"""
SparseTopkEmbCache — Sparse top-K attention delta @ gene_emb features.

Uses precomputed per-cell sparse attention (K=300) to compute delta top-K
weighted gene embeddings at training time. Filters 99.3% noise compared to
dense attention delta.

HDF5 layout (from precompute_sparse_attn.py):
    /attn_values        (N, G_full, K) float16  — top-K attention values per row
    /attn_indices       (N, G_full, K) int16    — column indices in G_full space
    /cell_names         (N,) string
    /valid_gene_mask    (G_full,) bool
"""

import h5py
import numpy as np
import torch


class SparseTopkEmbCache:
    """
    Sparse top-K attention delta @ gene_emb cache.

    At lookup time:
    1. Read sparse attention for src/tgt cells from HDF5
    2. Scatter to dense, compute delta = tgt - src
    3. Take top-K (default 30) by |delta|
    4. Weighted sum with gene embeddings → (B, G_sub, 512)
    5. Normalize with pre-computed statistics
    """

    def __init__(self, h5_path, gene_emb, top_k=30, target_std=1.0,
                 norm_n_pairs=2000, seed=42):
        """
        Args:
            h5_path: path to sparse attention HDF5 cache
            gene_emb: (G_full, D) CPU tensor of gene embeddings
            top_k: number of top delta entries to keep per gene
            target_std: target standard deviation for normalization
            norm_n_pairs: number of random cell pairs for norm statistics
            seed: random seed for norm sampling
        """
        self.h5_path = h5_path
        self.top_k = top_k
        self.target_std = target_std

        self.h5 = h5py.File(h5_path, "r")
        self.attn_values = self.h5["attn_values"]    # (N, G_full, K)
        self.attn_indices = self.h5["attn_indices"]   # (N, G_full, K)
        self.G_full = self.attn_values.shape[1]
        self.K_sparse = self.attn_values.shape[2]

        cell_names = self.h5["cell_names"].asstr()[:]
        self.name_to_idx = {name: i for i, name in enumerate(cell_names)}

        if "valid_gene_mask" in self.h5:
            self.valid_gene_mask = torch.from_numpy(
                self.h5["valid_gene_mask"][:].astype(bool))
        else:
            self.valid_gene_mask = torch.ones(self.G_full, dtype=torch.bool)

        self.gene_emb = gene_emb  # (G_full, D) CPU tensor
        assert gene_emb.shape[0] == self.G_full, (
            f"gene_emb shape {gene_emb.shape} != G_full {self.G_full}")

        print(f"  SparseTopkEmbCache: {len(self.name_to_idx)} cells, "
              f"G_full={self.G_full}, K_sparse={self.K_sparse}, top_k={top_k}")
        print(f"  gene_emb shape: {gene_emb.shape}, "
              f"valid genes: {self.valid_gene_mask.sum().item()}")

        # Compute normalization statistics
        self._compute_norm_stats(norm_n_pairs, seed)

    def _compute_norm_stats(self, n_pairs, seed):
        """Sample random cell pairs and compute norm statistics for the 512-d output."""
        rng = np.random.RandomState(seed)
        cell_names_list = list(self.name_to_idx.keys())
        n_cells = len(cell_names_list)

        src_idx = rng.randint(0, n_cells, size=n_pairs)
        tgt_idx = rng.randint(0, n_cells, size=n_pairs)

        # Sample gene subset for faster norm estimation
        valid_positions = torch.where(self.valid_gene_mask)[0]
        n_sample_genes = min(500, len(valid_positions))
        sample_perm = rng.choice(len(valid_positions), n_sample_genes, replace=False)
        sample_gene_idx = valid_positions[sample_perm]
        sample_gene_idx = torch.sort(sample_gene_idx)[0]

        D = self.gene_emb.shape[1]
        sum_x = torch.zeros(D, dtype=torch.float64)
        sum_x2 = torch.zeros(D, dtype=torch.float64)
        total_n = 0

        batch_size = 64
        print(f"  Computing norm stats from {n_pairs} cell pairs, "
              f"{n_sample_genes} sampled genes...")
        for i in range(0, n_pairs, batch_size):
            batch_end = min(i + batch_size, n_pairs)
            batch_src = [cell_names_list[j] for j in src_idx[i:batch_end]]
            batch_tgt = [cell_names_list[j] for j in tgt_idx[i:batch_end]]

            feats = self._compute_features(
                batch_src, batch_tgt, sample_gene_idx, torch.device("cpu"))
            flat = feats.reshape(-1, D).double()
            sum_x += flat.sum(dim=0)
            sum_x2 += (flat ** 2).sum(dim=0)
            total_n += flat.shape[0]

        self.norm_mean = (sum_x / total_n).float()
        self.norm_var = (sum_x2 / total_n - (sum_x / total_n) ** 2).float()
        # Clamp variance to avoid division by zero
        self.norm_var = self.norm_var.clamp(min=1e-8)

        print(f"  Norm stats computed: mean [{self.norm_mean.min():.4f}, "
              f"{self.norm_mean.max():.4f}], "
              f"std [{self.norm_var.sqrt().min():.4f}, "
              f"{self.norm_var.sqrt().max():.4f}]")

    def _compute_features(self, src_cell_names, tgt_cell_names, gene_indices, device):
        """
        Compute raw (unnormalized) sparse topk @ gene_emb features.

        Args:
            src_cell_names: list of str, control cell names
            tgt_cell_names: list of str, perturbation cell names
            gene_indices: (G_sub,) tensor or None (all genes)
            device: torch device for computation

        Returns:
            (B, G_sub, D) tensor of raw features
        """
        B = len(src_cell_names)
        D = self.gene_emb.shape[1]

        if gene_indices is not None:
            gene_idx_np = gene_indices.cpu().numpy()
            G_sub = len(gene_idx_np)
        else:
            gene_idx_np = None
            G_sub = self.G_full

        # Step 1: Collect unique cells, read HDF5 once
        seen = {}
        unique_names = []
        for n in src_cell_names + tgt_cell_names:
            if n not in seen:
                seen[n] = len(unique_names)
                unique_names.append(n)

        unique_h5_idx = [self.name_to_idx[n] for n in unique_names]
        sorted_order = np.argsort(unique_h5_idx)
        sorted_h5_idx = [unique_h5_idx[i] for i in sorted_order]

        # Read from HDF5 (sorted for sequential access)
        raw_vals = self.attn_values[sorted_h5_idx]   # (U, G_full, K) float16
        raw_idxs = self.attn_indices[sorted_h5_idx]  # (U, G_full, K) int16

        # Unsort to match unique_names order
        unsort = np.argsort(sorted_order)
        raw_vals = raw_vals[unsort]
        raw_idxs = raw_idxs[unsort]

        # Select gene subset in numpy (before GPU transfer)
        if gene_idx_np is not None:
            raw_vals = raw_vals[:, gene_idx_np, :]  # (U, G_sub, K)
            raw_idxs = raw_idxs[:, gene_idx_np, :]

        # Step 2: Map to src/tgt batch order
        src_map = [seen[n] for n in src_cell_names]
        tgt_map = [seen[n] for n in tgt_cell_names]

        # Step 3: Convert to torch and move to device
        src_vals = torch.from_numpy(raw_vals[src_map].astype(np.float32)).to(device)
        src_idxs = torch.from_numpy(raw_idxs[src_map].astype(np.int64)).to(device)
        tgt_vals = torch.from_numpy(raw_vals[tgt_map].astype(np.float32)).to(device)
        tgt_idxs = torch.from_numpy(raw_idxs[tgt_map].astype(np.int64)).to(device)
        gene_emb_d = self.gene_emb.to(device)

        # Step 4: Process in chunks (100 genes per chunk to limit memory)
        chunk_size = 100
        output = torch.zeros(B, G_sub, D, device=device)

        for c_start in range(0, G_sub, chunk_size):
            c_end = min(c_start + chunk_size, G_sub)
            c_len = c_end - c_start

            sv = src_vals[:, c_start:c_end, :]  # (B, c_len, K)
            si = src_idxs[:, c_start:c_end, :]
            tv = tgt_vals[:, c_start:c_end, :]
            ti = tgt_idxs[:, c_start:c_end, :]

            # Scatter sparse entries to dense attention rows
            src_dense = torch.zeros(B, c_len, self.G_full, device=device)
            tgt_dense = torch.zeros(B, c_len, self.G_full, device=device)
            src_dense.scatter_(-1, si, sv)
            tgt_dense.scatter_(-1, ti, tv)

            # Delta attention
            delta = tgt_dense - src_dense  # (B, c_len, G_full)

            # Top-k by absolute delta value
            _, topk_idx = delta.abs().topk(self.top_k, dim=-1)  # (B, c_len, top_k)
            topk_delta = delta.gather(-1, topk_idx)  # (B, c_len, top_k)

            # Gather gene embeddings at top-k positions
            flat_idx = topk_idx.reshape(-1)  # (B * c_len * top_k,)
            topk_emb = gene_emb_d[flat_idx].reshape(
                B, c_len, self.top_k, D)  # (B, c_len, top_k, D)

            # Weighted sum: delta_values * gene_emb → (B, c_len, D)
            chunk_feat = (topk_delta.unsqueeze(-1) * topk_emb).sum(dim=2)
            output[:, c_start:c_end, :] = chunk_feat

        return output

    def lookup_delta(self, src_cell_names, tgt_cell_names, gene_indices, device=None):
        """
        Compute normalized sparse topk @ gene_emb features for (src, tgt) pairs.

        Args:
            src_cell_names: list of str, control cell identifiers
            tgt_cell_names: list of str, perturbation cell identifiers
            gene_indices: (G_sub,) tensor, gene subset indices
            device: target torch device

        Returns:
            (B, G_sub, D) tensor, normalized features
        """
        if device is None:
            device = torch.device("cpu")

        feats = self._compute_features(
            src_cell_names, tgt_cell_names, gene_indices, device)

        # Normalize: (x - mean) / sqrt(var) * target_std
        eps = 1e-6
        norm_mean = self.norm_mean.to(device)
        norm_var = self.norm_var.to(device)
        feats = (feats - norm_mean) / (norm_var.sqrt() + eps)
        feats = feats * self.target_std

        return feats

    def close(self):
        self.h5.close()

    def __del__(self):
        try:
            self.h5.close()
        except Exception:
            pass


def _read_sparse_batch(h5_values, h5_indices, name_to_idx,
                       src_cell_names, tgt_cell_names, gene_idx_np=None):
    """
    Shared HDF5 reading logic for sparse caches.

    Returns:
        src_vals, src_idxs, tgt_vals, tgt_idxs: numpy arrays (B, G_sub, K)
    """
    seen = {}
    unique_names = []
    for n in src_cell_names + tgt_cell_names:
        if n not in seen:
            seen[n] = len(unique_names)
            unique_names.append(n)

    unique_h5_idx = [name_to_idx[n] for n in unique_names]
    sorted_order = np.argsort(unique_h5_idx)
    sorted_h5_idx = [unique_h5_idx[i] for i in sorted_order]

    raw_vals = h5_values[sorted_h5_idx]
    raw_idxs = h5_indices[sorted_h5_idx]

    unsort = np.argsort(sorted_order)
    raw_vals = raw_vals[unsort]
    raw_idxs = raw_idxs[unsort]

    if gene_idx_np is not None:
        raw_vals = raw_vals[:, gene_idx_np, :]
        raw_idxs = raw_idxs[:, gene_idx_np, :]

    src_map = [seen[n] for n in src_cell_names]
    tgt_map = [seen[n] for n in tgt_cell_names]
    return raw_vals[src_map], raw_idxs[src_map], raw_vals[tgt_map], raw_idxs[tgt_map]


class SparsePCADeltaCache:
    """
    Sparse PCA delta cache: topk filtering + PCA projection.

    Same topk30 filtering as SparseTopkEmbCache, but projects through
    precomputed PCA basis (d-dim) instead of gene_emb (512-dim):
        1. scatter sparse K=300 → dense delta (G_full)
        2. topk(top_k) by |delta|
        3. delta_vals @ pca_basis[topk_idx] → (d,)
    """

    def __init__(self, h5_path, top_k=30, target_std=1.0,
                 norm_n_pairs=2000, seed=42):
        self.h5_path = h5_path
        self.top_k = top_k
        self.target_std = target_std

        self.h5 = h5py.File(h5_path, "r")
        self.attn_values = self.h5["attn_values"]
        self.attn_indices = self.h5["attn_indices"]
        self.G_full = self.attn_values.shape[1]
        self.K_sparse = self.attn_values.shape[2]

        cell_names = self.h5["cell_names"].asstr()[:]
        self.name_to_idx = {name: i for i, name in enumerate(cell_names)}

        if "valid_gene_mask" in self.h5:
            self.valid_gene_mask = torch.from_numpy(
                self.h5["valid_gene_mask"][:].astype(bool))
        else:
            self.valid_gene_mask = torch.ones(self.G_full, dtype=torch.bool)

        # Load PCA basis
        self.pca_basis = torch.from_numpy(self.h5["pca_basis"][:]).float()  # (G_full, d)
        self.pca_dim = self.pca_basis.shape[1]

        print(f"  SparsePCADeltaCache: {len(self.name_to_idx)} cells, "
              f"G_full={self.G_full}, K_sparse={self.K_sparse}, "
              f"top_k={top_k}, PCA dim={self.pca_dim}")

        self._compute_norm_stats(norm_n_pairs, seed)

    def _compute_norm_stats(self, n_pairs, seed):
        """Sample random cell pairs and compute norm statistics for PCA-d output."""
        rng = np.random.RandomState(seed)
        cell_names_list = list(self.name_to_idx.keys())
        n_cells = len(cell_names_list)

        src_idx = rng.randint(0, n_cells, size=n_pairs)
        tgt_idx = rng.randint(0, n_cells, size=n_pairs)

        valid_positions = torch.where(self.valid_gene_mask)[0]
        n_sample_genes = min(500, len(valid_positions))
        sample_perm = rng.choice(len(valid_positions), n_sample_genes, replace=False)
        sample_gene_idx = valid_positions[sample_perm]
        sample_gene_idx = torch.sort(sample_gene_idx)[0]

        D = self.pca_dim
        sum_x = torch.zeros(D, dtype=torch.float64)
        sum_x2 = torch.zeros(D, dtype=torch.float64)
        total_n = 0

        batch_size = 64
        print(f"  Computing PCA norm stats from {n_pairs} cell pairs, "
              f"{n_sample_genes} sampled genes...")
        for i in range(0, n_pairs, batch_size):
            batch_end = min(i + batch_size, n_pairs)
            batch_src = [cell_names_list[j] for j in src_idx[i:batch_end]]
            batch_tgt = [cell_names_list[j] for j in tgt_idx[i:batch_end]]

            feats = self._compute_features(
                batch_src, batch_tgt, sample_gene_idx, torch.device("cpu"))
            flat = feats.reshape(-1, D).double()
            sum_x += flat.sum(dim=0)
            sum_x2 += (flat ** 2).sum(dim=0)
            total_n += flat.shape[0]

        self.norm_mean = (sum_x / total_n).float()
        self.norm_var = (sum_x2 / total_n - (sum_x / total_n) ** 2).float()
        self.norm_var = self.norm_var.clamp(min=1e-8)

        print(f"  Norm stats computed: mean [{self.norm_mean.min():.4f}, "
              f"{self.norm_mean.max():.4f}], "
              f"std [{self.norm_var.sqrt().min():.4f}, "
              f"{self.norm_var.sqrt().max():.4f}]")

    def _compute_features(self, src_cell_names, tgt_cell_names, gene_indices, device):
        """
        Compute topk-filtered PCA-projected delta features.

        Flow per chunk:
        1. scatter sparse K=300 → dense (B, chunk, G_full)
        2. delta = tgt_dense - src_dense
        3. topk(top_k) by |delta|
        4. delta_vals @ pca_basis[topk_idx] → (B, chunk, pca_dim)

        Returns: (B, G_sub, pca_dim) tensor
        """
        B = len(src_cell_names)
        D = self.pca_dim

        gene_idx_np = gene_indices.cpu().numpy() if gene_indices is not None else None
        G_sub = len(gene_idx_np) if gene_idx_np is not None else self.G_full

        sv_np, si_np, tv_np, ti_np = _read_sparse_batch(
            self.attn_values, self.attn_indices, self.name_to_idx,
            src_cell_names, tgt_cell_names, gene_idx_np)

        src_vals = torch.from_numpy(sv_np.astype(np.float32)).to(device)
        src_idxs = torch.from_numpy(si_np.astype(np.int64)).to(device)
        tgt_vals = torch.from_numpy(tv_np.astype(np.float32)).to(device)
        tgt_idxs = torch.from_numpy(ti_np.astype(np.int64)).to(device)
        pca_d = self.pca_basis.to(device)  # (G_full, d)

        chunk_size = 100
        output = torch.zeros(B, G_sub, D, device=device)

        for c_start in range(0, G_sub, chunk_size):
            c_end = min(c_start + chunk_size, G_sub)
            c_len = c_end - c_start

            sv = src_vals[:, c_start:c_end, :]  # (B, c_len, K)
            si = src_idxs[:, c_start:c_end, :]
            tv = tgt_vals[:, c_start:c_end, :]
            ti = tgt_idxs[:, c_start:c_end, :]

            # Scatter sparse → dense
            src_dense = torch.zeros(B, c_len, self.G_full, device=device)
            tgt_dense = torch.zeros(B, c_len, self.G_full, device=device)
            src_dense.scatter_(-1, si, sv)
            tgt_dense.scatter_(-1, ti, tv)

            # Delta + topk
            delta = tgt_dense - src_dense  # (B, c_len, G_full)
            _, topk_idx = delta.abs().topk(self.top_k, dim=-1)  # (B, c_len, top_k)
            topk_delta = delta.gather(-1, topk_idx)  # (B, c_len, top_k)

            # Gather PCA basis at topk positions & weighted sum
            flat_idx = topk_idx.reshape(-1)
            topk_pca = pca_d[flat_idx].reshape(
                B, c_len, self.top_k, D)  # (B, c_len, top_k, d)
            chunk_feat = (topk_delta.unsqueeze(-1) * topk_pca).sum(dim=2)
            output[:, c_start:c_end, :] = chunk_feat

        return output

    def lookup_delta(self, src_cell_names, tgt_cell_names, gene_indices, device=None):
        """
        Compute normalized PCA-projected delta features for (src, tgt) pairs.

        Returns: (B, G_sub, pca_dim) tensor, normalized features
        """
        if device is None:
            device = torch.device("cpu")

        feats = self._compute_features(
            src_cell_names, tgt_cell_names, gene_indices, device)

        eps = 1e-6
        norm_mean = self.norm_mean.to(device)
        norm_var = self.norm_var.to(device)
        feats = (feats - norm_mean) / (norm_var.sqrt() + eps)
        feats = feats * self.target_std

        return feats

    def close(self):
        self.h5.close()

    def __del__(self):
        try:
            self.h5.close()
        except Exception:
            pass