Add additional overload signatures for shared methods to resolve pyre…

… errors (pytorch#1406)

Summary:

Add a few additional overload signatures to shared methods for resolving pyre errors

Also remove separate cases for typing Literal since the split was necessary due to previous support for Python < 3.8

Differential Revision: D64677349

captum/_utils/common.py

@@ -86,6 +86,10 @@ def _is_tuple(inputs: Tuple[Tensor, ...]) -> Literal[True]: ...
 def _is_tuple(inputs: Tensor) -> Literal[False]: ...
 
 
+@typing.overload
+def _is_tuple(inputs: TensorOrTupleOfTensorsGeneric) -> bool: ...
+
+
 def _is_tuple(inputs: Union[Tensor, Tuple[Tensor, ...]]) -> bool:
     return isinstance(inputs, tuple)
 

captum/_utils/typing.py

@@ -2,25 +2,11 @@
 
 # pyre-strict
 
-from typing import (
-    List,
-    Optional,
-    overload,
-    Protocol,
-    Tuple,
-    TYPE_CHECKING,
-    TypeVar,
-    Union,
-)
+from typing import List, Literal, Optional, overload, Protocol, Tuple, TypeVar, Union
 
 from torch import Tensor
 from torch.nn import Module
 
-if TYPE_CHECKING:
-    from typing import Literal
-else:
-    Literal = {True: bool, False: bool, (True, False): bool, "pt": str}
-
 TensorOrTupleOfTensorsGeneric = TypeVar(
     "TensorOrTupleOfTensorsGeneric", Tensor, Tuple[Tensor, ...]
 )

captum/attr/_utils/common.py

@@ -82,6 +82,12 @@ def _format_input_baseline(  # type: ignore
 ) -> Tuple[Tuple[Tensor, ...], Tuple[Union[Tensor, int, float], ...]]: ...
 
 
+@typing.overload
+def _format_input_baseline(  # type: ignore
+    inputs: TensorOrTupleOfTensorsGeneric, baselines: BaselineType
+) -> Tuple[Tuple[Tensor, ...], Tuple[Union[Tensor, int, float], ...]]: ...
+
+
 def _format_input_baseline(
     inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType
 ) -> Tuple[Tuple[Tensor, ...], Tuple[Union[Tensor, int, float], ...]]:
@@ -236,6 +242,21 @@ def _compute_conv_delta_and_format_attrs(
 ) -> Union[Tensor, Tuple[Tensor, Tensor]]: ...
 
 
+@typing.overload
+def _compute_conv_delta_and_format_attrs(
+    attr_algo: "GradientAttribution",
+    return_convergence_delta: bool,
+    attributions: Tuple[Tensor, ...],
+    start_point: Union[int, float, Tensor, Tuple[Union[int, float, Tensor], ...]],
+    end_point: Union[Tensor, Tuple[Tensor, ...]],
+    additional_forward_args: Any,
+    target: TargetType,
+    is_inputs_tuple: bool = False,
+) -> Union[
+    Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]
+]: ...
+
+
 # FIXME: GradientAttribution is provided as a string due to a circular import.
 # This should be fixed when common is refactored into separate files.
 def _compute_conv_delta_and_format_attrs(