add colqwen stuff and pass ruff

apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann.py

@@ -4,7 +4,7 @@
 # pip install tqdm
 # pip install pillow
 
-#%%
+# %%
 from pdf2image import convert_from_path
 
 pdf_path = "pdfs/2004.12832v2.pdf"
@@ -13,9 +13,7 @@ images = convert_from_path(pdf_path)
 for i, image in enumerate(images):
     image.save(f"pages/page_{i + 1}.png", "PNG")
 
-#%%
-import numpy as np
-import concurrent.futures
+# %%
 import os
 from pathlib import Path
 
@@ -24,6 +22,7 @@ _repo_root = Path(__file__).resolve().parents[3]
 _leann_core_src = _repo_root / "packages" / "leann-core" / "src"
 _leann_hnsw_pkg = _repo_root / "packages" / "leann-backend-hnsw"
 import sys
+
 if str(_leann_core_src) not in sys.path:
     sys.path.append(str(_leann_core_src))
 if str(_leann_hnsw_pkg) not in sys.path:
@@ -34,26 +33,30 @@ from leann_multi_vector import LeannMultiVector
 
 class LeannRetriever(LeannMultiVector):
     pass
-#%%
+
+
+# %%
+from typing import cast
+
+import torch
 from colpali_engine.models import ColPali
 from colpali_engine.models.paligemma.colpali.processing_colpali import ColPaliProcessor
-from colpali_engine.utils.processing_utils import BaseVisualRetrieverProcessor
 from colpali_engine.utils.torch_utils import ListDataset, get_torch_device
 from torch.utils.data import DataLoader
-import torch
-from typing import List, cast
 
 # Auto-select device: CUDA > MPS (mac) > CPU
 _device_str = (
-    "cuda" if torch.cuda.is_available() else (
-        "mps" if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available() else "cpu"
+    "cuda"
+    if torch.cuda.is_available()
+    else (
+        "mps"
+        if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available()
+        else "cpu"
     )
 )
 device = get_torch_device(_device_str)
 # Prefer fp16 on GPU/MPS, bfloat16 on CPU
-_dtype = (
-    torch.float16 if _device_str in ("cuda", "mps") else torch.bfloat16
-)
+_dtype = torch.float16 if _device_str in ("cuda", "mps") else torch.bfloat16
 model_name = "vidore/colpali-v1.2"
 
 model = ColPali.from_pretrained(
@@ -77,21 +80,21 @@ dataloader = DataLoader(
     collate_fn=lambda x: processor.process_queries(x),
 )
 
-qs: List[torch.Tensor] = []
+qs: list[torch.Tensor] = []
 for batch_query in dataloader:
     with torch.no_grad():
         batch_query = {k: v.to(model.device) for k, v in batch_query.items()}
         embeddings_query = model(**batch_query)
     qs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
 print(qs[0].shape)
-#%%
+# %%
 
 
-from tqdm import tqdm
-from PIL import Image
-import os
 import re
 
+from PIL import Image
+from tqdm import tqdm
+
 page_filenames = sorted(os.listdir("./pages"), key=lambda n: int(re.search(r"\d+", n).group()))
 images = [Image.open(os.path.join("./pages", name)) for name in page_filenames]
 
@@ -102,7 +105,7 @@ dataloader = DataLoader(
     collate_fn=lambda x: processor.process_images(x),
 )
 
-ds: List[torch.Tensor] = []
+ds: list[torch.Tensor] = []
 for batch_doc in tqdm(dataloader):
     with torch.no_grad():
         batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
@@ -111,7 +114,7 @@ for batch_doc in tqdm(dataloader):
 
 print(ds[0].shape)
 
-#%%
+# %%
 # Build HNSW index via LeannRetriever primitives and run search
 index_path = "./indexes/colpali.leann"
 retriever = LeannRetriever(index_path=index_path, dim=int(ds[0].shape[-1]))
@@ -129,6 +132,3 @@ for query in qs:
     query_np = query.float().numpy()
     result = retriever.search(query_np, topk=1)
     print(filepaths[result[0][1]])
-
-
-