import os
import shutil
from datetime import datetime, timezone
from typing import Optional

from src.envs import API, QUEUE_REPO, PROJ_DIR


def _file_name(path_or_obj) -> str:
    if path_or_obj is None:
        return ""
    if isinstance(path_or_obj, dict) and "name" in path_or_obj:
        return os.path.basename(path_or_obj["name"])
    if hasattr(path_or_obj, "name"):
        return os.path.basename(path_or_obj.name)
    return os.path.basename(str(path_or_obj))


def queue_student_submission(
    group_id: str,
    alias: Optional[str],
    state_dict_file: Optional[str],
    model_py_file: str,
    preproc_py_file: str,
) -> tuple[str, str]:
    """
    Uploads submitted files to the private queue dataset for offline evaluation.
    Layout in repo:
      {PROJ_DIR}/{group_id} + {alias}/{timestamp}/model.py
      {PROJ_DIR}/{group_id} + {alias}/{timestamp}/preprocess.py
      {PROJ_DIR}/{group_id} + {alias}/{timestamp}/model.pt  (optional)
      {PROJ_DIR}/{group_id} + {alias}/{timestamp}/request.json
    """
    if not group_id or not group_id.strip():
        raise ValueError("Group ID is required.")
    group_id = group_id.strip()
    alias = alias.strip() if isinstance(alias, str) else alias
    ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
    alias_sanitized = (alias or "NA").replace("/", "_")
    base_path = f"{PROJ_DIR}/{group_id} + {alias_sanitized}/{ts}"

    # Prepare a small request.json metadata
    request_json = {
        "group_id": group_id,
        "alias": alias,
        "timestamp": ts,
        "status": "PENDING",
        "datasets": ["img_val"],
        "has_weights": bool(state_dict_file),
    }

    # Save metadata to a temp file for upload
    tmp_dir = os.path.abspath(os.path.join(".", ".tmp_queue_upload"))
    os.makedirs(tmp_dir, exist_ok=True)
    req_local = os.path.join(tmp_dir, f"request_{group_id}_{ts}.json")
    with open(req_local, "w") as f:
        import json

        json.dump(request_json, f)

    # Upload request
    API.upload_file(
        path_or_fileobj=req_local,
        path_in_repo=f"{base_path}/request.json",
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Queue request {group_id}/{ts}",
    )

    # Upload model and preprocess
    API.upload_file(
        path_or_fileobj=model_py_file,
        path_in_repo=f"{base_path}/model.py",
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Upload model.py for {group_id}/{ts}",
    )
    API.upload_file(
        path_or_fileobj=preproc_py_file,
        path_in_repo=f"{base_path}/preprocess.py",
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Upload preprocess.py for {group_id}/{ts}",
    )
    if state_dict_file:
        API.upload_file(
            path_or_fileobj=state_dict_file,
            path_in_repo=f"{base_path}/model.pt",
            repo_id=QUEUE_REPO,
            repo_type="dataset",
            commit_message=f"Upload model.pt for {group_id}/{ts}",
        )

    # Cleanup temporary
    try:
        shutil.rmtree(tmp_dir, ignore_errors=True)
    except Exception:
        pass

    return f"Submission queued for Group '{group_id}' at {ts}. Your model will be evaluated shortly.", ts