test1978

Add handler.py

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# ──────────────────────────────────────────────
# handler.py
#
# Custom handler for HF Inference Endpoints.
#
# When HF Inference Endpoints detects a handler.py
# in the repo root, it uses this class to serve
# predictions instead of the default Transformers
# pipeline.
#
# Expected repo layout:
#
#   your-username/breakthrough-model/
#   ├── handler.py              ← this file
#   ├── config_model.json       ← lists your Python modules + main
#   ├── config_data.json        ← lists data files + zone_db key
#   ├── breakthrough_mcvs.py    ← your UCT search / eval module
#   └── requirements.txt        ← runtime deps (numpy, huggingface_hub)
#
# config_model.json example:
#
#   {
#       "files": ["matrix_model.py", "abc_model.py", "breakthrough_mcvs.py"],
#       "main": "breakthrough_mcvs"
#   }
#
# config_data.json example:
#
#   {
#       "files": ["breakthrough_zone_db.npz"],
#       "zone_db": "breakthrough_zone_db.npz"
#   }
#
# Set the HF_DATA_REPO_ID environment variable to
# the HF dataset repo holding your data files.
#
# Request format (sent by Agladiator):
#
#   POST /
#   {
#       "inputs": {
#           "fen": "BBBBBBBB/BBBBBBBB/8/8/8/8/WWWWWWWW/WWWWWWWW w",
#           "player": "w",
#           "game_type": "breakthrough"
#       }
#   }
#
# Response format (what you must return):
#
#   {"move": "a2a3"}
#
# ──────────────────────────────────────────────
from __future__ import annotations

import importlib.util
import json
import logging
import os
import random
import sys
from pathlib import Path

import numpy as np

log = logging.getLogger(__name__)

# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
#  Breakthrough board constants (must match the
#  server's breakthrough_engine.py)
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
BOARD_SIZE = 8
_FILES = "abcdefgh"
_RANKS = "12345678"
WHITE, BLACK = "w", "b"
WHITE_PIECE, BLACK_PIECE, EMPTY = "W", "B", "."


class EndpointHandler:
    """Custom handler for Breakthrough AI on HF Inference Endpoints.

    Lifecycle:
      1. ``__init__`` is called once when the endpoint container starts.
         Use it to load your model artifacts (modules, zone DB, etc.).
      2. ``__call__`` is called for every inference request.
         It receives the parsed JSON body and must return a dict.
    """

    def __init__(self, path: str):
        """Load model artefacts from *path* (the cloned repo directory).

        Steps:
          1. Read ``config_model.json`` to discover module files and main module.
          2. Dynamically import each listed module.
          3. Read ``config_data.json``, download data files, load zone DB.
          4. Initialise the search engine.
        """
        self.path = Path(path)
        self.predict_fn = None
        self.zone_db = None
        self.module = None

        # ── 1. Read config_model.json ───────────
        model_config_path = self.path / "config_model.json"
        if not model_config_path.exists():
            log.warning("No config_model.json found in %s", path)
            model_config: dict = {}
        else:
            with open(model_config_path) as fh:
                model_config = json.load(fh)

        main_module_name = model_config.get("main", "")

        # ── 2. Import listed modules ────────────
        # Add repo dir to sys.path so relative imports work.
        if str(self.path) not in sys.path:
            sys.path.insert(0, str(self.path))

        for mod_file in model_config.get("files", []):
            mod_name = mod_file.removesuffix(".py")
            mod_path = self.path / mod_file
            if not mod_path.exists():
                log.warning("Module file %s not found — skipping.", mod_file)
                continue
            spec = importlib.util.spec_from_file_location(mod_name, str(mod_path))
            mod = importlib.util.module_from_spec(spec)
            sys.modules[mod_name] = mod
            spec.loader.exec_module(mod)
            log.info("Loaded module: %s", mod_name)
            # Mark the module matching "main" as the primary module.
            if mod_name == main_module_name:
                self.module = mod

        # ── 3. Read config_data.json & load zone DB ──
        data_config_path = self.path / "config_data.json"
        if not data_config_path.exists():
            log.warning("No config_data.json found in %s", path)
            data_config: dict = {}
        else:
            with open(data_config_path) as fh:
                data_config = json.load(fh)

        data_repo = os.environ.get("HF_DATA_REPO_ID", "")
        downloaded_files: dict[str, str] = {}

        if data_repo:
            try:
                from huggingface_hub import hf_hub_download
            except Exception:
                hf_hub_download = None
                log.warning(
                    "huggingface_hub not installed — cannot download data files. Add it to requirements.txt"
                )

            if hf_hub_download is not None:
                for data_file in data_config.get("files", []):
                    try:
                        local_path = hf_hub_download(
                            repo_id=data_repo,
                            filename=data_file,
                            repo_type="dataset",
                        )
                        downloaded_files[data_file] = local_path
                        log.info("Downloaded %s from %s", data_file, data_repo)
                    except Exception:
                        log.warning(
                            "Failed to download %s from %s — skipping.",
                            data_file,
                            data_repo,
                        )
        else:
            log.warning(
                "HF_DATA_REPO_ID not set — skipping data file downloads."
            )

        zone_db_filename = data_config.get("zone_db", "")
        if zone_db_filename and zone_db_filename in downloaded_files:
            self.zone_db = np.load(
                downloaded_files[zone_db_filename], allow_pickle=True
            )
            log.info("Zone DB loaded from data repo %s", data_repo)
        elif zone_db_filename:
            local_zone = self.path / zone_db_filename
            if local_zone.exists():
                self.zone_db = np.load(str(local_zone), allow_pickle=True)
                log.info("Zone DB loaded from local file %s", local_zone)
            else:
                log.warning("No zone DB found for '%s'.", zone_db_filename)

        # ── 4. Initialise the predictor ─────────
        self._init_predictor()
        log.info("EndpointHandler initialised successfully.")

    # ──────────────────────────────────────────
    #  Predictor initialisation
    # ──────────────────────────────────────────
    def _init_predictor(self):
        """Discover and configure the prediction function from the
        loaded module.  Tries several common patterns:

        1. ``module.get_move(fen, player, zone_db=...)``
        2. ``module.UCTSearcher(zone_db=...).search(fen, player)``
        3. ``module.predict(fen, player)``
        """
        if self.module is None:
            log.warning("No module loaded — predictions will use random fallback.")
            return

        # Pattern 1: module exposes a top-level get_move()
        if hasattr(self.module, "get_move"):
            raw_fn = self.module.get_move
            self.predict_fn = lambda fen, player: raw_fn(
                fen, player, zone_db=self.zone_db
            )
            log.info("Using module.get_move() for predictions.")
            return

        # Pattern 2: module exposes a UCTSearcher class
        if hasattr(self.module, "UCTSearcher"):
            searcher_cls = self.module.UCTSearcher
            kwargs = {}
            if self.zone_db is not None:
                kwargs["zone_db"] = self.zone_db
            searcher = searcher_cls(**kwargs)
            self.predict_fn = lambda fen, player: searcher.search(fen, player)
            log.info("Using module.UCTSearcher for predictions.")
            return

        # Pattern 3: module exposes a predict()
        if hasattr(self.module, "predict"):
            raw_fn = self.module.predict
            self.predict_fn = lambda fen, player: raw_fn(fen, player)
            log.info("Using module.predict() for predictions.")
            return

        log.warning(
            "Module %s has no get_move / UCTSearcher / predict — "
            "predictions will use random fallback.",
            self.module.__name__,
        )

    # ──────────────────────────────────────────
    #  Inference entry point
    # ──────────────────────────────────────────
    def __call__(self, data: dict) -> dict:
        """Handle an inference request.

        Expected *data*::

            {"inputs": {"fen": "...", "player": "w"}}

        Returns::

            {"move": "a2a3"}

        Falls back to a random legal move if the model fails.
        """
        inputs = data.get("inputs", data)
        fen = inputs.get("fen", "")
        player = inputs.get("player", WHITE)

        if not fen:
            return {"error": "Missing 'fen' in request."}

        # Try model prediction
        if self.predict_fn is not None:
            try:
                move = self.predict_fn(fen, player)
                if move and isinstance(move, str):
                    return {"move": move}
            except Exception:
                log.exception("Model prediction failed for FEN=%s", fen)

        # Fallback: random legal move
        moves = self._legal_moves(fen)
        if not moves:
            return {"error": "No legal moves available.", "move": "0000"}

        # Prefer captures (diagonal moves in Breakthrough)
        captures = [m for m in moves if m[0] != m[2]]
        move = random.choice(captures) if captures else random.choice(moves)
        return {"move": move}

    # ──────────────────────────────────────────
    #  Minimal legal-move generator (self-contained)
    # ──────────────────────────────────────────
    @staticmethod
    def _legal_moves(fen: str) -> list[str]:
        """Return all legal UCI moves for the given Breakthrough FEN.

        This is a minimal, self-contained implementation so the
        handler does not depend on the server's breakthrough_engine.
        """
        parts = fen.strip().split()
        turn = parts[1] if len(parts) > 1 else WHITE

        # Parse ranks → grid
        grid: list[list[str]] = []
        for rank_str in parts[0].split("/"):
            row: list[str] = []
            for ch in rank_str:
                if ch.isdigit():
                    row.extend([EMPTY] * int(ch))
                else:
                    row.append(ch)
            grid.append(row)

        piece = WHITE_PIECE if turn == WHITE else BLACK_PIECE
        direction = -1 if turn == WHITE else 1
        moves: list[str] = []

        for r in range(BOARD_SIZE):
            for c in range(BOARD_SIZE):
                if grid[r][c] != piece:
                    continue
                nr = r + direction
                if nr < 0 or nr >= BOARD_SIZE:
                    continue
                # Straight forward on empty
                if grid[nr][c] == EMPTY:
                    moves.append(
                        f"{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - r]}"
                        f"{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - nr]}"
                    )
                # Diagonals — capture or empty
                for dc in (-1, 1):
                    nc = c + dc
                    if nc < 0 or nc >= BOARD_SIZE:
                        continue
                    if grid[nr][nc] == piece:
                        continue  # own piece
                    moves.append(
                        f"{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - r]}"
                        f"{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}"
                    )

        return moves

	# ──────────────────────────────────────────────
	# handler.py
	#
	# Custom handler for HF Inference Endpoints.
	#
	# When HF Inference Endpoints detects a handler.py
	# in the repo root, it uses this class to serve
	# predictions instead of the default Transformers
	# pipeline.
	#
	# Expected repo layout:
	#
	# your-username/breakthrough-model/
	# ├── handler.py ← this file
	# ├── config_model.json ← lists your Python modules + main
	# ├── config_data.json ← lists data files + zone_db key
	# ├── breakthrough_mcvs.py ← your UCT search / eval module
	# └── requirements.txt ← runtime deps (numpy, huggingface_hub)
	#
	# config_model.json example:
	#
	# {
	# "files": ["matrix_model.py", "abc_model.py", "breakthrough_mcvs.py"],
	# "main": "breakthrough_mcvs"
	# }
	#
	# config_data.json example:
	#
	# {
	# "files": ["breakthrough_zone_db.npz"],
	# "zone_db": "breakthrough_zone_db.npz"
	# }
	#
	# Set the HF_DATA_REPO_ID environment variable to
	# the HF dataset repo holding your data files.
	#
	# Request format (sent by Agladiator):
	#
	# POST /
	# {
	# "inputs": {
	# "fen": "BBBBBBBB/BBBBBBBB/8/8/8/8/WWWWWWWW/WWWWWWWW w",
	# "player": "w",
	# "game_type": "breakthrough"
	# }
	# }
	#
	# Response format (what you must return):
	#
	# {"move": "a2a3"}
	#
	# ──────────────────────────────────────────────
	from __future__ import annotations

	import importlib.util
	import json
	import logging
	import os
	import random
	import sys
	from pathlib import Path

	import numpy as np

	log = logging.getLogger(__name__)

	# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
	# Breakthrough board constants (must match the
	# server's breakthrough_engine.py)
	# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
	BOARD_SIZE = 8
	_FILES = "abcdefgh"
	_RANKS = "12345678"
	WHITE, BLACK = "w", "b"
	WHITE_PIECE, BLACK_PIECE, EMPTY = "W", "B", "."


	class EndpointHandler:
	"""Custom handler for Breakthrough AI on HF Inference Endpoints.

	Lifecycle:
	1. ``__init__`` is called once when the endpoint container starts.
	Use it to load your model artifacts (modules, zone DB, etc.).
	2. ``__call__`` is called for every inference request.
	It receives the parsed JSON body and must return a dict.
	"""

	def __init__(self, path: str):
	"""Load model artefacts from path (the cloned repo directory).

	Steps:
	1. Read ``config_model.json`` to discover module files and main module.
	2. Dynamically import each listed module.
	3. Read ``config_data.json``, download data files, load zone DB.
	4. Initialise the search engine.
	"""
	self.path = Path(path)
	self.predict_fn = None
	self.zone_db = None
	self.module = None

	# ── 1. Read config_model.json ───────────
	model_config_path = self.path / "config_model.json"
	if not model_config_path.exists():
	log.warning("No config_model.json found in %s", path)
	model_config: dict = {}
	else:
	with open(model_config_path) as fh:
	model_config = json.load(fh)

	main_module_name = model_config.get("main", "")

	# ── 2. Import listed modules ────────────
	# Add repo dir to sys.path so relative imports work.
	if str(self.path) not in sys.path:
	sys.path.insert(0, str(self.path))

	for mod_file in model_config.get("files", []):
	mod_name = mod_file.removesuffix(".py")
	mod_path = self.path / mod_file
	if not mod_path.exists():
	log.warning("Module file %s not found — skipping.", mod_file)
	continue
	spec = importlib.util.spec_from_file_location(mod_name, str(mod_path))
	mod = importlib.util.module_from_spec(spec)
	sys.modules[mod_name] = mod
	spec.loader.exec_module(mod)
	log.info("Loaded module: %s", mod_name)
	# Mark the module matching "main" as the primary module.
	if mod_name == main_module_name:
	self.module = mod

	# ── 3. Read config_data.json & load zone DB ──
	data_config_path = self.path / "config_data.json"
	if not data_config_path.exists():
	log.warning("No config_data.json found in %s", path)
	data_config: dict = {}
	else:
	with open(data_config_path) as fh:
	data_config = json.load(fh)

	data_repo = os.environ.get("HF_DATA_REPO_ID", "")
	downloaded_files: dict[str, str] = {}

	if data_repo:
	try:
	from huggingface_hub import hf_hub_download
	except Exception:
	hf_hub_download = None
	log.warning(
	"huggingface_hub not installed — cannot download data files. Add it to requirements.txt"
	)

	if hf_hub_download is not None:
	for data_file in data_config.get("files", []):
	try:
	local_path = hf_hub_download(
	repo_id=data_repo,
	filename=data_file,
	repo_type="dataset",
	)
	downloaded_files[data_file] = local_path
	log.info("Downloaded %s from %s", data_file, data_repo)
	except Exception:
	log.warning(
	"Failed to download %s from %s — skipping.",
	data_file,
	data_repo,
	)
	else:
	log.warning(
	"HF_DATA_REPO_ID not set — skipping data file downloads."
	)

	zone_db_filename = data_config.get("zone_db", "")
	if zone_db_filename and zone_db_filename in downloaded_files:
	self.zone_db = np.load(
	downloaded_files[zone_db_filename], allow_pickle=True
	)
	log.info("Zone DB loaded from data repo %s", data_repo)
	elif zone_db_filename:
	local_zone = self.path / zone_db_filename
	if local_zone.exists():
	self.zone_db = np.load(str(local_zone), allow_pickle=True)
	log.info("Zone DB loaded from local file %s", local_zone)
	else:
	log.warning("No zone DB found for '%s'.", zone_db_filename)

	# ── 4. Initialise the predictor ─────────
	self._init_predictor()
	log.info("EndpointHandler initialised successfully.")

	# ──────────────────────────────────────────
	# Predictor initialisation
	# ──────────────────────────────────────────
	def _init_predictor(self):
	"""Discover and configure the prediction function from the
	loaded module. Tries several common patterns:

	1. ``module.get_move(fen, player, zone_db=...)``
	2. ``module.UCTSearcher(zone_db=...).search(fen, player)``
	3. ``module.predict(fen, player)``
	"""
	if self.module is None:
	log.warning("No module loaded — predictions will use random fallback.")
	return

	# Pattern 1: module exposes a top-level get_move()
	if hasattr(self.module, "get_move"):
	raw_fn = self.module.get_move
	self.predict_fn = lambda fen, player: raw_fn(
	fen, player, zone_db=self.zone_db
	)
	log.info("Using module.get_move() for predictions.")
	return

	# Pattern 2: module exposes a UCTSearcher class
	if hasattr(self.module, "UCTSearcher"):
	searcher_cls = self.module.UCTSearcher
	kwargs = {}
	if self.zone_db is not None:
	kwargs["zone_db"] = self.zone_db
	searcher = searcher_cls(**kwargs)
	self.predict_fn = lambda fen, player: searcher.search(fen, player)
	log.info("Using module.UCTSearcher for predictions.")
	return

	# Pattern 3: module exposes a predict()
	if hasattr(self.module, "predict"):
	raw_fn = self.module.predict
	self.predict_fn = lambda fen, player: raw_fn(fen, player)
	log.info("Using module.predict() for predictions.")
	return

	log.warning(
	"Module %s has no get_move / UCTSearcher / predict — "
	"predictions will use random fallback.",
	self.module.__name__,
	)

	# ──────────────────────────────────────────
	# Inference entry point
	# ──────────────────────────────────────────
	def __call__(self, data: dict) -> dict:
	"""Handle an inference request.

	Expected data::

	{"inputs": {"fen": "...", "player": "w"}}

	Returns::

	{"move": "a2a3"}

	Falls back to a random legal move if the model fails.
	"""
	inputs = data.get("inputs", data)
	fen = inputs.get("fen", "")
	player = inputs.get("player", WHITE)

	if not fen:
	return {"error": "Missing 'fen' in request."}

	# Try model prediction
	if self.predict_fn is not None:
	try:
	move = self.predict_fn(fen, player)
	if move and isinstance(move, str):
	return {"move": move}
	except Exception:
	log.exception("Model prediction failed for FEN=%s", fen)

	# Fallback: random legal move
	moves = self._legal_moves(fen)
	if not moves:
	return {"error": "No legal moves available.", "move": "0000"}

	# Prefer captures (diagonal moves in Breakthrough)
	captures = [m for m in moves if m[0] != m[2]]
	move = random.choice(captures) if captures else random.choice(moves)
	return {"move": move}

	# ──────────────────────────────────────────
	# Minimal legal-move generator (self-contained)
	# ──────────────────────────────────────────
	@staticmethod
	def _legal_moves(fen: str) -> list[str]:
	"""Return all legal UCI moves for the given Breakthrough FEN.

	This is a minimal, self-contained implementation so the
	handler does not depend on the server's breakthrough_engine.
	"""
	parts = fen.strip().split()
	turn = parts[1] if len(parts) > 1 else WHITE

	# Parse ranks → grid
	grid: list[list[str]] = []
	for rank_str in parts[0].split("/"):
	row: list[str] = []
	for ch in rank_str:
	if ch.isdigit():
	row.extend([EMPTY] * int(ch))
	else:
	row.append(ch)
	grid.append(row)

	piece = WHITE_PIECE if turn == WHITE else BLACK_PIECE
	direction = -1 if turn == WHITE else 1
	moves: list[str] = []

	for r in range(BOARD_SIZE):
	for c in range(BOARD_SIZE):
	if grid[r][c] != piece:
	continue
	nr = r + direction
	if nr < 0 or nr >= BOARD_SIZE:
	continue
	# Straight forward on empty
	if grid[nr][c] == EMPTY:
	moves.append(
	f"{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - r]}"
	f"{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - nr]}"
	)
	# Diagonals — capture or empty
	for dc in (-1, 1):
	nc = c + dc
	if nc < 0 or nc >= BOARD_SIZE:
	continue
	if grid[nr][nc] == piece:
	continue # own piece
	moves.append(
	f"{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - r]}"
	f"{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}"
	)

	return moves