AIStoryBoard/python/venv/Lib/site-packages/torch/jit/_recursive.py

# mypy: allow-untyped-defs
import collections
import functools
import inspect
import textwrap
import types
import warnings

import torch
import torch._jit_internal as _jit_internal
from torch._sources import fake_range
from torch.jit._builtins import _find_builtin
from torch.jit._check import AttributeTypeIsSupportedChecker
from torch.jit._state import _add_script_class, _get_script_class, _python_cu
from torch.jit.frontend import (
    get_class_properties,
    get_default_args,
    get_jit_class_def,
    get_jit_def,
)
from torch.nn import Module


ScriptMethodStub = collections.namedtuple(
    "ScriptMethodStub", ("resolution_callback", "def_", "original_method")
)
PropertyStub = collections.namedtuple("PropertyStub", ("resolution_callback", "def_"))


# TODO: there should be a more principled way of doing this.
ignored_attributes = [
    "_version",
    "_parameters",
    "_buffers",
    "_non_persistent_buffers_set",
    "_backward_hooks",
    "_backward_pre_hooks",
    "_forward_hooks",
    "_forward_hooks_with_kwargs",
    "_forward_pre_hooks",
    "_forward_pre_hooks_with_kwargs",
    "_forward_hooks_always_called",
    "_state_dict_hooks",
    "_state_dict_pre_hooks",
    "_load_state_dict_pre_hooks",
    "_load_state_dict_post_hooks",
    "_modules",
    "_initializing",
    "dump_patches",
]


def _compile_and_register_class(obj, rcb, qualified_name):
    script_class = _get_script_class(obj)

    if not script_class:
        ast = get_jit_class_def(obj, obj.__name__)
        defaults = torch.jit.frontend.get_default_args_for_class(obj)
        script_class = torch._C._jit_script_class_compile(
            qualified_name, ast, defaults, rcb
        )
        _add_script_class(obj, script_class)

    return script_class


def make_stub(func, name):
    rcb = _jit_internal.createResolutionCallbackFromClosure(func)
    ast = get_jit_def(func, name, self_name="RecursiveScriptModule")
    return ScriptMethodStub(rcb, ast, func)


def make_stub_from_method(nn_module, method_name):
    func = getattr(nn_module, method_name)
    if isinstance(func, ScriptMethodStub):
        return func
    # Make sure the name present in the resulting AST will match the name
    # requested here. The only time they don't match is if you do something
    # like:
    #   def _forward(self):
    #       pass
    #   forward = _forward
    # In this case, the actual function object will have the name `_forward`,
    # even though we requested a stub for `forward`.
    return make_stub(func, method_name)


def make_stubs_from_exported_methods(mod):
    stubs = []
    for name in dir(mod):
        item = getattr(mod, name, None)
        if (
            _jit_internal.get_torchscript_modifier(item)
            is _jit_internal.FunctionModifiers.EXPORT
        ):
            stubs.append(make_stub_from_method(mod, name))

    return stubs


def jit_ignored_properties(module):
    user_annotated_ignored_attributes = getattr(
        module, "__jit_ignored_attributes__", []
    )

    def get_properties_names(module):
        return {k for k, v in vars(module).items() if isinstance(v, property)}

    properties = get_properties_names(type(module))
    user_annoted_ignored_properties = set()

    for ignored_attr in user_annotated_ignored_attributes:
        if ignored_attr in properties:
            user_annoted_ignored_properties.add(ignored_attr)
    return user_annoted_ignored_properties


# base types that can be constants
# in addition, tuples and lists of these base types are also considered constants
# If you edit this list, then you also need to edit the handlers in
# ConstantValue in jit/script/init.cpp
_constant_types = (
    bool,
    float,
    int,
    str,
    type(None),
    torch.device,
    torch.layout,
    torch.dtype,
    torch.qscheme,
)


def _get_valid_constant(attr, v, owner_type):
    if isinstance(v, _constant_types):
        return v
    elif isinstance(v, (tuple, list)):
        return tuple(_get_valid_constant(attr, x, owner_type) for x in v)
    constants = ", ".join(torch.typename(typ) for typ in _constant_types)
    raise TypeError(
        textwrap.dedent(
            f"""
        '{torch.typename(type(v))}' object in attribute '{owner_type}.{attr}' is not a valid constant.
        Valid constants are:
        1. a nn.ModuleList
        2. a value of type {{{constants}}}
        3. a list or tuple of (2)
        """
        )
    )


class SourceContext(torch._C._jit_tree_views.SourceRangeFactory):
    pass


def get_annotations(obj):
    # In Python-3.10+ it is recommended to use inspect.get_annotations
    # See https://docs.python.org/3.10/howto/annotations.html
    # But also, in 3.10 annotations from base class are not inherited
    # by unannotated derived one, so they must be manually extracted
    annotations = inspect.get_annotations(obj)
    if annotations:
        return annotations

    def get_cls_annotations(cls):
        cls_annotations = inspect.get_annotations(cls)
        if cls_annotations:
            return cls_annotations
        for base in cls.__bases__:
            cls_annotations = get_cls_annotations(base)
            if cls_annotations:
                return cls_annotations
        return {}

    cls = obj if isinstance(obj, type) else type(obj)
    return get_cls_annotations(cls)


def infer_concrete_type_builder(nn_module, share_types=True):
    """
    Build a ConcreteModuleTypeBuilder from an nn.Module.

    This ConcreteModuleType doesn't have a JIT type associated with it yet, it
    must be filled in by the caller.
    """
    concrete_type_builder = torch._C.ConcreteModuleTypeBuilder(type(nn_module))
    if isinstance(nn_module, (torch.nn.ModuleDict)):
        concrete_type_builder.set_module_dict()
    if isinstance(nn_module, (torch.nn.ModuleList, torch.nn.Sequential)):
        concrete_type_builder.set_module_list()
    if isinstance(nn_module, (torch.nn.ParameterList)):
        concrete_type_builder.set_parameter_list()
    if isinstance(nn_module, (torch.nn.ParameterDict)):
        concrete_type_builder.set_parameter_dict()

    class_annotations = get_annotations(nn_module)
    if isinstance(nn_module, (torch.ao.quantization.QuantWrapper)):
        class_annotations = {}

    # Get user-annotated ignored attributes.
    user_annotated_ignored_attributes = getattr(
        nn_module, "__jit_ignored_attributes__", []
    )
    concrete_type_builder.add_ignored_attributes(user_annotated_ignored_attributes)
    ignored_properties = jit_ignored_properties(nn_module)

    # try to infer the type from type annotation or from the object itself
    def infer_type(name, item):
        # The forward function from Module is special; never use this annotations; we
        # need to infer type directly using JIT.  I originally wanted to write
        # this test as isinstance(class_annotations[name], Callable) but
        # isinstance on typing things doesn't seem to work: isinstance(list, Callable)
        # is also true!
        inferred = False
        try:
            if (
                name in class_annotations
                and class_annotations[name]
                != torch.nn.Module.__annotations__["forward"]
            ):
                ann_to_type = torch.jit.annotations.ann_to_type(
                    class_annotations[name], fake_range()
                )
                attr_type = torch._C.InferredType(ann_to_type)
            elif isinstance(item, torch.jit.Attribute):
                ann_to_type = torch.jit.annotations.ann_to_type(item.type, fake_range())
                attr_type = torch._C.InferredType(ann_to_type)
            else:
                attr_type = torch._C._jit_try_infer_type(item)
                inferred = True
        except RuntimeError as re:
            raise RuntimeError(f"Error inferring type for {name}: {item}: {re}") from re

        return attr_type, inferred

    added_names = set()

    for name, item in nn_module._parameters.items():
        if name in user_annotated_ignored_attributes:
            continue

        assert item is None or isinstance(item, torch.Tensor)
        attr_type, _ = infer_type(name, item)
        # We currently have the invariant in various places in our code
        # that parameters must be Tensors. However, the nn.Module API also
        # allows NoneType parameters. These parameters are not returned as
        # part of `parameters()` and its variants, but are available
        # through direct attribute access.
        concrete_type_builder.add_attribute(name, attr_type.type(), True, False)
        added_names.add(name)

    for name, item in nn_module._buffers.items():
        if name in user_annotated_ignored_attributes:
            continue

        assert item is None or isinstance(item, torch.Tensor)
        attr_type, _ = infer_type(name, item)
        concrete_type_builder.add_attribute(name, attr_type.type(), False, True)
        added_names.add(name)

    for name, item in nn_module._modules.items():
        if name in user_annotated_ignored_attributes:
            continue

        attr_type, _ = infer_type(name, item)
        if item is None:
            # Modules can be None. We don't have direct support for optional
            # Modules, so the register it as an NoneType attribute instead.
            concrete_type_builder.add_attribute(name, attr_type.type(), False, False)
            continue
        if attr_type.success():
            assert attr_type.type().is_interface_type()
            # if the type can be inferred, it should be a module interface type
            sub_concrete_type = torch._C.ConcreteModuleType.from_jit_type(
                attr_type.type()
            )
        else:
            # otherwise we get the concrete module type for item and add it to concrete_type
            sub_concrete_type = get_module_concrete_type(item, share_types)
        concrete_type_builder.add_module(name, sub_concrete_type)

        added_names.add(name)

    # populate constants_set
    constants_set = set(getattr(nn_module, "__constants__", ()))

    # Constants annotated via `Final[T]` rather than being added to `__constants__`
    for name, ann in class_annotations.items():
        if torch._jit_internal.is_final(ann):
            constants_set.add(name)

    for name in constants_set:
        if name in added_names:
            # TODO: We should really error in this case, but its bc-breaking so
            # we need to warn for at least one release
            if name in nn_module._modules:
                hint = "submodule"
            elif name in nn_module._buffers:
                hint = "buffer"
            elif name in nn_module._parameters:
                hint = "parameter"
            else:
                raise AssertionError(
                    "added_names must be submodule, parameter, or buffer"
                )

            warnings.warn(
                f"'{name}' was found in ScriptModule constants, "
                f" but it is a non-constant {hint}. Consider removing it.",
                stacklevel=2,
            )
            continue
        if not hasattr(nn_module, name):
            # TODO: We should really error in this case, but its bc-breaking so
            # we need to warn for at least one release
            warnings.warn(
                f"'{name}' was found in ScriptModule constants, "
                "but was not actually set in __init__. "
                "Consider removing it.",
                stacklevel=2,
            )
            continue
        value = getattr(nn_module, name)
        concrete_type_builder.add_constant(
            name, _get_valid_constant(name, value, type(nn_module).__name__)
        )
        added_names.add(name)

    # populate overloads
    overloads = getattr(nn_module, "__overloads__", {})
    # update with any annotated overloads
    overloads.update(
        get_overload_name_mapping(
            get_overload_annotations(nn_module, ignored_properties)
        )
    )
    for name, overloaded_names in overloads.items():
        concrete_type_builder.add_overload(name, overloaded_names)

    for name, value in nn_module.__dict__.items():
        if name in ignored_attributes or name.startswith("__"):
            # Python objects have lots of random attributes attached to them;
            # PyTorch adds a few more. Prevent these from getting compiled.
            continue

        if name in user_annotated_ignored_attributes:
            continue

        if name in added_names:
            # Don't re-add anything we already added
            continue

        isoverloadpacket = isinstance(value, torch._ops.OpOverloadPacket)
        if isoverloadpacket:
            value = value.op
        # Handle Python function attributes
        if inspect.isfunction(value):
            try:
                scripted_fn = torch.jit.script(value)
                concrete_type_builder.add_function_attribute(
                    name, torch._C._jit_try_infer_type(scripted_fn).type(), value
                )
            except Exception as e:
                # If we fail to script the function, it isn't a hard error.
                # Instead, we will add it to the list of attributes we failed
                # to convert, with the compilation error.
                hint = (
                    "(This function exists as an attribute on the Python module, "
                    "but we failed to compile it to a TorchScript function. "
                    f"\nThe error stack is reproduced here:\n{e})"
                )
                concrete_type_builder.add_failed_attribute(name, hint)

            continue

        # Handle calls to builtin functions (either bespoke builtins from torch.jit._builtins or
        # a call to an aten function like torch.add)
        builtin_symbol_name = _find_builtin(value)
        if builtin_symbol_name:
            concrete_type_builder.add_builtin_function(name, builtin_symbol_name)
            continue

        # Handle Script function attributes
        if isinstance(value, torch.jit.ScriptFunction):
            concrete_type_builder.add_function_attribute(
                name, torch._C._jit_try_infer_type(value).type(), value
            )
            continue

        # If we got here, this is a regular "data" attribute, add it to the concrete type
        attr_type, inferred = infer_type(name, value)
        if attr_type.success():
            concrete_type_builder.add_attribute(name, attr_type.type(), False, False)
        else:
            # TODO: could add more detail here. For example, what the user should do
            # when the pytype is `list` or `NoneType`
            inferred_msg = (
                "Its type was inferred; try adding a type annotation for the attribute."
                if inferred
                else ""
            )
            additional_info = f"{attr_type.reason()}. {inferred_msg}"
            hint = (
                "(This attribute exists on the Python module, "
                f"but we failed to convert Python type: '{torch.typename(type(value))}' "
                f"to a TorchScript type. {additional_info})"
            )
            concrete_type_builder.add_failed_attribute(name, hint)

    # add hooks to concrete type
    for hook in nn_module._forward_hooks.values():
        concrete_type_builder.add_forward_hook(hook)
    for pre_hook in nn_module._forward_pre_hooks.values():
        concrete_type_builder.add_forward_pre_hook(pre_hook)

    return concrete_type_builder


class ConcreteTypeStore:
    type_store: dict[type[Module], list[torch._C.ConcreteModuleType]]
    methods_compiled: set[torch._C.ConcreteModuleType]

    def __init__(self) -> None:
        # Python module type => List[ConcreteModuleType)]
        self.type_store = {}
        # ConcreteTypes that have had their methods already compiled
        self.methods_compiled = set()

    def get_or_create_concrete_type(self, nn_module):
        """Infer a ConcreteType from this `nn.Module` instance. Underlying JIT types are reused if possible."""
        concrete_type_builder = infer_concrete_type_builder(nn_module)

        nn_module_type = type(nn_module)
        if nn_module_type not in self.type_store:
            self.type_store[nn_module_type] = []

        # Search the type store for an already-available JIT type
        known_types = self.type_store[nn_module_type]
        for known_type in known_types:
            if known_type.equals(concrete_type_builder):
                return known_type

        # We didn't find anything; generate a new JIT type from this concrete type
        concrete_type = concrete_type_builder.build()
        self.type_store[nn_module_type].append(concrete_type)
        return concrete_type


concrete_type_store = ConcreteTypeStore()


def create_methods_and_properties_from_stubs(
    concrete_type, method_stubs, property_stubs
) -> None:
    method_defs = [m.def_ for m in method_stubs]
    method_rcbs = [m.resolution_callback for m in method_stubs]
    method_defaults = [get_default_args(m.original_method) for m in method_stubs]

    property_defs = [p.def_ for p in property_stubs]
    property_rcbs = [p.resolution_callback for p in property_stubs]

    concrete_type._create_methods_and_properties(
        property_defs, property_rcbs, method_defs, method_rcbs, method_defaults
    )


def create_hooks_from_stubs(concrete_type, hook_stubs, pre_hook_stubs) -> None:
    hook_defs = [h.def_ for h in hook_stubs]
    hook_rcbs = [h.resolution_callback for h in hook_stubs]

    pre_hook_defs = [h.def_ for h in pre_hook_stubs]
    pre_hook_rcbs = [h.resolution_callback for h in pre_hook_stubs]

    concrete_type._create_hooks(hook_defs, hook_rcbs, pre_hook_defs, pre_hook_rcbs)


def get_module_concrete_type(nn_module, share_types=True):
    """
    Get a concrete type for nn_modules.

    If share_types is True, the concrete type is fetched from concrete_type_store.
    If it is False, a new concrete type is created without first searching concrete_type_store.

    Args:
        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
        share_types = Whether to share underlying JIT types between modules (if possible).

    Returns:
        A concrete type for nn_module.
    """
    assert isinstance(nn_module, Module)
    if isinstance(nn_module, torch.jit.ScriptModule) and hasattr(
        nn_module, "_concrete_type"
    ):
        return nn_module._concrete_type

    if share_types:
        # Look into the store of cached JIT types
        concrete_type = concrete_type_store.get_or_create_concrete_type(nn_module)
    else:
        # Get a concrete type directly, without trying to reuse an existing JIT
        # type from the type store.
        concrete_type_builder = infer_concrete_type_builder(nn_module, share_types)
        concrete_type_builder.set_poisoned()
        concrete_type = concrete_type_builder.build()

    return concrete_type


def create_script_class(obj):
    """
    Create and return a RecursiveScriptClass instance from a Python object.

    Arguments:
        obj: A Python object.
    """
    qualified_class_name = _jit_internal._qualified_name(type(obj))
    rcb = _jit_internal.createResolutionCallbackForClassMethods(type(obj))
    # Script the type of obj if it hasn't already been scripted.
    _compile_and_register_class(type(obj), rcb, qualified_class_name)
    class_ty = _python_cu.get_class(qualified_class_name)
    # Create an empty torch._C.ScriptObject with the scripted type.
    cpp_object = torch._C._create_object_with_type(class_ty)
    # Copy all of the attributes over to the torch._C.ScriptObject.
    for name, value in obj.__dict__.items():
        cpp_object.setattr(name, value)

    # Wrap the torch._C.ScriptObject in a RecursiveScriptClass instance.
    return wrap_cpp_class(cpp_object)


def create_script_module(nn_module, stubs_fn, share_types=True, is_tracing=False):
    """
    Create a new ScriptModule from an nn.Module.

    Args:
        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
        stubs_fn:  Lambda that takes an nn.Module and generates a list of ScriptMethodStubs to compile.
        share_types:  Whether to share underlying JIT types between modules (if possible).
            NOTE: Only set to False this when we cannot guarantee type sharing will work
                correctly. This only happens today for traced modules, where the same
                module can produce different traced methods depending on the inputs.
        is_tracing: Whether this function is called during tracing or scripting. If tracing,
                we don't need to do AttributeTypeIsSupportedChecker because all the unsupported
                attributes will be baked as constant in the tracing graph. In addition,
                this check significantly slows down the traced modules when the module size is big.
    """
    assert not isinstance(nn_module, torch.jit.RecursiveScriptModule)
    check_module_initialized(nn_module)
    concrete_type = get_module_concrete_type(nn_module, share_types)
    if not is_tracing:
        AttributeTypeIsSupportedChecker().check(nn_module)
    return create_script_module_impl(nn_module, concrete_type, stubs_fn)


def create_script_module_impl(nn_module, concrete_type, stubs_fn):
    """
    Convert an nn.Module to a RecursiveScriptModule.

    Args:
        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
        concrete_type:  The fully initialized ConcreteType of the module.
        stubs_fn:  Lambda that takes an nn.Module and generates a list of ScriptMethodStubs to compile.
    """
    cpp_module = torch._C._create_module_with_type(concrete_type.jit_type)
    method_stubs = stubs_fn(nn_module)
    property_stubs = get_property_stubs(nn_module)
    hook_stubs, pre_hook_stubs = get_hook_stubs(nn_module)
    ignored_properties = jit_ignored_properties(nn_module)

    def init_fn(script_module) -> None:
        # Initialize the ScriptModule:
        # 1. Copy the attributes/parameters/buffers from the original `nn_module` to the new ScriptModule.
        for name in concrete_type.get_attributes():
            orig_value = getattr(nn_module, name)
            orig_value = (
                orig_value.value
                if isinstance(orig_value, torch.jit.Attribute)
                else orig_value
            )
            cpp_module.setattr(name, orig_value)

        # 2. Copy the submodules from the original `nn_module` to the new ScriptModule,
        #    recursively scripting them.
        for name, sub_concrete_type in concrete_type.get_modules():
            orig_value = getattr(nn_module, name)
            assert isinstance(orig_value, Module), (
                f"Expected Module but got {type(orig_value)}"
            )
            module_type = sub_concrete_type.jit_type
            if isinstance(module_type, torch._C.InterfaceType):
                # use the interface inference rule to compile the module
                scripted = interface_script(module_type, orig_value)
            elif isinstance(orig_value, torch.jit.ScriptModule):
                scripted = orig_value
            else:
                # always reuse the provided stubs_fn to infer the methods to compile
                scripted = create_script_module_impl(
                    orig_value, sub_concrete_type, stubs_fn
                )

            cpp_module.setattr(name, scripted)
            script_module._modules[name] = scripted

        # 3. Copy @ignored/@unused methods and attrs from the original `nn_module` to the new ScriptModule.
        #    This ensures we can access these Python methods on the ScriptModule.
        for name in dir(nn_module):
            if name in ignored_properties:
                continue
            item = getattr(nn_module, name, None)
            if inspect.ismethod(item) and _jit_internal.is_ignored_fn(item):
                unbound_function = getattr(nn_module, name).__func__
                bound_method = unbound_function.__get__(script_module)
                setattr(script_module, name, bound_method)
            elif concrete_type.is_ignored_attribute(name):
                setattr(script_module, name, item)

        # For convenience, attach the concrete type to the new ScriptModule
        script_module._concrete_type = concrete_type

    # Actually create the ScriptModule, initializing it with the function we just defined
    script_module = torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)

    # Compile methods if necessary
    if concrete_type not in concrete_type_store.methods_compiled:
        create_methods_and_properties_from_stubs(
            concrete_type, method_stubs, property_stubs
        )
        # Create hooks after methods to ensure no name collisions between hooks and methods.
        # If done before, hooks can overshadow methods that aren't exported.
        create_hooks_from_stubs(concrete_type, hook_stubs, pre_hook_stubs)
        torch._C._run_emit_module_hook(cpp_module)
        concrete_type_store.methods_compiled.add(concrete_type)

    # Copy the forward hooks and pre-hooks to the new ScriptModule
    # to allow the hooks to be run from eager as ScriptFunctions
    for idx, fn in enumerate(script_module._c._get_forward_pre_hooks()):
        script_module._forward_pre_hooks[idx] = fn
    for idx, fn in enumerate(script_module._c._get_forward_hooks()):
        script_module._forward_hooks[idx] = fn

    # Special handling so methods like __len__ work in script methods on classes derived from containers
    if (
        isinstance(
            nn_module, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)
        )
        and "__len__" not in cpp_module._method_names()
    ):
        script_module.define(f"def __len__(self):\n   return {len(nn_module)}\n")
    if (
        isinstance(nn_module, torch.nn.ModuleDict)
        and "__contains__" not in cpp_module._method_names()
    ):
        if len(nn_module.keys()):
            keys = repr(list(nn_module.keys()))
            script_module.define(
                f"def __contains__(self, key: str):\n   return key in {keys}\n"
            )
        else:
            script_module.define("def __contains__(self, key: str):\n   return False\n")

    # Make the compiled methods available to the Python ScriptModule class.
    for method_stub in method_stubs:
        if method_stub.original_method is None:
            # define()'d methods don't have an Python original_method, so we
            # don't need to do any Python re-wrapping stuff
            continue

        name = method_stub.original_method.__name__
        if name != method_stub.def_.name().name:
            # TODO: Why skip this? Because @torch.jit._overload_method will
            # mangle the name of the function.
            continue
        script_method = cpp_module._get_method(name)

        # Wrap the original to propagate docstrings and such.
        # TODO: we don't currently do this functions that are recursively
        # compiled, we should.
        wrapped_script_method = functools.wraps(method_stub.original_method)(
            script_method
        )

        # Add the methods to the script_module directly. This ensures they will
        # be found first when `name` is looked up (as opposed to the stubs or
        # nn.Module.forward)
        script_module.__dict__[name] = wrapped_script_method

    # Make module properties available on the Python ScriptModule class.
    for property_stub in property_stubs:
        property_name = property_stub.def_.name().name
        fget = cpp_module._get_method(property_stub.def_.getter_name().name)
        # Setter is optional, so it may not exist.
        setter_name = property_stub.def_.setter_name()
        fset = cpp_module._get_method(setter_name.name) if setter_name else None
        script_module.__dict__[property_name] = property(property_name, fget, fset)  # type: ignore[arg-type]

    # copy over python methods to script module if they aren't defined on the script module
    # this is currently an internal api used only on module containers
    for name in dir(nn_module):
        if name in ignored_properties:
            continue
        item = getattr(nn_module, name, None)
        if (
            _jit_internal.get_torchscript_modifier(item)
            is _jit_internal.FunctionModifiers.COPY_TO_SCRIPT_WRAPPER
        ):
            add_python_attr_to_scripted_model(script_module, nn_module, name)

    return script_module


# We define shims of certain attributes on the RecursiveScriptModule to support
# magic methods. To check if a script model defines an attribute we need
# to also check that the attribute is not the shim
def script_model_defines_attr(script_model, attr):
    script_attr = getattr(script_model, attr, None)
    if script_attr is None:
        return False
    default_attr = getattr(torch.jit.RecursiveScriptModule, attr, None)
    if default_attr is None:
        return False
    return script_attr != default_attr


def add_python_attr_to_scripted_model(script_model, orig, attr) -> None:
    if hasattr(orig, attr) and script_model_defines_attr(script_model, attr):
        setattr(script_model, attr, getattr(orig, attr))


def get_overload_annotations(mod, jit_ignored_properties):
    # original function => [(mangled overload name, overload function)]
    overloads = {}

    for name in dir(type(mod)):
        if name in jit_ignored_properties:
            continue
        item = getattr(mod, name, None)
        if not callable(item):
            continue

        # builtin functions like repr() in python 2 do not have __module__ defined
        if hasattr(item, "__module__") and item.__module__ is not None:
            method_overloads = _jit_internal._get_overloaded_methods(
                item, mod.__class__
            )
            if method_overloads is None:
                continue

            # pyrefly: ignore [missing-attribute]
            if item.__func__ in method_overloads:
                raise RuntimeError(
                    _jit_internal.get_overload_no_implementation_error_message(
                        "method", item.__func__
                    )
                )

            names = [name + "__" + str(i) for i in range(len(method_overloads))]
            overloads[item] = list(zip(names, method_overloads))

    return overloads


def get_overload_name_mapping(overload_info):
    # Same format as __overloads__
    # original function => [overload names]
    overload_name_mappings: dict[str, list[str]] = {}
    for orig_fn, overloads in overload_info.items():
        original_name = orig_fn.__name__
        if original_name not in overload_name_mappings:
            overload_name_mappings[original_name] = []

        for overload_name, _ in overloads:
            overload_name_mappings[original_name].append(overload_name)
    return overload_name_mappings


def _check_no_signature(func) -> None:
    signature = torch.jit.annotations.get_signature(
        func, None, fake_range(), inspect.ismethod(func)
    )
    if signature is None:
        qual_name = _jit_internal._qualified_name(func)
        raise RuntimeError(
            f"Must explicitly add type annotations to overloaded functions: {qual_name}"
        )


def make_stubs_for_overloads(overload_info):
    overload_stubs = []
    for orig_fn, overloads in overload_info.items():
        orig_ast = get_jit_def(
            orig_fn, orig_fn.__name__, self_name="RecursiveScriptModule"
        )
        for overload_name, overload_fn in overloads:
            _check_no_signature(overload_fn)
            over_ast = get_jit_def(
                overload_fn, overload_fn.__name__, self_name="RecursiveScriptModule"
            )
            new_ast = torch._C._replace_overloaded_method_decl(
                over_ast.decl(), orig_ast, overload_name
            )
            _rcb = _jit_internal.createResolutionCallbackFromClosure(orig_fn)
            overload_stubs.append(ScriptMethodStub(_rcb, new_ast, overload_fn))
    return overload_stubs


def check_module_initialized(mod) -> None:
    assert isinstance(mod, torch.nn.Module)
    if not hasattr(mod, "_parameters"):
        raise RuntimeError(
            f"'{torch.typename(type(mod))}' has not been initialized, did you forget to call 'super()'?"
        )

    # This is to avoid importing torch.distributed.nn
    if not hasattr(mod, "remote_parameters"):
        for name, param in mod._parameters.items():
            if param is not None and torch.nn.parameter.is_lazy(param):
                raise RuntimeError(
                    f"'{torch.typename(type(mod))}' has uninitialized parameters {name}. Did you forget to run a forward pass?"
                )
        for name, buf in mod._buffers.items():
            if buf is not None and torch.nn.parameter.is_lazy(buf):
                raise RuntimeError(
                    f"'{torch.typename(type(mod))}' has uninitialized buffers {name}. Did you forget to run a forward pass?"
                )


def infer_methods_to_compile(nn_module):
    """Implement the default rules for which methods should act as starting points for compilation.

    (TODO add a link when the rules are published).
    """
    check_module_initialized(nn_module)
    ignored_properties = jit_ignored_properties(nn_module)

    methods: list[str] = []
    if hasattr(nn_module, "forward") and not _jit_internal.is_ignored_fn(
        nn_module.forward
    ):
        forward_func = getattr(nn_module.forward, "__func__", None)
        module_forward = getattr(torch.nn.Module, "forward", None)
        if forward_func != module_forward:
            methods = ["forward"]

    exported = []
    for name in dir(nn_module):
        if name in ignored_properties:
            continue
        item = getattr(nn_module, name, None)
        if (
            _jit_internal.get_torchscript_modifier(item)
            is _jit_internal.FunctionModifiers.EXPORT
        ):
            exported.append(name)

    methods = methods + exported

    overload_name_mappings = dict(getattr(nn_module, "__overloads__", {}))
    overload_info = get_overload_annotations(nn_module, ignored_properties)
    overload_name_mappings.update(get_overload_name_mapping(overload_info))
    overload_stubs = make_stubs_for_overloads(overload_info)

    nn_module.__overloads__ = overload_name_mappings

    # we shouldn't directly compile overloaded methods, just its overloads
    def ignore_overloaded(method_name):
        return method_name not in overload_name_mappings

    filtered_methods = filter(ignore_overloaded, methods)

    # Unique the methods. We don't want to use a set to store the methods because it
    # introduces non-determinism to compile order.
    uniquer: set[str] = set()
    uniqued_methods = []
    for name in filtered_methods:
        if name in uniquer:
            continue
        uniqued_methods.append(name)
        uniquer.add(name)

    stubs = [make_stub_from_method(nn_module, method) for method in uniqued_methods]
    return overload_stubs + stubs


def get_hook_stubs(nn_module):
    """Return forward hook and pre_hook ScriptModuleStubs."""
    check_module_initialized(nn_module)
    hook_map: dict = {}

    hook_stubs = []
    for hook in nn_module._forward_hooks.values():
        if hook.__name__ in hook_map:
            if id(hook) != id(hook_map[hook.__name__]):
                raise RuntimeError(
                    f"Hook '{hook.__name__}' on {type(nn_module).__name__} "
                    "has at least two different python definitions."
                    " Please use unique names for all hooks."
                )
        else:
            hook_map[hook.__name__] = hook
        hook_stubs.append(make_stub(hook, hook.__name__))

    pre_hook_stubs = []
    for pre_hook in nn_module._forward_pre_hooks.values():
        if pre_hook.__name__ in hook_map:
            if id(pre_hook) != id(hook_map[pre_hook.__name__]):
                raise RuntimeError(
                    f"Pre-hook '{pre_hook.__name__}' on {type(nn_module).__name__} "
                    "has at least two different python definitions."
                    " Please use unique names for all hooks."
                )
        else:
            hook_map[pre_hook.__name__] = pre_hook
        pre_hook_stubs.append(make_stub(pre_hook, pre_hook.__name__))

    return hook_stubs, pre_hook_stubs


def get_property_stubs(nn_module):
    """Create property stubs for the properties of the module by creating method stubs for the getter and setter."""
    module_ty = type(nn_module)
    properties_asts = get_class_properties(module_ty, self_name="RecursiveScriptModule")
    rcbs = {}

    for name in dir(module_ty):
        item = getattr(module_ty, name, None)
        if isinstance(item, property):
            if not item.fget:
                raise RuntimeError(
                    f"Property {name} of {nn_module.__name__} must have a getter"
                )

            rcbs[name] = _jit_internal.createResolutionCallbackFromClosure(item.fget)

    stubs = [PropertyStub(rcbs[ast.name().name], ast) for ast in properties_asts]
    return stubs


def interface_script(mod_interface, nn_module):
    """
    Make a ScriptModule from an nn.Module, using the interface methods rule for determining which methods to compile.

    Args:
        mod_interface: the interface type that the module have
        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
    """
    if isinstance(nn_module, torch.jit.ScriptModule):
        return nn_module

    check_module_initialized(nn_module)

    def infer_interface_methods_to_compile(nn_module):
        """Rule to infer the methods from the interface type.

        It is used to know which methods need to act as starting points for compilation.
        """
        stubs = [
            make_stub_from_method(nn_module, method)
            for method in mod_interface.getMethodNames()
        ]
        return stubs

    return create_script_module(nn_module, infer_interface_methods_to_compile)


def try_compile_fn(fn, loc):
    if _jit_internal.is_ignored_fn(fn):
        # Don't do anything for @ignore'd functions
        return None

    if isinstance(fn, torch.nn.Module):
        # Since modules are callable pybind recognizes them as functions, but
        # don't do anything for them
        return None

    if not inspect.isfunction(fn) and not inspect.ismethod(fn):
        raise RuntimeError(
            f"`{fn}` is not a function. Recursive scripting only supports "
            "Python functions or methods currently.\n"
            f"Consider manually annotating `{fn}` with @torch.jit.script."
        )

    # The object returned by __prepare_scriptable__ might have a different closure.
    # Resolve it here to get the right resolution callback.
    fn = fn.__prepare_scriptable__() if hasattr(fn, "__prepare_scriptable__") else fn  # type: ignore[operator]

    # We don't have the actual scope where the function was defined, but we can
    # extract the necessary info from the closed over variables on the function
    # object
    rcb = _jit_internal.createResolutionCallbackFromClosure(fn)
    return torch.jit.script(fn, _rcb=rcb)


def wrap_cpp_class(cpp_class):
    """Wrap this torch._C.Object in a Python RecursiveScriptClass."""
    return torch.jit.RecursiveScriptClass(cpp_class)


def wrap_cpp_module(cpp_module):
    """Wrap this torch._C.ScriptModule in a Python ScriptModule, recursively for all submodules."""

    def init_fn(script_module) -> None:
        for name, cpp_module in torch._C.ModuleDict(script_module._c).items():
            setattr(script_module, name, wrap_cpp_module(cpp_module))
        script_module._concrete_type = torch._C.ConcreteModuleType.from_jit_type(
            script_module._c._type()
        )

        for idx, fn in enumerate(script_module._c._get_forward_pre_hooks()):
            script_module._forward_pre_hooks[idx] = fn
        for idx, fn in enumerate(script_module._c._get_forward_hooks()):
            script_module._forward_hooks[idx] = fn

    return torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)


def compile_unbound_method(concrete_type, fn):
    if _jit_internal.is_ignored_fn(fn):
        return None
    stub = make_stub(fn, fn.__name__)
    with torch._jit_internal._disable_emit_hooks():
        # We don't want to call the hooks here since the graph that is calling
        # this function is not yet complete
        create_methods_and_properties_from_stubs(concrete_type, (stub,), ())
    return stub


def lazy_bind(concrete_type, unbound_method):
    """
    Return a function that lazily binds `unbound_method` to a provided Module IValue, then invokes the method.

    We do this so that any Python shenanigans that
    will poison type sharing are impossible at compile time.
    """

    def lazy_binding_method(cpp_module, *args):
        def init_fn(script_module) -> None:
            orig_class = concrete_type.py_class

            # Copy @ignored/@unused methods from the original module to the new one.
            # This ensures they are available during execution.
            for name in dir(orig_class):
                item = getattr(orig_class, name, None)
                if _jit_internal.is_ignored_fn(item):
                    setattr(script_module, name, item)

            # Copy constants over so they are available during execution.
            for name, value in concrete_type.get_constants().items():
                setattr(script_module, name, value)

        script_module = torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
        method = types.MethodType(unbound_method, script_module)
        return method(*args)

    # make the lazy binding method "look like" the original method
    lazy_binding_method.original_fn = unbound_method  # type: ignore[attr-defined]
    lazy_binding_method.__name__ = unbound_method.__name__
    torch._jit_internal.copy_torchscript_modifier(unbound_method, lazy_binding_method)

    return lazy_binding_method