Reduce and tighten comments and docstrings across the test suite (#6429)

* Reduce and tighten comments and docstrings in tests

Shorten verbose comments and docstrings across the test suite without
changing any test logic. Remove narration that restates the next line,
collapse long module and test docstrings to a single line, and drop banner
separators. Keep regression context (issue and PR references, run ids),
skip reasons, mocking and timing rationale, license headers, lint and type
directives, and commented-out code.

Comments and docstrings only: an AST signature check confirms no code,
assertions, or string literals changed, and the suite byte-compiles cleanly.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

tests/python/test_fast_language_model_text_only.py

@@ -63,7 +63,7 @@ def _param_default(method, name):
 
 
 def _load_text_only_namespace():
-    # Exec the text-only helpers from _utils into one namespace (no unsloth import),
+    # Exec the _utils text-only helpers into one namespace (no unsloth import),
     # in dependency order so cross-references resolve.
     source = _source(UTILS_PATH)
     import transformers
@@ -123,8 +123,7 @@ def test_fast_language_model_forwards_text_only_to_fast_model():
     source = _source(LOADER_PATH)
     method = _class_method(ast.parse(source), "FastLanguageModel", "from_pretrained")
 
-    # text_only defaults False (opt-in, not forced True), and both FastModel
-    # delegations forward it.
+    # text_only defaults False (opt-in); both FastModel delegations forward it.
     text_only_default = _param_default(method, "text_only")
     assert isinstance(text_only_default, ast.Constant) and text_only_default.value is False
 
@@ -206,22 +205,14 @@ def test_fast_base_model_text_only_bypasses_vision_auto_model():
 
 
 def test_gemma3_text_only_model_class_resolves_and_has_no_vision_tower():
-    """Tiny end-to-end: build a Gemma3 text-only config, instantiate the
-    matching model class with shrunken hidden sizes, assert it has the
-    text language model attributes and no vision tower attribute.
-
-    This is the integration check the AST-only tests were missing -- it
-    proves the text-only routing actually produces a model that can be
-    instantiated and that the resulting model is purely text. We use
-    shrunken hidden sizes so the test is fast and CPU-only.
-    """
+    """End-to-end: a tiny Gemma3 text-only model instantiates with text LM attrs and no vision tower."""
     transformers = pytest.importorskip("transformers")
     helper = _load_text_only_helper()
 
     full_config = transformers.Gemma3Config()
     text_config = helper(full_config, "google/gemma-3-27b-it")
 
-    # Shrink for a cheap CPU instantiation; keep the shape attrs read at construction.
+    # Shrink for cheap CPU instantiation.
     text_config.num_hidden_layers = 1
     text_config.hidden_size = 32
     text_config.intermediate_size = 32
@@ -233,10 +224,9 @@ def test_gemma3_text_only_model_class_resolves_and_has_no_vision_tower():
     model_class = transformers.AutoModelForCausalLM._model_mapping[type(text_config)]
     model = model_class(text_config)
 
-    # Positive checks: text language model surface is present.
     assert hasattr(model, "lm_head"), "text-only Gemma3 model should expose lm_head"
 
-    # Negative checks: no vision tower / multimodal projector remains.
+    # No vision tower / multimodal projector remains.
     assert not hasattr(
         model, "vision_tower"
     ), "text-only Gemma3 model should not have a vision_tower"
@@ -246,7 +236,7 @@ def test_gemma3_text_only_model_class_resolves_and_has_no_vision_tower():
 
 
 def test_helper_defined_once_in_utils_and_imported():
-    # _get_text_only_config is defined only in _utils and imported by loader + vision.
+    # _get_text_only_config defined only in _utils, imported by loader + vision.
     def _defines(path):
         return any(
             isinstance(n, ast.FunctionDef) and n.name == "_get_text_only_config"
@@ -274,7 +264,7 @@ def _load_util_func(name):
 
 
 def test_text_only_guard_predicate_across_vlm_families():
-    # Text-only is taken only when the resolved class remaps VLM weights.
+    # Text-only taken only when the resolved class remaps VLM weights.
     transformers = pytest.importorskip("transformers")
     from transformers import AutoModelForCausalLM
 
@@ -309,7 +299,7 @@ def test_text_only_guard_predicate_across_vlm_families():
 
 
 def test_text_only_helper_preserves_quantization_config():
-    # quantization_config must survive the strip so pre-quantized repos still load. A
+    # quantization_config must survive the strip so pre-quantized repos load. A
     # sentinel object avoids a bitsandbytes dependency on transformers 4.51.3.
     transformers = pytest.importorskip("transformers")
     helper = _load_text_only_helper()
@@ -318,13 +308,13 @@ def test_text_only_helper_preserves_quantization_config():
     config.quantization_config = sentinel
     text_config = helper(config, "google/gemma-3-27b-it")
     assert getattr(text_config, "quantization_config", None) is sentinel
-    # The parent's shared text sub-config must not be mutated by the carry-over.
+    # The parent's shared text sub-config must not be mutated.
     assert getattr(config.get_text_config(), "quantization_config", None) is None
 
 
 def test_text_only_key_mapping_targets_published_prefixes():
-    # The mapping must remap the published VLM decoder prefixes and only apply on
-    # transformers >=5 (on 4.x base_model_prefix handles it and a mapping hurts).
+    # Remap the published VLM decoder prefixes, applying only on transformers >=5
+    # (on 4.x base_model_prefix handles it and a mapping hurts).
     transformers = pytest.importorskip("transformers")
     get_key_mapping = _load_util_func("_get_text_only_key_mapping")
     mapping = get_key_mapping(transformers.Gemma3Config(), transformers.Gemma3TextConfig())
@@ -338,8 +328,8 @@ def test_text_only_key_mapping_targets_published_prefixes():
 
 
 def test_gemma3_text_only_loads_real_language_weights_from_vlm_checkpoint(tmp_path):
-    # Regression for PR #5816: text-only loading of a Gemma 3 VLM checkpoint must load the
-    # real language weights, not random ones. Fails on tf >=5 without the key_mapping fix.
+    # PR #5816: text-only loading of a Gemma 3 VLM checkpoint must load real
+    # language weights, not random ones. Fails on tf >=5 without the key_mapping fix.
     transformers = pytest.importorskip("transformers")
     torch = pytest.importorskip("torch")
     import shutil
@@ -391,7 +381,7 @@ def test_gemma3_text_only_loads_real_language_weights_from_vlm_checkpoint(tmp_pa
     save_dir = tmp_path / "vlm"
     full_model.save_pretrained(save_dir, safe_serialization = True)
 
-    # tf >=5 saves under an outer "model." prefix; strip it to reproduce the real
+    # tf >=5 saves under an outer "model." prefix; strip it to reproduce the
     # language_model.model.* layout the published Gemma 3 checkpoints use.
     real_dir = tmp_path / "real"
     real_dir.mkdir()