Update dependency pyright to ^1.1.382 (#2575)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: Kyle Finley <kyle@finley.sh>

package.json

@@ -1,7 +1,7 @@
 {
   "devDependencies": {
     "cspell": "^8.14.4",
-    "pyright": "^1.1.377"
+    "pyright": "^1.1.382"
   },
   "name": "runway",
   "version": "0.0.0"

runway/cfngin/blueprints/base.py

@@ -47,6 +47,8 @@
 class CFNParameter:
     """Wrapper around a value to indicate a CloudFormation Parameter."""
 
+    value: list[Any] | str
+
     def __init__(self, name: str, value: bool | float | list[Any] | str | Any) -> None:
         """Instantiate class.
 

runway/cfngin/dag/__init__.py

@@ -306,7 +306,7 @@ def from_dict(self, graph_dict: dict[str, Iterable[str] | Any]) -> None:
         for ind_node, dep_nodes in graph_dict.items():
             if not isinstance(dep_nodes, collections.abc.Iterable):
                 raise TypeError(f"{ind_node}: dict values must be lists")
-            for dep_node in dep_nodes:
+            for dep_node in cast("list[str]", dep_nodes):
                 self.add_edge(ind_node, dep_node)
 
     def reset_graph(self) -> None:

runway/cfngin/hooks/docker/hook_data.py

@@ -25,7 +25,7 @@ def client(self) -> DockerClient:
         return DockerClient.from_env()
 
     @overload
-    def update_context(self, context: CfnginContext = ...) -> DockerHookData: ...
+    def update_context(self, context: CfnginContext) -> DockerHookData: ...
 
     @overload
     def update_context(self, context: None = ...) -> None: ...

runway/cfngin/hooks/docker/image/_build.py

@@ -135,7 +135,7 @@ def _set_docker(
                 v.setdefault("tag", repo)
             elif isinstance(v, DockerImageBuildApiOptions) and not v.tag:
                 v.tag = repo
-        return v
+        return v  # pyright: ignore[reportUnknownVariableType]
 
     @field_validator("ecr_repo", mode="before")
     @classmethod

runway/cfngin/hooks/ssm/parameter.py

@@ -110,10 +110,10 @@ def _convert_policies(cls, v: list[dict[str, Any]] | str | Any) -> str:
     @classmethod
     def _convert_tags(cls, v: dict[str, str] | list[dict[str, str]] | Any) -> list[dict[str, str]]:
         """Convert tags to acceptable value."""
-        if isinstance(v, list):
-            return v
-        if isinstance(v, dict):
-            return [{"Key": k, "Value": v} for k, v in v.items()]
+        if isinstance(v, list):  # TODO (kyle): improve with `typing.TypeIs` narrowing
+            return cast("list[dict[str, str]]", v)
+        if isinstance(v, dict):  # TODO (kyle): improve with `typing.TypeIs` narrowing
+            return [{"Key": k, "Value": v} for k, v in cast("dict[str, str]", v).items()]
         raise ValueError(
             f"unexpected type {type(v)}; permitted: dict[str, str] | list[dict[str, str]] | none"
         )

runway/cfngin/lookups/handlers/file.py

@@ -6,7 +6,7 @@
 import collections.abc
 import json
 import re
-from typing import TYPE_CHECKING, Any, Callable, ClassVar, overload
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, cast, overload
 
 import yaml
 from pydantic import field_validator
@@ -121,7 +121,7 @@ def parameterized_codec(raw: str, b64: Literal[False] = ...) -> GenericHelperFn:
 
 
 @overload
-def parameterized_codec(raw: str, b64: Literal[True] = ...) -> Base64: ...
+def parameterized_codec(raw: str, b64: Literal[True]) -> Base64: ...
 
 
 def parameterized_codec(raw: str, b64: bool = False) -> Any:
@@ -175,9 +175,13 @@ def _parameterize_obj(
     if isinstance(obj, str):
         return _parameterize_string(obj)
     if isinstance(obj, collections.abc.Mapping):
-        return {key: _parameterize_obj(value) for key, value in obj.items()}
+        # TODO (kyle): improve with `typing.TypeIs` narrowing
+        return {
+            key: _parameterize_obj(value) for key, value in cast("Mapping[str, Any]", obj).items()
+        }
     if isinstance(obj, collections.abc.Sequence):
-        return [_parameterize_obj(item) for item in obj]
+        # TODO (kyle): improve with `typing.TypeIs` narrowing
+        return [_parameterize_obj(item) for item in cast("Sequence[Any]", obj)]
     return obj
 
 

runway/cfngin/plan.py

@@ -50,7 +50,7 @@ def json_serial(obj: set[Any] | Any) -> Any:
 
     """
     if isinstance(obj, set):
-        return list(obj)
+        return list(obj)  # pyright: ignore[reportUnknownArgumentType]
     raise TypeError
 
 

runway/context/_runway.py

@@ -65,7 +65,7 @@ def no_color(self) -> bool:
         """
         colorize = self.env.vars.get("RUNWAY_COLORIZE")  # explicitly enable/disable
         try:
-            if isinstance(colorize, bool):
+            if isinstance(colorize, bool):  # pyright: ignore[reportUnnecessaryIsInstance]
                 # catch False
                 return not colorize
             if colorize and isinstance(colorize, str):  # type: ignore

runway/core/components/_module_path.py

@@ -158,7 +158,7 @@ def parse_obj(
                 definition=obj.path,
                 deploy_environment=deploy_environment,
             )
-        if isinstance(obj, (type(None), Path, str)):
+        if isinstance(obj, (type(None), Path, str)):  # pyright: ignore[reportUnnecessaryIsInstance]
             return cls(
                 cache_dir=cache_dir,
                 definition=obj,

runway/env_mgr/tfenv.py

@@ -198,9 +198,9 @@ def _flatten_lists(data: dict[str, Any] | list[Any] | Any) -> dict[str, Any] | A
                 data: Dict with lists to flatten.
 
             """
-            if not isinstance(data, dict):
+            if not isinstance(data, dict):  # TODO (kyle): improve with `typing.TypeIs` narrowing
                 return data
-            copy_data = data.copy()
+            copy_data = cast("dict[str, Any]", data).copy()
             for attr, val in copy_data.items():
                 if isinstance(val, list):
                     if len(cast("list[Any]", val)) == 1:
@@ -210,7 +210,7 @@ def _flatten_lists(data: dict[str, Any] | list[Any] | Any) -> dict[str, Any] | A
                         data[attr] = [_flatten_lists(v) for v in cast("list[Any]", val)]
                 elif isinstance(val, dict):
                     data[attr] = _flatten_lists(cast("dict[str, Any]", val))
-            return data
+            return cast("dict[str, Any]", data)
 
         try:
             result: dict[str, Any] | list[dict[str, Any]] = load_terraform_module(