import collections
import dataclasses
import enum
import functools
import inspect
import math
import numbers
import operator
import re
import types
from abc import ABCMeta
from typing import Any, Union

import numpy as np
from functorch.experimental.ops import PyOperator

import torch
from torch.fx.immutable_collections import immutable_list

from .. import config, mutation_guard, replay_record, skipfiles
from ..allowed_functions import is_allowed, is_builtin_callable, is_numpy
from ..exc import unimplemented
from ..guards import GuardBuilder, GuardSource
from ..side_effects import SideEffects
from ..source import (
    AttrSource,
    ConstantSource,
    GetItemSource,
    GlobalSource,
    GlobalWeakRefSource,
    is_constant_source,
    LocalSource,
    RandomValueSource,
    Source,
    TupleIteratorGetItemSource,
)
from ..utils import (
    clone_input,
    fake_tensors_available,
    get_fake_value,
    get_real_value,
    getfile,
    global_key_name,
    is_namedtuple,
    is_numpy_int_type,
    is_typing,
    istensor,
    istype,
    odict_values,
    preserve_rng_state,
    tuple_iterator,
    tuple_iterator_getitem,
    tuple_iterator_len,
    wrap_to_fake_tensor_and_record,
)

from .base import MutableLocal, typestr
from .builtin import BuiltinVariable
from .constant import ConstantVariable, EnumVariable
from .dicts import (
    ConstDictVariable,
    DataClassVariable,
    DefaultDictVariable,
    HFPretrainedConfigVariable,
)
from .functions import UserFunctionVariable
from .lists import (
    ListIteratorVariable,
    ListVariable,
    NamedTupleVariable,
    RangeVariable,
    SizeVariable,
    SliceVariable,
    TupleVariable,
)
from .misc import (
    AutogradFunctionVariable,
    GetAttrVariable,
    InspectSignatureVariable,
    LambdaVariable,
    NumpyVariable,
    PythonModuleVariable,
    SkipFilesVariable,
    TypingVariable,
)
from .nn_module import UnspecializedNNModuleVariable
from .tensor import (
    DynamicShapeVariable,
    TensorVariable,
    TensorWithTFOverrideVariable,
    UnspecializedNumpyVariable,
    UnspecializedPythonVariable,
)
from .torch import (
    tensor_dunder_fns,
    torch_special_class_types,
    TorchPyOperator,
    TorchVariable,
)
from .user_defined import UserDefinedClassVariable, UserDefinedObjectVariable


class _missing:
    pass


@dataclasses.dataclass
class GraphArg:
    source: Source
    example: Any
    is_unspecialized: bool

    def __post_init__(self):
        if isinstance(self.example, torch._subclasses.fake_tensor.FakeTensor):
            raise AssertionError("Fake Tensor observed in TorchDynamo Fx graph inputs")

    def load(self, tx):
        return self.source.reconstruct(tx)

    def get_examples(self):
        return [self.example]

    def __len__(self):
        return 1

    def erase(self):
        self.example = None


class VariableBuilder:
    """Wrap a python value in a VariableTracker() instance"""

    def __init__(
        self,
        tx,
        source: Source,
    ):
        super(VariableBuilder, self).__init__()
        self.tx = tx
        self.source = source
        self.name = source.name()

    def __call__(self, value):
        if value in self.tx.output.side_effects:
            # TODO(jansel): add guard for alias relationship
            return self.tx.output.side_effects[value]
        return self._wrap(value).clone(**self.options())

    @staticmethod
    @functools.lru_cache(None)
    def _common_constants():
        return set(range(17)).union(
            {
                20,
                30,
                40,
                32,
                64,
                96,
                128,
                144,
                240,
                256,
                672,
                1024,
                2048,
                4096,
                0.1,
                0.01,
                0.001,
                0.5,
                0.05,
                800,
                1.873536229133606,
                4.135166556742356,  # Work around for vision_maskrcnn where torch.clamp can't be on different devices
            }
        )

    @staticmethod
    def list_type(value):
        if is_namedtuple(value):
            return functools.partial(NamedTupleVariable, tuple_cls=type(value))
        return {
            tuple: TupleVariable,
            list: ListVariable,
            odict_values: ListVariable,
            torch.nn.ParameterList: ListVariable,
            torch.nn.ModuleList: ListVariable,
        }[type(value)]

    def get_source(self):
        return self.source

    def options(self):
        return {"source": self.get_source()}

    def make_guards(self, *guards):
        source = self.get_source()
        if (
            isinstance(source, ConstantSource)
            or source.guard_source() == GuardSource.CONSTANT
        ):
            return None
        return {source.make_guard(guard) for guard in guards}

    def _wrap(self, value):
        make_guards = self.make_guards
        if istype(value, (torch.SymInt, torch.SymFloat)):
            return self.wrap_sym(value)
        if istensor(value):
            return self.wrap_tensor(value)
        elif istype(value, (tuple, list, odict_values)) or is_namedtuple(value):
            # One can index a tensor with a list/tuple. Therefore, we need to
            # have a stricter match.
            if istype(value, (tuple, list)) and all(
                [isinstance(x, int) or is_numpy_int_type(x) or x is None for x in value]
            ):
                guards = self.make_guards(GuardBuilder.EQUALS_MATCH)
            else:
                guards = self.make_guards(GuardBuilder.LIST_LENGTH)
            output = [
                VariableBuilder(self.tx, GetItemSource(self.get_source(), i))(
                    item
                ).add_guards(guards)
                for i, item in enumerate(value)
            ]
            result = self.list_type(value)(output, guards=guards)
            if istype(value, list):
                return self.tx.output.side_effects.track_list(
                    self.source, value, result
                )
            return result
        elif istype(value, tuple_iterator):
            guards = self.make_guards(GuardBuilder.TUPLE_ITERATOR_LEN)
            output = [
                VariableBuilder(
                    self.tx, TupleIteratorGetItemSource(self.get_source(), i)
                )(tuple_iterator_getitem(value, i)).add_guards(guards)
                for i in range(tuple_iterator_len(value))
            ]
            return ListIteratorVariable(
                output, mutable_local=MutableLocal(), guards=guards
            )
        elif istype(value, range):
            guards = self.make_guards(GuardBuilder.EQUALS_MATCH)
            return RangeVariable(value=value, guards=guards)
        elif istype(
            value, (dict, collections.defaultdict, collections.OrderedDict)
        ) and all(
            map(
                lambda k: ConstantVariable.is_literal(k)
                or self.tensor_can_be_dict_key(k),
                value.keys(),
            )
        ):
            guards = self.make_guards(GuardBuilder.DICT_KEYS)

            # store key variables in global location for reconstruction
            for key in value.keys():
                if self.tensor_can_be_dict_key(key):
                    self.tx.store_dict_key(global_key_name(key), key)

            def index_source(key):
                if self.tensor_can_be_dict_key(key):
                    return GlobalWeakRefSource(global_key_name(key))
                else:
                    return key

            result = dict(
                [
                    (
                        k,
                        VariableBuilder(
                            self.tx, GetItemSource(self.get_source(), index_source(k))
                        )(value[k]).add_guards(guards),
                    )
                    for k in value.keys()
                ]
            )

            if istype(value, collections.defaultdict):
                result = DefaultDictVariable(
                    result, type(value), value.default_factory, guards=guards
                )
            else:
                result = ConstDictVariable(result, type(value), guards=guards)

            return self.tx.output.side_effects.track_dict(self.source, value, result)
        elif isinstance(value, torch.nn.Module):
            if mutation_guard.is_dynamic_nn_module(value):
                # created dynamically, don't specialize on it
                result = UnspecializedNNModuleVariable(
                    value, guards=make_guards(GuardBuilder.TYPE_MATCH)
                )
                if not SideEffects.cls_supports_mutation_side_effects(type(value)):
                    # don't allow STORE_ATTR mutation with custom __setattr__
                    return result
                return self.tx.output.side_effects.track_object_existing(
                    self.source, value, result
                )
            elif issubclass(
                value.__class__, torch.nn.parallel.distributed.DistributedDataParallel
            ):
                return UnspecializedNNModuleVariable(
                    value, guards=make_guards(GuardBuilder.TYPE_MATCH)
                )
            else:
                return self.tx.output.register_attr_or_module(
                    value,
                    self.name,
                    source=self.get_source(),
                    # Guards are added inside register_attr_or_module
                )
        elif ConstantVariable.is_literal(value) or istype(
            value, (torch.Size, torch.device, torch.dtype)
        ):
            if type(value) in (int, float) and not config.specialize_int_float:
                # unspecializing int/float by default, but still
                # specialize for the following conditions
                if (
                    value in self._common_constants()
                    or isinstance(self.source, GlobalSource)
                    or isinstance(self.source, GetItemSource)
                    or (
                        isinstance(self.source, AttrSource)
                        and isinstance(self.source.base, GlobalSource)
                    )
                ):
                    return ConstantVariable(
                        value=value,
                        guards=make_guards(GuardBuilder.CONSTANT_MATCH),
                    )
                else:
                    return self.wrap_unspecialized_primitive(value)
            else:
                return ConstantVariable(
                    value=value,
                    guards=make_guards(GuardBuilder.CONSTANT_MATCH),
                )
        elif isinstance(value, frozenset) and (
            all(is_allowed(x) or ConstantVariable.is_literal(x) for x in value)
        ):
            # For frozenset, we can guard by object ID instead of value
            # equality, this allows us to handle non-literal values
            return ConstantVariable(
                value=value,
                guards=make_guards(GuardBuilder.ID_MATCH),
            )
        elif isinstance(value, enum.Enum):
            return EnumVariable(
                value=value,
                guards=make_guards(GuardBuilder.ID_MATCH),
            )
        elif is_builtin_callable(value):
            return BuiltinVariable(
                value,
                guards=make_guards(GuardBuilder.BUILTIN_MATCH),
            )
        elif is_allowed(value):
            return TorchVariable(
                value,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif is_typing(value):
            # typing.List, typing.Mapping, etc.
            return TypingVariable(
                value,
                guards=make_guards(GuardBuilder.ID_MATCH),
            )
        elif value is inspect.signature:
            return LambdaVariable(
                InspectSignatureVariable.create,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif value is dataclasses.fields:
            return LambdaVariable(
                _dataclasses_fields_lambda,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif is_numpy(value):
            return NumpyVariable(
                value,
                guards=make_guards(
                    GuardBuilder.FUNCTION_MATCH
                    if callable(value)
                    else GuardBuilder.TYPE_MATCH
                ),
            )
        elif value in tensor_dunder_fns:
            return TorchVariable(
                value,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif (
            istype(value, (type, types.FunctionType))
            and skipfiles.check(getfile(value), allow_torch=True)
            and not inspect.getattr_static(value, "_torchdynamo_inline", False)
        ):
            return SkipFilesVariable(
                value, guards=make_guards(GuardBuilder.FUNCTION_MATCH)
            )
        elif istype(value, (type, ABCMeta)):
            # TODO(whc) the following seems preferable but breaks some tests, debug
            # elif inspect.isclass(value):
            return UserDefinedClassVariable(
                value, guards=make_guards(GuardBuilder.FUNCTION_MATCH)
            )
        elif value in tensor_dunder_fns:
            return TorchVariable(
                value,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif istype(value, types.FunctionType):
            return UserFunctionVariable(
                value,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif istype(value, (types.ModuleType, replay_record.DummyModule)):
            return PythonModuleVariable(
                value,
                guards=make_guards(GuardBuilder.PYMODULE_MATCH),
            )
        elif type(value) is torch.autograd.function.FunctionMeta:
            return AutogradFunctionVariable(
                value, guards=make_guards(GuardBuilder.FUNCTION_MATCH)
            )
        elif (
            isinstance(value, types.BuiltinFunctionType)
            and type(getattr(value, "__self__", None))
            is torch.autograd.function.FunctionMeta
            and getattr(value, "__name__", "") == "apply"
            and value == getattr(value.__self__, "apply", None)
        ):
            # handle aliased autograd function `apply` calls
            return GetAttrVariable(
                AutogradFunctionVariable(
                    value.__self__, guards=make_guards(GuardBuilder.FUNCTION_MATCH)
                ),
                "apply",
            )
        elif isinstance(value, (int, float, np.number)):
            return self.wrap_unspecialized_primitive(value)
        elif DataClassVariable.is_matching_object(value):
            return DataClassVariable.wrap(self, value).add_guards(
                make_guards(GuardBuilder.TYPE_MATCH)
            )
        elif HFPretrainedConfigVariable.is_matching_object(value):
            return HFPretrainedConfigVariable(
                value, guards=make_guards(GuardBuilder.TYPE_MATCH)
            )
        elif isinstance(value, slice):
            items = [
                VariableBuilder(self.tx, AttrSource(self.get_source(), k))(
                    getattr(value, k)
                )
                for k in ("start", "stop", "step")
            ]
            return SliceVariable(items, guards=make_guards(GuardBuilder.TYPE_MATCH))
        elif isinstance(value, PyOperator):
            return TorchPyOperator(
                value,
                guards=self.make_guards(
                    GuardBuilder.TYPE_MATCH, GuardBuilder.NAME_MATCH
                ),
            )
        elif type(value).__name__ == "builtin_function_or_method" and isinstance(
            value.__self__, torch_special_class_types
        ):
            return TorchVariable(
                value,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        else:
            result = UserDefinedObjectVariable(
                value,
                guards=self.make_guards(GuardBuilder.TYPE_MATCH),
            )
            if not SideEffects.cls_supports_mutation_side_effects(type(value)):
                # don't allow STORE_ATTR mutation with custom __setattr__
                return result
            return self.tx.output.side_effects.track_object_existing(
                self.source, value, result
            )

    def tensor_can_be_dict_key(self, value):
        # only allow Parameter and another specific Tensor can be used as dict key
        return (
            isinstance(value, torch.nn.Parameter)
            or isinstance(self.source, AttrSource)
            and self.source.member == "state"
            and isinstance(self.source.base, LocalSource)
        )

    def tensor_should_specialize(self):
        return (
            self.source
            and isinstance(self.source, GetItemSource)
            and isinstance(self.source.base, GetItemSource)
            and self.source.base.index == "params"
            and isinstance(self.source.base.base, GetItemSource)
            and isinstance(self.source.base.base.base, AttrSource)
            and self.source.base.base.base.member == "param_groups"
            and isinstance(self.source.base.base.base.base, LocalSource)
            and (
                isinstance(
                    self.tx.f_locals[self.source.base.base.base.base.local_name],
                    torch.optim.Optimizer,
                )
                if self.source.base.base.base.base.local_name in self.tx.f_locals.keys()
                else True
            )
        )

    def wrap_sym(self, value: Union[torch.SymInt, torch.SymFloat]):
        if not is_constant_source(self.get_source()):
            self.tx.output.graphargs.append(GraphArg(self.get_source(), value, False))
        elif is_constant_source(self.get_source()):
            return self.tx.output.register_attr_or_module(
                value,
                re.sub(r"[^a-zA-Z0-9]+", "_", self.name),
                source=None,
                dyn_shape=value
                # shape Guards live their own rich life via shape_env
            )
        return DynamicShapeVariable.create(
            tx=self.tx,
            proxy=self.tx.output.create_graph_input(
                re.sub(r"[^a-zA-Z0-9]+", "_", self.name), type(value)
            ),
            dyn_shape=value
            # shape Guards live their own rich life via shape_env
        )

    def wrap_tensor(self, value: torch.Tensor):
        if self.get_source().guard_source().is_nn_module():
            return self.tx.output.register_attr_or_module(
                value,
                self.name,
                source=self.get_source(),
                # Guards are done inside register_attr_or_module
                # guards=self.make_guards(GuardBuilder.TENSOR_MATCH),
            )
        else:
            if not is_constant_source(self.get_source()):
                self.tx.output.graphargs.append(
                    GraphArg(self.get_source(), value, False)
                )
            # Disable __torch_function__ to prevent cloning of `value` to hit
            # us
            with torch._C.DisableTorchFunction():
                if is_constant_source(self.get_source()):
                    return self.tx.output.register_attr_or_module(
                        value,
                        re.sub(r"[^a-zA-Z0-9]+", "_", self.name),
                        source=None,
                        # Guards are added inside register_attr_or_module
                    )
                tensor_variable = wrap_fx_proxy(
                    tx=self.tx,
                    proxy=self.tx.output.create_graph_input(
                        re.sub(r"[^a-zA-Z0-9]+", "_", self.name), type(value)
                    ),
                    example_value=value,
                    guards=self.make_guards(GuardBuilder.TENSOR_MATCH),
                    should_specialize=self.tensor_should_specialize(),
                )
            if torch.overrides.has_torch_function_unary(value):
                subclass_torch_function__func = value.__torch_function__.__func__
                subclass_type = type(value)
                return TensorWithTFOverrideVariable(
                    tensor_variable,
                    self.get_source(),
                    subclass_torch_function__func,
                    subclass_type,
                )
            return tensor_variable

    def wrap_unspecialized_primitive(self, value):
        if self.name in self.tx.output.unspec_variable_map:
            return self.tx.output.unspec_variable_map[self.name]
        else:
            wrapped_value = torch.tensor(value)
            if not is_constant_source(self.get_source()):
                self.tx.output.graphargs.append(
                    GraphArg(self.get_source(), wrapped_value, True)
                )
            if not isinstance(self.get_source(), RandomValueSource):
                guards = {self.get_source().make_guard(GuardBuilder.TYPE_MATCH, True)}
                options = {"guards": guards}
            else:
                options = {}
            options.update({"source": self.get_source()})
            options.update({"raw_value": value})

            proxy = self.tx.output.create_graph_input(
                re.sub(r"[^a-zA-Z0-9]+", "_", self.name), type(wrapped_value)
            )

            if isinstance(value, np.number):
                unspec_var = wrap_fx_proxy_cls(
                    UnspecializedNumpyVariable,
                    tx=self.tx,
                    proxy=proxy,
                    example_value=wrapped_value,
                    **options,
                )
            else:
                unspec_var = wrap_fx_proxy_cls(
                    UnspecializedPythonVariable,
                    tx=self.tx,
                    proxy=proxy,
                    example_value=wrapped_value,
                    **options,
                )
            self.tx.output.unspec_variable_map[self.name] = unspec_var
            return unspec_var


def _dataclasses_fields_lambda(obj):
    if isinstance(obj, UserDefinedObjectVariable):
        value = obj.value
    elif isinstance(obj, DataClassVariable):
        value = obj.user_cls
    else:
        unimplemented(f"Dataclass fields handling fails for type {obj}")
    items = []
    for field in dataclasses.fields(value):
        source = None
        if obj.source:
            source = GetItemSource(
                AttrSource(obj.source, "__dataclass_fields__"), field.name
            )
        items.append(UserDefinedObjectVariable(field, source=source).add_options(obj))
    return TupleVariable(items).add_options(obj)


def wrap_fx_proxy(tx, proxy, example_value=None, **options):
    return wrap_fx_proxy_cls(
        target_cls=TensorVariable,
        tx=tx,
        proxy=proxy,
        example_value=example_value,
        **options,
    )


# Note: Unfortunate split due to some gross classes existing that subclass TensorVariable
# Should be compositional instead
def wrap_fx_proxy_cls(target_cls, tx, proxy, example_value=None, **options):
    if "guards" in options and options["guards"] is not None:
        tx.output.guards.update(options["guards"])

    assert "example_value" not in proxy.node.meta
    if not config.dynamic_propagation:
        if isinstance(example_value, torch.Tensor):
            options.update(target_cls.specialize(example_value))
        return target_cls(proxy, **options)

    use_fake_tensors = fake_tensors_available and config.fake_tensor_propagation

    initial_example_value = example_value

    def _clone_input(value):
        if isinstance(value, torch.Tensor):
            use_fake_tensors = fake_tensors_available and config.fake_tensor_propagation
            # tensor subclasses will not be converted to FakeTensors and need to be cloned
            if not use_fake_tensors or not isinstance(
                value, torch._subclasses.fake_tensor.FakeTensor
            ):
                # NB: ensure strides are preserved
                value = clone_input(value)

        return value

    with preserve_rng_state():
        if example_value is None:
            if use_fake_tensors:
                example_value = get_fake_value(proxy.node, tx)
            else:
                example_value = get_real_value(proxy.node, tx.output)

        else:
            proxy.tracer.real_value_cache[proxy.node] = _clone_input(example_value)
            if use_fake_tensors:
                fake_wrapper = functools.partial(wrap_to_fake_tensor_and_record, tx=tx)
                example_value = fake_wrapper(example_value)

    if isinstance(example_value, torch.Tensor):
        is_parameter = isinstance(example_value, torch.nn.Parameter)
        should_specialize = options.pop("should_specialize", False)
        if is_parameter or should_specialize:
            specialized_value = initial_example_value
        else:
            specialized_value = None

        example_value = _clone_input(example_value)
        proxy.node.meta["example_value"] = example_value
        specialized_props = target_cls.specialize(example_value)
        if use_fake_tensors and isinstance(
            example_value, torch._subclasses.fake_tensor.FakeTensor
        ):
            specialized_props["class_type"] = (
                torch.nn.Parameter if is_parameter else torch.Tensor
            )

        specialized_props["specialized_value"] = specialized_value

        options.update(specialized_props)
        return target_cls(proxy, **options)
    elif (
        hasattr(proxy.node.target, "__name__")
        and proxy.node.target.__name__ == "set_state"
        and isinstance(proxy.node.target.__self__, torch._C.Generator)
        or proxy.node.target == torch.random.set_rng_state
    ):
        from . import TorchVariable

        return TorchVariable(proxy.node.target)
    elif (
        proxy.node.target == torch._C._DisableFuncTorch
        or proxy.node.target == torch.cuda._is_in_bad_fork
    ):
        from . import UserDefinedObjectVariable

        return UserDefinedObjectVariable(example_value)
    elif istype(example_value, (int, bool, float)) and config.dynamic_shapes:
        proxy.node.meta["example_value"] = example_value
        return DynamicShapeVariable.create(tx, proxy, example_value, **options)
    elif istype(example_value, torch.Size) and config.dynamic_shapes:
        proxy.node.meta["example_value"] = example_value
        sizes = []
        for i, v in enumerate(example_value):
            proxy_i = proxy[i]
            sizes.append(DynamicShapeVariable.create(tx, proxy_i, v, **options))
        return SizeVariable(sizes, proxy, **options)
    elif istype(example_value, int) and proxy.node.target in (
        torch.seed,
        operator.mod,
        # some mac builds are missing torch.distributed.get_rank()
        getattr(torch.distributed, "get_rank", _missing),
        getattr(torch.distributed, "get_world_size", _missing),
    ):
        if config.dynamic_shapes:
            proxy.node.meta["example_value"] = example_value
            return DynamicShapeVariable.create(tx, proxy, example_value, **options)
        else:
            return ConstantVariable(example_value, **options)
    elif istype(example_value, torch.Size) and all(
        [isinstance(x, int) for x in example_value]
    ):
        sizes = [ConstantVariable(x) for x in example_value]
        return SizeVariable(sizes, **options)
    elif isinstance(example_value, (tuple, list)):
        unpacked = []
        for i, val in enumerate(example_value):
            if val is None:
                # nn.MultiheadAttention() can return None, see issue #175
                unpacked.append(
                    ConstantVariable(None, **options),
                )
            else:
                unpacked.append(
                    wrap_fx_proxy(
                        tx,
                        proxy.tracer.create_proxy(
                            "call_function", operator.getitem, (proxy, i), {}
                        ),
                        example_value=val,
                        **options,
                    )
                )
        if istype(example_value, tuple):
            return TupleVariable(unpacked, **options)
        elif istype(example_value, (list, immutable_list)):
            return ListVariable(unpacked, mutable_local=MutableLocal(), **options)
        else:
            assert (
                example_value.__class__.__module__ == "torch.return_types"
                or hasattr(example_value, "_fields")
            ), ("namedtuple?")
            return NamedTupleVariable(unpacked, example_value.__class__, **options)
    elif example_value is None or proxy.node.target is torch.manual_seed:
        return ConstantVariable(None, **options)
    elif (
        isinstance(example_value, int)
        and proxy.node.target is torch._utils._element_size
    ):
        proxy.node.meta["example_value"] = example_value
        return ConstantVariable(example_value, **options)
    elif (
        isinstance(example_value, numbers.Number)
        and (proxy.node.target == "item" or proxy.node.target in {math.sqrt, math.pow})
        and config.capture_scalar_outputs
    ):
        if use_fake_tensors:
            # item raw value should not be accessed
            return wrap_fx_proxy_cls(
                FakeItemVariable,
                tx=tx,
                proxy=proxy,
                example_value=torch.tensor(example_value),
                **options,
            )
        else:
            return wrap_fx_proxy_cls(
                UnspecializedPythonVariable,
                tx=tx,
                proxy=proxy,
                example_value=torch.tensor(example_value),
                raw_value=None if use_fake_tensors else example_value,
                need_unwrap=False,
                **options,
            )
    elif isinstance(example_value, (torch.SymInt, torch.SymFloat)):
        proxy.node.meta["example_value"] = example_value
        return DynamicShapeVariable(proxy, example_value, **options)
    else:
        raise AssertionError(
            "torch.* op returned non-Tensor "
            + f"{typestr(example_value)} {proxy.node.op} {proxy.node.target}"
        )