Remove old file: load_dataset.py

load_dataset.py

@@ -1,140 +0,0 @@
-"""
-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)
-        # Documents live under assets/; accept a checkout root or the assets dir itself.
-        if (self.local_dir / "assets").is_dir():
-            self.assets_dir = self.local_dir / "assets"
-        else:
-            self.assets_dir = self.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.assets_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.assets_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.assets_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.assets_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.assets_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()