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# Copyright 2026 the HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


from collections.abc import Callable
from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F

from ... import initialization as init
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...integrations import use_experts_implementation, use_kernel_forward_from_hub
from ...masking_utils import create_causal_mask
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults
from ...utils.output_capturing import capture_outputs
from .configuration_glm_moe_dsa import GlmMoeDsaConfig


@use_kernel_forward_from_hub("RMSNorm")
class GlmMoeDsaRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
        """
        GlmMoeDsaRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        input_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.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(
    x: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
    unsqueeze_dim: int = 1,
) -> torch.Tensor:
    """
    Applies Rotary Position Embedding to a single tensor.

    This is the transformers equivalent of DeepSeek V3.2's `apply_rotary_emb(x, freqs_cis, interleaved)`.
    Instead of using complex-number `freqs_cis`, we use pre-split `(cos, sin)` tensors from RotaryEmbedding.

    Args:
        x (`torch.Tensor`): Input tensor of shape `[..., head_dim]`.
        cos (`torch.Tensor`): Cosine part from RotaryEmbedding, shape `[batch, seq_len, head_dim]`.
        sin (`torch.Tensor`): Sine part from RotaryEmbedding, shape `[batch, seq_len, head_dim]`.
        unsqueeze_dim (`int`): Dimension along which to unsqueeze cos/sin for broadcasting.
            Use `1` when x is `[B, H, S, D]` (BHSD) and `2` when x is `[B, S, H, D]` (BSHD).

    Returns:
        `torch.Tensor`: Tensor with rotary embeddings applied, same shape as input.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)

    # Split-half (NeoX/Llama style): (x[:d/2], x[d/2:])
    # This matches llama's apply_rotary_pos_emb logic.
    x_rotated = (x * cos) + (rotate_half(x) * sin)
    return x_rotated


class GlmMoeDsaIndexer(nn.Module):
    """
    Dynamic Sparse Attention (DSA) indexer for selecting top-k tokens.

    The Indexer has its own lightweight projections (wq_b, wk) separate from the
    main MLA attention. It uses non-interleaved (NeoX/Llama) RoPE, unlike the main attention
    which uses interleaved RoPE.

    **Cache strategy**: The Indexer manages its own key cache (`_cached_keys`) separately
    from the DynamicCache used by MLA attention, since DynamicCache is sized for exactly
    `num_hidden_layers` attention layers. Keys are concatenated along the sequence dimension
    during autoregressive decode.
    """

    def __init__(self, config: "GlmMoeDsaConfig", layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx

        self.hidden_size: int = config.hidden_size
        self.n_heads: int = config.index_n_heads
        self.head_dim: int = config.index_head_dim
        self.qk_rope_head_dim: int = config.qk_rope_head_dim
        self.index_topk: int = config.index_topk
        self.q_lora_rank: int = config.q_lora_rank

        # Named to match checkpoint: wq_b, wk, k_norm
        self.wq_b = nn.Linear(self.q_lora_rank, self.n_heads * self.head_dim, bias=False)
        self.wk = nn.Linear(self.hidden_size, self.head_dim, bias=False)
        self.k_norm = nn.LayerNorm(self.head_dim, eps=1e-6)
        # Named to match checkpoint: weights_proj
        # In the reference, this is fp32; the HF FP8 checkpoint stores a bf16 tensor.
        # Keeping it as a plain Linear prevents FP8 conversion (see `_keep_in_fp32_modules`).
        self.weights_proj = nn.Linear(self.hidden_size, self.n_heads, bias=False)
        self.softmax_scale = self.head_dim**-0.5

        # Indexer maintains its own key cache (not in DynamicCache, which is sized for attention layers only)
        self._cached_keys: torch.Tensor | None = None

    @torch.no_grad()
    def forward(
        self,
        hidden_states: torch.Tensor,  # [B, S, hidden]
        q_resid: torch.Tensor,  # [B, S, q_lora_rank]
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None,
        use_cache: bool = False,
    ) -> torch.LongTensor:
        """
        Computes top-k token indices for sparse attention (DSA).

        This is the bf16 equivalent of the reference Indexer which uses `rotate_activation` (Hadamard transform)
        and `fp8_index` (FP8 quantized scoring kernel). Since the Hadamard transform is orthogonal (dot products
        are preserved: Hq·Hk = q·k), and FP8 quantization is a precision optimization, we skip both and compute
        scores directly in bf16/fp32.

        The scoring logic computes:
            index_score[b,s,t] = Σ_h (weight[b,s,h] · softmax_scale · q[b,s,h,:] · k[b,t,:])

        Args:
            hidden_states: Input hidden states `[B, S, hidden_size]`.
            q_resid: Query residual from `q_a_layernorm(q_a_proj(x))`, shape `[B, S, q_lora_rank]`.
            position_embeddings: `(cos, sin)` from RotaryEmbedding.
            attention_mask: Causal mask, broadcastable to `[B, S, T]`.
            use_cache: Whether to store/update the indexer's own key cache for autoregressive decode.

        Returns:
            `torch.LongTensor`: Top-k token indices of shape `[B, S, topk]`.
        """
        batch_size, seq_len, _ = hidden_states.shape
        cos, sin = position_embeddings

        # === Queries ===
        q = self.wq_b(q_resid)  # [B, S, H*D]
        q = q.view(batch_size, seq_len, self.n_heads, self.head_dim)  # [B, S, H, D]
        q_pe, q_nope = torch.split(q, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim], dim=-1)
        q_pe = apply_rotary_pos_emb(q_pe, cos, sin, unsqueeze_dim=2)  # [B, S, H, rope_D]
        q = torch.cat([q_pe, q_nope], dim=-1)  # [B, S, H, D]

        # === Keys ===
        k = self.k_norm(self.wk(hidden_states))  # [B, S, D]
        k_pe, k_nope = torch.split(k, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim], dim=-1)
        k_pe = apply_rotary_pos_emb(k_pe.unsqueeze(2), cos, sin, unsqueeze_dim=2).squeeze(2)  # [B, S, rope_D]
        k = torch.cat([k_pe, k_nope], dim=-1)  # [B, S, D]

        # === Key cache (managed by the indexer, not DynamicCache) ===
        # Reset cache on prefill (new prompt) to avoid stale keys / batch-size mismatch
        if seq_len > 1:
            self._cached_keys = None

        if use_cache:
            if self._cached_keys is not None:
                k_cached = torch.cat([self._cached_keys, k], dim=1)  # [B, T, D]
            else:
                k_cached = k
            self._cached_keys = k_cached
        else:
            k_cached = k

        # === Scoring ===
        # Reference: weights = weights_proj(x.float()) * n_heads^(-0.5)
        # Reference: weights = weights.unsqueeze(-1) * q_scale * softmax_scale
        # Reference: index_score = fp8_index(q_fp8, weights, k_cache, k_scale_cache)
        #
        # In bf16 mode (no FP8), q_scale = 1. The fp8_index kernel computes:
        #   score[b,s,t] = sum_h(weights[b,s,h] * dot(q[b,s,h,:], k[b,t,:]))
        # where weights already absorbs n_heads^(-0.5) and softmax_scale.

        # Don't force fp32 inputs here: the checkpoint stores `weights_proj.weight` in bf16.
        # Use native dtype for matmul, then upcast the result for scoring stability.
        weights = self.weights_proj(hidden_states).float() * (self.n_heads**-0.5)  # [B, S, H]

        # q·k^T per head: [B, S, H, D] @ [B, T, D]^T → [B, S, H, T]
        scores = torch.einsum("bshd,btd->bsht", q.float(), k_cached.float()) * self.softmax_scale

        # Weight per head and sum across heads → [B, S, T]
        index_scores = torch.einsum("bsht,bsh->bst", scores, weights)

        if attention_mask is not None:
            index_scores = index_scores + attention_mask

        total_len = index_scores.shape[-1]
        topk = min(self.index_topk, total_len)
        topk_indices = index_scores.topk(topk, dim=-1).indices  # [B, S, topk]
        return topk_indices


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class GlmMoeDsaAttention(nn.Module):
    """
    Multi-head Latent Attention (MLA) with Dynamic Sparse Attention (DSA) indexer.

    This follows the same architecture as DeepSeek V3.2's MLA:
      - Query: x → q_a_proj → RMSNorm → q_b_proj → split(q_nope, q_pe) → RoPE(q_pe)
      - KV:    x → kv_a_proj → split(kv_compressed, k_pe) → RMSNorm(kv_compressed) → kv_b_proj
                                                           → RoPE(k_pe)
      - Cache: fully expanded key_states [B, H, T, qk_head_dim] and value_states [B, H, T, v_head_dim]
      - Indexer: selects top-k tokens via DSA, applied as an additive -inf mask on attention scores

    **Caching strategy**: follows the DeepSeek V3 transformers convention of fully expanding K/V
    before caching. This ensures compatibility with DynamicCache, StaticCache, flash attention,
    and SDPA backends. The reference's compressed-cache decode path (which avoids the kv_b_proj
    expansion at decode time) is a future optimization that would require a dedicated MLA cache class.

    **FP8 compatibility**: all weight accesses use standard nn.Linear forward calls (never
    raw `.weight` access), so FP8-quantized checkpoints work transparently.
    """

    def __init__(self, config: GlmMoeDsaConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.attention_dropout = config.attention_dropout
        self.num_heads = config.num_attention_heads

        self.q_lora_rank = config.q_lora_rank
        self.qk_rope_head_dim = config.qk_rope_head_dim
        self.kv_lora_rank = config.kv_lora_rank
        self.v_head_dim = config.v_head_dim
        self.qk_nope_head_dim = config.qk_nope_head_dim
        self.qk_head_dim = config.qk_head_dim

        self.is_causal = True

        # Query projection (with optional LoRA)
        if self.q_lora_rank is None:
            self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
        else:
            self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
            self.q_a_layernorm = GlmMoeDsaRMSNorm(config.q_lora_rank)
            self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)

        # Key-Value projections (MLA compressed path)
        self.kv_a_proj_with_mqa = nn.Linear(
            config.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=config.attention_bias,
        )
        self.kv_a_layernorm = GlmMoeDsaRMSNorm(self.kv_lora_rank)
        self.kv_b_proj = nn.Linear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
        )

        # Output projection
        self.o_proj = nn.Linear(
            self.num_heads * self.v_head_dim,
            config.hidden_size,
            bias=config.attention_bias,
        )

        self.scaling = self.qk_head_dim ** (-0.5)

        self.indexer = GlmMoeDsaIndexer(config, layer_idx)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
        batch_size, seq_length = hidden_states.shape[:-1]
        cos, sin = position_embeddings

        # ===== Query path =====
        if self.q_lora_rank is None:
            query_states = self.q_proj(hidden_states)
            q_resid = None
        else:
            q_resid = self.q_a_layernorm(self.q_a_proj(hidden_states))  # [B, S, q_lora_rank]
            query_states = self.q_b_proj(q_resid)
        query_states = query_states.view(batch_size, seq_length, -1, self.qk_head_dim).transpose(1, 2)
        # Split nope/rope, apply RoPE, recombine — layout: [B, H, S, D]
        q_nope, q_pe = torch.split(query_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
        q_pe = apply_rotary_pos_emb(q_pe, cos, sin, unsqueeze_dim=1)  # BHSD format

        # ===== KV path =====
        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)  # [B, S, kv_rank + rope_D]
        k_compressed, k_pe = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        k_compressed = self.kv_a_layernorm(k_compressed)  # [B, S, kv_rank]

        # Expand KV through kv_b_proj
        kv_expanded = self.kv_b_proj(k_compressed)  # [B, S, H * (nope_D + v_D)]
        kv_expanded = kv_expanded.view(batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim)
        k_nope, value_states = torch.split(kv_expanded, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
        k_nope = k_nope.transpose(1, 2)  # [B, H, S, nope_D]
        value_states = value_states.transpose(1, 2)  # [B, H, S, v_D]

        # RoPE on k_pe (single-head rope stream)
        k_pe = k_pe.view(batch_size, 1, seq_length, self.qk_rope_head_dim)  # [B, 1, S, rope_D]
        k_pe = apply_rotary_pos_emb(k_pe, cos, sin, unsqueeze_dim=1)  # BHSD format
        k_pe = k_pe.expand(-1, k_nope.shape[1], -1, -1)  # [B, H, S, rope_D]

        # Assemble full Q and K
        query_states = torch.cat([q_nope, q_pe], dim=-1)  # [B, H, S, qk_head_dim]
        key_states = torch.cat([k_nope, k_pe], dim=-1)  # [B, H, S, qk_head_dim]

        # Cache update
        if past_key_values is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)

        # ===== Indexer (DSA sparse mask) =====
        # attention_mask is [B, 1, S, T] (4D) for eager and (2D) otherwise but indexer works with [B, S, T] (3D)
        indexer_mask = (
            attention_mask[:, 0, :, :]
            if attention_mask is not None and attention_mask.dim() == 4
            else attention_mask.unsqueeze(1)
            if attention_mask is not None
            else None
        )
        topk_indices = self.indexer(
            hidden_states,
            q_resid,
            position_embeddings,
            indexer_mask,
            use_cache=past_key_values is not None,
        )  # [B, S, topk]

        # Build combined DSA + causal mask: -inf everywhere except selected top-k positions
        total_len = key_states.shape[2]
        index_mask = torch.full(
            (batch_size, seq_length, total_len),
            float("-inf"),
            device=hidden_states.device,
            dtype=query_states.dtype,
        )
        index_mask.scatter_(-1, topk_indices, 0.0)  # [B, S, T]
        index_mask = index_mask.unsqueeze(1)  # [B, 1, S, T]
        if attention_mask is not None and attention_mask.dim() == 4:
            causal_mask = attention_mask[..., :total_len]
            combined_mask = index_mask + causal_mask
        else:
            combined_mask = (
                attention_mask.masked_fill(index_mask == float("-inf"), float("-inf"))
                if attention_mask is not None
                else index_mask
            )

        # Flash attention head_dim padding (qk_head_dim != v_head_dim)
        if is_flash_attention_requested(self.config) and self.qk_head_dim != self.v_head_dim:
            value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim])

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            combined_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            indices=topk_indices,  # flash_mla_with_kvcache
            **kwargs,
        )

        if is_flash_attention_requested(self.config) and self.qk_head_dim != self.v_head_dim:
            attn_output = attn_output[:, :, :, : self.v_head_dim]

        attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class GlmMoeDsaMLP(nn.Module):
    def __init__(self, config, intermediate_size=None):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size if intermediate_size is None else 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):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


class GlmMoeDsaTopkRouter(nn.Module):
    def __init__(self, config: GlmMoeDsaConfig):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.n_routed_experts = config.n_routed_experts
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_group = config.n_group
        self.topk_group = config.topk_group
        self.norm_topk_prob = config.norm_topk_prob

        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
        self.register_buffer("e_score_correction_bias", torch.zeros((self.n_routed_experts), dtype=torch.float32))

    def forward(self, hidden_states):
        hidden_states = hidden_states.view(-1, self.config.hidden_size)
        router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
        return router_logits


@use_experts_implementation
class GlmMoeDsaNaiveMoe(nn.Module):
    """Collection of expert weights stored as 3D tensors."""

    def __init__(self, config):
        super().__init__()
        self.num_experts = config.num_local_experts
        self.hidden_dim = config.hidden_size
        self.intermediate_dim = config.moe_intermediate_size
        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim))
        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim))
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(
        self,
        hidden_states: torch.Tensor,
        top_k_index: torch.Tensor,
        top_k_weights: torch.Tensor,
    ) -> torch.Tensor:
        final_hidden_states = torch.zeros_like(hidden_states)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
            expert_mask = expert_mask.permute(2, 1, 0)
            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

        for expert_idx in expert_hit:
            expert_idx = expert_idx[0]
            if expert_idx == self.num_experts:
                continue
            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
            current_state = hidden_states[token_idx]
            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
            current_hidden_states = self.act_fn(gate) * up
            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))

        return final_hidden_states


class GlmMoeDsaMoE(nn.Module):
    """
    A mixed expert module containing shared experts.
    """

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.experts = GlmMoeDsaNaiveMoe(config)
        self.gate = GlmMoeDsaTopkRouter(config)
        self.shared_experts = GlmMoeDsaMLP(
            config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
        )
        self.n_routed_experts = config.n_routed_experts
        self.n_group = config.n_group
        self.topk_group = config.topk_group
        self.norm_topk_prob = config.norm_topk_prob
        self.routed_scaling_factor = config.routed_scaling_factor
        self.top_k = config.num_experts_per_tok

    def route_tokens_to_experts(self, router_logits):
        router_logits = router_logits.sigmoid()
        router_logits_for_choice = router_logits + self.gate.e_score_correction_bias
        group_scores = (
            router_logits_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group)
            .topk(2, dim=-1)[0]
            .sum(dim=-1)
        )
        group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
        group_mask = torch.zeros_like(group_scores)
        group_mask.scatter_(1, group_idx, 1)
        score_mask = (
            group_mask.unsqueeze(-1)
            .expand(-1, self.n_group, self.n_routed_experts // self.n_group)
            .reshape(-1, self.n_routed_experts)
        )
        scores_for_choice = router_logits_for_choice.masked_fill(~score_mask.bool(), 0.0)
        topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1]
        topk_weights = router_logits.gather(1, topk_indices)
        if self.norm_topk_prob:
            denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
            topk_weights /= denominator
        topk_weights = topk_weights * self.routed_scaling_factor
        return topk_indices, topk_weights

    def forward(self, hidden_states):
        residuals = hidden_states
        orig_shape = hidden_states.shape
        router_logits = self.gate(hidden_states)
        topk_indices, topk_weights = self.route_tokens_to_experts(router_logits)
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
        hidden_states = self.experts(hidden_states, topk_indices, topk_weights).view(*orig_shape)
        hidden_states = hidden_states + self.shared_experts(residuals)
        return hidden_states


class GlmMoeDsaDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: GlmMoeDsaConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = GlmMoeDsaAttention(config, layer_idx)

        if config.mlp_layer_types[layer_idx] == "sparse":
            self.mlp = GlmMoeDsaMoE(config)
        else:
            self.mlp = GlmMoeDsaMLP(config)

        self.input_layernorm = GlmMoeDsaRMSNorm(config.hidden_size, config.rms_norm_eps)
        self.post_attention_layernorm = GlmMoeDsaRMSNorm(config.hidden_size, config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        use_cache: bool | None = False,
        cache_position: torch.LongTensor | None = None,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states
        return hidden_states


@auto_docstring
class GlmMoeDsaPreTrainedModel(PreTrainedModel):
    config: GlmMoeDsaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["GlmMoeDsaDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn = False  # flash-mla kernels need a bit more work in the way we enable them!
    _supports_sdpa = True
    _supports_flex_attn = False

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": GlmMoeDsaDecoderLayer,
        "attentions": GlmMoeDsaAttention,
    }
    _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.78.*"]
    # NOTE: FP8 quantization uses `_keep_in_fp32_modules` (not `_strict`) to decide which modules to NOT convert.
    # We must keep `indexer.weights_proj` as a plain Linear to match the checkpoint (no `weight_scale_inv`).
    _keep_in_fp32_modules = ["indexer.weights_proj"]
    _compatible_flash_implementations = ["kernels-community/flash-mla"]

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if isinstance(module, GlmMoeDsaTopkRouter):
            init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            init.zeros_(module.e_score_correction_bias)
        elif isinstance(module, GlmMoeDsaNaiveMoe):
            init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range)
            init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range)


class GlmMoeDsaRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: GlmMoeDsaConfig, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)

    @staticmethod
    def compute_default_rope_parameters(
        config: GlmMoeDsaConfig | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0)
        head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
        dim = int(head_dim * partial_rotary_factor)

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


@auto_docstring
class GlmMoeDsaModel(GlmMoeDsaPreTrainedModel):
    def __init__(self, config: GlmMoeDsaConfig):
        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(
            [GlmMoeDsaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = GlmMoeDsaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = GlmMoeDsaRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    @merge_with_config_defaults
    @capture_outputs
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        cache_position: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

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

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position: torch.Tensor = (
                torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = create_causal_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_embeddings=position_embeddings,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


@auto_docstring
class GlmMoeDsaForCausalLM(GlmMoeDsaPreTrainedModel, GenerationMixin):
    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
    _tp_plan = {"lm_head": "colwise_gather_output"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = GlmMoeDsaModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CausalLMOutputWithPast:
        r"""
        Example:

        ```python
        >>> from transformers import AutoTokenizer, GlmMoeDsaForCausalLM

        >>> model = GlmMoeDsaForCausalLM.from_pretrained("meta-glm_moe_dsa/GlmMoeDsa-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-glm_moe_dsa/GlmMoeDsa-2-7b-hf")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""
        outputs: BaseModelOutputWithPast = 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,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = ["GlmMoeDsaPreTrainedModel", "GlmMoeDsaModel", "GlmMoeDsaForCausalLM"]
