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"""Inference-only Flash model compatible with HuggingFace weights."""

import typing
from collections.abc import Callable, Iterable
from itertools import islice

import torch
from torch import nn
from transformers import PretrainedConfig

from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import (
    FusedMoE,
    fused_moe_make_expert_params_mapping,
)
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.utils.int8_utils import block_dequant
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.model_executor.models.deepseek_v2 import DeepseekV2MLAAttention
from vllm.sequence import IntermediateTensors

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

logger = init_logger(__name__)


class FlashConfig(PretrainedConfig):
    """Flash model configuration."""

    model_type = "longcat_flash"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=131072,
        hidden_size=4096,
        intermediate_size=8192,
        num_layers=28,
        num_hidden_layers=None,
        num_attention_heads=96,
        num_key_value_heads=128,
        ep_size=1,
        kv_lora_rank=512,
        q_lora_rank=1536,
        qk_rope_head_dim=64,
        v_head_dim=128,
        qk_nope_head_dim=128,
        num_experts_per_tok=None,
        norm_topk_prob=False,
        max_position_embeddings=8192,
        initializer_range=0.02,
        rms_norm_eps=1e-05,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=100000,
        eos_token_id=100001,
        pretraining_tp=1,
        tie_word_embeddings=False,
        rope_parameters=None,
        attention_bias=False,
        attention_dropout=0.0,
        mla_scale_q_lora=False,
        mla_scale_kv_lora=False,
        dtype="bfloat16",
        params_dtype="bfloat16",
        router_dtype="float32",
        router_bias=False,
        topk_method=None,
        routed_scaling_factor=1.0,
        zero_expert_num=0,
        zero_expert_type=None,
        nextn_use_scmoe=False,
        **kwargs,
    ):
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            dtype=dtype,
            params_dtype=params_dtype,
            router_dtype=router_dtype,
            topk_method=topk_method,
            router_bias=router_bias,
            nextn_use_scmoe=nextn_use_scmoe,
            **kwargs,
        )
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.num_hidden_layers = (
            num_hidden_layers if num_hidden_layers is not None else num_layers
        )
        self.num_attention_heads = num_attention_heads
        self.ep_size = ep_size
        self.kv_lora_rank = kv_lora_rank
        self.q_lora_rank = q_lora_rank
        self.qk_rope_head_dim = qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.qk_nope_head_dim = qk_nope_head_dim
        self.num_experts_per_tok = num_experts_per_tok
        self.norm_topk_prob = norm_topk_prob
        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.pretraining_tp = pretraining_tp
        self.use_cache = use_cache
        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
        rope_scaling = kwargs.pop("rope_scaling", None)
        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
        rope_theta = kwargs.pop("rope_theta", 1000000.0)
        if "rope_theta" not in rope_parameters:
            rope_parameters["rope_theta"] = rope_theta
        self.rope_parameters = rope_parameters
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.mla_scale_q_lora = mla_scale_q_lora
        self.mla_scale_kv_lora = mla_scale_kv_lora
        self.zero_expert_num = zero_expert_num
        self.zero_expert_type = zero_expert_type
        self.routed_scaling_factor = routed_scaling_factor
        self.hidden_act = "silu"
        self.intermediate_size = (
            self.ffn_hidden_size
            if hasattr(self, "ffn_hidden_size")
            else intermediate_size
        )
        if hasattr(self, "moe_intermediate_size"):
            self.moe_intermediate_size = self.moe_intermediate_size
        elif hasattr(self, "expert_ffn_hidden_size"):
            self.moe_intermediate_size = self.expert_ffn_hidden_size
        else:
            self.moe_intermediate_size = self.intermediate_size


class FlashMLP(nn.Module):
    """Flash MLP layer."""

    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: QuantizationConfig | None = None,
        reduce_results: bool = True,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if x.numel() == 0:
            return x

        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class LongcatRouter(nn.Module):
    def __init__(
        self,
        config: FlashConfig,
        zero_expert_num: int,
        router_params_dtype: torch.dtype,
        prefix: str = "",
    ):
        super().__init__()
        self.n_routed_experts = (
            config.n_routed_experts
            if hasattr(config, "n_routed_experts")
            else config.num_experts[0]
        )
        self.n_routed_experts = self.n_routed_experts + zero_expert_num
        self.classifier = ReplicatedLinear(
            config.hidden_size,
            self.n_routed_experts,
            bias=config.router_bias,
            params_dtype=router_params_dtype,
            quant_config=None,
            prefix=f"{prefix}.classifier",
        )
        self.e_score_correction_bias = nn.Parameter(
            torch.zeros((self.n_routed_experts), dtype=router_params_dtype)
        )

    def forward(self, hidden_states):
        logits, _ = self.classifier(hidden_states)
        return logits


class LongcatMoe(nn.Module):
    def __init__(
        self,
        config: FlashConfig,
        num_experts: int,
        top_k: int,
        hidden_size: int,
        intermediate_size: int,
        params_dtype: torch.dtype | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        enable_eplb: bool = False,
    ):
        super().__init__()
        self.hidden_size = hidden_size
        # Gate always runs at half / full precision for now.
        self.router_params_dtype = params_dtype
        if config.router_dtype == "float32":
            self.router_params_dtype = torch.float32

        self.router = LongcatRouter(
            config=config,
            zero_expert_num=config.zero_expert_num,
            router_params_dtype=self.router_params_dtype,
            prefix=f"{prefix}.gate",
        )

        assert config.zero_expert_type is not None
        self.experts = FusedMoE(
            zero_expert_type=config.zero_expert_type,
            e_score_correction_bias=self.router.e_score_correction_bias,
            num_experts=num_experts,
            top_k=top_k,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            params_dtype=params_dtype,
            renormalize=False,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
            enable_eplb=enable_eplb,
            routed_scaling_factor=config.routed_scaling_factor,
            router_logits_dtype=self.router_params_dtype,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)

        # Align to FusedMoE padded hidden size to avoid dim mismatch
        padded_hidden = self.experts.hidden_size
        if hidden_dim < padded_hidden:
            hidden_states_padded = torch.nn.functional.pad(
                hidden_states,
                (0, padded_hidden - hidden_dim),
                mode="constant",
                value=0.0,
            )
        else:
            hidden_states_padded = hidden_states

        router_logits_full = self.router(
            hidden_states_padded.to(self.router_params_dtype)
        )

        # FusedMoE handles routing memoization and zero expert computation
        # internally. Pass full router_logits (including zero experts) so that
        # zero experts can be properly identified in routing.
        final_hidden_states = self.experts(
            hidden_states=hidden_states_padded,
            router_logits=router_logits_full,  # Full logits (includes zero experts)
        )

        # Crop back to original hidden dimension if padded earlier
        if padded_hidden != hidden_dim:
            final_hidden_states = final_hidden_states[..., :hidden_dim]

        return final_hidden_states.view(num_tokens, hidden_dim)


class FlashDecoderLayer(nn.Module):
    """Flash decoder layer with dual attention and MLP structure."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        config: FlashConfig,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        enable_eplb: bool = False,
    ) -> None:
        super().__init__()
        self.layer_idx = int(prefix.split(sep=".")[-1])
        self.hidden_size = config.hidden_size
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)

        # Dual attention structure
        self.self_attn = nn.ModuleList(
            [
                DeepseekV2MLAAttention(
                    vllm_config=vllm_config,
                    config=config,
                    hidden_size=self.hidden_size,
                    num_heads=config.num_attention_heads,
                    qk_nope_head_dim=config.qk_nope_head_dim,
                    qk_rope_head_dim=config.qk_rope_head_dim,
                    v_head_dim=config.v_head_dim,
                    q_lora_rank=(
                        config.q_lora_rank if hasattr(config, "q_lora_rank") else None
                    ),
                    kv_lora_rank=config.kv_lora_rank,
                    max_position_embeddings=max_position_embeddings,
                    cache_config=cache_config,
                    quant_config=None
                    if "self_attn" in getattr(config, "disable_quant_module", [])
                    else quant_config,
                    prefix=f"{prefix}.self_attn.{i}",
                )
                for i in range(2)
            ]
        )
        self.input_layernorm = nn.ModuleList(
            [RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
        )
        self.post_attention_layernorm = nn.ModuleList(
            [RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
        )

        # Dual MLP structure
        self.mlps = nn.ModuleList(
            [
                FlashMLP(
                    hidden_size=self.hidden_size,
                    intermediate_size=config.intermediate_size,
                    hidden_act=config.hidden_act,
                    quant_config=None
                    if "mlps" in getattr(config, "disable_quant_module", [])
                    else quant_config,
                    prefix=f"{prefix}.mlps.{i}",
                )
                for i in range(2)
            ]
        )

        self.mlp = LongcatMoe(
            config=config,
            num_experts=config.n_routed_experts
            if hasattr(config, "n_routed_experts")
            else config.num_experts[self.layer_idx],
            top_k=config.moe_topk
            if hasattr(config, "moe_topk")
            else config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            quant_config=quant_config,
            prefix=(f"{prefix}.mlp"),
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm[0](hidden_states)
        else:
            hidden_states, residual = self.input_layernorm[0](hidden_states, residual)

        hidden_states = self.self_attn[0](
            positions=positions,
            hidden_states=hidden_states,
            llama_4_scaling=None,
        )

        hidden_states, residual = self.post_attention_layernorm[0](
            hidden_states, residual
        )

        # moe
        hidden_states_copy = hidden_states.clone()
        moe_hidden_states = self.mlp(hidden_states_copy)

        # first mlp
        hidden_states = self.mlps[0](hidden_states)

        hidden_states, residual = self.input_layernorm[1](hidden_states, residual)

        # second_attn
        hidden_states = self.self_attn[1](
            positions=positions,
            hidden_states=hidden_states,
            llama_4_scaling=None,
        )
        hidden_states, residual = self.post_attention_layernorm[1](
            hidden_states, residual
        )

        # second_mlp
        hidden_states = self.mlps[1](hidden_states)

        hidden_states = hidden_states + moe_hidden_states

        return hidden_states, residual


@support_torch_compile
class FlashModel(nn.Module):
    """Flash model."""

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config

        self.vocab_size = config.vocab_size

        if get_pp_group().is_first_rank:
            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size,
                config.hidden_size,
                prefix=maybe_prefix(prefix, "embed_tokens"),
            )
        else:
            self.embed_tokens = PPMissingLayer()
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: FlashDecoderLayer(
                vllm_config,
                config,
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=prefix,
            ),
            prefix=f"{prefix}.layers",
        )
        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )

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

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = 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)
            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(
                positions,
                hidden_states,
                residual,
            )

        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )

        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        return fused_moe_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.n_routed_experts
            if hasattr(self.config, "n_routed_experts")
            else self.config.num_experts[0],
        )

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            ("fused_qkv_a_proj", "q_a_proj", 0),
            ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]

        expert_params_mapping = self.get_expert_mapping()
        loaded_params: set[str] = set()

        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                if "mlp" in name and "mlps" not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if (
                    name.endswith(".bias") or name.endswith("_bias")
                ) and name not in params_dict:
                    continue
                # Skip mtp
                if ".mtp." in name:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                is_expert_weight = False
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    is_expert_weight = True
                    name_mapped = name.replace(weight_name, param_name)
                    # Skip mtp
                    if ".mtp." in name_mapped:
                        continue
                    if (
                        name_mapped.endswith(".bias") or name_mapped.endswith("_bias")
                    ) and name not in params_dict:
                        continue
                    if is_pp_missing_parameter(name, self):
                        continue
                    param = params_dict[name_mapped]
                    weight_loader = param.weight_loader
                    weight_loader = typing.cast(
                        Callable[..., bool], param.weight_loader
                    )
                    success = weight_loader(
                        param,
                        loaded_weight,
                        name_mapped,
                        shard_id=shard_id,
                        expert_id=expert_id,
                        return_success=True,
                    )
                    if success:
                        name = name_mapped
                        break
                else:
                    if is_expert_weight:
                        # We've checked that this is an expert weight
                        # However it's not mapped locally to this rank
                        # So we simply skip it
                        continue
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    # Skip loading kv_scale from ckpts towards new design.
                    if name.endswith(".kv_scale") and name not in params_dict:
                        continue
                    # Skip mtp
                    if ".mtp." in name:
                        continue
                    if name is None:
                        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)
        for layer_id in range(self.config.num_hidden_layers):
            for i in range(2):
                if isinstance(self.layers[layer_id], PPMissingLayer):
                    continue
                self_attn = self.layers[layer_id].self_attn[i]
                if hasattr(
                    self.quant_config, "weight_block_size"
                ) and self_attn.kv_b_proj.weight.dtype in (
                    torch.float8_e4m3fn,
                    torch.float8_e4m3fnuz,
                ):
                    weight_block_size = self.quant_config.weight_block_size
                    if weight_block_size is not None:
                        assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
                        dtype = torch.get_default_dtype()
                        w = block_dequant(
                            self_attn.kv_b_proj.weight,
                            self_attn.kv_b_proj.weight_scale_inv,
                            weight_block_size,
                        ).to(dtype)
                else:
                    w = self_attn.kv_b_proj.weight

                w_kc, w_vc = w.unflatten(
                    0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
                ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
                self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
                self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
                if self.config.mla_scale_q_lora:
                    self_attn.q_a_layernorm.weight.data *= (
                        self.config.hidden_size / self.config.q_lora_rank
                    ) ** 0.5
                if self.config.mla_scale_kv_lora:
                    self_attn.kv_a_layernorm.weight.data *= (
                        self.config.hidden_size / self.config.kv_lora_rank
                    ) ** 0.5
        return loaded_params


class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
    """Flash model for causal language modeling."""

    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
        quant_config = vllm_config.quant_config

        self.config = config
        config.intermediate_size = (
            config.ffn_hidden_size
            if hasattr(config, "ffn_hidden_size")
            else config.intermediate_size
        )

        self.quant_config = quant_config

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

        if get_pp_group().is_last_rank:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "lm_head"),
            )
        else:
            self.lm_head = PPMissingLayer()

        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

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

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

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

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