import collections
import contextlib
import dataclasses
import enum
import functools
import inspect
import logging
import operator
import re
import types
from typing import List, NamedTuple, Optional, Union

import torch

from torch import SymInt
from torch._guards import GuardSource
from torch._ops import HigherOrderOperator
from torch._subclasses.fake_tensor import FakeTensor
from torch.fx.experimental.symbolic_shapes import (
    DimConstraint,
    DimDynamic,
    RelaxedUnspecConstraint,
)
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
from ..side_effects import SideEffects
from ..source import (
    AttrSource,
    ConstantSource,
    GetItemSource,
    GlobalWeakRefSource,
    is_constant_source,
    LocalSource,
    RandomValueSource,
    Source,
    TupleIteratorGetItemSource,
)
from ..utils import (
    clone_input,
    get_fake_value,
    getfile,
    global_key_name,
    HAS_NUMPY,
    is_namedtuple,
    is_numpy_int_type,
    is_typing,
    istype,
    np,
    odict_values,
    preserve_rng_state,
    tensor_always_has_static_shape,
    tuple_iterator,
    tuple_iterator_getitem,
    tuple_iterator_len,
    wrap_fake_exception,
)

from .base import MutableLocal, typestr, VariableTracker
from .builtin import BuiltinVariable
from .constant import ConstantVariable, EnumVariable
from .ctx_manager import CUDAStreamVariable, NullContextVariable
from .dicts import (
    ConstDictVariable,
    DataClassVariable,
    DefaultDictVariable,
    HFPretrainedConfigVariable,
)
from .functions import UserFunctionVariable, UserMethodVariable
from .lists import (
    ListVariable,
    NamedTupleVariable,
    RangeVariable,
    SizeVariable,
    SliceVariable,
    TupleIteratorVariable,
    TupleVariable,
)
from .misc import (
    AutogradFunctionContextVariable,
    AutogradFunctionVariable,
    ComptimeVariable,
    GetAttrVariable,
    InspectSignatureVariable,
    LambdaVariable,
    NumpyVariable,
    PythonModuleVariable,
    SkipFilesVariable,
    TypingVariable,
)
from .nn_module import FSDPManagedNNModuleVariable, UnspecializedNNModuleVariable
from .tensor import (
    SymNodeVariable,
    TensorVariable,
    TensorWithTFOverrideVariable,
    UnspecializedPythonVariable,
)
from .torch import (
    tensor_dunder_fns,
    torch_special_class_types,
    TorchHigherOrderOperator,
    TorchVariable,
)
from .user_defined import UserDefinedClassVariable, UserDefinedObjectVariable


log = logging.getLogger(__name__)


DimList = List


class _missing:
    pass


@dataclasses.dataclass
class GraphArg:
    source: Source
    # TODO: storing a SymInt here but not a FakeTensor is a pretty strange
    # thing to do.  Probably should have example (which stores an int) and
    # fake_example
    example: Union[torch.Tensor, torch.SymInt]
    is_unspecialized: bool
    fake_tensor: Optional[torch._subclasses.fake_tensor.FakeTensor]
    # UnspecializedPythonVariable often masquerades as a tensor.
    # We MUST NOT generate shape guard code
    # that actually tries to access tensor properties on these values.
    # is_tensor lets us tell if this graph arg actually is a tensor
    # or not.
    is_tensor: bool = True

    def __post_init__(self):
        if isinstance(self.example, torch.Tensor):
            assert isinstance(
                self.fake_tensor, torch._subclasses.fake_tensor.FakeTensor
            )
        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 erase(self):
        self.example = None


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

    def __init__(
        self,
        tx,
        source: Source,
    ):
        assert source is not None
        super().__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 {
            # We zero-one specialize shapes, so specialize these constants
            # too
            0,
            1,
            # NB: There used to be more constants here, but honestly it was
            # pretty confusing.  Note we specialize floats by default, and
            # DON'T specialize ints by default.  This all only matters with
            # dynamic_shapes
        }

    @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}

    @classmethod
    @functools.lru_cache(None)
    def _type_dispatch(cls):
        # NB: Careful not to close over self to avoid ref cycle from lru_cache
        entries = [
            (
                (torch.Tensor, torch.nn.Parameter, torch._subclasses.FakeTensor),
                cls.wrap_tensor,
            ),
            ((tuple, list, odict_values), cls.wrap_listlike),
            (tuple_iterator, cls.wrap_tuple_iterator),
            ((slice, range), cls.wrap_slice_range),
            (
                (
                    int,
                    float,
                    bool,
                    type(None),
                    str,
                    torch.Size,
                    torch.device,
                    torch.dtype,
                ),
                cls.wrap_literal,
            ),
        ]

        result = {}
        for ts, fn in entries:
            for t in ts if isinstance(ts, tuple) else (ts,):
                assert t not in result
                result[t] = fn

        return result

    @classmethod
    @functools.lru_cache(None)
    def _id_dispatch(cls):
        from ..comptime import comptime

        entries = [
            (
                inspect.signature,
                lambda self, value: LambdaVariable(
                    InspectSignatureVariable.create,
                    source=self.source,
                    guards=self.make_guards(GuardBuilder.FUNCTION_MATCH),
                ),
            ),
            (comptime, lambda self, value: ComptimeVariable()),
            (
                dataclasses.fields,
                lambda self, value: LambdaVariable(
                    _dataclasses_fields_lambda,
                    source=self.source,
                    guards=self.make_guards(GuardBuilder.FUNCTION_MATCH),
                ),
            ),
            (
                tensor_dunder_fns,
                lambda self, value: TorchVariable(
                    value,
                    source=self.source,
                    guards=self.make_guards(GuardBuilder.FUNCTION_MATCH),
                ),
            ),
        ]

        result = {}
        for ts, fn in entries:
            for t in ts if isinstance(ts, (tuple, list)) else (ts,):
                assert t not in result
                result[id(t)] = fn

        return result

    def _wrap(self, value):
        make_guards = self.make_guards

        # Handle exact type() match
        type_dispatch = self._type_dispatch().get(type(value))
        if type_dispatch is not None:
            return type_dispatch(self, value)

        # Handle exact id() match
        id_dispatch = self._id_dispatch().get(id(value))
        if id_dispatch is not None:
            return id_dispatch(self, value)

        # Note - There are some nested values where types mismatch!
        # We want to get those out and wrap those.
        value = inspect.getattr_static(value, "_torchdynamo_inline", value)

        # Everything else (NB: order matters!)
        if istype(value, config.traceable_tensor_subclasses):
            return self.wrap_tensor(value)
        elif is_namedtuple(value):
            return self.wrap_listlike(value)
        elif istype(
            value, (dict, collections.defaultdict, collections.OrderedDict)
        ) and all(
            (
                ConstantVariable.is_literal(k)
                or self.tensor_can_be_dict_key(k)
                or isinstance(k, enum.Enum)
                for k in value.keys()
            )
        ):
            if not value and self.get_source().is_nn_module():
                # It is faster to guard on 'false' property than to guard
                # on actual dict keys, but we can't do this fast guard in general because
                # it omits a crucial type check that ensures the value is actually still a dict at runtime.

                # Why is this OK for (specialized) nnmodules? We set up a setattr hook
                # to check for module property mutations, which does a reasonable,
                # but not completely secure job ensuring a property wasn't changed.
                guards = self.make_guards(GuardBuilder.BOOL_FALSE)
            else:
                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 = {
                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):
            return self.wrap_module(value)
        elif ConstantVariable.is_literal(value):  # non-atomic literals
            return self.wrap_literal(value)
        elif istype(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,
                source=self.source,
                guards=make_guards(GuardBuilder.ID_MATCH),
            )
        elif isinstance(value, enum.Enum):
            return EnumVariable(
                value=value,
                source=self.source,
                guards=make_guards(GuardBuilder.ID_MATCH),
            )
        elif is_builtin_callable(value):
            return BuiltinVariable(
                value,
                source=self.source,
                guards=make_guards(GuardBuilder.BUILTIN_MATCH),
            )
        elif is_allowed(value):
            return TorchVariable(
                value,
                source=self.source,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif is_typing(value):
            # typing.List, typing.Mapping, etc.
            return TypingVariable(
                value,
                source=self.source,
                guards=make_guards(GuardBuilder.ID_MATCH),
            )
        elif is_numpy(value):
            return NumpyVariable(
                value,
                source=self.source,
                guards=make_guards(
                    GuardBuilder.FUNCTION_MATCH
                    if callable(value)
                    else GuardBuilder.TYPE_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,
                source=self.source,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        # NB: These can't be put in type_dispatch, they have to run later
        elif istype(value, (types.FunctionType, torch.jit.ScriptFunction)):
            return UserFunctionVariable(
                value,
                source=self.source,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif istype(value, (types.ModuleType, replay_record.DummyModule)):
            return PythonModuleVariable(
                value,
                source=self.source,
                guards=make_guards(GuardBuilder.PYMODULE_MATCH),
            )
        elif istype(value, torch.autograd.function.FunctionMeta):
            return AutogradFunctionVariable(
                value,
                source=self.source,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif isinstance(value, torch.autograd.function.FunctionCtx):
            # The autograd.function context
            return self.tx.output.side_effects.track_object_existing(
                self.source,
                value,
                AutogradFunctionContextVariable(
                    value,
                    source=self.source,
                    guards=make_guards(GuardBuilder.TYPE_MATCH),
                ),
            )
        elif (
            isinstance(value, types.MethodType)
            and istype(
                getattr(value, "__self__", None), 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__,
                    source=self.source,
                    guards=make_guards(GuardBuilder.FUNCTION_MATCH),
                ),
                "apply",
            )
        elif HAS_NUMPY and isinstance(value, 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, HigherOrderOperator):
            return TorchHigherOrderOperator(
                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),
            )
        elif isinstance(value, torch.cuda.streams.Stream):
            return CUDAStreamVariable(
                None,
                value,
                source=self.source,
                guards=self.make_guards(GuardBuilder.ID_MATCH),
            )
        elif issubclass(type(value), type):
            # TODO(whc) the following seems preferable but breaks some tests, debug
            # elif inspect.isclass(value):
            return UserDefinedClassVariable(
                value,
                source=self.source,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif isinstance(value, types.MethodType) and isinstance(
            value.__self__, torch.nn.Module
        ):
            # don't let MethodTypes fall through to UserDefinedObject,
            # which doesn't support 'CALL_FUNCTION'

            # TODO(whc): Why do we limit this to methods on NNModules?
            # I don't have a good reason for this, but it preserves the existing behavior
            # for MBartForConditionalGeneration, which generates many graph breaks and OOMs otherwise.
            # I suspect we probably want to relax this check and dig deeper there.

            # In order to construct a MethodVariable in Dynamo, we start with an actual method obj from python,
            # but need to separately wrap its underlying `__func__` and its `self` argument.  We wrap `self` here
            # and then `__func__` gets wrapped inside UserMethodVariable.
            self_obj = VariableBuilder(
                self.tx, source=AttrSource(self.source, "__self__")
            )(value.__self__)
            assert self_obj and isinstance(
                self_obj, VariableTracker
            ), "Failed to produce a valid self obj"
            return UserMethodVariable(
                value.__func__,
                self_obj,
                source=self.source,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        elif (
            istype(value, contextlib.nullcontext)
            and inspect.getattr_static(value, "enter_result", None) is None
        ):
            return NullContextVariable(
                source=self.source,
                guards=make_guards(GuardBuilder.FUNCTION_MATCH),
            )
        else:
            result = UserDefinedObjectVariable(
                value,
                source=self.source,
                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_listlike(self, value: Union[tuple, list, odict_values, NamedTuple]):
        # 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
            )
            and not config.dynamic_shapes
        ):
            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

    def wrap_tuple_iterator(self, 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 TupleIteratorVariable(
            output, mutable_local=MutableLocal(), guards=guards
        )

    def wrap_slice_range(self, value: Union[slice, range]):
        items = [
            VariableBuilder(self.tx, AttrSource(self.get_source(), k))(
                getattr(value, k)
            )
            for k in ("start", "stop", "step")
        ]
        if isinstance(value, slice):
            return SliceVariable(
                items, guards=self.make_guards(GuardBuilder.TYPE_MATCH)
            )
        else:
            return RangeVariable(
                items, guards=self.make_guards(GuardBuilder.EQUALS_MATCH)
            )

    def wrap_module(self, value: torch.nn.Module):
        from ..eval_frame import OptimizedModule

        if istype(value, OptimizedModule):
            guards = self.make_guards(GuardBuilder.TYPE_MATCH)
            self.source = AttrSource(self.source, "_orig_mod")
            return self.wrap_module(value._orig_mod).add_guards(guards)

        if (
            isinstance(value, (torch.nn.RNN, torch.nn.GRU, torch.nn.LSTM))
            and not config.allow_rnn
        ):
            unimplemented("TorchDynamo purposely graph breaks on RNN, GRU, LSTMs")
        if mutation_guard.is_dynamic_nn_module(value):
            # created dynamically, don't specialize on it
            result = UnspecializedNNModuleVariable(
                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
            )
        elif issubclass(
            value.__class__, torch.nn.parallel.distributed.DistributedDataParallel
        ):
            return UnspecializedNNModuleVariable(
                value, guards=self.make_guards(GuardBuilder.TYPE_MATCH)
            )
        elif getattr(value, "_is_fsdp_managed_module", False):
            # See note [Dynamo treats FSDP wrapped modules as UnspecializedNNModule]
            # in fully_sharded_data_parallel.py for more information

            # we can't do this assert inside FSDP constructor,
            # since we don't know yet whether dynamo will be used
            assert getattr(
                value, "_fsdp_use_orig_params", False
            ), "Dynamo only supports FSDP with use_orig_params=True"

            # Note on FSDP guarding
            # 1. We expect FSDP wrapping mutates an nn module irreversably (no way to de-wrap).
            # 2. Eager FSDP already assumes (requires, but without enforcement) that users don't mutate their
            #    model parameters/structure after FSDP wrapping, because FSDP wouldn't notice or update its FlatParams.
            #
            # Due to (1), once we enter this path we expect not to go back nor have to guard on type
            # or _is_fsdp_managed_module.
            #
            # TODO(whc) We could add a guard on the opposite case, where a user compiled/ran
            # pre-FSDP-wrapped model, then wrapped, to ensure that we recompile with the FSDP handling.
            #
            # Due to (2), we skip guards on inner contents of fsdp_managed modules, by using FSDPNNModuleSource as the
            # guard source.  This behavior is gated on config.skip_fsdp_guards.
            #
            # ID_MATCH is required to disambiguate cases as simple as a unit test that constructs 2 models and wraps
            # them differently with different FSDP configs.  (test_dynamo_distributed.py -k test_fsdp_aot_eager)
            return FSDPManagedNNModuleVariable(
                value,
                guards=self.make_guards(GuardBuilder.TYPE_MATCH, GuardBuilder.ID_MATCH),
                source=self.get_source(),
            )
        else:
            return self.tx.output.register_attr_or_module(
                value,
                self.name,
                source=self.get_source(),
                # Guards are added inside register_attr_or_module
            )

    def wrap_literal(self, value):
        unspec = not config.specialize_int and config.dynamic_shapes
        if unspec and type(value) is torch.Size:
            return SizeVariable(
                [
                    VariableBuilder(self.tx, GetItemSource(self.get_source(), i))(v)
                    for i, v in enumerate(value)
                ],
                guards=self.make_guards(GuardBuilder.LIST_LENGTH),
            )
        elif unspec and type(value) is int:
            # unspecializing int by default, but still
            # specialize for the following conditions
            if (
                value in self._common_constants()
                # Assume integers from global variables want to be specialized
                or not self.source.guard_source().is_local()
                # Assume that integers that came from NN modules want to be
                # specialized (as we don't expect users to be changing the
                # NN modules on the fly)
                or self.source.guard_source().is_nn_module()
            ):
                return ConstantVariable(
                    value=value,
                    guards=self.make_guards(GuardBuilder.CONSTANT_MATCH),
                )
            else:
                return self.wrap_unspecialized_primitive(value)
        else:
            return ConstantVariable(
                value=value,
                guards=self.make_guards(GuardBuilder.CONSTANT_MATCH),
            )

    def wrap_tensor(self, value: torch.Tensor):
        source = self.get_source()

        if (
            source.guard_source().is_nn_module()
            and not source.guard_source().is_fsdp_module()
        ):
            return self.tx.output.register_attr_or_module(
                value,
                self.name,
                source=source,
                # Guards are done inside register_attr_or_module
                # guards=self.make_guards(GuardBuilder.TENSOR_MATCH),
            )

        if is_constant_source(source):
            return self.tx.output.register_attr_or_module(
                value,
                re.sub(r"[^a-zA-Z0-9]+", "_", self.name),
                source=source,
                # Guards are added inside register_attr_or_module
            )

        if type(value) in config.traceable_tensor_subclasses:
            # Ordinarily, we would fakeify a tensor so that it can get dynamic
            # shapes and be computed on without triggering actual operations.
            # However, how can we fakeify a tensor subclass?  Ordinary
            # inheritance (nor multiple inheritance) won't work work.
            #
            # Instead, our plan is to *manually simulate* the tensor subclass
            # inheriting from a fake tensor with dynamo.  This means our
            # data representation for a tensor subclass will be a fake tensor
            # + tensor subclass type + any extra data the subclass may have
            # been storing on the tensor.  Because all Python accesses are
            # mediated through TensorWithTFOverrideVariable, we can ensure
            # that we dispatch differently, e.g., according to
            # __torch_function__
            #
            # To simplify things for now, the __dict__ tracking bits haven't
            # been implemented yet, but they can be added into this design at
            # a later point in time.
            ignore_subclass = True
        else:
            assert type(value) in (torch.Tensor, torch.nn.Parameter)
            ignore_subclass = False

        is_duplicate_tensor = source in self.tx.output.input_source_to_var
        if is_duplicate_tensor:
            return self.tx.output.input_source_to_var[source]

        tensor_proxy = self.tx.output.create_graph_input(
            re.sub(r"[^a-zA-Z0-9]+", "_", self.name), type(value)
        )
        tensor_variable = wrap_fx_proxy(
            tx=self.tx,
            proxy=tensor_proxy,
            example_value=value,
            guards=self.make_guards(GuardBuilder.TENSOR_MATCH),
            should_specialize=self.tensor_should_specialize(),
            ignore_subclass=ignore_subclass,
            source=source,
        )
        self.tx.output.input_source_to_var[source] = tensor_variable
        assert "tensor_dict" not in tensor_proxy.node.meta
        tensor_proxy.node.meta["tensor_dict"] = value.__dict__.copy()

        # TODO: I think the result is guaranteed to be fake with
        # ignore_subclass changes
        fake_tensor_value = None
        example_value = tensor_variable.proxy.node.meta["example_value"]
        if isinstance(example_value, torch._subclasses.fake_tensor.FakeTensor):
            fake_tensor_value = example_value

        grapharg = GraphArg(source, value, False, fake_tensor_value)
        tensor_proxy.node.meta["grapharg"] = grapharg
        self.tx.output.add_symbol_bindings(grapharg)

        if type(value) in config.traceable_tensor_subclasses:
            subclass_torch_function__func = value.__torch_function__.__func__
            subclass_type = type(value)
            # NB: This is slightly misnamed, a tensor subclass might not have
            # any explicit __torch_function__ implementation and is relying
            # on the default inherited from torch.Tensor
            return TensorWithTFOverrideVariable(
                tensor_variable,
                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:
            # NB: We do not do float.  For motivation, see
            # https://docs.google.com/document/d/1INSCdYu1PxXcr43HrD82OudeEuS-qxQe1yZmLg2wy6A/edit
            # but the general idea is that we generate kernels that can
            # take unspecialized floats and use them in sizevar computation
            if (
                config.dynamic_shapes
                and isinstance(value, int)
                and not is_constant_source(self.get_source())
            ):
                if value < 0 or torch._dynamo.config.specialize_int:
                    # Negative values don't create_symbol correctly,
                    # so make sure we do a constant in this case.
                    #
                    # Also, if specialize_int is False, also return
                    # a constant (but this should have been handled
                    # in the caller, TBH)
                    return ConstantVariable(
                        value=value,
                        guards=self.make_guards(GuardBuilder.CONSTANT_MATCH),
                    )

                shape_env = self.tx.output.shape_env

                dynamic_dim = DimDynamic.DYNAMIC

                wrapped_value = shape_env.create_symintnode(
                    # TODO: This is wrong wrong wrong, create_symbol will
                    # generate something that is non-negative, but this is
                    # not a sound assumption to make.
                    # Not fixing as this was a preexisting condition.
                    shape_env.create_symbol(
                        value,
                        source=self.source,
                        dynamic_dim=dynamic_dim,
                        constraint_dim=None,
                    ),
                    hint=value,
                )
                self.tx.output.tracked_fakes.append(
                    TrackedFake(wrapped_value, self.source, None)
                )
            else:
                wrapped_value = torch.tensor(value)
            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()})
            if isinstance(wrapped_value, torch.Tensor):
                options.update({"raw_value": value})

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

            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
            if not is_constant_source(self.get_source()):
                if self.tx.export and not isinstance(self.get_source(), LocalSource):
                    raise AssertionError(
                        "Dynamo attempts to add additional input during export: value={}, source={}".format(
                            wrapped_value, self.get_source()
                        )
                    )
                fake_tensor_value = None
                example_value = unspec_var.proxy.node.meta["example_value"]
                if isinstance(example_value, torch._subclasses.fake_tensor.FakeTensor):
                    fake_tensor_value = example_value
                proxy.node.meta["grapharg"] = GraphArg(
                    self.get_source(),
                    wrapped_value,
                    isinstance(wrapped_value, torch.Tensor),
                    fake_tensor_value,
                    is_tensor=False,
                )
            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, ignore_subclass=False, **options
):
    from ..symbolic_convert import InstructionTranslatorBase

    assert isinstance(tx, InstructionTranslatorBase)
    if "guards" in options and options["guards"] is not None:
        tx.output.guards.update(options["guards"])

    assert "example_value" not in proxy.node.meta, f"{proxy.node.meta['example_value']}"

    initial_example_value = example_value

    def _clone_input(value):
        if isinstance(value, torch.Tensor):
            # tensor subclasses will not be converted to FakeTensors and need to be cloned
            if 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:
            example_value = get_fake_value(proxy.node, tx)

        # Handle recursive calls here
        elif isinstance(example_value, FakeTensor):
            pass

        elif isinstance(example_value, torch.Tensor):
            if tx.export:
                # The legacy behavior for real value cache with subclasses was
                # to perform a clone WITHOUT preserving the subclass.  It's
                # not entirely clear this is what you actually want though.
                with torch._C.DisableTorchFunctionSubclass():
                    proxy.tracer.real_value_cache[proxy.node] = _clone_input(
                        example_value
                    )
            # NB: If we're ignoring subclass, then the expectation is you will
            # take the returned TensorVariable and wrap it into a more
            # accurate TensorVariable that is able to track subclass-ness;
            # otherwise this is wrong!
            kwargs = {
                "ignore_subclass": ignore_subclass,
                "is_tensor": target_cls is TensorVariable,
            }
            assert "source" in options and options["source"] is not None
            kwargs["source"] = options["source"]
            example_value = wrap_to_fake_tensor_and_record(
                example_value, tx=tx, **kwargs
            )

    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

        # NB: In most (all?) cases, this does not actually do a clone.
        # (WARNING: this means that if we mutate metadata on the fake
        # tensor, the stored example value will update too!)
        example_value = _clone_input(example_value)
        proxy.node.meta["example_value"] = example_value
        specialized_props = target_cls.specialize(example_value)
        if isinstance(example_value, torch._subclasses.fake_tensor.FakeTensor):
            # NB: This will be wrong for ignore_subclass; fix it up later!
            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 SymNodeVariable.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(SymNodeVariable.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 SymNodeVariable.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)):
        proxy.node.meta["example_value"] = example_value
        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, (torch.SymInt, torch.SymFloat, torch.SymBool)):
        proxy.node.meta["example_value"] = example_value
        return SymNodeVariable(proxy, example_value, **options)
    elif proxy.node.target in [torch.cuda.streams.Stream, torch.cuda.current_stream]:
        proxy.node.meta["example_value"] = example_value
        return CUDAStreamVariable(proxy, example_value, **options)
    else:
        unimplemented(
            "torch.* op returned non-Tensor "
            + f"{typestr(example_value)} {proxy.node.op} {proxy.node.target}"
        )


# Tracks the sources of all fake tensors we wrap in Dynamo.
# Used by shape guard computation.
@dataclasses.dataclass
class TrackedFake:
    fake: Union[FakeTensor, SymInt]
    source: Source
    # Is None when fake is SymInt
    constraint_dims: Optional[DimList[DimConstraint]]

    def __hash__(self) -> int:
        return hash((self.fake, self.source.name()))

    def __eq__(self, other: object) -> bool:
        if isinstance(other, TrackedFake):
            return self.fake is other.fake and self.source.name() == other.source.name()
        return False


def wrap_to_fake_tensor_and_record(
    e, tx, ignore_subclass=False, *, source: Optional[Source], is_tensor: bool
):
    if type(e) in (torch.Tensor, torch.nn.Parameter) or (
        ignore_subclass and isinstance(e, torch.Tensor)
    ):
        assert source is not None
        static_shapes, reason = tensor_always_has_static_shape(
            e, is_tensor, guard_source=source.guard_source()
        )

        name = source.name()

        # Prep for automatic dynamic
        curr_sizes = None
        if name not in tx.output.frame_state:
            # If there is no entry for this source, add the tensor to frame state with its current static size.
            # E.g., {} -> {"x": [2, 4]}
            curr_sizes = list(e.size())
        else:
            curr_sizes = tx.output.frame_state[name]
            if curr_sizes is not None:
                if e.ndim != len(curr_sizes):
                    # If there is already an entry, and the dim mismatches, replace the frame state entry with None.
                    # E.g. {"x": [2, 3, 4]} -> {"x": None}
                    curr_sizes = None
                else:
                    # If there is already an entry, and the dim matches, for every size in the frame state which
                    # disagrees with the current static size, replace it with None. E.g., {"x": [2, 3]} -> {"x": [2, None]}
                    for i, dim in enumerate(curr_sizes):
                        if e.size()[i] != dim:
                            curr_sizes[i] = None

        # TODO: index export_constraints ahead of time so we don't have to
        # do a linear scan every time here
        t_id = id(e)
        dim2constraint = {}
        if tx.output.export_constraints:
            for constraint in tx.output.export_constraints:
                if constraint.t_id == t_id:
                    if constraint.dim in dim2constraint:
                        from torch.fx.experimental.symbolic_shapes import (
                            StrictMinMaxConstraint,
                        )

                        dim2constraint[constraint.dim] = StrictMinMaxConstraint(
                            vr=constraint.constraint_range.vr
                            & dim2constraint[constraint.dim].vr,
                            warn_only=False,
                        )
                    else:
                        dim2constraint[constraint.dim] = constraint.constraint_range

        dynamic_dims = None
        constraint_dims = None
        if tx.fake_mode.shape_env is not None:
            dynamic_dims = []
            constraint_dims = []
            for i in range(e.dim()):
                # NB: mark dynamic has precedence over static
                marked_dynamic = i in getattr(e, "_dynamo_dynamic_indices", set())
                marked_weak_dynamic = i in getattr(
                    e, "_dynamo_weak_dynamic_indices", set()
                )
                marked_static = i in getattr(e, "_dynamo_static_indices", set())

                # NB: both static and dynamic have precedence over
                automatic_dynamic = config.automatic_dynamic_shapes and (
                    curr_sizes is None or curr_sizes[i] is None
                )

                # Reflect the user directive in the frame_state
                # For dynamic, apply None always
                if marked_dynamic:
                    curr_sizes[i] = None

                # We will process constraints first, as they will imply that we
                # have a dynamic dimension
                # Precedence: export constraints > eager constraints
                constraint = dim2constraint.get(i)
                if constraint is None:
                    if marked_dynamic and not config.allow_ignore_mark_dynamic:
                        constraint = RelaxedUnspecConstraint(warn_only=False)
                    elif not marked_static and automatic_dynamic:
                        constraint = RelaxedUnspecConstraint(warn_only=True)
                constraint_dims.append(constraint)

                # Now, figure out if the dim is dynamic/duck/static
                if constraint is not None or marked_dynamic or marked_weak_dynamic:
                    # NB: We could assert static_shapes is False here, but it
                    # seems better to allow the user to override policy in this
                    # case
                    dynamic = DimDynamic.DYNAMIC
                elif static_shapes or config.assume_static_by_default or marked_static:
                    dynamic = DimDynamic.STATIC
                else:
                    dynamic = DimDynamic.DUCK
                dynamic_dims.append(dynamic)

        tx.output.frame_state[name] = curr_sizes

        log.debug(
            "wrap_to_fake %s %s %s %s",
            source.name(),
            tuple(e.shape),
            dynamic_dims,
            constraint_dims,
        )
        fake_e = wrap_fake_exception(
            lambda: tx.fake_mode.from_tensor(
                e,
                ignore_subclass=ignore_subclass,
                source=source,
                dynamic_dims=dynamic_dims,
                constraint_dims=constraint_dims,
            )
        )
        if is_tensor and not (static_shapes and source.is_nn_module()):
            tx.output.tracked_fakes.append(TrackedFake(fake_e, source, constraint_dims))
        return fake_e
    else:
        return e