[Automated Commit] Format Codebase

Author: github-actions[bot]

multimodal/vl2l/src/mlperf_inference_multimodal_vl2l/cli.py

@@ -70,7 +70,9 @@ def benchmark(
     )
     logger.info("Running VL2L benchmark with settings: {}", settings)
     logger.info("Running VL2L benchmark with dataset: {}", dataset)
-    logger.info("Running VL2L benchmark with OpenAI API endpoint: {}", endpoint)
+    logger.info(
+        "Running VL2L benchmark with OpenAI API endpoint: {}",
+        endpoint)
     logger.info("Running VL2L benchmark with random seed: {}", random_seed)
     test_settings, log_settings = settings.to_lgtype()
     task = ShopifyGlobalCatalogue(

multimodal/vl2l/src/mlperf_inference_multimodal_vl2l/evaluation.py

@@ -51,7 +51,8 @@ def get_hierarchical_components(
     intersection_count = 0
 
     # Iterate through the paths simultaneously
-    for pred_cat, true_cat in zip(predicted_categories, true_categories, strict=False):
+    for pred_cat, true_cat in zip(
+            predicted_categories, true_categories, strict=False):
         if pred_cat == true_cat:
             intersection_count += 1
         else:

multimodal/vl2l/src/mlperf_inference_multimodal_vl2l/schema.py

@@ -196,7 +196,8 @@ class TestSettings(BaseModelWithAttributeDescriptionsFromDocstrings):
         mode="before",
     )
     @classmethod
-    def parse_timedelta(cls, value: timedelta | float | str) -> timedelta | str:
+    def parse_timedelta(cls, value: timedelta | float |
+                        str) -> timedelta | str:
         """Parse timedelta from seconds (int/float/str) or ISO 8601 format."""
         if isinstance(value, timedelta):
             return value
@@ -222,9 +223,12 @@ def to_lgtype(self) -> lg.TestSettings:
         settings.server_target_latency_ns = round(
             self.server_target_latency.total_seconds() * 1e9,
         )
-        settings.ttft_latency = round(self.server_ttft_latency.total_seconds() * 1e9)
-        settings.tpot_latency = round(self.server_tpot_latency.total_seconds() * 1e9)
-        settings.min_duration_ms = round(self.min_duration.total_seconds() * 1000)
+        settings.ttft_latency = round(
+            self.server_ttft_latency.total_seconds() * 1e9)
+        settings.tpot_latency = round(
+            self.server_tpot_latency.total_seconds() * 1e9)
+        settings.min_duration_ms = round(
+            self.min_duration.total_seconds() * 1000)
         settings.min_query_count = self.min_query_count
         settings.performance_sample_count_override = (
             self.performance_sample_count_override
@@ -414,5 +418,6 @@ def ensure_content_is_list(
                 == "pydantic_core._pydantic_core"
                 and message["content"].__class__.__name__ == "ValidatorIterator"
             ):
-                message["content"] = list(message["content"])  # type: ignore[arg-type]
+                message["content"] = list(
+                    message["content"])  # type: ignore[arg-type]
         return messages

multimodal/vl2l/src/mlperf_inference_multimodal_vl2l/task.py

@@ -145,7 +145,9 @@ def estimated_num_performance_samples(self) -> int:
         """
         estimation_indices = random.sample(
             range(self.total_num_samples),
-            k=min(MAX_NUM_ESTIMATION_PERFORMANCE_SAMPLES, self.total_num_samples),
+            k=min(
+                MAX_NUM_ESTIMATION_PERFORMANCE_SAMPLES,
+                self.total_num_samples),
         )
         estimation_samples = [
             self.formulate_loaded_sample(self.dataset[i]) for i in estimation_indices
@@ -205,7 +207,8 @@ def _unload_samples_from_ram(query_sample_indices: list[int]) -> None:
             _unload_samples_from_ram,
         )
 
-    async def _query_endpoint_async_batch(self, query_sample: lg.QuerySample) -> None:
+    async def _query_endpoint_async_batch(
+            self, query_sample: lg.QuerySample) -> None:
         """Query the endpoint through the async OpenAI API client."""
         try:
             sample = self.loaded_samples[query_sample.index]
@@ -282,7 +285,8 @@ async def _query_endpoint_async_batch(self, query_sample: lg.QuerySample) -> Non
                 ],
             )
 
-    async def _query_endpoint_async_stream(self, query_sample: lg.QuerySample) -> None:
+    async def _query_endpoint_async_stream(
+            self, query_sample: lg.QuerySample) -> None:
         """Query the endpoint through the async OpenAI API client."""
         ttft_set = False
         try: