fix quality/type lint issues

Signed-off-by: dalthecow <[email protected]>

Author: DaltheCow

src/guidellm/presentation/data_models.py

@@ -1,9 +1,9 @@
-import httpx
 import random
 from collections import defaultdict
 from math import ceil
 from typing import TYPE_CHECKING, Any, Optional, Union
 
+import httpx
 from pydantic import BaseModel, computed_field
 
 if TYPE_CHECKING:
@@ -14,8 +14,6 @@
 from guidellm.dataset.in_memory import InMemoryDatasetCreator
 from guidellm.dataset.synthetic import SyntheticDatasetConfig, SyntheticDatasetCreator
 from guidellm.objects.statistics import DistributionSummary
-from guidellm.preprocess.dataset import TokensConfig
-
 
 
 class Bucket(BaseModel):
@@ -73,29 +71,32 @@ def from_data(cls, request_loader: Any):
             FileDatasetCreator,
             HFDatasetsCreator,
         ]
-        dataset_name = ""
+        dataset_name = None
         data = request_loader.data
         data_args = request_loader.data_args
         processor = request_loader.processor
         processor_args = request_loader.processor_args
-        
+
         for creator in creators:
-            if creator.is_supported(data, None):
-                random_seed = 42
-                dataset = creator.handle_create(data, data_args, processor, processor_args, random_seed)
-                dataset_name = creator.extract_dataset_name(dataset)
-                if dataset_name is None or dataset_name == "":
-                    if creator == SyntheticDatasetCreator:
-                        data_dict = SyntheticDatasetConfig.parse_str(data)
-                        dataset_name = data_dict.source
-                    if creator == FileDatasetCreator or isinstance(creator, HFDatasetsCreator):
-                        dataset_name = data
-                    if creator == InMemoryDatasetCreator:
-                        dataset_name = "In-memory"
+            if not creator.is_supported(data, None):
+                continue
+            random_seed = 42
+            dataset = creator.handle_create(
+                data, data_args, processor, processor_args, random_seed
+            )
+            dataset_name = creator.extract_dataset_name(dataset)
+            if dataset_name is None or dataset_name == "":
+                if creator == SyntheticDatasetCreator:
+                    data_dict = SyntheticDatasetConfig.parse_str(data)
+                    dataset_name = data_dict.source
+                if creator == FileDatasetCreator or isinstance(
+                    creator, HFDatasetsCreator
+                ):
+                    dataset_name = data
+                if creator == InMemoryDatasetCreator:
+                    dataset_name = "In-memory"
                 break
-        return cls(
-            name=dataset_name or ""
-        )
+        return cls(name=dataset_name or "")
 
 
 class RunInfo(BaseModel):
@@ -111,15 +112,15 @@ def from_benchmarks(cls, benchmarks: list["GenerativeBenchmark"]):
             bm.run_stats.start_time for bm in benchmarks if bm.start_time is not None
         )
         response = httpx.get(f"https://huggingface.co/api/models/{model}")
-        modelJson = response.json()
-            
+        model_json = response.json()
+
         return cls(
-            model=Model(name=model, size=modelJson.get("usedStorage", 0)),
+            model=Model(name=model, size=model_json.get("usedStorage", 0)),
             task="N/A",
             timestamp=timestamp,
             dataset=Dataset.from_data(benchmarks[0].request_loader),
         )
-    
+
 
 class Distribution(BaseModel):
     statistics: Optional[DistributionSummary] = None