tune coverage

Author: xadupre

.coveragerc

@@ -3,6 +3,16 @@ exclude_lines =
     pragma: no cover
     if __name__ == .__main__.:
     @onnxscript.*
+    except ImportError:
+    except ImportError as e:
+    except Exception as e:
+    except subprocess.TimeoutExpired as e:
+    raise AssertionError(
+    raise NotImplementedError(
+    if patch_parse_processor_args:
+    if patch_DynamicCache:
+     def make_sliding_window_cache(
+     def make_hybrid_cache(
 
 [run]
 include =

onnx_diagnostic/helpers/_log_helper.py

@@ -357,7 +357,7 @@ def apply_excel_style(
         CubeViewDef.HighLightKind.GREEN: Font(color="00AA00"),
         CubeViewDef.HighLightKind.RED: Font(color="FF0000"),
     }
-    if verbose:
+    if verbose:  # pragma: no cover
         from tqdm import tqdm
 
         sheet_names = tqdm(list(workbook.sheetnames))

onnx_diagnostic/helpers/bench_run.py

@@ -180,7 +180,7 @@ def run_benchmark(
         temp_output_data is None or "temp" in temp_output_data
     ), f"Unexpected value for {temp_output_data!r}"
     assert configs, f"No configuration was given (script_name={script_name!r})"
-    if verbose:
+    if verbose:  # pragma: no cover
         from tqdm import tqdm
 
         loop = tqdm(configs)

onnx_diagnostic/helpers/log_helper.py

@@ -264,7 +264,7 @@ def to_images(
 
     @classmethod
     def _make_loop(cls, ensemble, verbose):
-        if verbose:
+        if verbose:  # pragma: no cover
             from tqdm import tqdm
 
             loop = tqdm(ensemble)

onnx_diagnostic/torch_export_patches/eval/__init__.py

@@ -85,7 +85,7 @@ def evaluation(
 
     sorted_cases = sorted(cases.items())
     loop = list(itertools.product(sorted_cases, dynamic, exporters))
-    if verbose:
+    if verbose:  # pragma: no cover
         try:
             import tqdm
 

onnx_diagnostic/torch_export_patches/patches/_patch_transformers_generation_mixin.py

@@ -134,7 +134,7 @@ def branch_3(input_ids, cache_position):
             )
         return inputs_embeds, input_ids
 
-    def prepare_inputs_for_generation(
+    def prepare_inputs_for_generation(  # pragma: no cover
         self,
         input_ids: torch.LongTensor,
         past_key_values: Optional[Cache] = None,