Rewrite loader to read from local HF checkout instead of S3

load_dataset.py

@@ -0,0 +1,135 @@
+"""
+FCC Invoices Verified Augmented - Dataset Loader
+
+Usage:
+    from huggingface_hub import snapshot_download
+    from load_dataset import FCCInvoicesDataset
+
+    local_dir = snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset")
+    ds = FCCInvoicesDataset(local_dir=local_dir)
+
+    # Iterate all (document, pipeline) pairs
+    for sample in ds:
+        print(sample["doc_id"], sample["pipeline_name"])
+        gt = sample["ground_truth"]["inference_result"]
+        print(gt["Agency"], gt["GrossTotal"])
+
+    # Get the local path to a single noisy PDF
+    path = ds.pdf_path(doc_id="033f718b16cb597c065930410752c294", pipeline_name="custom13")
+
+Requirements:
+    pip install huggingface_hub
+"""
+
+import json
+from pathlib import Path
+from typing import Iterator
+
+
+PIPELINES = [
+    "default", "archetype3", "archetype4", "archetype7", "archetype9",
+    "archetype10", "archetype11", "custom12", "custom13", "custom14",
+    "custom15", "custom16", "custom17", "custom18", "custom19",
+    "custom20", "custom21", "custom22",
+]
+
+
+class FCCInvoicesDataset:
+    """Lazy iterator over all (document, pipeline) pairs in a local ConfBench checkout."""
+
+    def __init__(self, local_dir: str):
+        self.local_dir = Path(local_dir)
+        self._doc_ids: list[str] | None = None
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def doc_ids(self) -> list[str]:
+        """Return all document IDs (md5 hashes)."""
+        if self._doc_ids is None:
+            self._doc_ids = sorted(
+                p.name for p in self.local_dir.iterdir()
+                if p.is_dir() and (p / "metadata.json").exists()
+            )
+        return self._doc_ids
+
+    def load_ground_truth(self, doc_id: str) -> dict:
+        """Read and parse gt.json for a document."""
+        return json.loads((self.local_dir / doc_id / "gt.json").read_text())
+
+    def load_metadata(self, doc_id: str) -> dict:
+        """Read and parse metadata.json (pipeline manifest) for a document."""
+        return json.loads((self.local_dir / doc_id / "metadata.json").read_text())
+
+    def pdf_path(self, doc_id: str, pipeline_name: str) -> str:
+        """Return the local path to a noisy PDF."""
+        return str(self.local_dir / doc_id / pipeline_name / f"{pipeline_name}_noisy.pdf")
+
+    def original_pdf_path(self, doc_id: str) -> str:
+        """Return the local path to the original clean PDF."""
+        return str(self.local_dir / doc_id / "original.pdf")
+
+    def get_sample(self, doc_id: str, pipeline_name: str) -> dict:
+        """Return a single sample dict."""
+        gt = self.load_ground_truth(doc_id)
+        return {
+            "doc_id":        doc_id,
+            "pipeline_name": pipeline_name,
+            "pipeline_type": "archetype" if not pipeline_name.startswith("custom") else "custom",
+            "original_pdf":  self.original_pdf_path(doc_id),
+            "noisy_pdf":     self.pdf_path(doc_id, pipeline_name),
+            "ground_truth":  gt,
+        }
+
+    def __iter__(self) -> Iterator[dict]:
+        """Yield one dict per (document, pipeline) pair."""
+        for doc_id in self.doc_ids():
+            meta = self.load_metadata(doc_id)
+            gt   = self.load_ground_truth(doc_id)
+            for pipeline in meta.get("pipelines", []):
+                name = pipeline["pipeline_name"]
+                yield {
+                    "doc_id":        doc_id,
+                    "pipeline_name": name,
+                    "pipeline_type": "archetype" if not name.startswith("custom") else "custom",
+                    "original_pdf":  self.original_pdf_path(doc_id),
+                    "noisy_pdf":     self.pdf_path(doc_id, name),
+                    "ground_truth":  gt,
+                }
+
+    def __len__(self) -> int:
+        return len(self.doc_ids()) * len(PIPELINES)
+
+
+# ------------------------------------------------------------------
+# CLI convenience
+# ------------------------------------------------------------------
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="FCC Invoices dataset helper")
+    parser.add_argument("--local-dir", default=".", help="Path to local ConfBench checkout")
+    sub = parser.add_subparsers(dest="cmd")
+
+    p_list = sub.add_parser("list", help="List all document IDs")
+
+    p_gt = sub.add_parser("gt", help="Print ground truth for a document")
+    p_gt.add_argument("doc_id")
+
+    p_path = sub.add_parser("path", help="Print the local path to a noisy PDF")
+    p_path.add_argument("doc_id")
+    p_path.add_argument("pipeline_name")
+
+    args = parser.parse_args()
+    ds = FCCInvoicesDataset(local_dir=args.local_dir)
+
+    if args.cmd == "list":
+        for d in ds.doc_ids():
+            print(d)
+    elif args.cmd == "gt":
+        print(json.dumps(ds.load_ground_truth(args.doc_id), indent=2))
+    elif args.cmd == "path":
+        print(ds.pdf_path(args.doc_id, args.pipeline_name))
+    else:
+        parser.print_help()