"""Runnable example: load the WOD-E2E Fast-dDrive SASD Parquet shards and reconstruct one
sample, framework-free (numpy + pyarrow only). Works on a TPU host, GPU box, or CPU.

  python load_example.py            # reads local ./train-*.parquet
  python load_example.py <dir>      # reads <dir>/train-*.parquet
"""
import glob, json, os, sys
import numpy as np
import pyarrow.parquet as pq

d = sys.argv[1] if len(sys.argv) > 1 else "."
info = json.load(open(os.path.join(d, "dataset_info_train.json")))
ADT = info["array_dtypes"]

paths = sorted(glob.glob(os.path.join(d, "train-*.parquet")))
row = pq.read_table(paths[0]).to_pylist()[0]

def dec(name):
    return np.frombuffer(row[name], np.dtype(ADT[name])).reshape(tuple(row[name + "_shape"]))

print(f"shards={len(paths)}  num_samples={info['num_samples']}  sample_id={row['sample_id']}")
print(f"L={row['L']}  n_blocks={row['n_blocks']}")
for name in ADT:
    a = dec(name)
    print(f"  {name:16s} {str(a.shape):14s} {a.dtype}")
print("input_ids[:12] =", dec("input_ids")[:12].tolist())