Update level_classifier_tool_2.py

level_classifier_tool_2.py

@@ -22,40 +22,40 @@ class HFEmbeddingBackend:
 
     def __post_init__(self):
     # 1) Try to disable Spaces ZeroGPU monkey-patch proactively
-    os.environ.setdefault("SPACES_ZERO_DISABLED", "1")
-    try:
-        # If Spaces was already imported somewhere, explicitly disable its patch.
-        from spaces import zero as _spaces_zero  # safe import; no-op if not installed
-        if hasattr(_spaces_zero, "disable"):
-            _spaces_zero.disable()
-    except Exception:
-        pass
-
-    # 2) Keep attention off Flash/MemEfficient (avoid vectorized mask paths)
-    try:
-        torch.backends.cuda.sdp_kernel(enable_math=True, enable_flash=False, enable_mem_efficient=False)
-    except Exception:
-        pass
-
-    # 3) Load tokenizer/model and force eager attention (non-vmap route)
-    self.TOK = AutoTokenizer.from_pretrained(self.model_name)
-    self.MODEL = AutoModel.from_pretrained(self.model_name, attn_implementation="eager")
-
-    # (extra safety) disable any sliding/windowed attention that can trigger the vmap mask path
-    try:
-        if hasattr(self.MODEL.config, "sliding_window"):
-            self.MODEL.config.sliding_window = None
-        if hasattr(self.MODEL, "generation_config") and hasattr(self.MODEL.generation_config, "sliding_window"):
-            self.MODEL.generation_config.sliding_window = None
-    except Exception:
-        pass
-
-    try:
-        self.MODEL.config.attn_implementation = "eager"
-    except Exception:
-        pass
-
-    self.MODEL.to(self.device).eval()
+        os.environ.setdefault("SPACES_ZERO_DISABLED", "1")
+        try:
+            # If Spaces was already imported somewhere, explicitly disable its patch.
+            from spaces import zero as _spaces_zero  # safe import; no-op if not installed
+            if hasattr(_spaces_zero, "disable"):
+                _spaces_zero.disable()
+        except Exception:
+            pass
+    
+        # 2) Keep attention off Flash/MemEfficient (avoid vectorized mask paths)
+        try:
+            torch.backends.cuda.sdp_kernel(enable_math=True, enable_flash=False, enable_mem_efficient=False)
+        except Exception:
+            pass
+    
+        # 3) Load tokenizer/model and force eager attention (non-vmap route)
+        self.TOK = AutoTokenizer.from_pretrained(self.model_name)
+        self.MODEL = AutoModel.from_pretrained(self.model_name, attn_implementation="eager")
+    
+        # (extra safety) disable any sliding/windowed attention that can trigger the vmap mask path
+        try:
+            if hasattr(self.MODEL.config, "sliding_window"):
+                self.MODEL.config.sliding_window = None
+            if hasattr(self.MODEL, "generation_config") and hasattr(self.MODEL.generation_config, "sliding_window"):
+                self.MODEL.generation_config.sliding_window = None
+        except Exception:
+            pass
+    
+        try:
+            self.MODEL.config.attn_implementation = "eager"
+        except Exception:
+            pass
+    
+        self.MODEL.to(self.device).eval()
 
     def encode(self, texts: Iterable[str], batch_size: int = 32) -> "Tuple[torch.Tensor, List[str]]":
         """