From 5207768b6e533c0509218376942309d1c7bac22f Mon Sep 17 00:00:00 2001
From: Dan Redding <125183946+dangotbanned@users.noreply.github.com>
Date: Wed, 4 Sep 2024 09:58:29 +0100
Subject: [PATCH] refactor: Remove `channels` parameter in
 `infer_encoding_types` (#3564)

* test: Monkeypatch channels global

Removes the dependency in `test_infer_encoding_types`

* refactor: Remove `channels` parameter in `infer_encoding_types`

Was kept, but only needed for tests since #3444.
As `infer_encoding_types` is not public API - this is a safe remove, no need for deprecation
---
 altair/utils/core.py     | 29 ++---------------------------
 tests/utils/test_core.py | 35 +++++++++++++++++++++++++++++------
 2 files changed, 31 insertions(+), 33 deletions(-)

diff --git a/altair/utils/core.py b/altair/utils/core.py
index f5ef659b1..c8b47f9f8 100644
--- a/altair/utils/core.py
+++ b/altair/utils/core.py
@@ -33,7 +33,6 @@
 
 if TYPE_CHECKING:
     import typing as t
-    from types import ModuleType
 
     import pandas as pd
     from narwhals.typing import IntoExpr
@@ -812,22 +811,6 @@ class _ChannelCache:
     channel_to_name: dict[type[SchemaBase], str]
     name_to_channel: dict[str, dict[_ChannelType, type[SchemaBase]]]
 
-    @classmethod
-    def from_channels(cls, channels: ModuleType, /) -> _ChannelCache:
-        # - This branch is only kept for tests that depend on mocking `channels`.
-        # - No longer needs to pass around `channels` reference and rebuild every call.
-        c_to_n = {
-            c: c._encoding_name
-            for c in channels.__dict__.values()
-            if isinstance(c, type)
-            and issubclass(c, SchemaBase)
-            and hasattr(c, "_encoding_name")
-        }
-        self = cls.__new__(cls)
-        self.channel_to_name = c_to_n
-        self.name_to_channel = _invert_group_channels(c_to_n)
-        return self
-
     @classmethod
     def from_cache(cls) -> _ChannelCache:
         global _CHANNEL_CACHE
@@ -925,9 +908,7 @@ def _reduce(it: Iterator[tuple[type[Any], str]]) -> Any:
     return {k: _reduce(chans) for k, chans in grouper}
 
 
-def infer_encoding_types(
-    args: tuple[Any, ...], kwargs: dict[str, Any], channels: ModuleType | None = None
-):
+def infer_encoding_types(args: tuple[Any, ...], kwargs: dict[str, Any]):
     """
     Infer typed keyword arguments for args and kwargs.
 
@@ -937,8 +918,6 @@ def infer_encoding_types(
         Sequence of function args
     kwargs : MutableMapping
         Dict of function kwargs
-    channels : ModuleType
-        The module containing all altair encoding channel classes.
 
     Returns
     -------
@@ -946,11 +925,7 @@ def infer_encoding_types(
         All args and kwargs in a single dict, with keys and types
         based on the channels mapping.
     """
-    cache = (
-        _ChannelCache.from_channels(channels)
-        if channels
-        else _ChannelCache.from_cache()
-    )
+    cache = _ChannelCache.from_cache()
     # First use the mapping to convert args to kwargs based on their types.
     for arg in args:
         el = next(iter(arg), None) if isinstance(arg, (list, tuple)) else arg
diff --git a/tests/utils/test_core.py b/tests/utils/test_core.py
index 295e2b38f..d43f88dd8 100644
--- a/tests/utils/test_core.py
+++ b/tests/utils/test_core.py
@@ -11,6 +11,7 @@
 from pandas.api.types import infer_dtype
 
 import altair as alt
+from altair.utils import core
 from altair.utils.core import infer_encoding_types, parse_shorthand, update_nested
 from tests import skip_requires_pyarrow
 
@@ -267,18 +268,40 @@ def test_update_nested():
 
 
 @pytest.fixture
-def channels():
+def channels() -> types.ModuleType:
     channels = types.ModuleType("channels")
     exec(FAKE_CHANNELS_MODULE, channels.__dict__)
     return channels
 
 
+@pytest.fixture
+def channels_cached(channels) -> core._ChannelCache:
+    """Previously ``_ChannelCache.from_channels``."""
+    cached = core._ChannelCache.__new__(core._ChannelCache)
+    cached.channel_to_name = {
+        c: c._encoding_name
+        for c in channels.__dict__.values()
+        if isinstance(c, type)
+        and issubclass(c, alt.SchemaBase)
+        and hasattr(c, "_encoding_name")
+    }
+    cached.name_to_channel = core._invert_group_channels(cached.channel_to_name)
+    return cached
+
+
 def _getargs(*args, **kwargs):
     return args, kwargs
 
 
-# NOTE: Dependent on a no longer needed implementation detail
-def test_infer_encoding_types(channels):
+def test_infer_encoding_types(
+    monkeypatch: pytest.MonkeyPatch, channels, channels_cached
+):
+    # Indirectly initialize `_CHANNEL_CACHE`
+    infer_encoding_types((), {})
+    # Replace with contents of `FAKE_CHANNELS_MODULE`
+    # Scoped to only this test
+    monkeypatch.setattr(core, "_CHANNEL_CACHE", channels_cached)
+
     expected = {
         "x": channels.X("xval"),
         "y": channels.YValue("yval"),
@@ -289,17 +312,17 @@ def test_infer_encoding_types(channels):
     args, kwds = _getargs(
         channels.X("xval"), channels.YValue("yval"), channels.StrokeWidthValue(4)
     )
-    assert infer_encoding_types(args, kwds, channels) == expected
+    assert infer_encoding_types(args, kwds) == expected
 
     # All keyword args
     args, kwds = _getargs(x="xval", y=alt.value("yval"), strokeWidth=alt.value(4))
-    assert infer_encoding_types(args, kwds, channels) == expected
+    assert infer_encoding_types(args, kwds) == expected
 
     # Mixed positional & keyword
     args, kwds = _getargs(
         channels.X("xval"), channels.YValue("yval"), strokeWidth=alt.value(4)
     )
-    assert infer_encoding_types(args, kwds, channels) == expected
+    assert infer_encoding_types(args, kwds) == expected
 
 
 def test_infer_encoding_types_with_condition():