remove flashinfer

nanonets-ocr.py

@@ -5,14 +5,11 @@
 #     "huggingface-hub[hf_transfer]",
 #     "pillow",
 #     "vllm",
-#     "flashinfer-python",
 #     "tqdm",
 #     "toolz",
 #     "torch",  # Added for CUDA check
 # ]
-# 
-# [[tool.uv.index]]
-# url = "https://flashinfer.ai/whl/cu121/torch2.4/"
+#
 # ///
 
 """
@@ -72,12 +69,12 @@ def make_ocr_message(
         pil_img = Image.open(image)
     else:
         raise ValueError(f"Unsupported image type: {type(image)}")
-    
+
     # Convert to base64 data URI
     buf = io.BytesIO()
     pil_img.save(buf, format="PNG")
     data_uri = f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}"
-    
+
     # Return message in vLLM format
     return [
         {
@@ -105,31 +102,33 @@ def main(
     private: bool = False,
 ):
     """Process images from HF dataset through OCR model."""
-    
+
     # Check CUDA availability first
     check_cuda_availability()
-    
+
     # Enable HF_TRANSFER for faster downloads
     os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
-    
+
     # Login to HF if token provided
     HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
     if HF_TOKEN:
         login(token=HF_TOKEN)
-    
+
     # Load dataset
     logger.info(f"Loading dataset: {input_dataset}")
     dataset = load_dataset(input_dataset, split=split)
-    
+
     # Validate image column
     if image_column not in dataset.column_names:
-        raise ValueError(f"Column '{image_column}' not found. Available: {dataset.column_names}")
-    
+        raise ValueError(
+            f"Column '{image_column}' not found. Available: {dataset.column_names}"
+        )
+
     # Limit samples if requested
     if max_samples:
         dataset = dataset.select(range(min(max_samples, len(dataset))))
         logger.info(f"Limited to {len(dataset)} samples")
-    
+
     # Initialize vLLM
     logger.info(f"Initializing vLLM with model: {model}")
     llm = LLM(
@@ -139,53 +138,55 @@ def main(
         gpu_memory_utilization=gpu_memory_utilization,
         limit_mm_per_prompt={"image": 1},
     )
-    
+
     sampling_params = SamplingParams(
         temperature=0.0,  # Deterministic for OCR
         max_tokens=max_tokens,
     )
-    
+
     # Process images in batches
     all_markdown = []
-    
+
     logger.info(f"Processing {len(dataset)} images in batches of {batch_size}")
-    
+
     # Process in batches to avoid memory issues
     for batch_indices in tqdm(
         partition_all(batch_size, range(len(dataset))),
         total=(len(dataset) + batch_size - 1) // batch_size,
-        desc="OCR processing"
+        desc="OCR processing",
     ):
         batch_indices = list(batch_indices)
         batch_images = [dataset[i][image_column] for i in batch_indices]
-        
+
         try:
             # Create messages for batch
             batch_messages = [make_ocr_message(img) for img in batch_images]
-            
+
             # Process with vLLM
             outputs = llm.chat(batch_messages, sampling_params)
-            
+
             # Extract markdown from outputs
             for output in outputs:
                 markdown_text = output.outputs[0].text.strip()
                 all_markdown.append(markdown_text)
-                
+
         except Exception as e:
             logger.error(f"Error processing batch: {e}")
             # Add error placeholders for failed batch
             all_markdown.extend(["[OCR FAILED]"] * len(batch_images))
-    
+
     # Add markdown column to dataset
     logger.info("Adding markdown column to dataset")
     dataset = dataset.add_column("markdown", all_markdown)
-    
+
     # Push to hub
     logger.info(f"Pushing to {output_dataset}")
     dataset.push_to_hub(output_dataset, private=private, token=HF_TOKEN)
-    
+
     logger.info("✅ OCR conversion complete!")
-    logger.info(f"Dataset available at: https://huggingface.co/datasets/{output_dataset}")
+    logger.info(
+        f"Dataset available at: https://huggingface.co/datasets/{output_dataset}"
+    )
 
 
 if __name__ == "__main__":
@@ -215,13 +216,15 @@ if __name__ == "__main__":
         print("   hfjobs run \\")
         print("     --flavor l4x1 \\")
         print("     --secret HF_TOKEN=... \\")
-        print("     uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \\")
+        print(
+            "     uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \\"
+        )
         print("       your-document-dataset \\")
         print("       your-markdown-output")
         print("\n" + "=" * 80)
         print("\nFor full help, run: uv run nanonets-ocr.py --help")
         sys.exit(0)
-    
+
     parser = argparse.ArgumentParser(
         description="OCR images to markdown using Nanonets-OCR-s",
         formatter_class=argparse.RawDescriptionHelpFormatter,
@@ -235,73 +238,60 @@ Examples:
 
   # Process subset for testing
   uv run nanonets-ocr.py large-dataset test-output --max-samples 100
-        """
-    )
-    
-    parser.add_argument(
-        "input_dataset",
-        help="Input dataset ID from Hugging Face Hub"
-    )
-    parser.add_argument(
-        "output_dataset",
-        help="Output dataset ID for Hugging Face Hub"
+        """,
     )
+
+    parser.add_argument("input_dataset", help="Input dataset ID from Hugging Face Hub")
+    parser.add_argument("output_dataset", help="Output dataset ID for Hugging Face Hub")
     parser.add_argument(
         "--image-column",
         default="image",
-        help="Column containing images (default: image)"
+        help="Column containing images (default: image)",
     )
     parser.add_argument(
         "--batch-size",
         type=int,
         default=8,
-        help="Batch size for processing (default: 8)"
+        help="Batch size for processing (default: 8)",
     )
     parser.add_argument(
         "--model",
         default="nanonets/Nanonets-OCR-s",
-        help="Model to use (default: nanonets/Nanonets-OCR-s)"
+        help="Model to use (default: nanonets/Nanonets-OCR-s)",
     )
     parser.add_argument(
         "--max-model-len",
         type=int,
         default=8192,
-        help="Maximum model context length (default: 8192)"
+        help="Maximum model context length (default: 8192)",
     )
     parser.add_argument(
         "--max-tokens",
         type=int,
         default=4096,
-        help="Maximum tokens to generate (default: 4096)"
+        help="Maximum tokens to generate (default: 4096)",
     )
     parser.add_argument(
         "--gpu-memory-utilization",
         type=float,
         default=0.7,
-        help="GPU memory utilization (default: 0.7)"
-    )
-    parser.add_argument(
-        "--hf-token",
-        help="Hugging Face API token"
+        help="GPU memory utilization (default: 0.7)",
     )
+    parser.add_argument("--hf-token", help="Hugging Face API token")
     parser.add_argument(
-        "--split",
-        default="train",
-        help="Dataset split to use (default: train)"
+        "--split", default="train", help="Dataset split to use (default: train)"
     )
     parser.add_argument(
         "--max-samples",
         type=int,
-        help="Maximum number of samples to process (for testing)"
+        help="Maximum number of samples to process (for testing)",
     )
     parser.add_argument(
-        "--private",
-        action="store_true",
-        help="Make output dataset private"
+        "--private", action="store_true", help="Make output dataset private"
     )
-    
+
     args = parser.parse_args()
-    
+
     main(
         input_dataset=args.input_dataset,
         output_dataset=args.output_dataset,
@@ -315,4 +305,4 @@ Examples:
         split=args.split,
         max_samples=args.max_samples,
         private=args.private,
-    )
+    )