Add Or Fix Type Checking

Apply fixes using this priority order (simplest first):

a. Narrow unions with isinstance() / if x is None / hasattr(). This is the primary tool for resolving union-type errors. ty narrows through all of these patterns, including the negative forms:

# Narrow X | None — use `if ...: raise`, never `assert`
if x is None:
    raise ValueError("x must not be None")
x.method()  # ty knows x is X here

# Narrow str | UploadFile
if isinstance(field, str):
    raise TypeError("Expected file upload, got string")
await field.read()  # ty knows field is UploadFile here

# Narrow broad union parameters early in a function body
# (common for methods accepting e.g. list | dict | BatchEncoding)
if isinstance(encoded_inputs, (list, tuple)):
    raise TypeError("Expected a mapping, got sequence")
encoded_inputs.keys()  # ty sees only the dict/mapping types now

b. Use local variables to help ty track narrowing across closures. When self.x is X | None and you need to pass it to nested functions or closures, ty cannot track that self.x stays non-None. Copy to a local variable and narrow the local:

manager = self.batching_manager
if manager is None:
    raise RuntimeError("Manager not initialized")
# Use `manager` (not `self.batching_manager`) in nested functions

c. Split chained calls when the intermediate type is a broad union. If func().method() fails because func() returns a union, split it:

# BAD: ty can't narrow through chained calls
result = func(return_dict=True).to(device)["input_ids"]

# GOOD: split, narrow, then chain
result = func(return_dict=True)
if not hasattr(result, "to"):
    raise TypeError("Expected dict-like result")
inputs = result.to(device)["input_ids"]

d. Fix incorrect type hints at the source. If a parameter is typed X | None but can never be None when actually called, remove None from the hint.

e. Annotate untyped attributes. Add type annotations to instance variables set in __init__ or elsewhere (for example self.foo: list[int] = []). Declare class-level attributes that are set dynamically later (for example _cache: Cache, _token_tensor: torch.Tensor | None).

f. Use @overload for methods with input-dependent return types. When a method returns different types based on the input type (e.g. __getitem__ with str vs int keys), use @overload to declare each signature separately:

from typing import overload

@overload
def __getitem__(self, item: str) -> ValueType: ...
@overload
def __getitem__(self, item: int) -> EncodingType: ...
@overload
def __getitem__(self, item: slice) -> dict[str, ValueType]: ...

def __getitem__(self, item: int | str | slice) -> ValueType | EncodingType | dict[str, ValueType]:
    ...  # actual implementation

This eliminates cast() calls at usage sites by giving the checker precise return types for each call pattern.

g. Make container classes generic to propagate value types. When a class like UserDict holds values whose type changes after transformation (e.g. lists → tensors after .to()), make the class generic so methods can return narrowed types:

from typing import Generic, overload
from typing_extensions import TypeVar

_V = TypeVar("_V", default=Any)  # default=Any keeps existing code working

class MyDict(UserDict, Generic[_V]):
    @overload
    def __getitem__(self, item: str) -> _V: ...
    # ...

    def to(self, device) -> MyDict[torch.Tensor]:
        # after .to(), values are tensors
        ...
        return self  # type: ignore[return-value]

The default=Any (from typing_extensions) means unparameterized usage like MyDict() stays MyDict[Any] — no existing code needs to change. Only methods that narrow the value type (like .to()) declare a specific return type. This eliminates cast() at all call sites.

h. Use self: "ProtocolType" for mixins. When a mixin accesses attributes from its host class, define a Protocol in src/transformers/_typing.py and annotate self on methods that need it. Apply this consistently to all methods in the mixin. Import under TYPE_CHECKING to avoid circular imports.

i. Use TypeGuard functions for dynamic module attributes (for example torch.npu, torch.xpu, torch.compiler). Instead of getattr(torch, "npu") or hasattr(torch, "npu") and torch.npu.is_available(), define a type guard function in src/transformers/_typing.py:

def has_torch_npu(mod: ModuleType) -> TypeGuard[Any]:
    return hasattr(mod, "npu") and mod.npu.is_available()

Then use it as a narrowing check: if has_torch_npu(torch): torch.npu.device_count(). After the guard, ty treats the module as Any, allowing attribute access without getattr() or cast(). See existing guards in _typing.py for all device backends.

Key rules for type guards:

• Use TypeGuard[Any] (not a Protocol) — this is the simplest form that works with ty and avoids losing the original module's known attributes.
• The guard function must be called directly in an if condition for narrowing to work. ty does NOT narrow through and conditions or if not guard: return.
• Import guards with from .._typing import has_torch_xxx (not via module attribute _typing.has_torch_xxx) — ty only resolves TypeGuard from direct imports.

j. Use getattr() / setattr() for dynamic model/config attributes. For runtime-injected fields (for example config/model flags), use getattr(obj, "field", default) for reads and setattr(obj, "field", value) for writes. Also use getattr() for third-party packages missing type stubs (for example getattr(safetensors, "__version__", "unknown")). Avoid getattr(torch, "npu") style — use type guards instead (see above).

k. Use cast() as a last resort before # type: ignore. Use when you've structurally validated the type but the checker can't see it: pattern-matched AST nodes, known-typed dict values, or validated API responses.

# After structural validation confirms the type:
stmt = cast(cst.Assign, node.body[0])
annotations = cast(list[Annotation], [])

Do not use cast() for module attribute narrowing — use type guards. Do not use cast() when @overload or generics can solve it at the source.

l. Use # type: ignore only for third-party stub defects. This means cases where the third-party package's type stubs are wrong or incomplete and there is no way to narrow or cast around it. Examples:

• A kwarg that exists at runtime but is missing from the stubs
• A method that exists but isn't declared in the stubs Always add the specific error code: # type: ignore[call-arg], not bare # type: ignore.

Things to never do:

• Never use assert for type narrowing. Asserts are stripped by python -O and must not be relied on for correctness. Use if ...: raise instead.
• Never use # type: ignore as a first resort. Exhaust all approaches above first.
• Do not use getattr(torch, "backend") to access dynamic device backends (npu, xpu, hpu, musa, mlu, neuron, compiler) — use type guards
• Do not use cast() for module attribute narrowing — use type guards
• Do not use cast() when @overload or generics can eliminate it at the source
• Do not add helper methods or abstractions just to satisfy the type checker (especially for only 1-2 occurrences)
• Do not pollute base classes with domain-specific fields; use Protocols
• Do not add if x is not None guards for values guaranteed non-None by the call chain; fix the annotation instead
• Do not use conditional inheritance patterns; annotate self instead

Organization:

• Keep shared Protocols and type aliases in src/transformers/_typing.py
• Import type-only symbols under if TYPE_CHECKING: to avoid circular deps
• Use from __future__ import annotations for PEP 604 syntax (X | Y)

Verify and close the PR loop:

• Re-run ty check on the same <target>
• Re-run make typing to confirm the type/model-rules step passes
• If working toward merge readiness, run make check-repo
• Ensure runtime behavior did not change and run relevant tests

Update CI coverage when adding new typed areas:

• Update ty_check_dirs in Makefile to include newly type-checked directories.