# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
#
# Copyright 2026 Sarvam AI team. All rights reserved.
#
# This code is based on Llama, Deepseek, and Bailing MoE implementations
# in this library. It has been modified from its original forms to
# accommodate Sarvam's MoE architectures.
#
# 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 __future__ import annotations

import math
from collections.abc import Iterable, Iterator
from itertools import islice

import torch
from torch import nn

from vllm.config import CacheConfig, ParallelConfig, VllmConfig
from vllm.distributed import (
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import SharedFusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.sequence import IntermediateTensors

from .bailing_moe import BailingMoeForCausalLM
from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)


def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
    if scale <= 1:
        return 1.0
    return 0.1 * mscale * math.log(scale) + 1.0


def _is_gate_expert_bias_name(name: str) -> bool:
    return name.endswith(".mlp.gate.e_score_correction_bias") or name.endswith(
        ".gate.e_score_correction_bias"
    )


def _zero_mean_tensor(t: torch.Tensor) -> torch.Tensor:
    if t.numel() == 0:
        return t
    return t - t.mean()


def _normalized_weights(
    weights: Iterable[tuple[str, torch.Tensor]],
) -> Iterator[tuple[str, torch.Tensor]]:
    for name, w in weights:
        if _is_gate_expert_bias_name(name):
            yield name, _zero_mean_tensor(w)
        else:
            yield name, w


class SarvamMLAAttention(nn.Module):
    def __init__(
        self,
        vllm_config: VllmConfig,
        config,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        self.hidden_size = config.hidden_size
        self.qk_nope_head_dim = config.qk_nope_head_dim
        self.qk_rope_head_dim = config.qk_rope_head_dim
        self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
        self.v_head_dim = config.v_head_dim

        self.q_lora_rank = getattr(config, "q_lora_rank", None)
        self.kv_lora_rank = config.kv_lora_rank

        self.total_num_heads = config.num_attention_heads
        tp_size = get_tensor_model_parallel_world_size()
        assert self.total_num_heads % tp_size == 0
        self.num_local_heads = self.total_num_heads // tp_size

        self.scaling = self.qk_head_dim**-0.5
        self.max_position_embeddings = config.max_position_embeddings

        if self.q_lora_rank is not None:
            self.q_a_proj = ReplicatedLinear(
                self.hidden_size,
                self.q_lora_rank,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_a_proj",
            )
            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
            self.q_b_proj = ColumnParallelLinear(
                self.q_lora_rank,
                self.total_num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_b_proj",
            )
            self.q_proj = None  # type: ignore
        else:
            self.q_proj = ColumnParallelLinear(
                self.hidden_size,
                self.total_num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_proj",
            )
            self.q_a_proj = None  # type: ignore
            self.q_a_layernorm = None  # type: ignore
            self.q_b_proj = None  # type: ignore

        # KV latent (MQA-style) A-proj
        self.kv_a_proj_with_mqa = ReplicatedLinear(
            self.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.kv_a_proj_with_mqa",
        )
        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)

        # KV B-proj produces per-head K_nope and V
        self.kv_b_proj = ColumnParallelLinear(
            self.kv_lora_rank,
            self.total_num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.kv_b_proj",
        )

        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.v_head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.rotary_emb = get_rope(
            self.qk_rope_head_dim,
            # rotary_dim=self.qk_rope_head_dim,
            max_position=config.max_position_embeddings,
            rope_parameters=config.rope_parameters,
            is_neox_style=False,
        )

        if config.rope_parameters.get("rope_type", None) == "deepseek_yarn":
            mscale_all_dim = config.rope_parameters.get("mscale_all_dim", False)
            scaling_factor = config.rope_parameters["factor"]
            mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
            self.scaling = self.scaling * mscale * mscale

        mla_modules = MLAModules(
            kv_a_layernorm=self.kv_a_layernorm,
            kv_b_proj=self.kv_b_proj,
            rotary_emb=self.rotary_emb,
            o_proj=self.o_proj,
            fused_qkv_a_proj=None,
            kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
            q_a_layernorm=self.q_a_layernorm if self.q_lora_rank is not None else None,
            q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
            q_proj=self.q_proj if self.q_lora_rank is None else None,
            indexer=None,
            indexer_rotary_emb=None,
            is_sparse=False,
            topk_indices_buffer=None,
        )

        self.mla_attn = MultiHeadLatentAttentionWrapper(
            self.hidden_size,
            self.num_local_heads,
            self.scaling,
            self.qk_nope_head_dim,
            self.qk_rope_head_dim,
            self.v_head_dim,
            self.q_lora_rank,
            self.kv_lora_rank,
            mla_modules,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=prefix,
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        return self.mla_attn(positions, hidden_states, llama_4_scaling=None)


class SarvamMLAMLP(nn.Module):
    def __init__(
        self,
        intermediate_size: int,
        config,
        quant_config: QuantizationConfig | None = None,
        reduce_results: bool = True,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.gate_up_proj = MergedColumnParallelLinear(
            config.hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            config.hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        self.act_fn = SiluAndMul()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class SarvamMLAMoE(nn.Module):
    def __init__(
        self,
        config,
        parallel_config: ParallelConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tensor_model_parallel_rank()
        self.hidden_size = config.hidden_size

        self.num_experts = config.num_experts
        self.top_k = config.num_experts_per_tok
        self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 2.5)

        self.n_group = getattr(config, "n_group", None)
        self.topk_group = getattr(config, "topk_group", None)
        self.use_grouped_topk = self.n_group is not None and self.topk_group is not None

        self.norm_expert_prob = getattr(config, "norm_topk_prob", True)

        router_dtype_cfg = getattr(config, "router_dtype", "fp32")
        if router_dtype_cfg is None:
            self.router_dtype = None
        elif router_dtype_cfg == "fp32":
            self.router_dtype = torch.float32
        else:
            self.router_dtype = torch.bfloat16

        self.gate = nn.Linear(
            self.hidden_size,
            self.num_experts,
            bias=False,
            dtype=self.router_dtype,
        )

        if getattr(config, "moe_router_enable_expert_bias", True):
            self.gate.e_score_correction_bias = nn.Parameter(
                torch.empty(
                    (self.num_experts,),
                    dtype=torch.float32,
                )
            )
        else:
            self.gate.e_score_correction_bias = None

        self.score_function = getattr(config, "score_function", "sigmoid")
        self.num_shared_experts = getattr(config, "num_shared_experts", 1)
        if self.num_shared_experts > 0:
            if hasattr(config, "moe_shared_expert_intermediate_size"):
                shared_int = config.moe_shared_expert_intermediate_size
            else:
                shared_int = config.moe_intermediate_size
            shared_int *= self.num_shared_experts
            self.shared_experts = SarvamMLAMLP(
                intermediate_size=shared_int,
                config=config,
                quant_config=quant_config,
                reduce_results=False,
                prefix=f"{prefix}.shared_experts",
            )
        else:
            self.shared_experts = None

        self.experts = SharedFusedMoE(
            shared_experts=self.shared_experts,
            num_experts=self.num_experts,
            top_k=self.top_k,
            hidden_size=self.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
            renormalize=self.norm_expert_prob,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
            scoring_func=self.score_function,
            e_score_correction_bias=self.gate.e_score_correction_bias,
            num_expert_group=self.n_group,
            topk_group=self.topk_group,
            use_grouped_topk=self.use_grouped_topk,
            routed_scaling_factor=self.routed_scaling_factor,
        )

    def maybe_get_fused_moe(self) -> SharedFusedMoE:
        return self.experts

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)
        router_logits = self.gate(
            hidden_states.to(self.router_dtype)
            if self.router_dtype is not None
            else hidden_states
        )
        router_logits = router_logits.to(hidden_states.dtype)
        final_hidden = self.experts(
            hidden_states=hidden_states,
            router_logits=router_logits,
        )

        if self.shared_experts is not None:
            shared_output, expert_output = final_hidden
        else:
            shared_output, expert_output = None, final_hidden

        # expert_output *= self.routed_scaling_factor

        if shared_output is not None:
            expert_output = expert_output + shared_output

        if self.tp_size > 1:
            expert_output = self.experts.maybe_all_reduce_tensor_model_parallel(
                expert_output
            )

        return expert_output.view(num_tokens, hidden_dim)


class SarvamMLABlock(nn.Module):
    def __init__(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
    ) -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        parallel_config = vllm_config.parallel_config
        layer_idx = int(prefix.split(".")[-1])
        hidden_size = config.hidden_size
        dense_intermediate = getattr(config, "intermediate_size", 16384)

        self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
        self.self_attn = SarvamMLAAttention(
            vllm_config=vllm_config,
            config=config,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.post_attention_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
        use_moe = hasattr(config, "num_experts") and config.num_experts is not None
        first_k_dense = getattr(config, "first_k_dense_replace", 1)
        moe_layer_freq = getattr(config, "moe_layer_freq", 1)
        if use_moe:
            is_moe_layer = layer_idx >= first_k_dense and (
                (layer_idx - first_k_dense) % moe_layer_freq == 0
            )
        else:
            is_moe_layer = False

        if is_moe_layer:
            self.mlp = SarvamMLAMoE(
                config=config,
                parallel_config=parallel_config,
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
            )
        else:
            self.mlp = SarvamMLAMLP(
                intermediate_size=dense_intermediate,
                config=config,
                quant_config=quant_config,
                reduce_results=True,
                prefix=f"{prefix}.mlp",
            )

    def forward(
        self,
        hidden_states: torch.Tensor,
        positions: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(hidden_states, residual)

        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )
        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual


class SarvamMLAModel(nn.Module):
    def __init__(
        self,
        *,
        vllm_config: VllmConfig,
        prefix: str = "",
    ) -> None:
        super().__init__()

        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config

        self.config = config
        self.vocab_size = config.vocab_size
        self.embed_dim = config.hidden_size
        self.tie_word_embeddings = getattr(config, "tie_word_embeddings", False)
        if get_pp_group().is_first_rank or (
            self.tie_word_embeddings and get_pp_group().is_last_rank
        ):
            self.embed_tokens = VocabParallelEmbedding(
                self.vocab_size,
                self.embed_dim,
                quant_config=quant_config,
                prefix=f"{prefix}.embed_tokens",
            )
        else:
            self.embed_tokens = PPMissingLayer()

        self.embedding_dropout = torch.nn.Dropout(
            getattr(config, "embedding_dropout", 0.0)
        )
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: SarvamMLABlock(
                vllm_config=vllm_config,
                prefix=prefix,
            ),
            prefix=f"{prefix}.layers",
        )
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )
        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.embed_input_ids(input_ids)
            hidden_states = self.embedding_dropout(hidden_states)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        for layer in islice(self.layers, self.start_layer, self.end_layer):
            hidden_states, residual = layer(
                hidden_states,
                positions,
                residual,
            )
        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
        if residual is None:
            hidden_states = self.norm(hidden_states)
        else:
            hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return SharedFusedMoE.make_expert_params_mapping(
            self,
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.num_experts,
        )

    def load_weights(
        self,
        weights: Iterable[tuple[str, torch.Tensor]],
    ) -> set[str]:
        """Load weights with stacked gate+up and MoE expert remapping."""
        weights = _normalized_weights(weights)
        stacked_params_mapping = [
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()
        expert_params_mapping = self.get_expert_mapping()

        for name, loaded_weight in weights:
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                if "mlp.experts" in name:
                    continue
                new_name = name.replace(weight_name, param_name)
                if new_name.endswith(".bias") and new_name not in params_dict:
                    continue
                if new_name not in params_dict:
                    continue
                if is_pp_missing_parameter(new_name, self):
                    continue

                param = params_dict[new_name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight, shard_id)
                loaded_params.add(new_name)
                break
            else:
                mapped = False
                for (
                    param_name,
                    weight_name,
                    expert_id,
                    shard_id,
                ) in expert_params_mapping:
                    if weight_name not in name:
                        continue

                    new_name = name.replace(weight_name, param_name)
                    if is_pp_missing_parameter(new_name, self):
                        continue
                    if new_name not in params_dict:
                        continue

                    param = params_dict[new_name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    loaded_params.add(new_name)
                    mapped = True
                    break

                if mapped:
                    continue

                if name.endswith(".bias") and name not in params_dict:
                    continue
                if name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue

                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
                loaded_params.add(name)

        return loaded_params


class SarvamMixtureOfExperts(MixtureOfExperts):
    def extract_moe_parameters(self, example_moe: SarvamMLAMoE | None) -> None:
        if example_moe is None:
            raise RuntimeError("No SarvamMLAMoE layer found in model.layers.")

        self.num_logical_experts = example_moe.num_experts
        self.num_routed_experts = example_moe.num_experts  # routed pool size
        self.num_shared_experts = getattr(example_moe.config, "num_shared_experts", 1)

        self.num_physical_experts = self.num_logical_experts
        self.num_local_physical_experts = self.num_logical_experts
        self.num_redundant_experts = 0

    def update_physical_experts_metadata(
        self,
        num_physical_experts: int,
        num_local_physical_experts: int,
    ) -> None:
        self.num_physical_experts = num_physical_experts
        self.num_local_physical_experts = num_local_physical_experts
        self.num_redundant_experts = num_physical_experts - self.num_logical_experts

        for moe in self.moe_mlp_layers:
            moe.n_physical_experts = num_physical_experts
            moe.n_local_physical_experts = num_local_physical_experts
            moe.n_redundant_experts = self.num_redundant_experts

            fused = moe.experts
            if hasattr(fused, "n_local_physical_experts"):
                fused.n_local_physical_experts = num_local_physical_experts
            if hasattr(fused, "n_physical_experts"):
                fused.n_physical_experts = num_physical_experts
            if hasattr(fused, "n_redundant_experts"):
                fused.n_redundant_experts = self.num_redundant_experts
            if hasattr(fused, "update_expert_map"):
                fused.update_expert_map()

    def set_eplb_state(self, eplb_state) -> None:
        self.eplb_state = eplb_state
        for moe in self.moe_layers:
            if hasattr(moe, "set_eplb_state"):
                moe.set_eplb_state(eplb_state)


class SarvamMLAForCausalLM(nn.Module, SupportsPP, SupportsLoRA, SarvamMixtureOfExperts):
    packed_modules_mapping = {
        "q_proj": ["q_proj"],
        "q_a_proj": ["q_a_proj"],
        "q_b_proj": ["q_b_proj"],
        "kv_a_proj_with_mqa": ["kv_a_proj_with_mqa"],
        "kv_b_proj": ["kv_b_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config

        self.model = SarvamMLAModel(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "model"),
        )

        self.tie_word_embeddings = getattr(config, "tie_word_embeddings", False)
        if get_pp_group().is_last_rank:
            if self.tie_word_embeddings:
                self.lm_head = self.model.embed_tokens
            else:
                self.lm_head = ParallelLMHead(
                    config.vocab_size,
                    config.hidden_size,
                    quant_config=quant_config,
                    prefix=maybe_prefix(prefix, "lm_head"),
                )
            self.logits_processor = LogitsProcessor(config.vocab_size)
        else:
            self.lm_head = PPMissingLayer()
            self.logits_processor = None  # type: ignore

        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

        self.expert_weights = []
        self.num_moe_layers = 0

        self.moe_layers = []
        self.moe_mlp_layers = []

        example_moe = None
        for layer in self.model.layers:
            if isinstance(layer, PPMissingLayer):
                continue
            if isinstance(layer.mlp, SarvamMLAMoE):
                example_moe = layer.mlp
                self.moe_mlp_layers.append(layer.mlp)
                self.moe_layers.append(layer.mlp.experts)
                self.num_moe_layers += 1

        self.extract_moe_parameters(example_moe)

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
        return self.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        if not get_pp_group().is_last_rank:
            return None
        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

    def load_weights(
        self,
        weights: Iterable[tuple[str, torch.Tensor]],
    ) -> set[str]:
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=(["lm_head."] if self.tie_word_embeddings else None),
        )
        return loader.load_weights(weights)

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return self.model.get_expert_mapping()


class SarvamMoEForCausalLM(BailingMoeForCausalLM):
    """Same as BailingMoeForCausalLM, but normalizes gate expert_bias pre-load."""

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        return super().load_weights(_normalized_weights(weights))