rearrange to clean up diff

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

Author: kylesayrs

src/compressed_tensors/compressors/quantized_compressors/base.py

@@ -189,19 +189,21 @@ def decompress(
                 path_to_model_or_tensors, names_to_scheme
             )
 
-    def decompress_from_state_dict(
+    def _decompress_from_path(
         self,
-        state_dict: Dict[str, torch.Tensor],
+        path_to_model: Union[str, Path, Dict[str, Any]],
         names_to_scheme: Dict[str, QuantizationScheme],
-    ) -> Generator[Tuple[str, Dict[str, torch.Tensor]], None, None]:
-        weight_mappings = get_nested_mappings_from_state_dict(
-            state_dict, self.compression_param_names
+        device: str,
+    ):
+        weight_mappings = get_nested_weight_mappings(
+            path_to_model, self.compression_param_names
         )
         for module_path in weight_mappings.keys():
             weight_data = {}
-            for param_name, param_value in weight_mappings[module_path].items():
-                weight_data[param_name] = param_value
-
+            for param_name, safe_path in weight_mappings[module_path].items():
+                full_name = merge_names(module_path, param_name)
+                with safe_open(safe_path, framework="pt", device=device) as f:
+                    weight_data[param_name] = f.get_tensor(full_name)
             if "weight_scale" in weight_data:
                 quant_args = names_to_scheme[module_path].weights
                 decompressed = self.decompress_weight(
@@ -210,21 +212,19 @@ def decompress_from_state_dict(
                 weight_data["weight"] = decompressed
                 yield module_path, weight_data
 
-    def _decompress_from_path(
+    def decompress_from_state_dict(
         self,
-        path_to_model: Union[str, Path, Dict[str, Any]],
+        state_dict: Dict[str, torch.Tensor],
         names_to_scheme: Dict[str, QuantizationScheme],
-        device: str,
-    ):
-        weight_mappings = get_nested_weight_mappings(
-            path_to_model, self.compression_param_names
+    ) -> Generator[Tuple[str, Dict[str, torch.Tensor]], None, None]:
+        weight_mappings = get_nested_mappings_from_state_dict(
+            state_dict, self.compression_param_names
         )
         for module_path in weight_mappings.keys():
             weight_data = {}
-            for param_name, safe_path in weight_mappings[module_path].items():
-                full_name = merge_names(module_path, param_name)
-                with safe_open(safe_path, framework="pt", device=device) as f:
-                    weight_data[param_name] = f.get_tensor(full_name)
+            for param_name, param_value in weight_mappings[module_path].items():
+                weight_data[param_name] = param_value
+
             if "weight_scale" in weight_data:
                 quant_args = names_to_scheme[module_path].weights
                 decompressed = self.decompress_weight(