"""GPU acceleration helpers for LiDAR pipeline.

Auto-selects the best NVIDIA GPU (highest compute capability first).
Uses CuPy Device API (not CUDA_VISIBLE_DEVICES) so JIT compilation
works correctly for any architecture (sm_89, sm_120, etc.).

All workers share the selected GPU.
"""

import logging
import os
import numpy as np
from scipy import ndimage

logger = logging.getLogger("lidar")

# ---------------------------------------------------------------------------
# GPU auto-detection via nvidia-smi (no CUDA context created)
# ---------------------------------------------------------------------------
_NUM_GPUS = 0
HAS_GPU = False
_gpu_name = None
_gpu_mem_gb = 0
_best_gpu_id: int | None = None
_gpu_reason = None


def _pick_gpu() -> list:
    """List all GPUs from the system, sorted by compute capability (highest first)."""
    try:
        import subprocess
        result = subprocess.run(
            ['nvidia-smi', '--query-gpu=index,name,compute_cap,memory.total',
             '--format=csv,noheader,nounits'],
            capture_output=True, text=True, timeout=5,
        )
        if result.returncode != 0:
            return []

        global _NUM_GPUS

        gpus = []
        for line in result.stdout.strip().split('\n'):
            parts = [p.strip() for p in line.split(',')]
            if len(parts) < 4:
                continue
            idx = int(parts[0])
            name = parts[1]
            cap_str = parts[2]
            mem_mi = int(parts[3])
            major, minor = (int(x) for x in cap_str.split('.'))
            score = major * 1000 + minor * 100 + mem_mi
            gpus.append((idx, name, cap_str, mem_mi, score, major))

        _NUM_GPUS = len(gpus)
        gpus.sort(key=lambda g: g[4], reverse=True)
        return gpus

    except (FileNotFoundError, subprocess.TimeoutExpired, Exception):
        return []


_candidate_gpus: list = []
try:
    _candidate_gpus = _pick_gpu() or []
except Exception:
    pass

# ---------------------------------------------------------------------------
# Lazy CuPy initialization — tries each GPU until one works
# ---------------------------------------------------------------------------
_xp = np
_cp = None
_cp_ndimage = None
_gpu_initialized = False


def _init_gpu():
    """Lazily initialize CuPy on first GPU use.

    Uses a subprocess to test each GPU (highest compute capability first).
    The subprocess sets CUDA_VISIBLE_DEVICES before importing CuPy and
    runs a warm-up kernel.  First GPU that passes wins.

    This is necessary because CUDA_VISIBLE_DEVICES must be set in the
    process environment BEFORE CuPy imports, not via os.environ in Python.
    """
    global _xp, _cp, _cp_ndimage, _gpu_initialized, HAS_GPU, _best_gpu_id, _gpu_name, _gpu_mem_gb
    if _gpu_initialized:
        return
    _gpu_initialized = True

    if not _candidate_gpus:
        _xp = np
        _cp = None
        _cp_ndimage = None
        HAS_GPU = False
        return

    # Test each GPU in a subprocess
    import subprocess
    _working_gpu = None
    for idx, name, cap_str, mem_mi, score, major in _candidate_gpus:
        result = subprocess.run(
            ['python3', '-c',
             'import cupy; a=cupy.array([1.0,2.0],dtype=cupy.float32); print(cupy.sum(a).get())'],
            capture_output=True, text=True, timeout=120,
            env={**os.environ, 'CUDA_VISIBLE_DEVICES': str(idx)},
        )
        if result.returncode == 0 and '3.0' in result.stdout:
            _working_gpu = (idx, name, mem_mi)
            break
        logger.warning(f"GPU {idx} ({name}, sm_{cap_str}) non compatible: "
                       f"{result.stderr.strip().splitlines()[-1] if result.stderr else 'inconnue'}")

    if _working_gpu is None:
        logger.info("Pas de GPU utilisable — mode CPU uniquement")
        _xp = np
        _cp = None
        _cp_ndimage = None
        HAS_GPU = False
        return

    idx, name, mem_mi = _working_gpu
    os.environ['CUDA_VISIBLE_DEVICES'] = str(idx)

    import cupy as _real_cupy
    import cupyx.scipy.ndimage as _real_cupy_ndimage

    _best_gpu_id = idx
    _gpu_name = name
    _gpu_mem_gb = mem_mi // 1024
    HAS_GPU = True
    _xp = _real_cupy
    _cp = _real_cupy
    _cp_ndimage = _real_cupy_ndimage


# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------

def num_gpus():
    """Return 1 if GPU is active, 0 otherwise."""
    return 1 if HAS_GPU else 0


def restrict_gpus(gpu_ids: list[int], set_env_var: bool = False):
    """No-op — GPU is auto-selected at import time."""
    pass


def set_active_gpu(gpu_id):
    """No-op — GPU is auto-selected at import time."""
    pass


def _gpu_available():
    """Check if GPU is usable right now."""
    if not HAS_GPU:
        return False
    try:
        _init_gpu()
        return _cp is not None
    except Exception:
        return False


def log_gpu_status():
    """Log GPU detection result. Called after logging is configured."""
    if _gpu_available():
        try:
            with _cp.cuda.Device(_best_gpu_id):
                name = _cp.cuda.runtime.getDeviceProperties(_best_gpu_id)['name']
                if isinstance(name, bytes):
                    name = name.decode()
                mem_gb = _cp.cuda.runtime.getDeviceProperties(_best_gpu_id)['totalGlobalMem'] // (1024**3)
                gpu_info = f"GPU: {name} ({mem_gb} Go VRAM) — ID {_best_gpu_id}"
        except Exception:
            gpu_info = f"GPU: {_gpu_name} ({_gpu_mem_gb} Go VRAM)"
        logger.info(gpu_info)

        # List other GPUs for info
        try:
            import subprocess
            result = subprocess.run(
                ['nvidia-smi', '--query-gpu=index,name,compute_cap',
                 '--format=csv,noheader,nounits'],
                capture_output=True, text=True, timeout=5,
            )
            if result.returncode == 0:
                for line in result.stdout.strip().split('\n'):
                    parts = [p.strip() for p in line.split(',')]
                    if len(parts) >= 3:
                        idx = int(parts[0])
                        if idx != _best_gpu_id:
                            cap = parts[2]
                            logger.info(f"  GPU {idx}: {parts[1]} (sm_{cap}) — disponible")
        except Exception:
            pass
    else:
        logger.info(f"Pas de GPU utilisable — mode CPU uniquement")


# ---------------------------------------------------------------------------
# Array transfer
# ---------------------------------------------------------------------------

def to_gpu(arr):
    """Send array to GPU if available, otherwise return as float32 numpy."""
    if _gpu_available():
        try:
            return _cp.asarray(arr.astype(np.float32))
        except Exception:
            pass
    return arr.astype(np.float32)


def to_cpu(arr):
    """Bring array back to CPU (numpy). No-op if already on CPU."""
    if _cp is not None and isinstance(arr, _cp.ndarray):
        try:
            return _cp.asnumpy(arr)
        except Exception:
            pass
    return arr


# ---------------------------------------------------------------------------
# Filters — GPU if array is on GPU, CPU otherwise
# ---------------------------------------------------------------------------

def xp_gaussian_filter(arr, sigma):
    if _cp is not None and isinstance(arr, _cp.ndarray):
        try:
            return _cp_ndimage.gaussian_filter(arr, sigma)
        except Exception:
            arr = to_cpu(arr)
    return ndimage.gaussian_filter(arr, sigma)


def xp_uniform_filter(arr, size):
    if _cp is not None and isinstance(arr, _cp.ndarray):
        try:
            return _cp_ndimage.uniform_filter(arr, size)
        except Exception:
            arr = to_cpu(arr)
    return ndimage.uniform_filter(arr, size)


def xp_minimum_filter(arr, footprint=None, size=None):
    if _cp is not None and isinstance(arr, _cp.ndarray):
        try:
            return _cp_ndimage.minimum_filter(arr, footprint=footprint, size=size)
        except Exception:
            arr = to_cpu(arr)
    return ndimage.minimum_filter(arr, footprint=footprint, size=size)


def xp_maximum_filter(arr, footprint=None, size=None):
    if _cp is not None and isinstance(arr, _cp.ndarray):
        try:
            return _cp_ndimage.maximum_filter(arr, footprint=footprint, size=size)
        except Exception:
            arr = to_cpu(arr)
    return ndimage.maximum_filter(arr, footprint=footprint, size=size)


# ---------------------------------------------------------------------------
# Misc
# ---------------------------------------------------------------------------

def gpu_cleanup():
    """Free GPU memory. Call between visualizations to prevent OOM."""
    if _cp is not None:
        try:
            _cp.get_default_memory_pool().free_all_blocks()
        except Exception:
            pass


def disable_gpu():
    """Disable GPU acceleration for the rest of this process."""
    global HAS_GPU, _xp, _cp, _cp_ndimage
    if not HAS_GPU:
        return
    logger.warning("GPU désactivé — passage en mode CPU pour la suite du processus")
    HAS_GPU = False
    _xp = np
    _cp = None
    _cp_ndimage = None


def is_gpu_active():
    """Check if GPU acceleration is currently active."""
    return HAS_GPU


def safe_gpu_call(func, *args, **kwargs):
    """Call a function with GPU arrays, retrying on CPU if GPU fails."""
    try:
        return func(*args, **kwargs)
    except Exception as e:
        err_msg = str(e)
        if _cp is not None and ('CUDA' in err_msg or 'cuda' in err_msg or 'GPU' in err_msg):
            logger.warning(f"Erreur GPU ({e.__class__.__name__}), retry en CPU...")
            disable_gpu()
            return func(*args, **kwargs)
        raise