Correct remaining typing.Literal imports

captum/_utils/common.py

@@ -5,13 +5,23 @@
 from enum import Enum
 from functools import reduce
 from inspect import signature
-from typing import Any, Callable, cast, Dict, List, overload, Sequence, Tuple, Union
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Dict,
+    List,
+    Literal,
+    overload,
+    Sequence,
+    Tuple,
+    Union,
+)
 
 import numpy as np
 import torch
 from captum._utils.typing import (
     BaselineType,
-    Literal,
     TargetType,
     TensorOrTupleOfTensorsGeneric,
     TupleOrTensorOrBoolGeneric,
@@ -71,23 +81,17 @@ def safe_div(
 
 
 @typing.overload
-# pyre-fixme[43]: The return type of overloaded function `_is_tuple` (`Literal[]`)
-#  is incompatible with the return type of the implementation (`bool`).
-# pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-# pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
 def _is_tuple(inputs: Tuple[Tensor, ...]) -> Literal[True]: ...
 
 
 @typing.overload
-# pyre-fixme[43]: The return type of overloaded function `_is_tuple` (`Literal[]`)
-#  is incompatible with the return type of the implementation (`bool`).
-# pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-# pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
 def _is_tuple(inputs: Tensor) -> Literal[False]: ...
 
 
 @typing.overload
-def _is_tuple(inputs: TensorOrTupleOfTensorsGeneric) -> bool: ...
+def _is_tuple(
+    inputs: TensorOrTupleOfTensorsGeneric,
+) -> bool: ...  # type: ignore
 
 
 def _is_tuple(inputs: Union[Tensor, Tuple[Tensor, ...]]) -> bool:
@@ -480,22 +484,14 @@ def _expand_and_update_feature_mask(n_samples: int, kwargs: dict) -> None:
 
 
 @typing.overload
-# pyre-fixme[43]: The implementation of `_format_output` does not accept all
-#  possible arguments of overload defined on line `449`.
 def _format_output(
-    # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     is_inputs_tuple: Literal[True],
     output: Tuple[Tensor, ...],
 ) -> Tuple[Tensor, ...]: ...
 
 
 @typing.overload
-# pyre-fixme[43]: The implementation of `_format_output` does not accept all
-#  possible arguments of overload defined on line `455`.
 def _format_output(
-    # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     is_inputs_tuple: Literal[False],
     output: Tuple[Tensor, ...],
 ) -> Tensor: ...
@@ -526,22 +522,14 @@ def _format_output(
 
 
 @typing.overload
-# pyre-fixme[43]: The implementation of `_format_outputs` does not accept all
-#  possible arguments of overload defined on line `483`.
 def _format_outputs(
-    # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     is_multiple_inputs: Literal[False],
     outputs: List[Tuple[Tensor, ...]],
 ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
 
 
 @typing.overload
-# pyre-fixme[43]: The implementation of `_format_outputs` does not accept all
-#  possible arguments of overload defined on line `489`.
 def _format_outputs(
-    # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     is_multiple_inputs: Literal[True],
     outputs: List[Tuple[Tensor, ...]],
 ) -> List[Union[Tensor, Tuple[Tensor, ...]]]: ...

captum/_utils/gradient.py

@@ -5,7 +5,18 @@
 import typing
 import warnings
 from collections import defaultdict
-from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Tuple, Union
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+)
 
 import torch
 from captum._utils.common import (
@@ -16,7 +27,6 @@
 )
 from captum._utils.sample_gradient import SampleGradientWrapper
 from captum._utils.typing import (
-    Literal,
     ModuleOrModuleList,
     TargetType,
     TensorOrTupleOfTensorsGeneric,
@@ -226,9 +236,6 @@ def _forward_layer_distributed_eval(
     # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     additional_forward_args: Any = None,
     attribute_to_layer_input: bool = False,
-    # pyre-fixme[9]: forward_hook_with_return has type `Literal[]`; used as `bool`.
-    # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     forward_hook_with_return: Literal[False] = False,
     require_layer_grads: bool = False,
 ) -> Dict[Module, Dict[device, Tuple[Tensor, ...]]]: ...
@@ -246,8 +253,6 @@ def _forward_layer_distributed_eval(
     additional_forward_args: Any = None,
     attribute_to_layer_input: bool = False,
     *,
-    # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     forward_hook_with_return: Literal[True],
     require_layer_grads: bool = False,
 ) -> Tuple[Dict[Module, Dict[device, Tuple[Tensor, ...]]], Tensor]: ...
@@ -675,7 +680,6 @@ def compute_layer_gradients_and_eval(
             target_ind=target_ind,
             additional_forward_args=additional_forward_args,
             attribute_to_layer_input=attribute_to_layer_input,
-            # pyre-fixme[6]: For 7th argument expected `Literal[]` but got `bool`.
             forward_hook_with_return=True,
             require_layer_grads=True,
         )

captum/_utils/progress.py

@@ -5,9 +5,7 @@
 import sys
 import warnings
 from time import time
-from typing import Any, cast, Iterable, Optional, Sized, TextIO
-
-from captum._utils.typing import Literal
+from typing import Any, cast, Iterable, Literal, Optional, Sized, TextIO
 
 try:
     from tqdm.auto import tqdm
@@ -75,10 +73,7 @@ def __enter__(self) -> "NullProgress":
         return self
 
     # pyre-fixme[2]: Parameter must be annotated.
-    # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     def __exit__(self, exc_type, exc_value, exc_traceback) -> Literal[False]:
-        # pyre-fixme[7]: Expected `Literal[]` but got `bool`.
         return False
 
     # pyre-fixme[3]: Return type must be annotated.
@@ -139,11 +134,8 @@ def __enter__(self) -> "SimpleProgress":
         return self
 
     # pyre-fixme[2]: Parameter must be annotated.
-    # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     def __exit__(self, exc_type, exc_value, exc_traceback) -> Literal[False]:
         self.close()
-        # pyre-fixme[7]: Expected `Literal[]` but got `bool`.
         return False
 
     # pyre-fixme[3]: Return type must be annotated.

captum/attr/_core/layer/layer_conductance.py

@@ -2,7 +2,7 @@
 
 # pyre-strict
 import typing
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -12,7 +12,7 @@
     _format_output,
 )
 from captum._utils.gradient import compute_layer_gradients_and_eval
-from captum._utils.typing import BaselineType, Literal, TargetType
+from captum._utils.typing import BaselineType, TargetType
 from captum.attr._utils.approximation_methods import approximation_parameters
 from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
 from captum.attr._utils.batching import _batch_attribution
@@ -86,8 +86,6 @@ def attribute(
         method: str = "gausslegendre",
         internal_batch_size: Union[None, int] = None,
         *,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
         attribute_to_layer_input: bool = False,
         grad_kwargs: Optional[Dict[str, Any]] = None,
@@ -105,9 +103,6 @@ def attribute(
         n_steps: int = 50,
         method: str = "gausslegendre",
         internal_batch_size: Union[None, int] = None,
-        # pyre-fixme[9]: return_convergence_delta has type `Literal[]`; used as `bool`.
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[False] = False,
         attribute_to_layer_input: bool = False,
         grad_kwargs: Optional[Dict[str, Any]] = None,

captum/attr/_core/layer/layer_deep_lift.py

@@ -2,7 +2,7 @@
 
 # pyre-strict
 import typing
-from typing import Any, Callable, cast, Dict, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, cast, Dict, Literal, Optional, Sequence, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -13,12 +13,7 @@
     ExpansionTypes,
 )
 from captum._utils.gradient import compute_layer_gradients_and_eval
-from captum._utils.typing import (
-    BaselineType,
-    Literal,
-    TargetType,
-    TensorOrTupleOfTensorsGeneric,
-)
+from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
 from captum.attr._utils.attribution import LayerAttribution
 from captum.attr._utils.common import (
@@ -101,8 +96,6 @@ def __init__(
 
     # Ignoring mypy error for inconsistent signature with DeepLift
     @typing.overload  # type: ignore
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `117`.
     def attribute(
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
@@ -111,27 +104,20 @@ def attribute(
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
         *,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
         attribute_to_layer_input: bool = False,
         custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None,
         grad_kwargs: Optional[Dict[str, Any]] = None,
     ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ...
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `104`.
     def attribute(
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
         baselines: BaselineType = None,
         target: TargetType = None,
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
-        # pyre-fixme[9]: return_convergence_delta has type `Literal[]`; used as `bool`.
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[False] = False,
         attribute_to_layer_input: bool = False,
         custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None,
@@ -382,8 +368,6 @@ def chunk_output_fn(out: TensorOrTupleOfTensorsGeneric) -> Sequence:
             inputs,
             additional_forward_args,
             target,
-            # pyre-fixme[31]: Expression `Literal[False])]` is not a valid type.
-            # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
             cast(Union[Literal[True], Literal[False]], len(attributions) > 1),
         )
 
@@ -464,8 +448,6 @@ def attribute(
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
         *,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
         attribute_to_layer_input: bool = False,
         custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None,
@@ -483,9 +465,6 @@ def attribute(
         target: TargetType = None,
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
-        # pyre-fixme[9]: return_convergence_delta has type `Literal[]`; used as `bool`.
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[False] = False,
         attribute_to_layer_input: bool = False,
         custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None,
@@ -686,10 +665,6 @@ def attribute(
             target=exp_target,
             additional_forward_args=exp_addit_args,
             return_convergence_delta=cast(
-                # pyre-fixme[31]: Expression `Literal[(True, False)]` is not a valid
-                #  type.
-                # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take
-                #  parameters.
                 Literal[True, False],
                 return_convergence_delta,
             ),

captum/attr/_core/layer/layer_gradient_shap.py

@@ -3,12 +3,12 @@
 # pyre-strict
 
 import typing
-from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, cast, Dict, List, Literal, Optional, Tuple, Union
 
 import numpy as np
 import torch
 from captum._utils.gradient import _forward_layer_eval, compute_layer_gradients_and_eval
-from captum._utils.typing import Literal, TargetType, TensorOrTupleOfTensorsGeneric
+from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._core.gradient_shap import _scale_input
 from captum.attr._core.noise_tunnel import NoiseTunnel
 from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
@@ -117,8 +117,6 @@ def attribute(
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
         *,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
         attribute_to_layer_input: bool = False,
     ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ...
@@ -135,9 +133,6 @@ def attribute(
         stdevs: Union[float, Tuple[float, ...]] = 0.0,
         target: TargetType = None,
         additional_forward_args: Any = None,
-        # pyre-fixme[9]: return_convergence_delta has type `Literal[]`; used as `bool`.
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[False] = False,
         attribute_to_layer_input: bool = False,
     ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
@@ -392,8 +387,6 @@ def __init__(
         self._multiply_by_inputs = multiply_by_inputs
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `385`.
     def attribute(
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
@@ -402,26 +395,19 @@ def attribute(
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
         *,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
         attribute_to_layer_input: bool = False,
         grad_kwargs: Optional[Dict[str, Any]] = None,
     ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ...
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `373`.
     def attribute(
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
         baselines: Union[Tensor, Tuple[Tensor, ...]],
         target: TargetType = None,
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
-        # pyre-fixme[9]: return_convergence_delta has type `Literal[]`; used as `bool`.
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[False] = False,
         attribute_to_layer_input: bool = False,
         grad_kwargs: Optional[Dict[str, Any]] = None,
@@ -505,8 +491,6 @@ def attribute(  # type: ignore
             inputs,
             additional_forward_args,
             target,
-            # pyre-fixme[31]: Expression `Literal[False])]` is not a valid type.
-            # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
             cast(Union[Literal[True], Literal[False]], len(attributions) > 1),
         )
 

captum/attr/_core/layer/layer_integrated_gradients.py

@@ -3,7 +3,7 @@
 # pyre-strict
 import functools
 import warnings
-from typing import Any, Callable, cast, List, overload, Tuple, Union
+from typing import Any, Callable, cast, List, Literal, overload, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -12,7 +12,7 @@
     _format_outputs,
 )
 from captum._utils.gradient import _forward_layer_eval, _run_forward
-from captum._utils.typing import BaselineType, Literal, ModuleOrModuleList, TargetType
+from captum._utils.typing import BaselineType, ModuleOrModuleList, TargetType
 from captum.attr._core.integrated_gradients import IntegratedGradients
 from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
 from captum.attr._utils.common import (
@@ -227,8 +227,6 @@ def layer_forward_hook(
         return _gradient_func
 
     @overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `112`.
     def attribute(
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
@@ -239,15 +237,11 @@ def attribute(
         n_steps: int,
         method: str,
         internal_batch_size: Union[None, int],
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[False],
         attribute_to_layer_input: bool,
     ) -> Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]]: ...
 
     @overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `126`.
     def attribute(  # type: ignore
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
@@ -258,8 +252,6 @@ def attribute(  # type: ignore
         n_steps: int,
         method: str,
         internal_batch_size: Union[None, int],
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
         attribute_to_layer_input: bool,
     ) -> Tuple[

captum/attr/_core/layer/layer_lrp.py

@@ -2,7 +2,7 @@
 
 # pyre-strict
 import typing
-from typing import Any, cast, List, Tuple, Union
+from typing import Any, cast, List, Literal, Tuple, Union
 
 from captum._utils.common import (
     _format_tensor_into_tuples,
@@ -15,7 +15,6 @@
     undo_gradient_requirements,
 )
 from captum._utils.typing import (
-    Literal,
     ModuleOrModuleList,
     TargetType,
     TensorOrTupleOfTensorsGeneric,
@@ -64,17 +63,13 @@ def __init__(self, model: Module, layer: ModuleOrModuleList) -> None:
             self.device_ids = cast(List[int], self.model.device_ids)
 
     @typing.overload  # type: ignore
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `77`.
     def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
         *,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
         attribute_to_layer_input: bool = False,
         verbose: bool = False,
@@ -84,17 +79,12 @@ def attribute(
     ]: ...
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `66`.
     def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
         # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
-        # pyre-fixme[9]: return_convergence_delta has type `Literal[]`; used as `bool`.
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[False] = False,
         attribute_to_layer_input: bool = False,
         verbose: bool = False,

captum/attr/_utils/common.py

@@ -3,7 +3,7 @@
 # pyre-strict
 import typing
 from inspect import signature
-from typing import Any, Callable, List, Tuple, TYPE_CHECKING, Union
+from typing import Any, Callable, List, Literal, Tuple, TYPE_CHECKING, Union
 
 import torch
 from captum._utils.common import (
@@ -12,12 +12,7 @@
     _format_tensor_into_tuples,
     _validate_input as _validate_input_basic,
 )
-from captum._utils.typing import (
-    BaselineType,
-    Literal,
-    TargetType,
-    TensorOrTupleOfTensorsGeneric,
-)
+from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._utils.approximation_methods import SUPPORTED_METHODS
 from torch import Tensor
 
@@ -206,8 +201,6 @@ def _format_and_verify_sliding_window_shapes(
 
 
 @typing.overload
-# pyre-fixme[43]: The implementation of `_compute_conv_delta_and_format_attrs` does
-#  not accept all possible arguments of overload defined on line `212`.
 def _compute_conv_delta_and_format_attrs(
     attr_algo: "GradientAttribution",
     return_convergence_delta: bool,
@@ -217,15 +210,11 @@ def _compute_conv_delta_and_format_attrs(
     # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     additional_forward_args: Any,
     target: TargetType,
-    # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     is_inputs_tuple: Literal[True],
 ) -> Union[Tuple[Tensor, ...], Tuple[Tuple[Tensor, ...], Tensor]]: ...
 
 
 @typing.overload
-# pyre-fixme[43]: The implementation of `_compute_conv_delta_and_format_attrs` does
-#  not accept all possible arguments of overload defined on line `199`.
 def _compute_conv_delta_and_format_attrs(
     attr_algo: "GradientAttribution",
     return_convergence_delta: bool,
@@ -235,9 +224,6 @@ def _compute_conv_delta_and_format_attrs(
     # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     additional_forward_args: Any,
     target: TargetType,
-    # pyre-fixme[9]: is_inputs_tuple has type `Literal[]`; used as `bool`.
-    # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
     is_inputs_tuple: Literal[False] = False,
 ) -> Union[Tensor, Tuple[Tensor, Tensor]]: ...
 

tests/attr/helpers/attribution_delta_util.py

@@ -4,8 +4,8 @@
 from typing import Tuple, Union
 
 import torch
-from captum._utils.typing import Tensor
 from tests.helpers import BaseTest
+from torch import Tensor
 
 
 def assert_attribution_delta(

tests/attr/layer/test_layer_lrp.py

@@ -65,7 +65,6 @@ def test_lrp_basic_attributions(self) -> None:
         relevance, delta = lrp.attribute(  # type: ignore
             inputs,
             classIndex.item(),
-            # pyre-fixme[6]: For 3rd argument expected `Literal[]` but got `bool`.
             return_convergence_delta=True,
         )
         assertTensorAlmostEqual(
@@ -82,7 +81,6 @@ def test_lrp_simple_attributions(self) -> None:
         relevance_upper, delta = lrp_upper.attribute(
             inputs,
             attribute_to_layer_input=True,
-            # pyre-fixme[6]: For 3rd argument expected `Literal[]` but got `bool`.
             return_convergence_delta=True,
         )
         lrp_lower = LayerLRP(model, model.linear)
@@ -185,7 +183,6 @@ def test_lrp_simple_attributions_all_layers_delta(self) -> None:
         relevance, delta = lrp.attribute(
             inputs,
             attribute_to_layer_input=True,
-            # pyre-fixme[6]: For 3rd argument expected `Literal[]` but got `bool`.
             return_convergence_delta=True,
         )
         self.assertEqual(len(relevance), len(delta))

tests/attr/test_interpretable_input.py

@@ -2,10 +2,9 @@
 
 # pyre-unsafe
 
-from typing import List, Optional, overload, Union
+from typing import List, Literal, Optional, overload, Union
 
 import torch
-from captum._utils.typing import Literal
 from captum.attr._utils.interpretable_input import TextTemplateInput, TextTokenInput
 from parameterized import parameterized
 from tests.helpers import BaseTest
@@ -22,10 +21,7 @@ def __init__(self, vocab_list) -> None:
     @overload
     def encode(self, text: str, return_tensors: None = None) -> List[int]: ...
     @overload
-    # pyre-fixme[43]: Incompatible overload. The implementation of
-    # `DummyTokenizer.encode` does not accept all possible arguments of overload.
-    # pyre-ignore[11]: Annotation `pt` is not defined as a type
-    def encode(self, text: str, return_tensors: Literal["pt"]) -> Tensor: ...  # type: ignore  # noqa: E501 line too long
+    def encode(self, text: str, return_tensors: Literal["pt"]) -> Tensor: ...
 
     def encode(
         self, text: str, return_tensors: Optional[str] = "pt"