src/artifacts.py

"""
Artifact Management
===================
Handles uploading trained models and results to HuggingFace Hub,
and downloading them for evaluation or transfer learning.

Supports both single-task models (MLP, CNN) and multi-task models (MTL).

The unified V1 repo (ascad-v1-models) uses the following structure:
  - Single-byte: desync{N}/{model_type}/byte{X}/  (model.h5, results.json, rank_curve.npy)
  - Multi-task:  desync{N}/{variant}/              (model.h5, results.json, rank_curve_byte{0..15}.npy)
"""

import json
import logging
import os
from typing import Dict, List, Optional

from huggingface_hub import HfApi, hf_hub_download

from .constants import HF_MLP_REPO, HF_CNN_REPO, HF_MTAN_REPO, HF_V1_REPO

logger = logging.getLogger(__name__)


# ---------------------------------------------------------------------------
# Repository mapping (legacy)
# ---------------------------------------------------------------------------

def _get_legacy_repo_id(model_type: str) -> str:
    """Map model type to legacy HuggingFace repository ID."""
    repos = {"mlp": HF_MLP_REPO, "cnn": HF_CNN_REPO, "mtan": HF_MTAN_REPO}
    if model_type not in repos:
        raise ValueError(f"Unknown model type: {model_type}")
    return repos[model_type]


def _build_hf_path(model_type: str, target_byte: int, desync: int) -> str:
    """Build the HuggingFace path prefix for a single-task model."""
    return f"desync{desync}/{model_type}/byte{target_byte}"


# ---------------------------------------------------------------------------
# Unified V1 upload helpers
# ---------------------------------------------------------------------------

def _ensure_v1_repo(api: HfApi) -> None:
    """Ensure the V1 repo exists."""
    try:
        api.create_repo(
            repo_id=HF_V1_REPO,
            repo_type="model",
            exist_ok=True,
            private=False,
        )
    except Exception as e:
        logger.warning("Could not create/verify repo %s: %s", HF_V1_REPO, e)


def _upload_files(
    api: HfApi,
    repo_id: str,
    local_dir: str,
    hf_prefix: str,
    filenames: List[str],
) -> int:
    """Upload a list of files to a HuggingFace repo. Returns count of uploaded files."""
    uploaded = 0
    for filename in filenames:
        local_path = os.path.join(local_dir, filename)
        if not os.path.isfile(local_path):
            logger.warning("File not found, skipping: %s", local_path)
            continue
        hf_path = f"{hf_prefix}/{filename}"
        try:
            logger.info("Uploading %s -> %s/%s", local_path, repo_id, hf_path)
            api.upload_file(
                path_or_fileobj=local_path,
                path_in_repo=hf_path,
                repo_id=repo_id,
                repo_type="model",
            )
            uploaded += 1
        except Exception as e:
            logger.error("Failed to upload %s: %s", filename, e)
    return uploaded


# ---------------------------------------------------------------------------
# Single-task upload (MLP, CNN)
# ---------------------------------------------------------------------------

def upload_model(
    model_dir: str,
    model_type: str,
    target_byte: int,
    desync: int,
    repo_id: Optional[str] = None,
    failed: bool = False,
) -> None:
    """
    Upload a trained single-task model directory to HuggingFace Hub.

    Uploads to both the unified V1 repo and the legacy repo (if no override).

    Args:
        model_dir: Local directory containing model.h5, results.json, rank_curve.npy.
        model_type: 'mlp' or 'cnn'.
        target_byte: Target byte index (0-15).
        desync: Desynchronization level (0, 50, or 100).
        repo_id: Override the default HuggingFace repo ID (skips V1 upload).
    """
    hf_prefix = _build_hf_path(model_type, target_byte, desync)
    if failed:
        hf_prefix = f"failed/{hf_prefix}"
    files = ["model.h5", "results.json", "rank_curve.npy"]
    api = HfApi()

    # Always upload to V1 repo
    _ensure_v1_repo(api)
    count = _upload_files(api, HF_V1_REPO, model_dir, hf_prefix, files)
    logger.info(
        "V1 upload complete: %s byte=%d desync=%d -> %s (%d files)",
        model_type, target_byte, desync, HF_V1_REPO, count,
    )

    # Also upload to legacy repo if no override
    if repo_id is None:
        legacy_repo = _get_legacy_repo_id(model_type)
    else:
        legacy_repo = repo_id
    count = _upload_files(api, legacy_repo, model_dir, hf_prefix, files)
    logger.info(
        "Legacy upload complete: %s byte=%d desync=%d -> %s (%d files)",
        model_type, target_byte, desync, legacy_repo, count,
    )


# ---------------------------------------------------------------------------
# Multi-task upload (MTL)
# ---------------------------------------------------------------------------

def upload_mtan_model(
    model_dir: str,
    variant: str,
    desync: int,
    repo_id: Optional[str] = None,
    hf_prefix_override: Optional[str] = None,
) -> None:
    """
    Upload a trained MTL multi-task model directory to HuggingFace Hub.

    Uploads to both the unified V1 repo and the legacy MTAN repo (if no override).

    Args:
        model_dir: Local directory containing model artifacts.
        variant: Model variant name (e.g., 'lmic_tsbn_v7b').
        desync: Desynchronization level (0, 50, or 100).
        repo_id: Override the default HuggingFace repo ID (skips V1 upload).
    """
    hf_prefix = hf_prefix_override if hf_prefix_override else f"desync{desync}/{variant}"
    api = HfApi()

    # Collect all files to upload
    files: List[str] = []
    for filename in ["model.h5", "results.json"]:
        if os.path.isfile(os.path.join(model_dir, filename)):
            files.append(filename)
    for byte_idx in range(16):
        filename = f"rank_curve_byte{byte_idx}.npy"
        if os.path.isfile(os.path.join(model_dir, filename)):
            files.append(filename)

    if not files:
        logger.error("No files found in %s to upload", model_dir)
        return

    # Always upload to V1 repo
    _ensure_v1_repo(api)
    count = _upload_files(api, HF_V1_REPO, model_dir, hf_prefix, files)
    logger.info(
        "V1 MTAN upload: variant=%s desync=%d -> %s (%d/%d files)",
        variant, desync, HF_V1_REPO, count, len(files),
    )

    # Also upload to legacy repo
    legacy_repo = repo_id if repo_id else HF_MTAN_REPO
    try:
        api.create_repo(
            repo_id=legacy_repo,
            repo_type="model",
            exist_ok=True,
            private=False,
        )
    except Exception as e:
        logger.warning("Could not create/verify repo %s: %s", legacy_repo, e)

    count = _upload_files(api, legacy_repo, model_dir, hf_prefix, files)
    logger.info(
        "Legacy MTAN upload: variant=%s desync=%d -> %s (%d/%d files)",
        variant, desync, legacy_repo, count, len(files),
    )


# ---------------------------------------------------------------------------
# Download functions
# ---------------------------------------------------------------------------

def download_mtan_model(
    variant: str,
    desync: int,
    local_dir: str,
    repo_id: Optional[str] = None,
) -> str:
    """
    Download a trained MTAN model from HuggingFace Hub.

    Tries V1 repo first, falls back to legacy repo.

    Args:
        variant: Model variant name (e.g., 'lmic_tsbn_v7b').
        desync: Desynchronization level (0, 50, or 100).
        local_dir: Local directory to save downloaded files.
        repo_id: Override the default HuggingFace repo ID.

    Returns:
        Path to the downloaded model directory.
    """
    if repo_id is None:
        repo_id = HF_V1_REPO

    hf_prefix = hf_prefix_override if hf_prefix_override else f"desync{desync}/{variant}"
    os.makedirs(local_dir, exist_ok=True)

    files_to_download = ["model.h5", "results.json"]
    for byte_idx in range(16):
        files_to_download.append(f"rank_curve_byte{byte_idx}.npy")

    for filename in files_to_download:
        hf_path = f"{hf_prefix}/{filename}"
        try:
            downloaded = hf_hub_download(
                repo_id=repo_id,
                filename=hf_path,
                local_dir=local_dir,
            )
            logger.info("Downloaded %s -> %s", hf_path, downloaded)
        except Exception as e:
            logger.warning("Could not download %s: %s", hf_path, e)

    return local_dir


def download_model(
    model_type: str,
    target_byte: int,
    desync: int,
    local_dir: str,
    repo_id: Optional[str] = None,
) -> str:
    """
    Download a trained single-task model from HuggingFace Hub.

    Tries V1 repo first, falls back to legacy repo.

    Args:
        model_type: 'mlp' or 'cnn'.
        target_byte: Target byte index (0-15).
        desync: Desynchronization level (0, 50, or 100).
        local_dir: Local directory to save downloaded files.
        repo_id: Override the default HuggingFace repo ID.

    Returns:
        Path to the downloaded model directory.
    """
    if repo_id is None:
        repo_id = HF_V1_REPO

    hf_prefix = _build_hf_path(model_type, target_byte, desync)
    os.makedirs(local_dir, exist_ok=True)

    for filename in ["model.h5", "results.json", "rank_curve.npy"]:
        hf_path = f"{hf_prefix}/{filename}"
        try:
            downloaded = hf_hub_download(
                repo_id=repo_id,
                filename=hf_path,
                local_dir=local_dir,
            )
            logger.info("Downloaded %s -> %s", hf_path, downloaded)
        except Exception as e:
            logger.warning("Could not download %s: %s", hf_path, e)

    return local_dir


# ---------------------------------------------------------------------------
# Audit / count (V1 repo)
# ---------------------------------------------------------------------------

def audit_repository(
    model_type: str,
    repo_id: Optional[str] = None,
) -> Dict[str, Dict[int, bool]]:
    """
    Audit a HuggingFace model repository to check which models exist.

    Returns:
        Nested dict: {desync_key: {byte_idx: has_model}}.
        Example: {"desync0": {0: True, 1: False, ...}, ...}
    """
    if repo_id is None:
        repo_id = HF_V1_REPO

    api = HfApi()
    try:
        files = api.list_repo_files(repo_id=repo_id, repo_type="model")
    except Exception as e:
        logger.error("Could not list files in %s: %s", repo_id, e)
        return {}

    result = {}
    for desync in [0, 50, 100]:
        desync_key = f"desync{desync}"
        result[desync_key] = {}
        for byte_idx in range(16):
            hf_path = f"{_build_hf_path(model_type, byte_idx, desync)}/model.h5"
            result[desync_key][byte_idx] = hf_path in files

    return result


def count_models(
    model_type: str,
    repo_id: Optional[str] = None,
) -> int:
    """Count the total number of models uploaded for a given type."""
    audit = audit_repository(model_type, repo_id)
    return sum(
        1 for desync_dict in audit.values()
        for exists in desync_dict.values()
        if exists
    )