
    **{"slots": True} if sys.version_info >= (3, 10) else {},
)
class CustomVarOperation(CachedVarOperation, Var[T]):
    """Base class for custom var operations."""

    _args: Tuple[Tuple[str, Var], ...] = dataclasses.field(default_factory=tuple)

    _return: CustomVarOperationReturn[T] = dataclasses.field(
        default_factory=lambda: CustomVarOperationReturn.create("")
    )

    @cached_property_no_lock
    def _cached_var_name(self) -> str:
        """Get the cached var name.

        Returns:
            The cached var name.
        """
        return str(self._return)

    @cached_property_no_lock
    def _cached_get_all_var_data(self) -> VarData | None:
        """Get the cached VarData.

        Returns:
            The cached VarData.
        """
        return VarData.merge(
            *map(
                lambda arg: arg[1]._get_all_var_data(),
                self._args,
            ),
            self._return._get_all_var_data(),
            self._var_data,
        )

    @classmethod
    def create(
        cls,
        args: Tuple[Tuple[str, Var], ...],
        return_var: CustomVarOperationReturn[T],
        _var_data: VarData | None = None,
    ) -> CustomVarOperation[T]:
        """Create a CustomVarOperation.

        Args:
            args: The arguments to the operation.
            return_var: The return var.
            _var_data: Additional hooks and imports associated with the Var.

        Returns:
            The CustomVarOperation.
        """
        return CustomVarOperation(
            _js_expr="",
            _var_type=return_var._var_type,
            _var_data=_var_data,
            _args=args,
            _return=return_var,
        )


class NoneVar(Var[None]):
    """A var representing None."""


@dataclasses.dataclass(
    eq=False,
    frozen=True,
    **{"slots": True} if sys.version_info >= (3, 10) else {},
)
class LiteralNoneVar(LiteralVar, NoneVar):
    """A var representing None."""

    _var_value: None = None

    def json(self) -> str:
        """Serialize the var to a JSON string.

        Returns:
            The JSON string.
        """
        return "null"

    @classmethod
    def create(
        cls,
        _var_data: VarData | None = None,
    ) -> LiteralNoneVar:
        """Create a var from a value.

        Args:
            _var_data: Additional hooks and imports associated with the Var.

        Returns:
            The var.
        """
        return LiteralNoneVar(
            _js_expr="null",
            _var_type=None,
            _var_data=_var_data,
        )


@dataclasses.dataclass(
    eq=False,
    frozen=True,
    **{"slots": True} if sys.version_info >= (3, 10) else {},
)
class ToNoneOperation(CachedVarOperation, NoneVar):
    """A var operation that converts a var to None."""

    _original_var: Var = dataclasses.field(
        default_factory=lambda: LiteralNoneVar.create()
    )

    @cached_property_no_lock
    def _cached_var_name(self) -> str:
        """Get the cached var name.

        Returns:
            The cached var name.
        """
        return str(self._original_var)

    @classmethod
    def create(
        cls,
        var: Var,
        _var_data: VarData | None = None,
    ) -> ToNoneOperation:
        """Create a ToNoneOperation.

        Args:
            var: The var to convert to None.
            _var_data: Additional hooks and imports associated with the Var.

        Returns:
            The ToNoneOperation.
        """
        return ToNoneOperation(
            _js_expr="",
            _var_type=None,
            _var_data=_var_data,
            _original_var=var,
        )


@dataclasses.dataclass(
    eq=False,
    frozen=True,
    **{"slots": True} if sys.version_info >= (3, 10) else {},
)
class StateOperation(CachedVarOperation, Var):
    """A var operation that accesses a field on an object."""

    _state_name: str = dataclasses.field(default="")
    _field: Var = dataclasses.field(default_factory=lambda: LiteralNoneVar.create())

    @cached_property_no_lock
    def _cached_var_name(self) -> str:
        """Get the cached var name.

        Returns:
            The cached var name.
        """
        return f"{str(self._state_name)}.{str(self._field)}"

    def __getattr__(self, name: str) -> Any:
        """Get an attribute of the var.

        Args:
            name: The name of the attribute.

        Returns:
            The attribute.
        """
        if name == "_js_expr":
            return self._cached_var_name

        return getattr(self._field, name)

    @classmethod
    def create(
        cls,
        state_name: str,
        field: Var,
        _var_data: VarData | None = None,
    ) -> StateOperation:
        """Create a DotOperation.

        Args:
            state_name: The name of the state.
            field: The field of the state.
            _var_data: Additional hooks and imports associated with the Var.

        Returns:
            The DotOperation.
        """
        return StateOperation(
            _js_expr="",
            _var_type=field._var_type,
            _var_data=_var_data,
            _state_name=state_name,
            _field=field,
        )


class ToOperation:
    """A var operation that converts a var to another type."""

    def __getattr__(self, name: str) -> Any:
        """Get an attribute of the var.

        Args:
            name: The name of the attribute.

        Returns:
            The attribute of the var.
        """
        return getattr(object.__getattribute__(self, "_original"), name)

    def __post_init__(self):
        """Post initialization."""
        object.__delattr__(self, "_js_expr")

    def __hash__(self) -> int:
        """Calculate the hash value of the object.

        Returns:
            int: The hash value of the object.
        """
        return hash(object.__getattribute__(self, "_original"))

    def _get_all_var_data(self) -> VarData | None:
        """Get all the var data.

        Returns:
            The var data.
        """
        return VarData.merge(
            object.__getattribute__(self, "_original")._get_all_var_data(),
            self._var_data,  # type: ignore
        )

    @classmethod
    def create(
        cls,
        value: Var,
        _var_type: GenericType | None = None,
        _var_data: VarData | None = None,
    ):
        """Create a ToOperation.

        Args:
            value: The value of the var.
            _var_type: The type of the Var.
            _var_data: Additional hooks and imports associated with the Var.

        Returns:
            The ToOperation.
        """
        return cls(
            _js_expr="",  # type: ignore
            _var_data=_var_data,  # type: ignore
            _var_type=_var_type or cls._default_var_type,  # type: ignore
            _original=value,  # type: ignore
        )


def get_uuid_string_var() -> Var:
    """Return a Var that generates a single memoized UUID via .web/utils/state.js.

    useMemo with an empty dependency array ensures that the generated UUID is
    consistent across re-renders of the component.

    Returns:
        A Var that generates a UUID at runtime.
    """
    from reflex.utils.imports import ImportVar
    from reflex.vars import Var

    unique_uuid_var = get_unique_variable_name()
    unique_uuid_var_data = VarData(
        imports={
            f"/{constants.Dirs.STATE_PATH}": {ImportVar(tag="generateUUID")},  # type: ignore
            "react": "useMemo",
        },
        hooks={f"const {unique_uuid_var} = useMemo(generateUUID, [])": None},
    )

    return Var(
        _js_expr=unique_uuid_var,
        _var_type=str,
        _var_data=unique_uuid_var_data,
    )


# Set of unique variable names.
USED_VARIABLES = set()


def get_unique_variable_name() -> str:
    """Get a unique variable name.

    Returns:
        The unique variable name.
    """
    name = "".join([random.choice(string.ascii_lowercase) for _ in range(8)])
    if name not in USED_VARIABLES:
        USED_VARIABLES.add(name)
        return name
    return get_unique_variable_name()


@dataclasses.dataclass(
    eq=True,
    frozen=True,
)
class VarData:
    """Metadata associated with a x."""

    # The name of the enclosing state.
    state: str = dataclasses.field(default="")

    # The name of the field in the state.
    field_name: str = dataclasses.field(default="")

    # Imports needed to render this var
    imports: ImmutableParsedImportDict = dataclasses.field(default_factory=tuple)

    # Hooks that need to be present in the component to render this var
    hooks: Tuple[str, ...] = dataclasses.field(default_factory=tuple)

    def __init__(
        self,
        state: str = "",
        field_name: str = "",
        imports: ImportDict | ParsedImportDict | None = None,
        hooks: dict[str, None] | None = None,
    ):
        """Initialize the var data.

        Args:
            state: The name of the enclosing state.
            field_name: The name of the field in the state.
            imports: Imports needed to render this var.
            hooks: Hooks that need to be present in the component to render this var.
        """
        immutable_imports: ImmutableParsedImportDict = tuple(
            sorted(
                ((k, tuple(sorted(v))) for k, v in parse_imports(imports or {}).items())
            )
        )
        object.__setattr__(self, "state", state)
        object.__setattr__(self, "field_name", field_name)
        object.__setattr__(self, "imports", immutable_imports)
        object.__setattr__(self, "hooks", tuple(hooks or {}))

    def old_school_imports(self) -> ImportDict:
        """Return the imports as a mutable dict.

        Returns:
            The imports as a mutable dict.
        """
        return dict((k, list(v)) for k, v in self.imports)

    @classmethod
    def merge(cls, *others: VarData | None) -> VarData | None:
        """Merge multiple var data objects.

        Args:
            *others: The var data objects to merge.

        Returns:
            The merged var data object.
        """
        state = ""
        field_name = ""
        _imports = {}
        hooks = {}
        for var_data in others:
            if var_data is None:
                continue
            state = state or var_data.state
            field_name = field_name or var_data.field_name
            _imports = imports.merge_imports(_imports, var_data.imports)
            hooks.update(
                var_data.hooks
                if isinstance(var_data.hooks, dict)
                else {k: None for k in var_data.hooks}
            )

        if state or _imports or hooks or field_name:
            return VarData(
                state=state,
                field_name=field_name,
                imports=_imports,
                hooks=hooks,
            )
        return None

    def __bool__(self) -> bool:
        """Check if the var data is non-empty.

        Returns:
            True if any field is set to a non-default value.
        """
        return bool(self.state or self.imports or self.hooks or self.field_name)

    @classmethod
    def from_state(cls, state: Type[BaseState] | str, field_name: str = "") -> VarData:
        """Set the state of the var.

        Args:
            state: The state to set or the full name of the state.
            field_name: The name of the field in the state. Optional.

        Returns:
            The var with the set state.
        """
        from reflex.utils import format

        state_name = state if isinstance(state, str) else state.get_full_name()
        return VarData(
            state=state_name,
            field_name=field_name,
            hooks={
                "const {0} = useContext(StateContexts.{0})".format(
                    format.format_state_name(state_name)
                ): None
            },
            imports={
                f"/{constants.Dirs.CONTEXTS_PATH}": [ImportVar(tag="StateContexts")],
                "react": [ImportVar(tag="useContext")],
            },
        )


def _decode_var_immutable(value: str) -> tuple[VarData | None, str]:
    """Decode the state name from a formatted var.

    Args:
        value: The value to extract the state name from.

    Returns:
        The extracted state name and the value without the state name.
    """
    var_datas = []
    if isinstance(value, str):
        # fast path if there is no encoded VarData
        if constants.REFLEX_VAR_OPENING_TAG not in value:
            return None, value

        offset = 0

        # Find all tags.
        while m := _decode_var_pattern.search(value):
            start, end = m.span()
            value = value[:start] + value[end:]

            serialized_data = m.group(1)

            if serialized_data.isnumeric() or (
                serialized_data[0] == "-" and serialized_data[1:].isnumeric()
            ):
                # This is a global immutable var.
                var = _global_vars[int(serialized_data)]
                var_data = var._get_all_var_data()

                if var_data is not None:
                    var_datas.append(var_data)
            offset += end - start

    return VarData.merge(*var_datas) if var_datas else None, value


# Compile regex for finding reflex var tags.
_decode_var_pattern_re = (
    rf"{constants.REFLEX_VAR_OPENING_TAG}(.*?){constants.REFLEX_VAR_CLOSING_TAG}"
)
_decode_var_pattern = re.compile(_decode_var_pattern_re, flags=re.DOTALL)

# Defined global immutable vars.
_global_vars: Dict[int, Var] = {}


def _extract_var_data(value: Iterable) -> list[VarData | None]:
    """Extract the var imports and hooks from an iterable containing a Var.

    Args:
        value: The iterable to extract the VarData from

    Returns:
        The extracted VarDatas.
    """
    from reflex.style import Style
    from reflex.vars import Var

    var_datas = []
    with contextlib.suppress(TypeError):
        for sub in value:
            if isinstance(sub, Var):
                var_datas.append(sub._var_data)
            elif not isinstance(sub, str):
                # Recurse into dict values.
                if hasattr(sub, "values") and callable(sub.values):
                    var_datas.extend(_extract_var_data(sub.values()))
                # Recurse into iterable values (or dict keys).
                var_datas.extend(_extract_var_data(sub))

    # Style objects should already have _var_data.
    if isinstance(value, Style):
        var_datas.append(value._var_data)
    else:
        # Recurse when value is a dict itself.
        values = getattr(value, "values", None)
        if callable(values):
            var_datas.extend(_extract_var_data(values()))
    return var_datas


# These names were changed in reflex 0.3.0
REPLACED_NAMES = {
    "full_name": "_var_full_name",
    "name": "_js_expr",
    "state": "_var_data.state",
    "type_": "_var_type",
    "is_local": "_var_is_local",
    "is_string": "_var_is_string",
    "set_state": "_var_set_state",
    "deps": "_deps",
}


dispatchers: Dict[GenericType, Callable[[Var], Var]] = {}


def transform(fn: Callable[[Var], Var]) -> Callable[[Var], Var]:
    """Register a function to transform a Var.

    Args:
        fn: The function to register.

    Returns:
        The decorator.

    Raises:
        TypeError: If the return type of the function is not a Var.
        TypeError: If the Var return type does not have a generic type.
        ValueError: If a function for the generic type is already registered.
    """
    return_type = fn.__annotations__["return"]

    origin = get_origin(return_type)

    if origin is not Var:
        raise TypeError(
            f"Expected return type of {fn.__name__} to be a Var, got {origin}."
        )

    generic_args = get_args(return_type)

    if not generic_args:
        raise TypeError(
            f"Expected Var return type of {fn.__name__} to have a generic type."
        )

    generic_type = get_origin(generic_args[0]) or generic_args[0]

    if generic_type in dispatchers:
        raise ValueError(f"Function for {generic_type} already registered.")

    dispatchers[generic_type] = fn

    return fn


def generic_type_to_actual_type_map(
    generic_type: GenericType, actual_type: GenericType
) -> Dict[TypeVar, GenericType]:
    """Map the generic type to the actual type.

    Args:
        generic_type: The generic type.
        actual_type: The actual type.

    Returns:
        The mapping of type variables to actual types.

    Raises:
        TypeError: If the generic type and actual type do not match.
        TypeError: If the number of generic arguments and actual arguments do not match.
    """
    generic_origin = get_origin(generic_type) or generic_type
    actual_origin = get_origin(actual_type) or actual_type

    if generic_origin is not actual_origin:
        if isinstance(generic_origin, TypeVar):
            return {generic_origin: actual_origin}
        raise TypeError(
            f"Type mismatch: expected {generic_origin}, got {actual_origin}."
        )

    generic_args = get_args(generic_type)
    actual_args = get_args(actual_type)

    if len(generic_args) != len(actual_args):
        raise TypeError(
            f"Number of generic arguments mismatch: expected {len(generic_args)}, got {len(actual_args)}."
        )

    # call recursively for nested generic types and merge the results
    return {
        k: v
        for generic_arg, actual_arg in zip(generic_args, actual_args)
        for k, v in generic_type_to_actual_type_map(generic_arg, actual_arg).items()
    }


def resolve_generic_type_with_mapping(
    generic_type: GenericType, type_mapping: Dict[TypeVar, GenericType]
):
    """Resolve a generic type with a type mapping.

    Args:
        generic_type: The generic type.
        type_mapping: The type mapping.

    Returns:
        The resolved generic type.
    """
    if isinstance(generic_type, TypeVar):
        return type_mapping.get(generic_type, generic_type)

    generic_origin = get_origin(generic_type) or generic_type

    generic_args = get_args(generic_type)

    if not generic_args:
        return generic_type

    mapping_for_older_python = {
        list: List,
        set: Set,
        dict: Dict,
        tuple: Tuple,
        frozenset: FrozenSet,
    }

    return mapping_for_older_python.get(generic_origin, generic_origin)[
        tuple(
            resolve_generic_type_with_mapping(arg, type_mapping) for arg in generic_args
        )
    ]


def resolve_arg_type_from_return_type(
    arg_type: GenericType, return_type: GenericType, actual_return_type: GenericType
) -> GenericType:
    """Resolve the argument type from the return type.

    Args:
        arg_type: The argument type.
        return_type: The return type.
        actual_return_type: The requested return type.

    Returns:
        The argument type without the generics that are resolved.
    """
    return resolve_generic_type_with_mapping(
        arg_type, generic_type_to_actual_type_map(return_type, actual_return_type)
    )


def dispatch(
    field_name: str,
    var_data: VarData,
    result_var_type: GenericType,
    existing_var: Var | None = None,
) -> Var:
    """Dispatch a Var to the appropriate transformation function.

    Args:
        field_name: The name of the field.
        var_data: The VarData associated with the Var.
        result_var_type: The type of the Var.
        existing_var: The existing Var to transform. Optional.

    Returns:
        The transformed Var.

    Raises:
        TypeError: If the return type of the function is not a Var.
        TypeError: If the Var return type does not have a generic type.
        TypeError: If the first argument of the function is not a Var.
        TypeError: If the first argument of the function does not have a generic type
    """
    result_origin_var_type = get_origin(result_var_type) or result_var_type

    if result_origin_var_type in dispatchers:
        fn = dispatchers[result_origin_var_type]
        fn_first_arg_type = list(inspect.signature(fn).parameters.values())[
            0
        ].annotation

        fn_return = inspect.signature(fn).return_annotation

        fn_return_origin = get_origin(fn_return) or fn_return

        if fn_return_origin is not Var:
            raise TypeError(
                f"Expected return type of {fn.__name__} to be a Var, got {fn_return}."
            )

        fn_return_generic_args = get_args(fn_return)

        if not fn_return_generic_args:
            raise TypeError(f"Expected generic type of {fn_return} to be a type.")

        arg_origin = get_origin(fn_first_arg_type) or fn_first_arg_type

        if arg_origin is not Var:
            raise TypeError(
                f"Expected first argument of {fn.__name__} to be a Var, got {fn_first_arg_type}."
            )

        arg_generic_args = get_args(fn_first_arg_type)

        if not arg_generic_args:
            raise TypeError(
                f"Expected generic type of {fn_first_arg_type} to be a type."
            )

        arg_type = arg_generic_args[0]
        fn_return_type = fn_return_generic_args[0]

        var = (
            Var(
                field_name,
                _var_data=var_data,
                _var_type=resolve_arg_type_from_return_type(
                    arg_type, fn_return_type, result_var_type
                ),
            ).guess_type()
            if existing_var is None
            else existing_var._replace(
                _var_type=resolve_arg_type_from_return_type(
                    arg_type, fn_return_type, result_var_type
                ),
                _var_data=var_data,
                _js_expr=field_name,
            ).guess_type()
        )

        return fn(var)

    if existing_var is not None:
        return existing_var._replace(
            _js_expr=field_name,
            _var_data=var_data,
            _var_type=result_var_type,
        ).guess_type()
    return Var(
        field_name,
        _var_data=var_data,
        _var_type=result_var_type,
    ).guess_type()