running isort again

olmocr/data/convertsilver_openai.py

@@ -11,7 +11,6 @@
 from cached_path import cached_path
 
 
-
 def setup_logging():
     """Configure logging for the script."""
     logging.basicConfig(level=logging.INFO, format="[%(asctime)s] %(levelname)s: %(message)s", handlers=[logging.StreamHandler(sys.stdout)])

olmocr/eval/runeval.py

@@ -13,7 +13,7 @@
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Dict, Optional, List
+from typing import Dict, List, Optional
 
 import boto3
 import zstandard

olmocr/prompts/__init__.py

@@ -1 +1,7 @@
-from .prompts import build_openai_silver_data_prompt, PageResponse, openai_response_format_schema, build_finetuning_prompt, extract_raw_text
+from .prompts import (
+    PageResponse,
+    build_finetuning_prompt,
+    build_openai_silver_data_prompt,
+    extract_raw_text,
+    openai_response_format_schema,
+)

olmocr/train/dataloader.py

@@ -15,7 +15,6 @@
 from olmocr.prompts.anchor import get_anchor_text
 from olmocr.s3_utils import parse_custom_id, parse_s3_path
 
-
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

olmocr/train/utils.py

@@ -17,18 +17,18 @@
 from datasets import Dataset, DatasetDict, concatenate_datasets
 from transformers import AutoProcessor
 
-from .core.cli import to_native_types
-from .core.config import AwsConfig, DataConfig, SourceConfig, TrainConfig, WandbConfig
-from .core.loggers import get_logger
-from .core.paths import copy_dir, is_local
-from .core.state import BeakerState
-
 from olmocr.train.dataloader import build_finetuning_dataset
 from olmocr.train.dataprep import (
     batch_prepare_data_for_molmo_training,
     batch_prepare_data_for_qwen2_training,
 )
 
+from .core.cli import to_native_types
+from .core.config import AwsConfig, DataConfig, SourceConfig, TrainConfig, WandbConfig
+from .core.loggers import get_logger
+from .core.paths import copy_dir, is_local
+from .core.state import BeakerState
+
 T = TypeVar("T")
 
 def accelerator_to_dtype(accelerator: Accelerator) -> torch.dtype: