"""EarthView (Satellogic 1 m, CC BY 4.0): build real same-place / different-date RGB pairs as 'no change' negatives.

Downloads N shards, keeps locations with >= 2 revisits, stores up to 3 revisit pairs per location as uint8
(K, 2, H, W, 3) at native 1 m. The trainer crops ~115 px and resizes to 192 (~0.6 m/px).
usage: python ev_prep.py N_SHARDS
"""
import os
import sys
from concurrent.futures import ThreadPoolExecutor

import numpy as np
import pyarrow.parquet as pq
from huggingface_hub import hf_hub_download

N = int(sys.argv[1]) if len(sys.argv) > 1 else 20
OUT = "/workspace/prep/ev_imgs.npy"


def to_rgb8(a):
    """(bands,H,W) raw -> (H,W,3) uint8 with a per-image percentile stretch (display-like)."""
    a = np.asarray(a, np.float32)[:3].transpose(1, 2, 0)
    lo, hi = np.percentile(a, 1), np.percentile(a, 99)
    return np.clip((a - lo) * 255.0 / max(hi - lo, 1e-6), 0, 255).astype(np.uint8)


def shard(i):
    p = hf_hub_download("satellogic/EarthView", f"satellogic/train-{i:05d}-of-07863.parquet", repo_type="dataset",
                        local_dir="/workspace/data/ev")
    rng = np.random.default_rng(i)
    out = []
    col = pq.read_table(p, columns=["rgb"])["rgb"].combine_chunks()
    nrev = np.diff(col.offsets.to_numpy())
    vals = col.flatten().flatten().flatten().flatten().to_numpy(zero_copy_only=False).astype(np.uint8)
    per = vals.size // max(nrev.sum(), 1)
    side = int(round((per / 3) ** 0.5))
    imgs = vals.reshape(-1, 3, side, side)
    start = np.concatenate([[0], np.cumsum(nrev)])
    for j in range(len(nrev)):                          # single-date tiles: keep first revisit
        x = to_rgb8(imgs[start[j]])
        if (x.max(2) > 0).mean() > 0.95 and x.std() > 8:
            out.append(x)
    print("shard", i, "rows", len(nrev), "multi-revisit", int((nrev >= 2).sum()), "max rev", int(nrev.max()), flush=True)
    os.remove(p)
    print("shard", i, len(out), flush=True)
    return out


if __name__ == "__main__":
    idx = np.random.default_rng(0).choice(7863, N, replace=False)
    with ThreadPoolExecutor(6) as ex:
        res = [r for rs in ex.map(shard, idx) for r in rs]
    shapes = {}
    for r in res:
        shapes[r.shape] = shapes.get(r.shape, 0) + 1
    s = max(shapes, key=shapes.get)
    arr = np.stack([r for r in res if r.shape == s])
    np.save(OUT, arr)
    print("EV_DONE", arr.shape, shapes, flush=True)