"""Resource-limited CPU single-file decoder; no network or neighboring model lookup."""

import contextlib
import json
import os
import resource
import sys
from pathlib import Path

# Apply limits before importing tensor/protobuf runtimes. Thread env also set by parent.
resource.setrlimit(resource.RLIMIT_CPU, (40, 40))
resource.setrlimit(resource.RLIMIT_AS, (8 * 1024**3, 8 * 1024**3))
resource.setrlimit(resource.RLIMIT_FSIZE, (16 * 1024**2, 16 * 1024**2))
resource.setrlimit(resource.RLIMIT_NOFILE, (64, 64))
resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
os.environ["CUDA_VISIBLE_DEVICES"] = ""
ROOT = Path(__file__).resolve().parents[1]
for root in (ROOT, ROOT.parent):
    sys.path.insert(0, str(root))

if __name__ == "__main__":
    try:
        payload = json.loads(sys.stdin.read(4096))
        with contextlib.redirect_stdout(sys.stderr):
            import torch
            from pretrained_upload import import_upload

            torch.set_num_threads(1)
            manifest = import_upload(
                payload["path"], payload["format"], payload["template"], payload["directory"]
            )
        print(json.dumps(manifest))
    except Exception as error:
        # Do not echo untrusted pickle/tensor names or absolute local paths.
        from pretrained import PretrainedError

        message = (
            str(error)
            if isinstance(error, PretrainedError)
            else "文件解析失败；请提供受支持的自包含Go2 legacy47 actor"
        )
        print(json.dumps({"error": message[:500]}))
        sys.exit(1)