[fix] pre-commit update

model/reward/instructor/tests/test_dataset.py

@@ -1,40 +1,41 @@
-from transformers import AutoTokenizer
+# -*- coding: utf-8 -*-
+from experimental_dataset import DataCollatorForSummaryScore, HFSummaryQuality
+from rank_datasets import DataCollatorForPairRank, HFSummary, WebGPT
 from torch.utils.data import DataLoader
-from rank_datasets import WebGPT, HFSummary, DataCollatorForPairRank
-from experimental_dataset import HFSummaryQuality, DataCollatorForSummaryScore
+from transformers import AutoTokenizer
+
 
 def test_hfsummary():
-    
+
     tokenizer = AutoTokenizer.from_pretrained("bigscience/mt0-large")
     collate_fn = DataCollatorForPairRank(tokenizer, max_length=200)
-    dataset = HFSummary('train')
+    dataset = HFSummary("train")
     print(len(dataset))
     dataloader = DataLoader(dataset, collate_fn=collate_fn, batch_size=8)
     for batch in dataloader:
-        batch['input_ids'].shape
- 
+        batch["input_ids"].shape
+
 
 def test_webgpt():
-    
+
     tokenizer = AutoTokenizer.from_pretrained("bigscience/mt0-large")
     collate_fn = DataCollatorForPairRank(tokenizer, max_length=200)
     dataset = WebGPT()
     dataloader = DataLoader(dataset, collate_fn=collate_fn, batch_size=32)
     for batch in dataloader:
-        print(batch['input_ids'].shape)
+        print(batch["input_ids"].shape)
 
 
 def test_hf_quality():
 
     tokenizer = AutoTokenizer.from_pretrained("bigscience/mt0-large")
     collate_fn = DataCollatorForSummaryScore(tokenizer, max_length=200)
-    dataset = HFSummaryQuality('validation', tokenizer)
+    dataset = HFSummaryQuality("validation", tokenizer)
     dataloader = DataLoader(dataset, collate_fn=collate_fn, batch_size=32)
     for batch in dataloader:
-        print(batch['input_ids'].shape)
-
+        print(batch["input_ids"].shape)
 
 
 if __name__ == "__main__":
     test_hf_quality()
-    # test_webgpt()
+    # test_webgpt()