from typing import Optional, Tuple
import os
import sys
import subprocess
import tempfile
import time
import numpy as np

from shared.utils.logging_config import get_logger
from shared.utils.backend import (
    HAS_CUML_PACKAGE, HAS_CUPY_PACKAGE,
    check_cuda_available, check_cuml_available,
)

logger = get_logger(__name__)

# Auto-enable scikit-learn-intelex (Intel oneDAL) acceleration for sklearn's
# PCA / TSNE / KMeans on CPU. Patches sklearn at import time so any downstream
# sklearn call gets the accelerated path transparently. Disable for debugging
# vanilla sklearn behavior with: EMB_EXPLORER_DISABLE_SKLEARNEX=1
if os.environ.get("EMB_EXPLORER_DISABLE_SKLEARNEX", "0") != "1":
    try:
        from sklearnex import patch_sklearn
        patch_sklearn()
        logger.info("scikit-learn-intelex enabled (CPU sklearn auto-accelerated)")
    except ImportError:
        logger.debug("scikit-learn-intelex not installed; using vanilla sklearn")

# Legacy module-level flags — now backed by lightweight find_spec() checks
# so importing this module no longer triggers heavy library loads.
# Functions that actually need the libraries import them locally.
HAS_CUML: bool = HAS_CUML_PACKAGE and HAS_CUPY_PACKAGE
HAS_CUDA: bool = False  # resolved lazily via check_cuda_available()


def _check_cuda() -> bool:
    """Check CUDA availability (cached after first call)."""
    global HAS_CUDA
    available, _ = check_cuda_available()
    HAS_CUDA = available
    return available


class VRAMExceededError(Exception):
    """Raised when GPU VRAM is exceeded during computation."""
    pass


class GPUArchitectureError(Exception):
    """Raised when GPU architecture is not supported."""
    pass


def get_gpu_memory_info() -> Optional[Tuple[int, int]]:
    """
    Get GPU memory info (used, total) in MB.

    Returns:
        Tuple of (used_mb, total_mb) or None if unavailable.
    """
    try:
        if HAS_CUML and _check_cuda():
            import cupy as cp
            meminfo = cp.cuda.Device().mem_info
            free_bytes, total_bytes = meminfo
            used_bytes = total_bytes - free_bytes
            return (used_bytes // (1024 * 1024), total_bytes // (1024 * 1024))
    except Exception:
        pass  # GPU memory query via CuPy failed; try PyTorch next

    try:
        import torch
        if torch.cuda.is_available():
            used = torch.cuda.memory_allocated() // (1024 * 1024)
            total = torch.cuda.get_device_properties(0).total_memory // (1024 * 1024)
            return (used, total)
    except Exception:
        pass  # GPU memory query via PyTorch failed; return None

    return None


def estimate_memory_requirement(n_samples: int, n_features: int, method: str) -> int:
    """
    Estimate memory requirement in MB for dimensionality reduction.

    Args:
        n_samples: Number of samples
        n_features: Number of features
        method: Reduction method (PCA, TSNE, UMAP)

    Returns:
        Estimated memory in MB
    """
    # Base memory for input data (float32)
    base_mb = (n_samples * n_features * 4) / (1024 * 1024)

    # Method-specific multipliers (empirical estimates)
    if method.upper() == "PCA":
        return int(base_mb * 2)  # Relatively low overhead
    elif method.upper() == "TSNE":
        return int(base_mb * 4 + (n_samples * n_samples * 4) / (1024 * 1024))  # Distance matrix
    elif method.upper() == "UMAP":
        return int(base_mb * 3 + (n_samples * 15 * 4) / (1024 * 1024))  # kNN graph
    else:
        return int(base_mb * 3)

def _prepare_embeddings(embeddings: np.ndarray, operation: str) -> np.ndarray:
    """Validate, cast to float32, and L2-normalize embeddings.

    L2 normalization projects vectors onto the unit hypersphere (magnitude 1).
    This stabilises cuML's NN-descent (prevents SIGFPE from large magnitudes)
    and is appropriate for contrastive-model embeddings (e.g. CLIP, BioCLIP)
    whose training objective is cosine-similarity based.

    Args:
        embeddings: Raw embedding matrix (n_samples, n_features).
        operation: Label for log messages (e.g. "reduce_dim", "kmeans").

    Returns:
        L2-normalized float32 embedding matrix.
    """
    n_samples, n_features = embeddings.shape

    # Cast to float32
    embeddings = np.ascontiguousarray(embeddings, dtype=np.float32)

    # Check for non-finite values
    n_nonfinite = (~np.isfinite(embeddings)).sum()
    if n_nonfinite > 0:
        logger.warning(f"[{operation}] {n_nonfinite} non-finite values found, replacing with 0")
        embeddings = np.nan_to_num(embeddings, nan=0.0, posinf=0.0, neginf=0.0)

    # L2 normalize
    norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
    n_zero = (norms.ravel() < 1e-10).sum()
    if n_zero > 0:
        logger.warning(f"[{operation}] {n_zero} near-zero-norm vectors found (will clamp to avoid division by zero)")
    embeddings = embeddings / np.maximum(norms, 1e-10)

    logger.info(f"[{operation}] Prepared embeddings: {n_samples} samples, {n_features} features, "
                f"dtype=float32, L2-normalized "
                f"(input norms: min={norms.min():.2f}, max={norms.max():.2f}, mean={norms.mean():.2f})")
    return embeddings


def reduce_dim(embeddings: np.ndarray, method: str = "PCA", seed: Optional[int] = None, n_workers: int = 1, backend: str = "auto"):
    """
    Reduce the dimensionality of embeddings to 2D using PCA, t-SNE, or UMAP.

    Args:
        embeddings (np.ndarray): The input feature embeddings of shape (n_samples, n_features).
        method (str, optional): The dimensionality reduction method, "PCA", "TSNE", or "UMAP". Defaults to "PCA".
        seed (int, optional): Random seed for reproducibility. Defaults to None (random).
        n_workers (int, optional): Number of parallel workers for t-SNE/UMAP. Defaults to 1.
        backend (str, optional): Backend to use - "auto", "sklearn", "cuml". Defaults to "auto".

    Returns:
        np.ndarray: The 2D reduced embeddings of shape (n_samples, 2).

    Raises:
        ValueError: If an unsupported method is provided.
    """
    n_samples, n_features = embeddings.shape
    logger.info(f"Dimensionality reduction: method={method}, samples={n_samples}, features={n_features}, backend={backend}")

    # Validate, cast, and L2-normalize
    embeddings = _prepare_embeddings(embeddings, "reduce_dim")

    # Determine which backend to use
    cuda_available = _check_cuda()
    use_cuml = False
    if backend == "cuml" and HAS_CUML and cuda_available:
        use_cuml = True
    elif backend == "auto" and HAS_CUML and cuda_available and n_samples > 5000:
        # Use cuML automatically for large datasets on GPU
        use_cuml = True

    start_time = time.time()
    if use_cuml:
        logger.info(f"Using cuML backend for {method}")
        result = _reduce_dim_cuml(embeddings, method, seed, n_workers)
    else:
        logger.info(f"Using sklearn backend for {method}")
        result = _reduce_dim_sklearn(embeddings, method, seed, n_workers)

    elapsed = time.time() - start_time
    logger.info(f"Dimensionality reduction completed in {elapsed:.2f}s")
    return result


def _reduce_dim_sklearn(embeddings: np.ndarray, method: str, seed: Optional[int], n_workers: int):
    """Dimensionality reduction using sklearn/umap backends."""
    from sklearn.decomposition import PCA
    from sklearn.manifold import TSNE

    # Use -1 (all available cores) instead of specific values > 1 to avoid
    # thread count restrictions on HPC clusters (OMP_NUM_THREADS, SLURM cgroups)
    effective_workers = -1 if n_workers > 1 else n_workers

    if method.upper() == "PCA":
        # Pass random_state so the randomized SVD solver (auto-selected for
        # large inputs) is reproducible when a seed is set; None keeps it random.
        reducer = PCA(n_components=2, random_state=seed)
    elif method.upper() == "TSNE":
        # Adjust perplexity to be valid for the sample size
        n_samples = embeddings.shape[0]
        perplexity = min(30, max(5, n_samples // 3))  # Ensure perplexity is reasonable

        if seed is not None:
            reducer = TSNE(n_components=2, perplexity=perplexity, random_state=seed, n_jobs=effective_workers)
        else:
            reducer = TSNE(n_components=2, perplexity=perplexity, n_jobs=effective_workers)
    elif method.upper() == "UMAP":
        from umap import UMAP
        # Adjust n_neighbors to be valid for the sample size
        n_samples = embeddings.shape[0]
        n_neighbors = min(15, max(2, n_samples - 1))

        if seed is not None:
            reducer = UMAP(n_components=2, n_neighbors=n_neighbors, random_state=seed, n_jobs=effective_workers)
        else:
            reducer = UMAP(n_components=2, n_neighbors=n_neighbors, n_jobs=effective_workers)
    else:
        raise ValueError("Unsupported method. Choose 'PCA', 'TSNE', or 'UMAP'.")
    return reducer.fit_transform(embeddings)


def _reduce_dim_cuml(embeddings: np.ndarray, method: str, seed: Optional[int], n_workers: int):
    """Dimensionality reduction using cuML GPU backends.

    Expects embeddings to already be L2-normalized float32 from _prepare_embeddings().
    """
    try:
        import cupy as cp

        if method.upper() == "UMAP":
            # cuML UMAP can crash with SIGFPE on certain data distributions
            # (NN-descent numerical instability).  SIGFPE is a signal, not a
            # Python exception, so try/except cannot catch it.  Run in an
            # isolated subprocess so the main process (Streamlit) survives.
            return _run_cuml_umap_subprocess(embeddings, seed)

        # PCA and TSNE are stable — run in-process
        embeddings_gpu = cp.asarray(embeddings, dtype=cp.float32)

        if method.upper() == "PCA":
            from cuml.decomposition import PCA as cuPCA
            # cuML PCA takes no random_state and needs none: its full-SVD solver
            # is deterministic, so results are already reproducible run-to-run.
            reducer = cuPCA(n_components=2)
        elif method.upper() == "TSNE":
            from cuml.manifold import TSNE as cuTSNE
            n_samples = embeddings.shape[0]
            perplexity = min(30, max(5, n_samples // 3))

            # Force the exact solver: cuML's default Barnes-Hut collapses to a
            # ~1D line on near-homogeneous data (#40). exact is O(N^2) but fine
            # at our interactive scale; a faster Barnes-Hut-with-guard can come later.
            if seed is not None:
                reducer = cuTSNE(n_components=2, perplexity=perplexity, method="exact", random_state=seed)
            else:
                reducer = cuTSNE(n_components=2, perplexity=perplexity, method="exact")
        else:
            raise ValueError("Unsupported method. Choose 'PCA', 'TSNE', or 'UMAP'.")

        result_gpu = reducer.fit_transform(embeddings_gpu)
        return cp.asnumpy(result_gpu)

    except RuntimeError as e:
        error_msg = str(e).lower()
        if "no kernel image" in error_msg or "cudaerrornokernel" in error_msg:
            logger.warning(f"cuML {method} not supported on this GPU architecture, falling back to sklearn")
        else:
            logger.warning(f"cuML reduction failed ({e}), falling back to sklearn")
        return _reduce_dim_sklearn(embeddings, method, seed, n_workers)
    except Exception as e:
        logger.warning(f"cuML reduction failed ({e}), falling back to sklearn")
        return _reduce_dim_sklearn(embeddings, method, seed, n_workers)


# Standalone script executed in a subprocess for cuML UMAP.
# Kept minimal: only imports cuml/cupy/numpy, no project dependencies.
_CUML_UMAP_SCRIPT = """\
import sys, numpy as np, cupy as cp
from cuml.manifold import UMAP as cuUMAP

input_path, output_path = sys.argv[1], sys.argv[2]
n_neighbors = int(sys.argv[3])
seed = int(sys.argv[4]) if sys.argv[4] else None

embeddings = np.load(input_path)
emb_gpu = cp.asarray(embeddings, dtype=cp.float32)

# Embeddings arrive L2-normalized from _prepare_embeddings().
# Verify as a safety net — re-normalize if needed (prevents SIGFPE from NN-descent).
norms = cp.linalg.norm(emb_gpu, axis=1)
if cp.abs(norms.mean() - 1.0) > 0.01:
    emb_gpu = emb_gpu / cp.maximum(norms.reshape(-1, 1), 1e-10)

kw = dict(n_components=2, n_neighbors=n_neighbors)
if seed is not None:
    kw["random_state"] = seed
reducer = cuUMAP(**kw)
result = reducer.fit_transform(emb_gpu)
np.save(output_path, cp.asnumpy(result))
"""


def _run_cuml_umap_subprocess(embeddings: np.ndarray, seed: Optional[int]) -> np.ndarray:
    """Run cuML UMAP in an isolated subprocess to survive SIGFPE crashes.

    cuML UMAP's NN-descent can trigger a floating-point exception (SIGFPE) on
    certain data distributions, which kills the entire process.  By running in
    a child process, the parent (Streamlit) survives and can fall back to
    sklearn UMAP.
    """
    n_samples = embeddings.shape[0]
    n_neighbors = min(15, max(2, n_samples - 1))

    # Use /dev/shm for fast IPC when available, else /tmp
    shm_dir = "/dev/shm" if os.path.isdir("/dev/shm") else tempfile.gettempdir()
    input_path = os.path.join(shm_dir, f"cuml_umap_in_{os.getpid()}.npy")
    output_path = os.path.join(shm_dir, f"cuml_umap_out_{os.getpid()}.npy")

    np.save(input_path, embeddings)
    seed_arg = str(seed) if seed is not None else ""

    try:
        logger.info(f"Running cuML UMAP in subprocess ({n_samples} samples, "
                    f"n_neighbors={n_neighbors})")
        result = subprocess.run(
            [sys.executable, "-c", _CUML_UMAP_SCRIPT,
             input_path, output_path, str(n_neighbors), seed_arg],
            capture_output=True, text=True, timeout=300,
        )

        if result.returncode == 0 and os.path.exists(output_path):
            reduced = np.load(output_path)
            logger.info("cuML UMAP subprocess completed successfully")
            return reduced

        stderr = result.stderr.strip()
        raise RuntimeError(
            f"cuML UMAP subprocess failed (rc={result.returncode}): "
            f"{stderr[-500:] if stderr else 'no stderr'}"
        )
    finally:
        for path in (input_path, output_path):
            try:
                os.unlink(path)
            except OSError:
                pass  # Best-effort cleanup of temp IPC files

def run_kmeans(embeddings: np.ndarray, n_clusters: int, seed: Optional[int] = None, n_workers: int = 1, backend: str = "auto"):
    """
    Perform KMeans clustering on the given embeddings.

    Args:
        embeddings (np.ndarray): The input feature embeddings of shape (n_samples, n_features).
        n_clusters (int): The number of clusters to form.
        seed (int, optional): Random seed for reproducibility. Defaults to None (random).
        n_workers (int, optional): Number of parallel workers (used by cuML if available).
        backend (str, optional): Clustering backend - "auto", "sklearn", or "cuml". Defaults to "auto".

    Returns:
        kmeans (KMeans or custom object): The fitted clustering object.
        labels (np.ndarray): Cluster labels for each sample.
    """
    n_samples = embeddings.shape[0]
    logger.info(f"KMeans clustering: n_clusters={n_clusters}, samples={n_samples}, backend={backend}")

    # Validate, cast, and L2-normalize
    embeddings = _prepare_embeddings(embeddings, "kmeans")

    start_time = time.time()

    # Determine which backend to use
    cuda_available = _check_cuda()
    if backend == "cuml" and HAS_CUML and cuda_available:
        logger.info("Using cuML backend for KMeans")
        result = _run_kmeans_cuml(embeddings, n_clusters, seed, n_workers)
    elif backend == "auto" and HAS_CUML and cuda_available and n_samples > 500:
        logger.info("Auto-selected cuML backend for KMeans (GPU available, large dataset)")
        result = _run_kmeans_cuml(embeddings, n_clusters, seed, n_workers)
    else:
        logger.info("Using sklearn backend for KMeans")
        result = _run_kmeans_sklearn(embeddings, n_clusters, seed)

    elapsed = time.time() - start_time
    logger.info(f"KMeans clustering completed in {elapsed:.2f}s")
    return result


def _run_kmeans_cuml(embeddings: np.ndarray, n_clusters: int, seed: Optional[int] = None, n_workers: int = 1):
    """KMeans using cuML GPU backend."""
    try:
        import cupy as cp
        from cuml.cluster import KMeans as cuKMeans

        # Convert to cupy array for GPU processing
        embeddings_gpu = cp.asarray(embeddings, dtype=cp.float32)

        # Create cuML KMeans object
        if seed is not None:
            kmeans = cuKMeans(
                n_clusters=n_clusters,
                random_state=seed,
                max_iter=300,
                init='k-means++',
                tol=1e-4
            )
        else:
            kmeans = cuKMeans(
                n_clusters=n_clusters,
                max_iter=300,
                init='k-means++',
                tol=1e-4
            )

        # Fit and predict on GPU
        labels_gpu = kmeans.fit_predict(embeddings_gpu)

        # Convert results back to numpy
        labels = cp.asnumpy(labels_gpu)
        centroids = cp.asnumpy(kmeans.cluster_centers_)
        
        # Create a simple object to mimic sklearn KMeans interface
        class cuMLKMeans:
            def __init__(self, centroids, labels):
                self.cluster_centers_ = centroids
                self.labels_ = labels
                self.n_clusters = len(centroids)
        
        return cuMLKMeans(centroids, labels), labels
        
    except Exception as e:
        logger.warning(f"cuML clustering failed ({e}), falling back to sklearn")
        return _run_kmeans_sklearn(embeddings, n_clusters, seed)


def _run_kmeans_sklearn(embeddings: np.ndarray, n_clusters: int, seed: Optional[int] = None):
    """KMeans using scikit-learn backend."""
    from sklearn.cluster import KMeans
    if seed is not None:
        kmeans = KMeans(n_clusters=n_clusters, random_state=seed)
    else:
        kmeans = KMeans(n_clusters=n_clusters)
    labels = kmeans.fit_predict(embeddings)
    return kmeans, labels