"""
Core variable tracking functionality for Dynamo. This module defines the fundamental
classes and systems used to track and manage variables during Dynamo's operation.

The module provides:
1. VariableTracker - The base class for tracking variables during compilation
2. MutationType system - Classes for tracking and managing mutations to variables
3. Source type management - Utilities for tracking variable origins and scope
4. Variable state management - Tools for managing variable state and transformations

These components form the foundation of Dynamo's variable handling system,
enabling accurate tracking and transformation of Python code into optimized
computations.
"""

import collections
import logging
from collections.abc import Callable, ItemsView, KeysView, Sequence, ValuesView
from enum import Enum
from typing import Any, NoReturn, Optional, TYPE_CHECKING

from torch._guards import Guard
from torch.fx.proxy import Node

from .. import graph_break_hints, variables
from ..current_scope_id import current_scope_id
from ..exc import raise_observed_exception, unimplemented
from ..guards import GuardBuilder, install_guard
from ..source import AttrSource, Source
from ..utils import cmp_name_to_op_mapping, istype


if TYPE_CHECKING:
    from ..codegen import PyCodegen
    from ..symbolic_convert import InstructionTranslator
    from .constant import ConstantVariable
    from .functions import UserFunctionVariable


log = logging.getLogger(__name__)


class SourceType(Enum):
    """
    This Enum divides VariableTracker into 2 cases, depending on the variable
    it represents:
    - already existed that Dynamo began tracking while introspection (Existing)
    - is a new variable that is created during Dynamo introspection (New)

    In general, we have these invariants:
    1. for `VariableTracker` associated with `Existing`, its `source` field must not be None.
    2. for `VariableTracker` associated with `New`, most of the time its
       `source` field is None, except for cases like side effect codegen for
       `AttributeMutationNew`, during which we generate a
       `LocalSource('tmp...')` for such variable, to facilitate codegen.
    """

    Existing = 0
    New = 1


class MutationType:
    """
    Base class for Variable.mutation_type. It encodes information about
    1. The type of mutation Dynamo allows on the variable.
    2. Whether the value represented by this variable already existed before
    Dynamo tracing.
    """

    def __init__(self, typ: SourceType) -> None:
        # In HigherOrderOperator tracing, we need to distinguish
        # between MutationTypes inside the HigherOrderOperator and
        # ones outside it. For example, it is not safe to mutate
        # `a` in the following example because it was constructed
        # in a different scope.
        #
        # def f(x):
        #     a = 1
        #     def g(x):
        #         nonlocal a
        #         a = 2
        #         return x
        #     return wrap(g, x) + a
        #
        # We use self.scope to distinguish this.
        # scope == 0: The object was an existing variable
        # scope == 1: The object was created while Dynamo
        #             was introspecting a function
        #             (and no HigherOrderOps were involved)
        # scope >= 2: The object was created through
        #             Dynamo introspection of a HigherOrderOp.
        #             The exact number corresponds to the level
        #             of nested HigherOrderOps.
        if typ is SourceType.Existing:
            self.scope = 0
        elif typ is SourceType.New:
            self.scope = current_scope_id()
        else:
            unimplemented(
                gb_type="Unsupported SourceType",
                context=f"MutationType.__init__ {self} {typ}",
                explanation=f"Dynamo does not support the type `{typ}`",
                hints=[
                    "This branch is not supposed to be reachable.",
                    *graph_break_hints.DYNAMO_BUG,
                ],
            )


class ValueMutationNew(MutationType):
    """
    This case of VariableTracker.mutation_type marker indicates
    1. Dynamo allows mutation on the value itself (rather than its attributes).
    2. The value is created by the bytecode Dynamo is tracing through.

    For instance, Dynamo could model a newly created list with this marker,
    indicating that while we need to model mutations to this list, we don't have
    to emit bytecode for these mutations if the list doesn't escape into the
    Python world.
    """

    def __init__(self) -> None:
        super().__init__(SourceType.New)

    def __hash__(self) -> int:
        return id(self)

    def __eq__(self, other: object) -> bool:
        return self is other


class ValueMutationExisting(MutationType):
    """
    This case of VariableTracker.mutation_type marker indicates
    1. Dynamo allows mutation on the value itself (rather than its attributes).
    2. The value exists before Dynamo tracing started.

    For instance, Dynamo could model a pre-existing list with this marker,
    indicating that if we encounter mutations to this list, we need to buffer
    and re-apply those mutations after the graph runs, since the list might be
    used afterwards in Python.
    """

    # A flag to indicate whether mutation happened on the associated
    # `VariableTracker`. This enables SideEffects to accurately and quickly
    # filter out which pre-existing values it needs to generate mutation for.
    is_modified: bool

    def __init__(self, is_modified: bool = False) -> None:
        super().__init__(SourceType.Existing)
        self.is_modified = is_modified


class AttributeMutation(MutationType):
    """
    This case of VariableTracker.mutation_type marker indicates that Dynamo
    allows mutation on the value's attributes.
    """


class AttributeMutationExisting(AttributeMutation):
    """
    This case of VariableTracker.mutation_type marker indicates
    1. Dynamo allows mutation on the value's attributes.
    2. The value exists before Dynamo tracing started.

    For instance, Dynamo could model a pre-existing object with this marker,
    indicating that if we encounter mutations to this object, we need to buffer
    then re-apply those mutations after the graph runs, since the object might
    be used afterwards in Python.
    """

    def __init__(self) -> None:
        super().__init__(SourceType.Existing)


class AttributeMutationNew(AttributeMutation):
    """
    This case of VariableTracker.mutation_type marker indicates
    1. Dynamo allows mutation on the value's attributes.
    2. The value is created by the bytecode Dynamo is tracing through.

    For instance, Dynamo could model a newly created object with this marker,
    indicating that while we need to model mutations to this object, we don't
    have to emit bytecode for these mutations if the object doesn't escape into
    the Python world.
    """

    def __init__(self, cls_source: Optional[Source] = None) -> None:
        super().__init__(SourceType.New)
        self.cls_source = cls_source


def _is_top_level_scope(scope_id: int) -> bool:
    return scope_id == 1


def is_side_effect_safe(m: MutationType) -> bool:
    scope_id = current_scope_id()

    # In the top-level scope (if no HigherOrderOperators are involved),
    # we are allowed to modify variables created in this scope as well
    # as existing variables.
    if _is_top_level_scope(scope_id):
        return True
    # Otherwise, only allow local mutation of variables created in the current scope
    return m.scope == scope_id


# This helps users of `as_python_constant` to catch unimplemented error with
# more information; it inherits `NotImplementedError` for backward
# compatibility reasons.
class AsPythonConstantNotImplementedError(NotImplementedError):
    vt: "VariableTracker"

    def __init__(self, vt: "VariableTracker") -> None:
        super().__init__(f"{vt} is not a constant")
        self.vt = vt


class VariableTrackerMeta(type):
    all_subclasses: list[type] = []

    def __new__(
        mcs: type, name: str, bases: tuple[type, ...], attrs: dict[str, Any]
    ) -> type:
        # Determine which metaclass to use based on the class attributes
        # Classes with _no_implicit_realize = True should NOT implicitly realize
        # (they need standard isinstance behavior to avoid infinite recursion)
        # Check if any base class has _no_implicit_realize set, or if it's in attrs
        no_implicit_realize = attrs.get("_no_implicit_realize", False) or any(
            getattr(base, "_no_implicit_realize", False) for base in bases
        )
        if no_implicit_realize or name == "VariableTracker":
            # Use base VariableTrackerMeta (no custom __instancecheck__)
            return super().__new__(VariableTrackerMeta, name, bases, attrs)
        else:
            # Use ImplicitRealizingVariableTrackerMeta for all other subclasses
            return super().__new__(
                ImplicitRealizingVariableTrackerMeta, name, bases, attrs
            )

    def __init__(
        cls: type, name: str, bases: tuple[type, ...], attrs: dict[str, Any]
    ) -> None:
        super().__init__(name, bases, attrs)  # type: ignore[misc]
        VariableTrackerMeta.all_subclasses.append(cls)


class ImplicitRealizingVariableTrackerMeta(VariableTrackerMeta):
    def __instancecheck__(self, instance: object) -> bool:
        """Make isinstance work with LazyVariableTracker"""
        if instancecheck(LazyVariableTracker, instance):
            return instance.lazy_isinstance(self)  # pyrefly: ignore[missing-attribute]
        return instancecheck(self, instance)


class VariableTracker(metaclass=VariableTrackerMeta):
    """
    Base class for tracked locals and stack values

    VariableTracker instances are immutable and should be copied in
    order to change them.

    Prefer the factory function VariableTracker.build() over VariableTracker.__init__().
    """

    # fields to leave unmodified in apply()
    _nonvar_fields = {
        "value",
        "guards",
        "source",
        "mutation_type",
        "parents_tracker",
        "user_code_variable_name",
    }

    def clone(self, **kwargs: Any) -> "VariableTracker":
        """Shallow copy with some (optional) changes"""
        args = dict(self.__dict__)
        args.update(kwargs)
        return self.__class__(**args)

    @classmethod
    def visit(
        cls,
        fn: Callable[["VariableTracker"], None],
        value: Any,
        cache: Optional[dict[int, Any]] = None,
    ) -> None:
        """
        Walk value and call fn on all the VariableTracker instances
        """
        if cache is None:
            cache = {}

        idx = id(value)
        if idx in cache:
            return
        # save `value` to keep it alive and ensure id() isn't reused
        cache[idx] = value

        if isinstance(value, VariableTracker):
            value = value.unwrap()
            fn(value)
            value = value.unwrap()  # calling fn() might have realized it
            nonvars = value._nonvar_fields
            for key, subvalue in value.__dict__.items():
                if key not in nonvars:
                    cls.visit(fn, subvalue, cache)
        elif istype(value, (list, tuple)):
            for subvalue in value:
                cls.visit(fn, subvalue, cache)
        elif istype(value, (dict, collections.OrderedDict)):
            for subvalue in value.values():
                cls.visit(fn, subvalue, cache)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}()"

    def debug_repr(self) -> str:
        # Intended to be overridden to provide more info
        try:
            return repr(self.as_python_constant())
        except NotImplementedError:
            return repr(self)

    def python_type(self) -> type:
        """
        Abstract method to be implemented by subclasses of VariableTracker.

        This method should return the type represented by the instance of the subclass.
        The purpose is to provide a standardized way to retrieve the Python type information
        of the variable being tracked.

        Returns:
            type: The Python type (such as int, str, list, etc.) of the variable tracked by
                the subclass. If the type cannot be determined or is not relevant,
                leaving it undefined or invoking super() is always sound.

        Note:
            This is an abstract method and may be overridden in subclasses.

        Example:
            class SetVariable(VariableTracker):
                def python_type(self):
                    return set

        Raises:
            NotImplementedError: If the method is not implemented in a subclass.
        """
        try:
            return type(self.as_python_constant())
        except NotImplementedError:
            raise NotImplementedError(f"{self} has no type") from None

    def python_type_name(self) -> str:
        try:
            return self.python_type().__name__
        except NotImplementedError:
            return "<unknown type>"

    def as_python_constant(self) -> Any:
        """For constants"""
        raise AsPythonConstantNotImplementedError(self)

    def guard_as_python_constant(self) -> Any:
        """Similar to as_python_constant(), but add ID_MATCH guards to try to force things to become constants"""
        try:
            return self.as_python_constant()
        except NotImplementedError:
            unimplemented(
                gb_type="Not a Python constant",
                context=f"guard_as_python_constant {self}",
                explanation=f"Failed to convert {self} into a Python constant.",
                hints=[],
            )

    def is_python_constant(self) -> bool:
        try:
            self.as_python_constant()
            return True
        except NotImplementedError:
            return False

    def is_constant_match(self, *values: Any) -> bool:
        """
        Check if this variable is a python constant matching one of the given values.

        Examples:
            var.is_constant_match(None)  # True if var is constant None
            var.is_constant_match(True, False)  # True if var is constant True or False
            var.is_constant_match(NotImplemented)  # True if var is constant NotImplemented
        """
        return False

    def is_constant_none(self) -> bool:
        """Check if this variable is a constant None value."""
        return False

    def make_guard(self, fn: Callable[..., Any]) -> Guard:
        if self.source:
            return self.source.make_guard(fn)
        raise NotImplementedError

    # TODO[@lucaskabela] - change this type to `InstructionTranslatorBase`
    # and cascade that (large blast radius)
    def const_getattr(self, tx: "InstructionTranslator", name: str) -> Any:
        """getattr(self, name) returning a python constant"""
        raise NotImplementedError

    def is_symnode_like(self) -> bool:
        """Return True for values that can participate in SymNode operations"""
        return False

    def is_tensor(self) -> bool:
        """Return True for TensorVariable instances"""
        return False

    def var_getattr(self, tx: "InstructionTranslator", name: str) -> "VariableTracker":
        """getattr(self, name) returning a new variable"""
        value = self.const_getattr(tx, name)
        if not variables.ConstantVariable.is_literal(value):
            raise NotImplementedError
        source = self.source and AttrSource(self.source, name)
        if source and not self.is_python_constant():
            # The second condition is to avoid guards on const getattr objects
            # like __code__.co_argcount
            install_guard(source.make_guard(GuardBuilder.CONSTANT_MATCH))
        return variables.ConstantVariable.create(value, source=source)

    def is_proxy(self) -> bool:
        try:
            self.as_proxy()
            return True
        except NotImplementedError:
            return False

    def as_proxy(self) -> Any:
        raise NotImplementedError(str(self))

    def maybe_fx_node(self) -> Optional[Node]:
        try:
            proxy = self.as_proxy()
            import torch.fx

            if isinstance(proxy, torch.fx.Proxy):
                return proxy.node
            return None
        except NotImplementedError:
            return None

    def reconstruct(self, codegen: "PyCodegen") -> None:
        raise NotImplementedError

    def unpack_var_sequence(self, tx: Any) -> list["VariableTracker"]:
        raise NotImplementedError

    def force_unpack_var_sequence(self, tx: Any) -> list["VariableTracker"]:
        # like unpack_var_sequence, but should only be used when it is
        # safe to eagerly (vs. lazily) unpack this variable.
        # e.g. map(f, x) is normally evaluated lazily but sometimes
        # we want to force eager unpacking, e.g. when converting to a list.
        # NOTE: this method is allowed to mutate the VariableTracker, so
        # it should only be called once.
        return self.unpack_var_sequence(tx)

    def has_unpack_var_sequence(self, tx: Any) -> bool:
        try:
            self.unpack_var_sequence(tx)
            return True
        except NotImplementedError:
            return False

    # NB: don't call force_unpack_var_sequence, especially if it mutates!
    def has_force_unpack_var_sequence(self, tx: Any) -> bool:
        return self.has_unpack_var_sequence(tx)

    # Forces unpacking the var sequence while also applying a function to each element.
    # Only use when it is safe to eagerly unpack this variable (like force_unpack_var_sequence).
    # INVARIANT: variable must satisfy has_force_unpack_var_sequence() == True!
    def force_apply_to_var_sequence(
        self, tx: Any, fn: Callable[["VariableTracker"], Any]
    ) -> None:
        assert self.has_force_unpack_var_sequence(tx)
        for v in self.unpack_var_sequence(tx):
            fn(v)

    def call_obj_hasattr(self, tx: Any, name: str) -> "ConstantVariable":
        unimplemented(
            gb_type="Unsupported hasattr call",
            context=f"call_obj_hasattr {self} {name}",
            explanation=f"Dynamo does not know how to trace the function `{self.debug_repr()}`",
            hints=[
                f"Avoid calling `hasattr({self.__class__.__name__}, {name})` in your code.",
                *graph_break_hints.SUPPORTABLE,
            ],
        )

    def call_function(
        self,
        tx: Any,
        args: Sequence["VariableTracker"],
        kwargs: dict[str, "VariableTracker"],
    ) -> "VariableTracker":
        unimplemented(
            gb_type="Unsupported function call",
            context=f"call_function {self} {args} {kwargs}",
            explanation=f"Dynamo does not know how to trace the function `{self.debug_repr()}`",
            hints=[
                f"Avoid calling `{self.debug_repr()}` in your code.",
                "Please report an issue to PyTorch.",
            ],
        )

    def call_method(
        self,
        tx: Any,
        name: str,
        args: list["VariableTracker"],
        kwargs: dict[str, "VariableTracker"],
    ) -> "VariableTracker":
        if name == "__len__" and self.has_unpack_var_sequence(tx):
            assert not (args or kwargs)
            return variables.ConstantVariable.create(len(self.unpack_var_sequence(tx)))
        elif (
            name == "__getattr__"
            and len(args) == 1
            and args[0].is_python_constant()
            and not kwargs
        ):
            return self.var_getattr(tx, args[0].as_python_constant())
        elif name in cmp_name_to_op_mapping and len(args) == 1 and not kwargs:
            other = args[0]
            if not isinstance(self, type(other)) and not (
                isinstance(self, variables.GetAttrVariable)
                or isinstance(other, variables.GetAttrVariable)
            ):
                # NB: GetAttrVariable is a special case because sometimes an
                # object can map to GetAttrVariable but other time as
                # SkipFunctionVariable if it is an input to the compiled
                # function, e.g. tensor.data_ptr
                return variables.ConstantVariable.create(NotImplemented)
            # NB : Checking for mutation is necessary because we compare
            # constant values
            if (
                not self.is_python_constant()
                or not other.is_python_constant()
                or tx.output.side_effects.has_pending_mutation(self)
                or tx.output.side_effects.has_pending_mutation(other)
            ):
                unimplemented(
                    gb_type="Builtin `operator.*` comparison with constant `self` failed",
                    context=f"call_method {self} {name} {args} {kwargs}",
                    explanation=f"Failed to compare {self} with {other}, "
                    + f"because {other} is not a Python constant or its mutation check fails.",
                    hints=[],
                )

            try:
                return variables.ConstantVariable.create(
                    cmp_name_to_op_mapping[name](
                        self.as_python_constant(), other.as_python_constant()
                    )
                )
            except Exception as e:
                raise_observed_exception(
                    type(e),
                    tx,
                    args=[list(map(variables.ConstantVariable.create, e.args))],
                )
        hints = [
            f"Avoid calling `{self.python_type_name()}.{name}` in your code.",
            "Please report an issue to PyTorch.",
        ]
        # additional hint for method calls on improperly constructed iterators
        if isinstance(self, variables.UserDefinedObjectVariable) and name in (
            "__iter__",
            "__next__",
        ):
            if isinstance(self.value, (KeysView, ItemsView, ValuesView)):
                hints.append(
                    "Consider moving the creation of dict view object (e.g. `dict.keys()`, `dict.items()`,) "
                    "to the compiled region, instead of passing it as an input to the compiled region."
                )
            hints.append(
                "Dynamo does not fully support tracing builtin iterators (e.g. `map`, `zip`, `enumerate`) "
                "passed in from uncompiled to compiled regions (e.g. `torch.compile(fn)(enumerate(...))`). "
                "This can happen unintentionally if a previous graph break happens with a builtin iterator "
                "in the local scope."
            )
            hints.append(
                "List/dict comprehensions in Python <= 3.11 result in implicit function calls, which Dynamo "
                "cannot trace as a top level frame. Possible workarounds are (1) use a loop instead of a comprehension, "
                "(2) fix any graph breaks in the function above the comprehension, (3) wrap the comprehension in a "
                "function, or (4) use Python 3.12+."
            )
        unimplemented(
            gb_type="Unsupported method call",
            context=f"call_method {self} {name} {args} {kwargs}",
            explanation=f"Dynamo does not know how to trace method `{name}` of class `{self.python_type_name()}`",
            hints=hints,
        )

    def call_tree_map(
        self,
        tx: Any,
        tree_map_fn: "UserFunctionVariable",
        map_fn: "VariableTracker",
        rest: Sequence["VariableTracker"],
        tree_map_kwargs: dict[str, "VariableTracker"],
    ) -> "VariableTracker":
        """Performance optimization to implement optree.tree_map faster than tracing it"""
        is_leaf_var = tree_map_kwargs.get("is_leaf")
        if is_leaf_var is not None and not is_leaf_var.is_constant_none():
            pred_result = is_leaf_var.call_function(tx, [self], {})
            try:
                leaf_decision = pred_result.as_python_constant()
            except NotImplementedError:
                return self._tree_map_fallback(
                    tx,
                    tree_map_fn,
                    map_fn,
                    rest,
                    tree_map_kwargs,
                )
            if leaf_decision:
                return map_fn.call_function(tx, [self, *rest], {})

        return self.call_tree_map_branch(
            tx,
            tree_map_fn,
            map_fn,
            rest,
            tree_map_kwargs,
        )

    def call_tree_map_branch(
        self,
        tx: Any,
        tree_map_fn: "UserFunctionVariable",
        map_fn: "VariableTracker",
        rest: Sequence["VariableTracker"],
        tree_map_kwargs: dict[str, "VariableTracker"],
    ) -> "VariableTracker":
        """Emulate optree.tree_map without is_leaf/none_is_leaf checks (handled above)"""
        return self._tree_map_fallback(
            tx,
            tree_map_fn,
            map_fn,
            rest,
            tree_map_kwargs,
        )

    def _tree_map_fallback(
        self,
        tx: Any,
        tree_map_fn: "UserFunctionVariable",
        map_fn: "VariableTracker",
        rest: Sequence["VariableTracker"],
        tree_map_kwargs: dict[str, "VariableTracker"],
    ) -> "VariableTracker":
        tree_map_fn_copy = tree_map_fn.clone()
        tree_map_fn_copy._maybe_call_tree_map_fastpath = lambda *args, **kwargs: None  # type: ignore[missing-attribute]
        log.debug(
            "tree_map fastpath fallback triggered for %s (rest=%s, kwargs=%s)",
            self,
            rest,
            tree_map_kwargs,
        )
        return tree_map_fn_copy.call_function(
            tx,
            [map_fn, self, *rest],
            tree_map_kwargs,
        )

    def set_name_hint(self, name: str) -> None:
        pass

    def realize(self) -> "VariableTracker":
        """Used by LazyVariableTracker to build the real VariableTracker"""
        return self

    def unwrap(self) -> "VariableTracker":
        """Used by LazyVariableTracker to return the real VariableTracker if it already exists"""
        return self

    def is_realized(self) -> bool:
        """Used by LazyVariableTracker to indicate an unrealized node"""
        return True

    def next_variable(self, tx: Any) -> "VariableTracker":
        unimplemented(
            gb_type="Unsupported next() call",
            context=f"next({self})",
            explanation=f"Dynamo does not know how to trace calling `next()` on variable `{self}`.",
            hints=[*graph_break_hints.USER_ERROR],
        )

    def is_strict_mode(self, tx: Any) -> bool:
        return bool(tx.strict_checks_fn and tx.strict_checks_fn(self))

    def is_mutable(self) -> bool:
        """Whether Dynamo allows mutation on this variable."""
        return not self.is_immutable()

    def is_immutable(self) -> bool:
        """Whether Dynamo bans mutation on this variable."""
        return self.mutation_type is None

    @staticmethod
    def build(
        tx: Any,
        value: Any,
        source: Optional[Source] = None,
    ) -> Any:
        """Create a new VariableTracker from a value and optional Source"""
        if source is None:
            return builder.SourcelessBuilder.create(tx, value)
        else:
            return variables.LazyVariableTracker.create(value, source)

    def is_python_hashable(self):
        """
        Unlike the variable tracker's own __hash__, this method checks whether
        the underlying Python object referenced by this variable tracker is hashable.
        """
        try:
            type_self = self.python_type()
        except NotImplementedError:
            type_self = type(self)

        unimplemented(
            gb_type="Dynamo cannot determine whether the underlying object is hashable",
            context=f"is_python_hashable {self}",
            explanation=f"Dynamo does not know whether the underlying python object for {self} is hashable",
            hints=[
                (
                    f"Consider using a different type of object as the dictionary key instead of {type_self}."
                ),
                *graph_break_hints.SUPPORTABLE,
            ],
        )

    def get_python_hash(self):
        """
        Unlike the variable tracker’s own __hash__, this method is used by
        ConstDictVariableTracker to compute the hash of the underlying key object.
        """
        unimplemented(
            gb_type="Dynamo cannot determine the hash of an object",
            context=f"get_python_hash {self}",
            explanation=f"Dynamo does not know the hash of the underlying python object for {self}",
            hints=[
                (
                    f"Consider using a different type of object as the dictionary key instead of {self.python_type()}."
                ),
                *graph_break_hints.SUPPORTABLE,
            ],
        )

    def is_python_equal(self, other):
        """
        NB - Deliberately not overriding the __eq__ method because that can
        disable the __hash__ for the vt itself.
        """
        unimplemented(
            gb_type="Dynamo cannot determine the equality comparison of an object",
            context=f"is_python_equal {self}",
            explanation=f"Dynamo does not know the equality comparison of the underlying python object for {self}",
            hints=[
                (
                    f"Consider using a different type of object as the dictionary key instead of {self.python_type()}."
                ),
                *graph_break_hints.SUPPORTABLE,
            ],
        )

    def __init__(
        self,
        *,
        source: Optional[Source] = None,
        mutation_type: Optional[MutationType] = None,
    ) -> None:
        super().__init__()
        self.source = source
        self.mutation_type = mutation_type

        # NOTE sometimes mutation_type is set afterwards for implementation
        # convenience, we don't validate those cases at the moment.
        if mutation_type is not None:
            if isinstance(mutation_type, (ValueMutationNew, AttributeMutationNew)):
                # If this fails, it's either
                # 1. one mistakenly passed in a source
                # 2. `mutation_type` is incorrect
                assert source is None
            else:
                assert isinstance(
                    mutation_type, (ValueMutationExisting, AttributeMutationExisting)
                )
                # If this fails, it's either
                # 1. one forgot to pass in a source
                # 2. `mutation_type` is incorrect
                assert source is not None


def raise_type_error_exc(tx: Any, msg_str: str) -> NoReturn:
    msg = variables.ConstantVariable.create(msg_str)
    raise_observed_exception(TypeError, tx, args=[msg])


def typestr(*objs: object) -> str:
    if len(objs) == 1:
        (obj,) = objs
        if isinstance(obj, VariableTracker):
            return str(obj)
        else:
            return type(obj).__name__
    else:
        return " ".join(map(typestr, objs))


instancecheck = type.__instancecheck__
from . import builder
from .lazy import LazyVariableTracker