# coding=utf-8
# Copyright 2026 VIDRAFT (비드래프트). All rights reserved.
#
# AETHER-V2-7way: 7-aware attention + 7×7 Latin Square 49-layer MoE
# Built upon HuggingFace Transformers conventions.
#
# Architecture:
#   - 49 layers organized as 7×7 Latin Square
#   - 7 distinct attention types (NSA, Differential, Full, Linear, Sliding, Compress, Hybrid)
#   - 25 experts per layer, top-7 active per token
#   - Each row of latin square = 1 cycle of 7 attention types
#   - Each column = different ordering (Latin square property)
#
# Layer index → (row, col) → attention type via LATIN_SQUARE_7x7
#
"""PyTorch AETHER-V2-7way model."""

from __future__ import annotations
import math
import warnings
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    MoeCausalLMOutputWithPast,
    MoeModelOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.generation import GenerationMixin
from transformers.utils import logging

from .configuration_aether_v2_7way import AETHERV27wayConfig

# 7-aware attention modules (already authored, in v2_attentions/)
from .nsa import NSAAttention
from .differential import DifferentialAttention

logger = logging.get_logger(__name__)


# =============================================================================
# 7×7 Latin Square — Layer → (attention_type, ffn_phase) 매핑
# =============================================================================
# Latin Square property: each row & column has each of {0..6} exactly once.
# row = layer // 7   (0..6)
# col = layer % 7    (0..6)
# attention_type = LATIN_SQUARE_7x7[row][col]
#
# 5-element cyclic FFN phase (:
# ffn_phase = layer % 5
#
LATIN_SQUARE_7x7 = [
    [0, 1, 2, 3, 4, 5, 6],   # row 0: identity
    [1, 2, 3, 4, 5, 6, 0],   # row 1: shift +1
    [2, 3, 4, 5, 6, 0, 1],   # row 2: shift +2
    [3, 4, 5, 6, 0, 1, 2],   # row 3: shift +3
    [4, 5, 6, 0, 1, 2, 3],   # row 4: shift +4
    [5, 6, 0, 1, 2, 3, 4],   # row 5: shift +5
    [6, 0, 1, 2, 3, 4, 5],   # row 6: shift +6
]

# Attention type names (0..6)
ATTN_TYPES = [
    "nsa",            # 0: Native Sparse Attention (3-branch)
    "differential",   # 1: Differential Attention (lambda-gated)
    "full",           # 2: Full Attention (standard)
    "linear",         # 3: Linear Attention (Mamba-style)
    "sliding",        # 4: Sliding Window Attention
    "compress",       # 5: Compress-only branch (NSA subset)
    "hybrid",         # 6: NSA+Differential combined
]


def get_attention_type(layer_idx: int) -> str:
    """Layer index → attention type via Latin Square."""
    row = layer_idx // 7
    col = layer_idx % 7
    type_idx = LATIN_SQUARE_7x7[row][col]
    return ATTN_TYPES[type_idx]


def get_ffn_phase(layer_idx: int) -> int:
    """Layer index → 5-element cyclic phase."""
    return layer_idx % 5


# =============================================================================
# Rotary Position Embedding (RoPE)
# =============================================================================
class AETHERV27wayRotaryEmbedding(nn.Module):
    def __init__(self, dim: int, max_pos: int = 4096, base: float = 10000.0, device=None):
        super().__init__()
        self.dim = dim
        self.max_pos = max_pos
        self.base = base
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self._build_cos_sin_cache(max_pos, device or torch.device("cpu"))

    def _build_cos_sin_cache(self, seq_len: int, device, dtype=torch.float32):
        t = torch.arange(seq_len, device=device, dtype=torch.float32)
        freqs = torch.outer(t, self.inv_freq)
        emb = torch.cat([freqs, freqs], dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

    @torch.no_grad()
    def forward(self, x: torch.Tensor, position_ids: torch.Tensor):
        if position_ids.max() >= self.cos_cached.size(0):
            self._build_cos_sin_cache(int(position_ids.max() + 1), x.device, x.dtype)
        cos = self.cos_cached[position_ids].to(x.dtype)
        sin = self.sin_cached[position_ids].to(x.dtype)
        return cos, sin


def rotate_half(x: torch.Tensor) -> torch.Tensor:
    x1, x2 = x.chunk(2, dim=-1)
    return torch.cat([-x2, x1], dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


# =============================================================================
# RMSNorm
# =============================================================================
class AETHERV27wayRMSNorm(nn.Module):
    def __init__(self, hidden_size: int, eps: float = 1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.eps = eps

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        in_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
        return self.weight * hidden_states.to(in_dtype)


# =============================================================================
# Standard Multi-Head Attention (Full Attention type)
# =============================================================================
class FullAttention(nn.Module):
    """Standard multi-head attention with GQA support."""

    def __init__(self, config: AETHERV27wayConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.num_kv_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
        self.head_dim = config.head_dim
        self.num_kv_groups = self.num_heads // self.num_kv_heads

        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

        self.rotary = AETHERV27wayRotaryEmbedding(
            self.head_dim, config.max_position_embeddings, config.rope_theta,
        )

    def _repeat_kv(self, x: torch.Tensor) -> torch.Tensor:
        if self.num_kv_groups == 1:
            return x
        bsz, n_kv, seq, dim = x.shape
        return x[:, :, None, :, :].expand(bsz, n_kv, self.num_kv_groups, seq, dim).reshape(
            bsz, n_kv * self.num_kv_groups, seq, dim,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[Cache]]:
        bsz, q_len, _ = hidden_states.size()

        q = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        k = self.k_proj(hidden_states).view(bsz, q_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
        v = self.v_proj(hidden_states).view(bsz, q_len, self.num_kv_heads, self.head_dim).transpose(1, 2)

        cos, sin = self.rotary(v, position_ids)
        q, k = apply_rotary_pos_emb(q, k, cos, sin)

        if past_key_value is not None:
            k, v = past_key_value.update(k, v, self.layer_idx)

        k = self._repeat_kv(k)
        v = self._repeat_kv(v)

        attn_out = F.scaled_dot_product_attention(
            q, k, v,
            attn_mask=(attention_mask.to(q.dtype) if attention_mask is not None else None),
            dropout_p=0.0 if not self.training else self.config.attention_dropout,
            is_causal=(attention_mask is None and q_len > 1),
        )
        attn_out = attn_out.transpose(1, 2).contiguous().view(bsz, q_len, -1)
        return self.o_proj(attn_out), past_key_value


# =============================================================================
# Linear Attention (Mamba-style, simplified)
# =============================================================================
class LinearAttention(nn.Module):
    """Linear attention (Mamba/RWKV-inspired) for long-context efficiency."""

    def __init__(self, config: AETHERV27wayConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = config.head_dim
        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.gate = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
        self.norm = AETHERV27wayRMSNorm(self.head_dim, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[Cache]]:
        bsz, q_len, _ = hidden_states.size()
        # causal mask handling: SDPA causal fallback (causal-safe)
        q = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        k = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        v = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        g = self.gate(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).sigmoid()

        # AetherCache fix: KV cache + causal only when prefill (q_len>1). Training path unchanged.
        if past_key_value is not None:
            k, v = past_key_value.update(k, v, self.layer_idx)
        out = F.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=(q_len > 1))
        out = out.transpose(1, 2).contiguous()  # (bsz, q_len, num_heads, head_dim)
        out = out * g
        out = self.norm(out).reshape(bsz, q_len, -1)
        return self.o_proj(out), past_key_value


# =============================================================================
# Sliding Window Attention
# =============================================================================
class SlidingWindowAttention(FullAttention):
    """Standard MHA but limited to local window for efficiency."""

    def __init__(self, config: AETHERV27wayConfig, layer_idx: int):
        super().__init__(config, layer_idx)
        self.window_size = getattr(config, "sliding_window_size", 512)

    def forward(self, hidden_states, attention_mask=None, position_ids=None,
                past_key_value=None, use_cache=False, **kwargs):
        bsz, q_len, _ = hidden_states.size()
        if attention_mask is None and q_len > self.window_size:
            mask = torch.ones(q_len, q_len, dtype=torch.bool, device=hidden_states.device)
            mask = torch.tril(mask) & torch.triu(mask, diagonal=-self.window_size)
            attention_mask = torch.where(mask, 0.0, float("-inf")).unsqueeze(0).unsqueeze(0)
        return super().forward(hidden_states, attention_mask, position_ids, past_key_value, use_cache, **kwargs)


# =============================================================================
# Compress Attention (NSA-subset, just compress branch)
# =============================================================================
class CompressAttention(nn.Module):
    """Compress branch: reduce KV cache via local average, then full attention on compressed."""

    def __init__(self, config: AETHERV27wayConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.compress_block = getattr(config, "compress_block_size", 16)
        self.full_attn = FullAttention(config, layer_idx)

    def forward(self, hidden_states, attention_mask=None, position_ids=None,
                past_key_value=None, use_cache=False, **kwargs):
        # per-token causal-safe block-mean
        # 임시 fallback: FullAttention causal (압축 효율 손실, 안전 우선)
        return self.full_attn(hidden_states, attention_mask, position_ids, past_key_value, use_cache)


# =============================================================================
# Hybrid Attention (NSA + Differential combined)
# =============================================================================
class HybridAttention(nn.Module):
    """Combine NSA + Differential outputs via learnable gate + final norm (stable).

    Fix v2 (2026-05-05): added post-merge GroupNorm + gate init=0 (sigmoid(0)=0.5 exact balance)
    + lightly scaled output to prevent 49-layer cumulative divergence.
    """

    def __init__(self, config: AETHERV27wayConfig, layer_idx: int):
        super().__init__()
        self.nsa = NSAAttention(config, layer_idx)
        self.diff = DifferentialAttention(config, layer_idx)
        # AetherCache: nsa caches the layer input, diff caches KV -> they MUST NOT share a slot.
        # (layer_idx is kept for lambda_init math; only the cache slot is offset.)
        self.nsa.cache_idx = layer_idx
        self.diff.cache_idx = int(getattr(config, "num_hidden_layers", 49)) + layer_idx
        # Per-channel gate (richer than scalar), init to 0 → sigmoid(0)=0.5 exact balance
        self.gate = nn.Parameter(torch.zeros(config.hidden_size))
        # per-token RMSNorm (causal-safe)
        self.merge_norm = AETHERV27wayRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(self, hidden_states, attention_mask=None, position_ids=None,
                past_key_value=None, use_cache=False, **kwargs):
        nsa_out, kv1 = self.nsa(hidden_states, attention_mask, position_ids, past_key_value, use_cache, **kwargs)
        diff_out, kv2 = self.diff(hidden_states, attention_mask, position_ids, past_key_value, use_cache, **kwargs)
        # Per-channel learnable mix: g (sigmoid) per channel
        g = torch.sigmoid(self.gate)  # shape (hidden_size,)
        out = g * nsa_out + (1.0 - g) * diff_out
        # per-token RMSNorm (causal-safe)
        out = self.merge_norm(out)
        return out, kv1 if kv1 is not None else kv2


# =============================================================================
# 7-aware Attention Dispatcher
# =============================================================================
def build_attention(config: AETHERV27wayConfig, layer_idx: int) -> nn.Module:
    """Pick attention type based on Latin Square index."""
    attn_type = get_attention_type(layer_idx)
    if attn_type == "nsa":
        return NSAAttention(config, layer_idx)
    elif attn_type == "differential":
        return DifferentialAttention(config, layer_idx)
    elif attn_type == "full":
        return FullAttention(config, layer_idx)
    elif attn_type == "linear":
        return LinearAttention(config, layer_idx)
    elif attn_type == "sliding":
        return SlidingWindowAttention(config, layer_idx)
    elif attn_type == "compress":
        return CompressAttention(config, layer_idx)
    elif attn_type == "hybrid":
        return HybridAttention(config, layer_idx)
    raise ValueError(f"Unknown attention type: {attn_type}")


# =============================================================================
# MoE Block: 25 experts, top-7 active per token
# =============================================================================
class AETHERV27wayMLP(nn.Module):
    """Single expert MLP (SwiGLU)."""

    def __init__(self, config: AETHERV27wayConfig, intermediate_size: Optional[int] = None):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = intermediate_size or config.expert_intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class AETHERV27waySparseMoE(nn.Module):
    """25-expert MoE with top-7 active routing.

    Each layer has a 5-phase cyclic FFN bias to encode cyclic phases.
    """

    def __init__(self, config: AETHERV27wayConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.num_experts = config.num_experts
        self.top_k = config.num_experts_per_tok
        self.ffn_phase = get_ffn_phase(layer_idx)  # 0..4 (5-element cycle)

        # Router: hidden → num_experts logits
        self.gate = nn.Linear(self.hidden_size, self.num_experts, bias=False)

        # 25 experts (each is a SwiGLU MLP)
        self.experts = nn.ModuleList([
            AETHERV27wayMLP(config) for _ in range(self.num_experts)
        ])

        # cyclic phase bias (learnable, 5 phases)
        self.phase_bias = nn.Parameter(torch.zeros(5, self.num_experts))

        # Optional shared expert (always active, optional)
        self.use_shared_expert = getattr(config, "use_shared_expert", True)
        if self.use_shared_expert:
            self.shared_expert = AETHERV27wayMLP(
                config, intermediate_size=config.expert_intermediate_size,
            )
            self.shared_expert_gate = nn.Linear(self.hidden_size, 1, bias=False)

    def _stacked_experts(self):
        """Expert weights stacked into [E, ...] tensors so a decode step can run all top_k
        experts as three bmm calls instead of 3*top_k separate GEMMs. Built once, on first
        use, and only for inference: costs one extra copy of the expert weights in VRAM.
        """
        stk = getattr(self, "_stk", None)
        if stk is None:
            with torch.no_grad():
                stk = (
                    torch.stack([e.gate_proj.weight for e in self.experts]),  # [E, I, H]
                    torch.stack([e.up_proj.weight for e in self.experts]),    # [E, I, H]
                    torch.stack([e.down_proj.weight for e in self.experts]),  # [E, H, I]
                )
            self._stk = stk
        return stk

    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        bsz, seq_len, dim = hidden_states.shape
        x = hidden_states.view(-1, dim)  # (bsz*seq, dim)

        # Routing
        router_logits = self.gate(x)  # (bsz*seq, num_experts)
        # Add 5-phase cyclic bias
        router_logits = router_logits + self.phase_bias[self.ffn_phase].unsqueeze(0)

        # Top-k selection
        routing_weights, selected_experts = torch.topk(router_logits, self.top_k, dim=-1)
        routing_weights = F.softmax(routing_weights, dim=-1)

        # Initialize output
        final_out = torch.zeros_like(x)

        # Per-expert dispatch. The old loop ran over every expert and called mask.any() to skip
        # the inactive ones -- but .any() and .nonzero() both sync the device, so a decoded token
        # paid num_experts x num_layers stalls just to decide what to skip. Both paths below keep
        # ascending-expert accumulation order, so results are unchanged.
        if x.shape[0] == 1 and not self.training:
            # Single-token decode. Each expert GEMM here is [1,H]x[H,I] -- far too small to keep
            # the GPU busy, so 3*top_k separate launches cost more than the math. Gather the
            # routed experts' weights with a device-side index (no host sync, static shape) and
            # run them as three bmm calls.
            wg, wu, wd = self._stacked_experts()
            idx = selected_experts[0]                                   # [k], stays on device
            xe = x.unsqueeze(0).expand(idx.shape[0], 1, dim)            # [k, 1, H]
            g = torch.bmm(xe, wg[idx].transpose(1, 2))                  # [k, 1, I]
            u = torch.bmm(xe, wu[idx].transpose(1, 2))                  # [k, 1, I]
            act = self.experts[0].act_fn(g) * u                         # [k, 1, I]
            o = torch.bmm(act, wd[idx].transpose(1, 2))                 # [k, 1, H]
            w = routing_weights[0].view(-1, 1, 1).to(o.dtype)
            final_out = (o * w).sum(0)                                  # [1, H]
        else:
            # unique() is sorted, so surviving experts keep ascending order; one sync per layer.
            for e in selected_experts.unique().tolist():
                mask = (selected_experts == e)
                token_idx, k_idx = mask.nonzero(as_tuple=True)
                expert_in = x[token_idx]
                expert_out = self.experts[e](expert_in)
                weight = routing_weights[token_idx, k_idx].unsqueeze(-1).to(expert_out.dtype)
                final_out.index_add_(0, token_idx, (expert_out * weight).to(final_out.dtype))

        # Shared expert
        if self.use_shared_expert:
            shared_out = self.shared_expert(x)
            shared_gate = torch.sigmoid(self.shared_expert_gate(x))
            final_out = final_out + (shared_out * shared_gate).to(final_out.dtype)

        final_out = final_out.view(bsz, seq_len, dim)
        return final_out, router_logits.view(bsz, seq_len, self.num_experts)


# =============================================================================
# Decoder Layer: Attention + MoE FFN with 7-aware + 5-phase logic
# =============================================================================
class AETHERV27wayDecoderLayer(nn.Module):
    def __init__(self, config: AETHERV27wayConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.attn_type = get_attention_type(layer_idx)
        self.ffn_phase = get_ffn_phase(layer_idx)

        # 7-aware attention (1 of 7 types based on Latin square)
        self.self_attn = build_attention(config, layer_idx)

        # MoE FFN with 5-phase cyclic bias
        self.mlp = AETHERV27waySparseMoE(config, layer_idx)

        # Norms
        self.input_layernorm = AETHERV27wayRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = AETHERV27wayRMSNorm(self.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_router_logits: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[Cache], Optional[torch.Tensor]]:
        # Self-attention with residual
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, kv = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            use_cache=use_cache,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # MoE FFN with residual
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states, router_logits = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states, kv)
        if output_router_logits:
            outputs = outputs + (router_logits,)
        else:
            outputs = outputs + (None,)
        return outputs


# =============================================================================
# Pretrained base
# =============================================================================
class AETHERV27wayPreTrainedModel(PreTrainedModel):
    config_class = AETHERV27wayConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["AETHERV27wayDecoderLayer"]
    _supports_cache_class = True
    _supports_static_cache = False

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, AETHERV27wayRMSNorm):
            module.weight.data.fill_(1.0)


# =============================================================================
# Main Model
# =============================================================================
class AETHERV27wayModel(AETHERV27wayPreTrainedModel):
    """49-layer decoder-only model with 7-aware attention + MoE."""

    def __init__(self, config: AETHERV27wayConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([
            AETHERV27wayDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)
        ])
        self.norm = AETHERV27wayRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.gradient_checkpointing = False
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple, MoeModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = output_hidden_states if output_hidden_states is not None else False
        output_router_logits = output_router_logits if output_router_logits is not None else self.config.output_router_logits
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("Cannot specify both input_ids and inputs_embeds")
        if input_ids is not None:
            bsz, seq_len = input_ids.shape
        elif inputs_embeds is not None:
            bsz, seq_len, _ = inputs_embeds.shape
        else:
            raise ValueError("Either input_ids or inputs_embeds must be provided")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        # TST superposition: bag s consecutive token-embeddings (FSDP-safe)
        _tst_bag = kwargs.get("tst_bag_size", 0)
        if _tst_bag and _tst_bag > 1:
            _b, _l, _d = inputs_embeds.shape
            inputs_embeds = inputs_embeds.view(_b, _l // _tst_bag, _tst_bag, _d).mean(dim=2)
            seq_len = inputs_embeds.shape[1]

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

        past_seen = past_key_values.get_seq_length() if past_key_values is not None else 0

        if position_ids is None:
            position_ids = torch.arange(
                past_seen, past_seen + seq_len, device=inputs_embeds.device,
            ).unsqueeze(0)

        hidden_states = inputs_embeds

        all_hidden_states = () if output_hidden_states else None
        all_router_logits = () if output_router_logits else None

        for layer_idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_out = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states, attention_mask, position_ids,
                    past_key_values, output_router_logits, use_cache,
                )
            else:
                layer_out = decoder_layer(
                    hidden_states=hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_router_logits=output_router_logits,
                    use_cache=use_cache,
                )

            hidden_states = layer_out[0]
            if output_router_logits:
                all_router_logits += (layer_out[2],)

        hidden_states = self.norm(hidden_states)
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        if not return_dict:
            return tuple(v for v in [
                hidden_states, past_key_values, all_hidden_states, None, all_router_logits,
            ] if v is not None)

        return MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=None,
            router_logits=all_router_logits,
        )


# =============================================================================
# Causal LM Wrapper
# =============================================================================
class AETHERV27wayForCausalLM(AETHERV27wayPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config: AETHERV27wayConfig):
        super().__init__(config)
        self.model = AETHERV27wayModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.router_aux_loss_coef = getattr(config, "router_aux_loss_coef", 0.001)
        self.num_experts = config.num_experts
        self.num_experts_per_tok = config.num_experts_per_tok
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def get_decoder(self):
        return self.model

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
        output_router_logits = output_router_logits if output_router_logits is not None else self.config.output_router_logits
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            tst_bag_size=kwargs.get("tst_bag_size", 0),
            return_dict=True,
        )
        hidden_states = outputs.last_hidden_state
        logits = self.lm_head(hidden_states).float()

        loss = None
        aux_loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss = F.cross_entropy(
                shift_logits.view(-1, self.vocab_size),
                shift_labels.view(-1),
                ignore_index=-100,
            )

        if output_router_logits and outputs.router_logits is not None:
            aux_loss = self._compute_router_aux_loss(outputs.router_logits, attention_mask)
            if loss is not None:
                loss = loss + self.router_aux_loss_coef * aux_loss

        if not return_dict:
            output = (logits,) + tuple(v for v in [
                outputs.past_key_values, outputs.hidden_states, None, outputs.router_logits, aux_loss,
            ] if v is not None)
            return (loss,) + output if loss is not None else output

        return MoeCausalLMOutputWithPast(
            loss=loss,
            aux_loss=aux_loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=None,
            router_logits=outputs.router_logits,
        )

    def _compute_router_aux_loss(self, router_logits: Tuple[torch.Tensor, ...], attention_mask=None):
        """Standard switch-transformer auxiliary loss for load balancing."""
        if router_logits is None or len(router_logits) == 0:
            return None
        # Each router_logits[i] shape: (bsz, seq, num_experts) → flatten to (n_tokens, num_experts)
        flat = []
        for r in router_logits:
            if r is None:
                continue
            flat.append(r.reshape(-1, self.num_experts))
        if not flat:
            return None
        all_router_logits = torch.cat(flat, dim=0)  # (total_tokens, num_experts)

        routing_weights = F.softmax(all_router_logits.float(), dim=-1)
        _, selected_experts = torch.topk(routing_weights, self.num_experts_per_tok, dim=-1)

        # Expert mask: (n_tokens, top_k, num_experts)
        expert_mask = F.one_hot(selected_experts, num_classes=self.num_experts).float()
        # Tokens-per-expert frequency: average over (n_tokens, top_k) dims → (num_experts,)
        tokens_per_expert = expert_mask.mean(dim=(0, 1))
        # Router prob per expert: (num_experts,)
        router_prob_per_expert = routing_weights.mean(dim=0)
        # aux_loss = num_experts * sum(token_freq * prob)
        return self.num_experts * torch.sum(tokens_per_expert * router_prob_per_expert)

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        **kwargs,
    ):
        if past_key_values is not None:
            input_ids = input_ids[:, -1:]
        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values is not None:
                position_ids = position_ids[:, -input_ids.shape[1]:]
        return {
            "input_ids": input_ids,
            "position_ids": position_ids,
            "past_key_values": past_key_values,
            "use_cache": kwargs.get("use_cache"),
            "attention_mask": attention_mask,
        }


# =============================================================================
# Helper: Latin Square Layer Map (for analysis / debugging)
# =============================================================================
def print_layer_map(num_layers: int = 49):
    """Print the Latin Square attention type map."""
    print(f"=== AETHER-V2-7way Layer Map ({num_layers} layers) ===")
    for L in range(num_layers):
        attn = get_attention_type(L)
        phase = get_ffn_phase(L)
        row = L // 7
        col = L % 7
        print(f"  L{L:02d} (row={row} col={col}): attn={attn:12s} ffn_phase={phase}")


__all__ = [
    "AETHERV27wayConfig",
    "AETHERV27wayModel",
    "AETHERV27wayForCausalLM",
    "AETHERV27wayPreTrainedModel",
    "AETHERV27wayDecoderLayer",
    "AETHERV27waySparseMoE",
    "build_attention",
    "get_attention_type",
    "get_ffn_phase",
    "LATIN_SQUARE_7x7",
    "ATTN_TYPES",
    "print_layer_map",
]