test(batches): restore alias-resolver coverage and fix harness after staging merge

Merging staging surfaced two test gaps. Restoring test_router.py to staging's
version while re-applying the new tests accidentally dropped the original
get_deployment_model_for_alias cases, which router_code_coverage.py (AST
call-graph) needs to see the public method exercised by a router-named test.

Staging also added an exact-kwargs batch-create contract test whose router is a
spec'd MagicMock. The create path now resolves the request alias to the
deployment's real model via get_deployment_model_for_alias, so the unconfigured
mock returned a MagicMock for "model". Wire that seam to the CREDS model so the
swap mirrors production and the payload assertion stays meaningful.

tests/test_litellm/proxy/batches_endpoints/test_endpoints.py

@@ -153,6 +153,13 @@ def _creds_lookup(*, model_id: str) -> Dict[str, str]:
     return dict(CREDS[model_id])
 
 
+def _alias_lookup(*, model_id: str) -> str:
+    # The endpoint swaps the request model for the deployment's real provider
+    # model before calling the provider; mirror that with the CREDS model so a
+    # wrong/hardcoded model_id KeyErrors instead of hiding.
+    return CREDS[model_id]["model"]
+
+
 @pytest.fixture
 def harness():
     """Seam harness. Patches only true I/O boundaries; pure encode/decode/merge
@@ -169,6 +176,7 @@ def harness():
     router.get_deployment_credentials_with_provider = MagicMock(
         side_effect=_creds_lookup
     )
+    router.get_deployment_model_for_alias = MagicMock(side_effect=_alias_lookup)
 
     read_body = AsyncMock(side_effect=lambda request: body_holder["body"])
     pre_call = AsyncMock(side_effect=lambda **kw: (body_holder["body"], MagicMock()))

tests/test_litellm/test_router.py

@@ -5174,6 +5174,48 @@ def test_is_deployment_blocked_static_helper_reflects_blocked_flag():
     )
 
 
+def test_get_deployment_model_for_alias_resolves_underlying_model():
+    """
+    The proxy batch-create path resolves a model-group alias to its deployment
+    so it can hand the provider the deployment's real model id, not the alias.
+    get_llm_provider cannot resolve a proxy alias, so without this the Bedrock
+    batch transform receives the alias as a modelId and AWS rejects it.
+    """
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "bedrock-batch-haiku",
+                "litellm_params": {
+                    "model": "bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+                    "aws_region_name": "us-east-1",
+                },
+                "model_info": {"id": "bedrock-batch-dep-0"},
+            }
+        ]
+    )
+
+    assert (
+        router.get_deployment_model_for_alias(model_id="bedrock-batch-haiku")
+        == "bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0"
+    )
+    # Resolving by deployment id returns the same underlying model.
+    assert (
+        router.get_deployment_model_for_alias(model_id="bedrock-batch-dep-0")
+        == "bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0"
+    )
+
+
+def test_get_deployment_model_for_alias_returns_none_for_unknown_model():
+    router = _router_with_two_deployments([False, False])
+    assert router.get_deployment_model_for_alias(model_id="does-not-exist") is None
+
+
+def test_get_deployment_model_for_alias_returns_none_for_blocked_deployment():
+    router = _router_with_two_deployments([True, False])
+    assert router.get_deployment_model_for_alias(model_id="dep-0") is None
+    assert router.get_deployment_model_for_alias(model_id="dep-1") == "openai/gpt-4o-1"
+
+
 def test_resolve_unblocked_deployment_resolves_alias_id_and_wildcard():
     """
     _resolve_unblocked_deployment underpins both the credential resolver and the