tools/pyi/gen_pyi.py

from __future__ import annotations

import argparse
import collections
import importlib
import sys
from pprint import pformat
from typing import Sequence
from unittest.mock import Mock, patch
from warnings import warn

from tools.autograd.gen_python_functions import (
    group_overloads,
    load_signatures,
    should_generate_py_binding,
)

from torchgen.api.python import (
    PythonSignatureGroup,
    PythonSignatureNativeFunctionPair,
    returns_structseq_pyi,
)
from torchgen.gen import parse_native_yaml, parse_tags_yaml
from torchgen.model import _TorchDispatchModeKey, DispatchKey, Variant
from torchgen.utils import FileManager


"""
This module implements generation of type stubs for PyTorch,
enabling use of autocomplete in IDEs like PyCharm, which otherwise
don't understand C extension modules.

At the moment, this module only handles type stubs for torch and
torch.Tensor.  It should eventually be expanded to cover all functions
which come are autogenerated.

Here's our general strategy:

- We start off with a hand-written __init__.pyi.in file.  This
  file contains type definitions for everything we cannot automatically
  generate, including pure Python definitions directly in __init__.py
  (the latter case should be pretty rare).

- We go through automatically bound functions based on the
  type information recorded in native_functions.yaml and
  generate type hints for them (generate_type_hints)

There are a number of type hints which we've special-cased;
read gen_pyi for the gory details.
"""


def get_py_torch_functions(
    python_funcs: Sequence[PythonSignatureNativeFunctionPair],
    method: bool = False,
) -> Sequence[PythonSignatureGroup]:
    """
    Get declarations (grouped by name) which should be generated
    as either functions in the "torch" module or methods on Tensor.
    """

    def should_bind_function(python_func: PythonSignatureNativeFunctionPair) -> bool:
        return (
            should_generate_py_binding(python_func.function)
            and not python_func.function.python_module
            and Variant.function in python_func.function.variants
        )

    def should_bind_method(python_func: PythonSignatureNativeFunctionPair) -> bool:
        return (
            should_generate_py_binding(python_func.function)
            and not python_func.function.python_module
            and Variant.method in python_func.function.variants
        )

    should_bind = should_bind_method if method else should_bind_function
    return group_overloads([f for f in python_funcs if should_bind(f)])


# TODO: Consider defining some aliases for our Union[...] types, to make
# the stubs to read on the human eye.

DEVICE_PARAM = "device: Optional[DeviceLikeType] = None"
FACTORY_PARAMS = f"dtype: Optional[_dtype] = None, {DEVICE_PARAM}, requires_grad: _bool = False, pin_memory: _bool = False"

# NOTE: specifying indices for Tensor.__getitem__
# We can imitate numpy's definition of ndarray.__getitem__ found in numpy/__init__.pyi:
#
# key: (
#     None
#     | slice
#     | ellipsis
#     | SupportsIndex
#     | _ArrayLikeInt_co
#     | tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...]
# )
#
# where:
#
# _ArrayLikeInt_co = _DualArrayLike[
#     dtype[Union[bool_, integer[Any]]],
#     Union[bool, int],
# ]
#
# and
#
# _DualArrayLike = Union[
#     _SupportsArray[_DType],
#     _NestedSequence[_SupportsArray[_DType]],
#     _T,
#     _NestedSequence[_T],
# ]
#
# Moreover, _NestedSequence is a Protocol that matches arbitrary nesting of list/tuple.
# We can substitute and simplify:
# _SupportsArray -> Tensor
# _ArrayLikeInt_co -> [bool | int | | Tensor | NestedSequence[bool | int] | NestedSequence[Tensor]]
# which leaves us with key: T | tuple[T, ...], where T is:
# T = (
#     None | bool | int | slice | ellipsis | SupportsIndex
#     | Tensor | _NestedSequence[Tensor] | _NestedSequence[bool | int]
# )

# NOTE: ellipsis is equal to type[Ellipsis] in stub files.
_leaf_types = "Union[None, _bool, _int, slice, ellipsis, Tensor]"  # not SupportsIndex!
_index = f"Union[SupportsIndex, {_leaf_types}, _NestedSequence[{_leaf_types}]]"
INDICES = f"indices: Union[{_index}, tuple[{_index}, ...]]"

blocklist = [
    "__init_subclass__",
    "__new__",
    "__subclasshook__",
    "cdist",
    "device",
    "grad",
    "requires_grad",
    "range",
    # defined in functional
    "einsum",
    # Somehow, these are defined in both _C and in functional. Ick!
    "broadcast_tensors",
    # Manually define named tensor type stubs in __init__.pyi.in
    "align_tensors",
    "meshgrid",
    "cartesian_prod",
    "block_diag",
    "norm",
    "chain_matmul",
    "stft",
    "tensordot",
    "split",
    "unique_consecutive",
    "atleast_1d",
    "atleast_2d",
    "atleast_3d",
    # These are handled specially by python_arg_parser.cpp
    "add",
    "add_",
    "add_out",
    "sub",
    "sub_",
    "sub_out",
    "mul",
    "mul_",
    "mul_out",
    "div",
    "div_",
    "div_out",
    "true_divide",
    "true_divide_",
    "true_divide_out",
    "floor_divide",
    "floor_divide_",
    "floor_divide_out",
    "to",
    "_to_copy",
    "copy_",
]

binary_ops = (
    "add",
    "sub",
    "mul",
    "div",
    "pow",
    "lshift",
    "rshift",
    "mod",
    "truediv",
    "matmul",
    "floordiv",
    "radd",
    "rsub",
    "rmul",
    "rtruediv",
    "rfloordiv",
    "rpow",  # reverse arithmetic
    "and",
    "or",
    "xor",
    "rand",
    "ror",
    "rxor",  # logic
    "iadd",
    "iand",
    "idiv",
    "ilshift",
    "imul",
    "ior",
    "irshift",
    "isub",
    "ixor",
    "ifloordiv",
    "imod",  # inplace ops
)
symmetric_comparison_ops = ("eq", "ne")
asymmetric_comparison_ops = ("ge", "gt", "lt", "le")
comparison_ops = symmetric_comparison_ops + asymmetric_comparison_ops

unary_ops = ("neg", "abs", "invert")
to_py_type_ops = ("bool", "float", "complex", "long", "index", "int", "nonzero")
all_ops = binary_ops + comparison_ops + unary_ops + to_py_type_ops


def sig_for_ops(opname: str) -> list[str]:
    """sig_for_ops(opname : str) -> List[str]

    Returns signatures for operator special functions (__add__ etc.)"""

    # we have to do this by hand, because they are hand-bound in Python

    assert opname.endswith("__") and opname.startswith("__"), f"Unexpected op {opname}"

    name = opname[2:-2]
    if name in binary_ops:
        return [f"def {opname}(self, other: Any) -> Tensor: ..."]
    elif name in comparison_ops:
        sig = f"def {opname}(self, other: Any) -> Tensor: ..."
        if name in symmetric_comparison_ops:
            # unsafe override https://github.com/python/mypy/issues/5704
            sig += "  # type: ignore[override]"
        return [sig]
    elif name in unary_ops:
        return [f"def {opname}(self) -> Tensor: ..."]
    elif name in to_py_type_ops:
        if name in {"bool", "float", "complex"}:
            tname = name
        elif name == "nonzero":
            tname = "bool"
        else:
            tname = "int"
        if tname in {"float", "int", "bool", "complex"}:
            tname = "builtins." + tname
        return [f"def {opname}(self) -> {tname}: ..."]
    else:
        raise Exception("unknown op", opname)  # noqa: TRY002


def generate_type_hints(sig_group: PythonSignatureGroup) -> list[str]:
    type_hints: list[str] = []

    # Some deprecated ops that are on the blocklist are still included in pyi
    if sig_group.signature.name in blocklist and not sig_group.signature.deprecated:
        return type_hints

    # deprecated signatures have separate entries for their functional and out variants
    # (as opposed to the native ops, which fuse the two into a single signature).
    # generate the functional variant here, if an out variant exists.
    if sig_group.signature.deprecated and sig_group.outplace is not None:
        type_hint = sig_group.signature.signature_str_pyi(skip_outputs=True)
        type_hints.append(type_hint)

    # PythonSignatureGroups that have both a functional + out variant get a single signature, with an optional out argument
    # Generates the out variant if one exists. Otherwise, generate the functional variant
    type_hint = sig_group.signature.signature_str_pyi(
        skip_outputs=sig_group.outplace is None
    )
    type_hints.append(type_hint)

    # Some operators also additionally have a vararg variant of their signature
    type_hint_vararg = sig_group.signature.signature_str_pyi_vararg(
        skip_outputs=sig_group.outplace is None
    )
    if type_hint_vararg:
        type_hints.append(type_hint_vararg)

    return type_hints


def get_max_pool_dispatch(name: str, arg_list: list[str]) -> dict[str, list[str]]:
    flag_pos = arg_list.index("{return_indices}")
    # If return_indices is positional arg, everything before should have no default
    arg_list_positional = (
        [
            ", ".join(single_arg.split(" = ")[0] for single_arg in arg.split(", "))
            for arg in arg_list[: flag_pos + 1]
        ]
        + ["/"]
        + arg_list[flag_pos + 1 :]
    )
    # Otherwise force return_indices to be kwarg
    arg_list_keyword = arg_list.copy()
    arg_list_keyword.insert(flag_pos, "*")
    tmpl = "def {name}({args}) -> {{return_type}}: ..."
    return {
        name: [
            tmpl.format(name=name, args=", ".join(arg_list)).format(
                return_indices="return_indices: Literal[False] = False",
                return_type="Tensor",
            ),
            tmpl.format(name=name, args=", ".join(arg_list_positional)).format(
                return_indices="return_indices: Literal[True]",
                return_type="Tuple[Tensor, Tensor]",
            ),
            tmpl.format(name=name, args=", ".join(arg_list_keyword)).format(
                return_indices="return_indices: Literal[True]",
                return_type="Tuple[Tensor, Tensor]",
            ),
        ]
    }


def gen_nn_functional(fm: FileManager) -> None:
    INPUT = "input: Tensor"
    KERNEL_SIZE = "kernel_size: Union[_int, _size]"
    STRIDE_PADDING = ", ".join(
        [
            "stride: Optional[Union[_int, _size]] = None",
            "padding: Union[_int, _size] = 0",
        ]
    )

    # TODO the list for `torch._C._nn` is nonexhaustive
    unsorted_c_nn_function_hints: dict[str, list[str]] = {}

    for d in (2, 3):
        unsorted_c_nn_function_hints.update(
            {
                f"avg_pool{d}d": [
                    f"def avg_pool{d}d({{}}) -> Tensor: ...".format(
                        ", ".join(
                            [
                                f"{INPUT}",
                                f"{KERNEL_SIZE}",
                                f"{STRIDE_PADDING}",
                                "ceil_mode: bool = False",
                                "count_include_pad: bool = True",
                                "divisor_override: Optional[int] = None",
                            ]
                        )
                    )
                ],
                f"fractional_max_pool{d}d": [
                    f"def fractional_max_pool{d}d({{}}) -> {{}}: ...".format(
                        ", ".join(
                            [
                                f"{INPUT}",
                                f"{KERNEL_SIZE}",
                                "output_size: Union[_int, _size]",
                                "_random_samples: Tensor",
                            ]
                        ),
                        "Tuple[Tensor, Tensor]",
                    )
                ],
                f"adaptive_max_pool{d}d": [
                    f"def adaptive_max_pool{d}d({{}}) -> {{}}: ...".format(
                        ", ".join([f"{INPUT}", "output_size: Union[_int, _size]"]),
                        "Tuple[Tensor, Tensor]",
                    )
                ],
            }
        )

    unsorted_c_nn_function_hints.update(
        {
            "hardtanh": [
                "def hardtanh({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "input: Tensor",
                            "min_val: float = ...",
                            "max_val: float = ...",
                            "*",
                            "out: Optional[Tensor] = None",
                        ]
                    )
                )
            ],
            "hardtanh_": [
                "def hardtanh_({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "input: Tensor",
                            "min_val: float = ...",
                            "max_val: float = ...",
                        ]
                    )
                )
            ],
            "elu_": ["def elu_(input: Tensor, alpha: float = ...) -> Tensor: ..."],
            "leaky_relu": [
                "def leaky_relu({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "input: Tensor",
                            "negative_slope: float = ...",
                            "*",
                            "out: Optional[Tensor] = None",
                        ]
                    )
                )
            ],
            "leaky_relu_": [
                f"def leaky_relu_({', '.join(['input: Tensor', 'negative_slope: float = ...'])}) -> Tensor: ..."
            ],
            "log_sigmoid": ["def log_sigmoid(input: Tensor) -> Tensor: ..."],
            "gelu": ["def gelu(input: Tensor, approximate: str = ...) -> Tensor: ..."],
            "softplus": [
                "def softplus({}) -> Tensor: ...".format(
                    ", ".join(
                        ["input: Tensor", "beta: float = ...", "threshold: float = ..."]
                    )
                )
            ],
            "softshrink": [
                "def softshrink(input: Tensor, lambd: float = ...) -> Tensor: ..."
            ],
            "hardsigmoid": [
                f"def hardsigmoid({', '.join(['input: Tensor', '*', 'out: Optional[Tensor] = None'])}) -> Tensor: ..."
            ],
            "linear": [
                "def linear({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "input: Tensor",
                            "weight: Tensor",
                            "bias: Optional[Tensor] = None",
                        ]
                    )
                )
            ],
            "pad": [
                "def pad({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "input: Tensor",
                            "pad: Sequence[int]",
                            "mode: str = ...",
                            "value: Optional[float] = None",
                        ]
                    )
                )
            ],
            "one_hot": [
                "def one_hot(tensor: Tensor, num_classes: int = ...) -> Tensor: ..."
            ],
            "scaled_dot_product_attention": [
                "def scaled_dot_product_attention({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "query: Tensor",
                            "key: Tensor",
                            "value: Tensor",
                            "attn_mask: Optional[Tensor] = None",
                            "dropout_p: float = 0.0",
                            "is_causal: bool = False",
                            "scale: Optional[float] = None",
                            "enable_gqa: bool = False",
                        ]
                    )
                )
            ],
        }
    )

    c_nn_function_hints: list[str] = []
    for _, hints in sorted(unsorted_c_nn_function_hints.items()):
        if len(hints) > 1:
            hints = ["@overload\n" + h for h in hints]
        c_nn_function_hints += hints

    # Functions imported into `torch.nn.functional` from `torch`, perhaps being filtered
    # through an `_add_docstr` call
    torch_imports = [
        "conv1d",
        "conv2d",
        "conv3d",
        "conv_transpose1d",
        "conv_transpose2d",
        "conv_transpose3d",
        "conv_tbc",
        "avg_pool1d",
        "adaptive_avg_pool1d",
        "relu_",
        "selu_",
        "celu_",
        "prelu",
        "rrelu_",
        "hardshrink",
        "bilinear",
        "pixel_shuffle",
        "pixel_unshuffle",
        "channel_shuffle",
        "native_channel_shuffle",
        "pairwise_distance",
        "pdist",
        "cosine_similarity",
    ]
    imported_hints = [f"from torch import {_} as {_}" for _ in torch_imports]

    # Functions imported into `torch.nn.functional` from `torch._C._nn`
    c_nn_imports = [
        "avg_pool2d",
        "avg_pool3d",
        "hardtanh_",
        "elu_",
        "leaky_relu_",
        "gelu",
        "softplus",
        "softshrink",
        "linear",
        "pad",
        "one_hot",
        "scaled_dot_product_attention",
    ]
    imported_hints += [f"from torch._C._nn import {_} as {_}" for _ in c_nn_imports]
    # This is from `torch._C._nn` but renamed
    imported_hints.append(
        "from torch._C._nn import log_sigmoid\nlogsigmoid = log_sigmoid"
    )

    # Functions generated by `torch._jit_internal.boolean_dispatch` in `nn.functional`
    unsorted_dispatched_hints: dict[str, list[str]] = {}

    for d in (1, 2, 3):
        unsorted_dispatched_hints.update(
            **get_max_pool_dispatch(
                f"max_pool{d}d",
                [
                    f"{INPUT}",
                    f"{KERNEL_SIZE}",
                    f"{STRIDE_PADDING}",
                    "dilation: Union[_int, _size] = 1",
                    "ceil_mode: bool = False",
                    "{return_indices}",
                ],
            ),
            **get_max_pool_dispatch(
                f"fractional_max_pool{d}d",
                [
                    f"{INPUT}",
                    f"{KERNEL_SIZE}",
                    "output_size: Optional[Union[_int, _size]] = None",
                    "output_ratio: Optional[_ratio_any_t] = None",
                    "{return_indices}",
                    "_random_samples: Optional[Tensor] = None",
                ],
            ),
            **get_max_pool_dispatch(
                f"adaptive_max_pool{d}d",
                [f"{INPUT}", "output_size: Union[_int, _size]", "{return_indices}"],
            ),
        )

    # There's no fractional_max_pool1d
    del unsorted_dispatched_hints["fractional_max_pool1d"]

    dispatched_hints: list[str] = []
    for _, hints in sorted(unsorted_dispatched_hints.items()):
        if len(hints) > 1:
            hints = ["@overload\n" + h for h in hints]
        dispatched_hints += hints

    fm.write_with_template(
        "torch/nn/functional.pyi",
        "torch/nn/functional.pyi.in",
        lambda: {
            "imported_hints": imported_hints,
            "dispatched_hints": dispatched_hints,
        },
    )
    fm.write_with_template(
        "torch/_C/_nn.pyi",
        "torch/_C/_nn.pyi.in",
        lambda: {
            "c_nn_function_hints": c_nn_function_hints,
        },
    )


"""
We gather the docstrings for torch with the following steps:
1. Mock torch and torch._C, which are the only dependencies of the docs files
2. Mock the _add_docstr function to save the docstrings
3. Import the docs files to trigger mocked _add_docstr and collect docstrings
"""


def gather_docstrs() -> dict[str, str]:
    docstrs = {}

    def mock_add_docstr(func: Mock, docstr: str) -> None:
        docstrs[func._extract_mock_name()] = docstr.strip()

    # sys.modules and sys.path are restored after the context manager exits
    with patch.dict(sys.modules), patch.object(sys, "path", sys.path + ["torch"]):
        # mock the torch module and torch._C._add_docstr
        sys.modules["torch"] = Mock(name="torch")
        sys.modules["torch._C"] = Mock(_add_docstr=mock_add_docstr)

        try:
            # manually import torch._torch_docs and torch._tensor_docs to trigger
            # the mocked _add_docstr and collect docstrings
            sys.modules["torch._torch_docs"] = importlib.import_module("_torch_docs")
            sys.modules["torch._tensor_docs"] = importlib.import_module("_tensor_docs")
        except ModuleNotFoundError:
            # Gracefully fail if these modules are not importable
            warn(
                "Failed to import _torch_docs/_tensor_docs, skipping docstring in pyi files."
            )

    return docstrs


def add_docstr_to_hint(docstr: str, hint: str) -> str:
    if "..." in hint:  # function or method
        assert hint.endswith("..."), f"Hint `{hint}` does not end with '...'"
        hint = hint[:-3]  # remove "..."
        return "\n    ".join([hint, 'r"""'] + docstr.split("\n") + ['"""', "..."])
    else:  # attribute or property
        return f'{hint}\nr"""{docstr}"""\n'


def gen_pyi(
    native_yaml_path: str,
    tags_yaml_path: str,
    deprecated_yaml_path: str,
    fm: FileManager,
) -> None:
    """gen_pyi()

    This function generates a pyi file for torch.
    """

    # Some of this logic overlaps with generate_python_signature in
    # tools/autograd/gen_python_functions.py; however, this
    # function is all about generating mypy type signatures, whereas
    # the other function generates are custom format for argument
    # checking.  If you are update this, consider if your change
    # also needs to update the other file.

    # Dictionary for NamedTuple definitions
    structseqs: dict[str, str] = {}

    # Generate type signatures for top-level functions
    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    unsorted_function_hints: dict[str, list[str]] = collections.defaultdict(list)

    for n, n1, n2 in [
        ("csr", "crow", "col"),
        ("csc", "ccol", "row"),
        ("bsr", "crow", "col"),
        ("bsc", "ccol", "row"),
    ]:
        unsorted_function_hints.update(
            {
                f"sparse_{n}_tensor": [
                    f"def sparse_{n}_tensor({{}}) -> Tensor: ...".format(
                        ", ".join(
                            [
                                f"{n1}_indices: Union[Tensor, List]",
                                f"{n2}_indices: Union[Tensor, List]",
                                "values: Union[Tensor, List]",
                                "size: Optional[_size] = None",
                                "*",
                                "dtype: Optional[_dtype] = None",
                                "device: Optional[DeviceLikeType] = None",
                                "requires_grad: _bool = False",
                                "check_invariants: Optional[_bool] = None",
                            ]
                        ),
                    )
                ],
            }
        )

    unsorted_function_hints.update(
        {
            "set_flush_denormal": ["def set_flush_denormal(mode: _bool) -> _bool: ..."],
            "get_default_dtype": ["def get_default_dtype() -> _dtype: ..."],
            "asarray": [
                "def asarray({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "obj: Any",
                            "*",
                            "dtype: Optional[_dtype] = None",
                            "device: Optional[DeviceLikeType] = None",
                            "copy: Optional[_bool] = None",
                            "requires_grad: _bool = False",
                        ]
                    )
                )
            ],
            "from_numpy": ["def from_numpy(ndarray) -> Tensor: ..."],
            "frombuffer": [
                "def frombuffer({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "buffer: Any",
                            "*",
                            "dtype: _dtype",
                            "count: int = -1",
                            "offset: int = 0",
                            "requires_grad: _bool = False",
                        ]
                    )
                )
            ],
            "numel": ["def numel(self: Tensor) -> _int: ..."],
            "as_tensor": [
                "def as_tensor({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "data: Any",
                            "dtype: Optional[_dtype] = None",
                            DEVICE_PARAM,
                        ]
                    )
                )
            ],
            "get_num_threads": ["def get_num_threads() -> _int: ..."],
            "set_num_threads": ["def set_num_threads(num: _int) -> None: ..."],
            "init_num_threads": ["def init_num_threads() -> None: ..."],
            "get_num_interop_threads": ["def get_num_interop_threads() -> _int: ..."],
            "set_num_interop_threads": [
                "def set_num_interop_threads(num: _int) -> None: ..."
            ],
            # These functions are explicitly disabled by
            # SKIP_PYTHON_BINDINGS because they are hand bound.
            # Correspondingly, we must hand-write their signatures.
            "tensor": [f"def tensor(data: Any, {FACTORY_PARAMS}) -> Tensor: ..."],
            "sparse_coo_tensor": [
                "def sparse_coo_tensor({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "indices: Tensor",
                            "values: Union[Tensor, List]",
                            "size: Optional[_size] = None",
                            "*",
                            "dtype: Optional[_dtype] = None",
                            "device: Optional[DeviceLikeType] = None",
                            "requires_grad: _bool = False",
                            "check_invariants: Optional[_bool] = None",
                            "is_coalesced: Optional[_bool] = None",
                        ]
                    )
                )
            ],
            "sparse_compressed_tensor": [
                "def sparse_compressed_tensor({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "compressed_indices: Union[Tensor, List]",
                            "plain_indices: Union[Tensor, List]",
                            "values: Union[Tensor, List]",
                            "size: Optional[_size] = None",
                            "*",
                            "dtype: Optional[_dtype] = None",
                            "layout: Optional[_layout] = None",
                            "device: Optional[DeviceLikeType] = None",
                            "requires_grad: _bool = False",
                            "check_invariants: Optional[_bool] = None",
                        ]
                    )
                )
            ],
            "_sync": ["def _sync(t: Tensor) -> None: ..."],
            "_is_functional_tensor": [
                "def _is_functional_tensor(t: Tensor) -> _bool: ..."
            ],
            "_is_functional_tensor_base": [
                "def _is_functional_tensor_base(t: Tensor) -> _bool: ..."
            ],
            "_from_functional_tensor": [
                "def _from_functional_tensor(t: Tensor) -> Tensor: ..."
            ],
            "_to_functional_tensor": [
                "def _to_functional_tensor(t: Tensor) -> Tensor: ..."
            ],
            "_functionalize_replace": [
                "def _functionalize_replace(self_: Tensor, other: Tensor) -> None: ..."
            ],
            "_functionalize_commit_update": [
                "def _functionalize_commit_update(t: Tensor) -> None: ..."
            ],
            "_functionalize_unsafe_set": [
                "def _functionalize_unsafe_set(dst: Tensor, src: Tensor) -> None: ..."
            ],
            "_functionalize_mark_mutation_hidden_from_autograd": [
                "def _functionalize_mark_mutation_hidden_from_autograd(t: Tensor) -> None: ..."
            ],
            "_functionalize_are_all_mutations_hidden_from_autograd": [
                "def _functionalize_are_all_mutations_hidden_from_autograd(t: Tensor) -> _bool: ..."
            ],
            "_functionalize_are_all_mutations_under_no_grad_or_inference_mode": [
                "def _functionalize_are_all_mutations_under_no_grad_or_inference_mode(t: Tensor) -> _bool: ..."
            ],
            "_functionalize_was_inductor_storage_resized": [
                "def _functionalize_was_inductor_storage_resized(t: Tensor) -> _bool: ..."
            ],
            "_functionalize_sync": ["def _functionalize_sync(t: Tensor) -> None: ..."],
            "_functionalize_was_storage_changed": [
                "def _functionalize_was_storage_changed(tensor: Tensor) -> _bool: ..."
            ],
            "_functionalize_set_storage_changed": [
                "def _functionalize_set_storage_changed(tensor: Tensor) -> _bool: ..."
            ],
            "_functionalize_has_metadata_mutation": [
                "def _functionalize_has_metadata_mutation(tensor: Tensor) -> _bool: ..."
            ],
            "_functionalize_apply_view_metas": [
                "def _functionalize_apply_view_metas(tensor: Tensor,  base: Tensor) -> Tensor: ..."
            ],
            "_functionalize_is_symbolic": [
                "def _functionalize_is_symbolic(tensor: Tensor) -> _bool: ..."
            ],
            "_enable_functionalization": [
                "def _enable_functionalization(*, reapply_views: _bool = False): ..."
            ],
            "_disable_functionalization": ["def _disable_functionalization(): ..."],
            "range": [
                "def range({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "start: Number",
                            "end: Number",
                            "step: Number = 1",
                            "*",
                            "out: Optional[Tensor] = None",
                            FACTORY_PARAMS,
                        ]
                    )
                )
            ],
            "arange": [
                "def arange({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "start: Number",
                            "end: Number",
                            "step: Number",
                            "*",
                            "out: Optional[Tensor] = None",
                            FACTORY_PARAMS,
                        ]
                    )
                ),
                "def arange({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "start: Number",
                            "end: Number",
                            "*",
                            "out: Optional[Tensor] = None",
                            FACTORY_PARAMS,
                        ]
                    )
                ),
                "def arange({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "end: Number",
                            "*",
                            "out: Optional[Tensor] = None",
                            FACTORY_PARAMS,
                        ]
                    )
                ),
            ],
            "linspace": [
                "def linspace({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "start: Number",
                            "end: Number",
                            "steps: Optional[_int] = None",
                            "*",
                            "out: Optional[Tensor] = None",
                            FACTORY_PARAMS,
                        ]
                    )
                )
            ],
            "logspace": [
                "def logspace({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "start: Number",
                            "end: Number",
                            "steps: Optional[_int] = None",
                            "base: _float = 10.0",
                            "*",
                            "out: Optional[Tensor] = None",
                            FACTORY_PARAMS,
                        ]
                    )
                )
            ],
            "randint": [
                "def randint({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "low: _int",
                            "high: _int",
                            "size: _size",
                            "*",
                            "generator: Optional[Generator] = None",
                            FACTORY_PARAMS,
                        ]
                    )
                ),
                "def randint({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "high: _int",
                            "size: _size",
                            "*",
                            "generator: Optional[Generator] = None",
                            FACTORY_PARAMS,
                        ]
                    )
                ),
            ],
            "full": [
                "def full({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "size: _size",
                            "fill_value: Union[Number, _complex]",
                            "*",
                            "out: Optional[Tensor] = None",
                            "layout: _layout = strided",
                            FACTORY_PARAMS,
                        ]
                    )
                ),
                "def full({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "size: _size",
                            "fill_value: Union[Number, _complex]",
                            "*",
                            "names: List[Union[str, None]]",
                            "layout: _layout = strided",
                            FACTORY_PARAMS,
                        ]
                    )
                ),
            ],
            "is_grad_enabled": ["def is_grad_enabled() -> _bool: ..."],
            "is_inference_mode_enabled": [
                "def is_inference_mode_enabled() -> _bool: ..."
            ],
            "nonzero": [
                "def nonzero(input: Tensor, *, as_tuple: Literal[False] = False, out: Optional[Tensor] = None) -> Tensor: ...",
                "def nonzero(input: Tensor, *, as_tuple: Literal[True]) -> Tuple[Tensor, ...]: ...",
            ],
            "dsmm": ["def dsmm(input: Tensor, mat2: Tensor) -> Tensor: ..."],
            "hsmm": ["def hsmm(input: Tensor, mat2: Tensor) -> Tensor: ..."],
            "saddmm": [
                "def saddmm({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "input: Tensor",
                            "mat1: Tensor",
                            "mat2: Tensor",
                            "*",
                            "beta: Number = 1",
                            "alpha: Number = 1",
                            "out: Optional[Tensor] = None",
                        ]
                    )
                )
            ],
            "spmm": ["def spmm(input: Tensor, mat2: Tensor) -> Tensor: ..."],
            "div": [
                "def div({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "input: Union[Tensor, Number]",
                            "other: Union[Tensor, Number]",
                            "*",
                            "rounding_mode: Optional[str] = None",
                            "out: Optional[Tensor] = None",
                        ]
                    )
                )
            ],
        }
    )
    for binop in ["true_divide", "floor_divide"]:
        unsorted_function_hints[binop].append(
            f"def {binop}(input: Union[Tensor, Number], other: Union[Tensor, Number], "
            "*, out: Optional[Tensor] = None) -> Tensor: ..."
        )
    for binop in ["mul"]:
        unsorted_function_hints[binop].append(
            f"def {binop}(input: Union[Tensor, Number, _complex], other: Union[Tensor, Number, _complex], "
            "*, out: Optional[Tensor] = None) -> Tensor: ..."
        )
    for binop in ["add", "sub"]:
        unsorted_function_hints[binop].append(
            f"def {binop}(input: Union[Tensor, Number, _complex], other: Union[Tensor, Number, _complex], "
            "*, alpha: Optional[Union[Number, _complex]] = 1, out: Optional[Tensor] = None) -> Tensor: ..."
        )

    native_functions = parse_native_yaml(
        native_yaml_path, tags_yaml_path
    ).native_functions
    native_functions = list(filter(should_generate_py_binding, native_functions))

    function_signatures = load_signatures(
        native_functions, deprecated_yaml_path, method=False, pyi=True
    )
    sig_groups = get_py_torch_functions(function_signatures)
    for group in sorted(sig_groups, key=lambda g: g.signature.name):
        name = group.signature.name
        unsorted_function_hints[name] += generate_type_hints(group)

        structseq = returns_structseq_pyi(group.signature)
        if structseq is not None and not group.signature.deprecated:
            # deprecated structseqs are currently not included for torch functions
            tuple_name, tuple_def = structseq
            if tuple_name in structseqs:
                assert structseqs[tuple_name] == tuple_def
            else:
                structseqs[tuple_name] = tuple_def

    def replace_special_case(hint: str) -> str:
        # NB: Keep this in sync with enum in aten/src/ATen/core/Reduction.h
        hint = hint.replace("at::Reduction::Mean", "1")
        hint = hint.replace(": Tensor = None", ": Optional[Tensor] = None")
        # Match both:
        # ": Union[Tensor, Tuple[Tensor, ...], List[Tensor]] = None"
        # ": Union[Tuple[Tensor, ...], List[Tensor]] = None"
        hint = hint.replace(
            "Tuple[Tensor, ...], List[Tensor]] = None",
            "Tuple[Tensor, ...], List[Tensor], None] = None",
        )
        return hint

    docstrs = gather_docstrs()
    function_hints = []
    for name, hints in sorted(unsorted_function_hints.items()):
        hints = [replace_special_case(h) for h in hints]
        if len(hints) > 1:
            hints = ["@overload\n" + h for h in hints]
        docstr = docstrs.get(f"torch.{name}")
        if docstr is not None:
            hints = [add_docstr_to_hint(docstr, h) for h in hints]
        function_hints += hints

    # Generate type signatures for Tensor methods
    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    unsorted_tensor_method_hints: dict[str, list[str]] = collections.defaultdict(list)
    unsorted_tensor_method_hints.update(
        {
            "size": [
                "def size(self, dim: None = None) -> Size: ...",
                "def size(self, dim: _int) -> _int: ...",
            ],
            "stride": [
                "def stride(self, dim: None = None) -> Tuple[_int, ...]: ...",
                "def stride(self, dim: _int) -> _int: ...",
            ],
            "new_ones": [
                f"def new_ones(self, size: _size, {FACTORY_PARAMS}) -> Tensor: ..."
            ],
            "new_tensor": [
                f"def new_tensor(self, data: Any, {FACTORY_PARAMS}) -> Tensor: ..."
            ],
            "__new__": ["def __new__(cls, *args, **kwargs) -> Self: ..."],
            # new and __init__ have the same signatures differ only in return type
            # Adapted from legacy_tensor_ctor and legacy_tensor_new
            "new": [
                f"def new(cls, *args: Any, {DEVICE_PARAM}) -> Self: ...",
                "def new(cls, storage: Storage) -> Self: ...",
                "def new(cls, other: Tensor) -> Self: ...",
                f"def new(cls, size: _size, *, {DEVICE_PARAM}) -> Self: ...",
            ],
            "__init__": [
                f"def __init__(self, *args: Any, {DEVICE_PARAM}) -> None: ...",
                "def __init__(self, storage: Storage) -> None: ...",
                "def __init__(self, other: Tensor) -> None: ...",
                f"def __init__(self, size: _size, *, {DEVICE_PARAM}) -> None: ...",
            ],
            "as_subclass": ["def as_subclass(self, cls: _Type[S]) -> S: ..."],
            "_make_subclass": [
                "@staticmethod    \ndef _make_subclass({}) -> S: ...".format(
                    ", ".join(
                        [
                            "cls: _Type[S]",
                            "data: Tensor",
                            "require_grad: _bool = False",
                            "dispatch_strides: _bool = False",
                            "dispatch_device: _bool = False",
                            "device_for_backend_keys: Optional[_device] = None",
                        ]
                    )
                )
            ],
            "__contains__": ["def __contains__(self, other: Any, /) -> _bool: ..."],
            "__getitem__": [f"def __getitem__(self, {INDICES}) -> Tensor: ..."],
            "__setitem__": [
                f"def __setitem__(self, {INDICES}, val: Union[Tensor, Number]) -> None: ..."
            ],
            "tolist": ["def tolist(self) -> List: ..."],
            "requires_grad_": [
                "def requires_grad_(self, mode: _bool = True) -> Tensor: ..."
            ],
            "element_size": ["def element_size(self) -> _int: ..."],
            "data_ptr": ["def data_ptr(self) -> _int: ..."],
            "dim": ["def dim(self) -> _int: ..."],
            "nonzero": [
                "def nonzero(self, *, as_tuple: Literal[False] = False) -> Tensor: ...",
                "def nonzero(self, *, as_tuple: Literal[True]) -> Tuple[Tensor, ...]: ...",
            ],
            "numel": ["def numel(self) -> _int: ..."],
            "ndimension": ["def ndimension(self) -> _int: ..."],
            "nelement": ["def nelement(self) -> _int: ..."],
            "cuda": [
                "def cuda({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "self",
                            "device: Optional[Union[_device, _int, str]] = None",
                            "non_blocking: _bool = False",
                            "memory_format: torch.memory_format = torch.preserve_format",
                        ]
                    )
                )
            ],
            "xpu": [
                "def xpu({}) -> Tensor: ...".format(
                    ", ".join(
                        [
                            "self",
                            "device: Optional[Union[_device, _int, str]] = None",
                            "non_blocking: _bool = False",
                            "memory_format: torch.memory_format = torch.preserve_format",
                        ]
                    )
                )
            ],
            "cpu": [
                "def cpu(self, memory_format: torch.memory_format = torch.preserve_format) -> Tensor: ..."
            ],
            "numpy": ["def numpy(self, *, force: _bool = False) -> numpy.ndarray: ..."],
            "apply_": ["def apply_(self, callable: Callable) -> Tensor: ..."],
            "map_": [
                "def map_(self, tensor: Tensor, callable: Callable) -> Tensor: ..."
            ],
            "map2_": [
                "def map2_(self, x: Tensor, y: Tensor, callable: Callable) -> Tensor: ..."
            ],
            "storage": ["def untyped_storage(self) -> UntypedStorage: ..."],
            "storage_type": ["def storage_type(self) -> Storage: ..."],
            "type": [
                "def type(self, dtype: None = None, non_blocking: _bool = False) -> str: ...",
                "def type(self, dtype: Union[str, _dtype], non_blocking: _bool = False) -> Tensor: ...",
            ],
            "get_device": ["def get_device(self) -> _int: ..."],
            "contiguous": [
                "def contiguous(self, memory_format=torch.contiguous_format) -> Tensor: ..."
            ],
            "has_names": ["def has_names(self) -> _bool: ..."],
            "is_contiguous": [
                "def is_contiguous(self, memory_format=torch.contiguous_format) -> _bool: ..."
            ],
            "_is_view": ["def _is_view(self) -> _bool: ..."],
            "is_cpu": ["is_cpu: _bool"],
            "is_cuda": ["is_cuda: _bool"],
            "is_leaf": ["is_leaf: _bool"],
            "is_nested": ["is_nested: _bool"],
            "is_sparse": ["is_sparse: _bool"],
            "is_sparse_csr": ["is_sparse_csr: _bool"],
            "is_quantized": ["is_quantized: _bool"],
            "is_meta": ["is_meta: _bool"],
            "is_mps": ["is_mps: _bool"],
            "is_mtia": ["is_mtia: _bool"],
            "is_maia": ["is_maia: _bool"],
            "is_mkldnn": ["is_mkldnn: _bool"],
            "is_vulkan": ["is_vulkan: _bool"],
            "is_ipu": ["is_ipu: _bool"],
            "storage_offset": ["def storage_offset(self) -> Union[_int, SymInt]: ..."],
            "to": [
                (
                    f"def to(self, {args}, non_blocking: _bool = False, copy: _bool = False, *, "
                    "memory_format: Optional[torch.memory_format] = None) -> Tensor: ..."
                )
                for args in [
                    "dtype: _dtype",
                    "device: Optional[DeviceLikeType] = None, dtype: Optional[_dtype] = None",
                    "other: Tensor",
                ]
            ],
            "item": ["def item(self) -> Number: ..."],
            "copy_": [
                "def copy_(self, src: Tensor, non_blocking: _bool = False) -> Tensor: ..."
            ],
            "set_": [
                "def set_(self, storage: Union[Storage, TypedStorage, UntypedStorage], "
                "offset: IntLikeType, size: _symsize, stride: _symsize) -> Tensor: ...",
                "def set_(self, storage: Union[Storage, TypedStorage, UntypedStorage]) -> Tensor: ...",
            ],
            "split": [
                "def split(self, split_size: _int, dim: _int = 0) -> Sequence[Tensor]: ...",
                "def split(self, split_size: Tuple[_int, ...], dim: _int = 0) -> Sequence[Tensor]: ...",
            ],
            "div": [
                "def div(self, other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None) -> Tensor: ..."
            ],
            "div_": [
                "def div_(self, other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None) -> Tensor: ..."
            ],
        }
    )
    for binop in ["true_divide", "floor_divide"]:
        for inplace in [False, True]:
            out_suffix = ", *, out: Optional[Tensor] = None"
            if inplace:
                binop += "_"
                out_suffix = ""
            unsorted_tensor_method_hints[binop].append(
                f"def {binop}(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat]{out_suffix})"
                " -> Tensor: ..."
            )
    for binop in ["mul"]:
        for inplace in [False, True]:
            out_suffix = ", *, out: Optional[Tensor] = None"
            if inplace:
                binop += "_"
                out_suffix = ""
            unsorted_tensor_method_hints[binop].append(
                f"def {binop}(self, other: Union[Tensor, Number, _complex, torch.SymInt, torch.SymFloat]{out_suffix})"
                " -> Tensor: ..."
            )
    for binop in ["add", "sub"]:
        for inplace in [False, True]:
            out_suffix = ", out: Optional[Tensor] = None"
            if inplace:
                binop += "_"
                out_suffix = ""
            unsorted_tensor_method_hints[binop].append(
                f"def {binop}(self, other: Union[Tensor, Number, _complex, torch.SymInt, torch.SymFloat], "
                f"*, alpha: Optional[Union[Number, _complex]] = 1{out_suffix})"
                " -> Tensor: ..."
            )
    simple_conversions = [
        "byte",
        "char",
        "double",
        "float",
        "half",
        "int",
        "long",
        "short",
        "bool",
        "bfloat16",
    ]
    for name in simple_conversions:
        unsorted_tensor_method_hints[name].append(f"def {name}(self) -> Tensor: ...")

    # pyi tensor methods don't currently include deprecated signatures for some reason
    # TODO: we should probably add them in
    tensor_method_signatures = load_signatures(
        native_functions,
        deprecated_yaml_path,
        method=True,
        skip_deprecated=True,
        pyi=True,
    )
    tensor_method_sig_groups = get_py_torch_functions(
        tensor_method_signatures, method=True
    )

    for group in sorted(tensor_method_sig_groups, key=lambda g: g.signature.name):
        name = group.signature.name
        unsorted_tensor_method_hints[name] += generate_type_hints(group)

        structseq = returns_structseq_pyi(group.signature)
        if structseq is not None and not group.signature.deprecated:
            # deprecated structseqs are currently not included for torch functions
            tuple_name, tuple_def = structseq
            if tuple_name in structseqs:
                assert structseqs[tuple_name] == tuple_def
            else:
                structseqs[tuple_name] = tuple_def

    for op in all_ops:
        name = f"__{op}__"
        unsorted_tensor_method_hints[name] += sig_for_ops(name)

    tensor_method_hints = []
    for name, hints in sorted(unsorted_tensor_method_hints.items()):
        if len(hints) > 1:
            hints = ["@overload\n" + h for h in hints]
        docstr = docstrs.get(f"torch._C.TensorBase.{name}")
        if docstr is not None:
            hints = [add_docstr_to_hint(docstr, h) for h in hints]
        tensor_method_hints += hints

    # TODO: Missing type hints for nn

    # Generate structseq definitions
    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    structseq_defs = [f"{defn}\n" for defn in structseqs.values()]

    # Generate type signatures for legacy classes
    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    legacy_storage_base_hints = ["class StorageBase(object): ..."]

    legacy_class_hints = []
    for c in (
        "DoubleTensor",
        "FloatTensor",
        "BFloat16Tensor",
        "LongTensor",
        "IntTensor",
        "ShortTensor",
        "HalfTensor",
        "CharTensor",
        "ByteTensor",
        "BoolTensor",
    ):
        legacy_class_hints.append(f"class {c}(Tensor): ...")

    # Generate type signatures for dtype classes
    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    # TODO: don't explicitly list dtypes here; get it from canonical
    # source
    dtype_class_hints = [
        f"{n}: dtype = ..."
        for n in [
            "float32",
            "float",
            "float64",
            "double",
            "float16",
            "bfloat16",
            "float8_e4m3fn",
            "float8_e4m3fnuz",
            "float8_e5m2",
            "float8_e5m2fnuz",
            "half",
            "uint8",
            "uint16",
            "uint32",
            "uint64",
            "int8",
            "int16",
            "short",
            "int32",
            "int",
            "int64",
            "long",
            "complex32",
            "complex64",
            "chalf",
            "cfloat",
            "complex128",
            "cdouble",
            "quint8",
            "qint8",
            "qint32",
            "bool",
            "quint4x2",
            "quint2x4",
            "bits1x8",
            "bits2x4",
            "bits4x2",
            "bits8",
            "bits16",
        ]
    ]

    # Generate __all__ directive
    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    # Include only the functions that contain hints, to prevent undefined
    # symbols to be included in the `__all__` directive.
    hinted_function_names = [
        name for name, hint in unsorted_function_hints.items() if hint
    ]
    all_symbols = sorted(list(structseqs.keys()) + hinted_function_names)
    all_directive = pformat(all_symbols, width=100, compact=True).split("\n")
    all_directive[0] = f"__all__ = {all_directive[0]}"

    # Dispatch key hints
    # ~~~~~~~~~~~~~~~~~~
    dispatch_key_hints = [f"{d.name}: DispatchKey = ..." for d in DispatchKey]
    torch_dispatch_mode_key_hints = [
        f"{k.name}: _TorchDispatchModeKey = ..." for k in _TorchDispatchModeKey
    ]

    # Tags Enum type hints
    # ~~~~~~~~~~~~~~~~~~~~

    tag_names = sorted(parse_tags_yaml(tags_yaml_path))
    tag_attributes = "\n".join(
        f"{name}: _int = {index}" for index, name in enumerate(tag_names)
    )

    # Write out the stub
    # ~~~~~~~~~~~~~~~~~~

    env = {
        "structseq_defs": structseq_defs,
        "function_hints": function_hints,
        "tensor_method_hints": tensor_method_hints,
        "legacy_class_hints": legacy_class_hints,
        "legacy_storage_base_hints": legacy_storage_base_hints,
        "dtype_class_hints": dtype_class_hints,
        "dispatch_key_hints": dispatch_key_hints,
        "torch_dispatch_mode_key_hints": torch_dispatch_mode_key_hints,
        "all_directive": all_directive,
        "tag_attributes": tag_attributes,
    }
    fm.write_with_template(
        "torch/_C/__init__.pyi",
        "torch/_C/__init__.pyi.in",
        lambda: env,
    )
    fm.write_with_template(
        "torch/_C/_VariableFunctions.pyi",
        "torch/_C/_VariableFunctions.pyi.in",
        lambda: env,
    )
    fm.write_with_template(
        "torch/_VF.pyi",
        "torch/_C/_VariableFunctions.pyi.in",
        lambda: env,
    )
    fm.write_with_template(
        "torch/return_types.pyi",
        "torch/_C/return_types.pyi.in",
        lambda: env,
    )
    gen_nn_functional(fm)


def main() -> None:
    parser = argparse.ArgumentParser(description="Generate type stubs for PyTorch")
    parser.add_argument(
        "--native-functions-path",
        metavar="NATIVE",
        default="aten/src/ATen/native/native_functions.yaml",
        help="path to native_functions.yaml",
    )
    parser.add_argument(
        "--tags-path",
        metavar="TAGS",
        default="aten/src/ATen/native/tags.yaml",
        help="path to tags.yaml",
    )
    parser.add_argument(
        "--deprecated-functions-path",
        metavar="DEPRECATED",
        default="tools/autograd/deprecated.yaml",
        help="path to deprecated.yaml",
    )
    parser.add_argument(
        "--out", metavar="OUT", default=".", help="path to output directory"
    )
    args = parser.parse_args()
    fm = FileManager(install_dir=args.out, template_dir=".", dry_run=False)
    gen_pyi(
        args.native_functions_path, args.tags_path, args.deprecated_functions_path, fm
    )


if __name__ == "__main__":
    main()