# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from collections.abc import Iterable
from itertools import islice

import torch
from torch import nn
from transformers import CohereConfig

from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (
    get_pp_group,
    get_tensor_model_parallel_world_size,
)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.fused_moe import (
    FusedMoE,
    fused_moe_make_expert_params_mapping,
)
from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
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 VocabParallelEmbedding
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
    row_parallel_weight_loader,
)
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors

from .commandr import LayerNorm
from .interfaces import SupportsPP, SupportsQuant
from .utils import (
    AutoWeightsLoader,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)


def is_prefix_dense_layer(config: CohereConfig, layer_idx: int) -> bool:
    """True when layer_idx lies in the contiguous dense MLP prefix."""
    if layer_idx >= len(config.mlp_layer_types):
        return False
    return all(t == "dense" for t in config.mlp_layer_types[: layer_idx + 1])


@torch.compile(backend=current_platform.simple_compile_backend)
def token_choice_with_bias(
    hidden_states: torch.Tensor,
    gating_output: torch.Tensor,
    topk: int,
    renormalize: bool,
):
    """Sigmoid -> top-k (-> renormalize) custom routing for Cohere2Moe."""
    assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"

    scores = gating_output.float().sigmoid()
    topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1, sorted=False)

    if renormalize:
        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)

    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)


@torch.compile(backend=current_platform.simple_compile_backend)
def rms_norm_func(hidden_states, weight, variance_epsilon):
    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 + variance_epsilon)
    hidden_states = weight.to(torch.float32) * hidden_states
    return hidden_states.to(input_dtype)


class RMSNorm(nn.Module):
    def __init__(self, param_shape=None, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(param_shape))
        self.variance_epsilon = eps
        set_weight_attrs(self.weight, {"weight_loader": row_parallel_weight_loader})

    def forward(self, hidden_states, residuals=None):
        hidden_states = rms_norm_func(hidden_states, self.weight, self.variance_epsilon)
        return hidden_states, residuals


def select_norm_impl(config: CohereConfig) -> tuple[type[nn.Module], float]:
    """Returns (norm_class, eps). Uses RMSNorm when config.rms_norm_eps is set,
    otherwise falls back to LayerNorm with config.layer_norm_eps."""
    rms_eps = getattr(config, "rms_norm_eps", None)
    if rms_eps is not None:
        return RMSNorm, rms_eps
    return LayerNorm, config.layer_norm_eps


class Cohere2MoeMLP(nn.Module):
    """Cohere MLP used as shared experts in the MoE block."""

    def __init__(
        self,
        config: CohereConfig,
        intermediate_size: int | None = None,
        quant_config: QuantizationConfig | None = None,
        reduce_results: bool = False,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = (
            intermediate_size
            if intermediate_size is not None
            else config.intermediate_size
        )
        self.gate_up_proj = MergedColumnParallelLinear(
            self.hidden_size,
            [self.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            self.intermediate_size,
            self.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):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class Cohere2MoeAttention(nn.Module):
    """Cohere MoE attention with sliding-window interleave."""

    def __init__(
        self,
        config: CohereConfig,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        tp_size = get_tensor_model_parallel_world_size()
        self.config = config
        self.layer_idx = extract_layer_index(prefix)
        self.hidden_size = config.hidden_size
        self.total_num_heads = config.num_attention_heads
        self.num_heads = self.total_num_heads // tp_size
        self.head_dim = getattr(
            config, "head_dim", self.hidden_size // self.total_num_heads
        )
        self.total_num_kv_heads = config.num_key_value_heads
        if self.total_num_kv_heads >= tp_size:
            assert self.total_num_kv_heads % tp_size == 0
        else:
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.max_position_embeddings = getattr(
            config, "model_max_length", None
        ) or getattr(config, "max_position_embeddings", 8192)
        self.qkv_proj = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=self.max_position_embeddings,
            rope_parameters=config.rope_parameters,
            is_neox_style=False,
        )

        self.sliding_window = None
        layer_types = getattr(config, "layer_types", None)
        if (
            layer_types is not None
            and layer_types[self.layer_idx] == "sliding_attention"
        ):
            self.sliding_window = config.sliding_window

        # Prefix-dense layers have full attention (no sliding window). When
        # prefix_dense_sliding_window_pattern == 1, they keep RoPE even though
        # they are not sliding-window layers.
        prefix_dense_sliding_window_pattern = getattr(
            config, "prefix_dense_sliding_window_pattern", 1
        )
        self.force_rope = bool(
            is_prefix_dense_layer(config, self.layer_idx)
            and prefix_dense_sliding_window_pattern == 1
        )

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            per_layer_sliding_window=self.sliding_window,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        if self.sliding_window or self.force_rope:
            q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output


class Cohere2Moe(nn.Module):
    """Tensor-parallel MoE block for Cohere2Moe with shared experts."""

    def __init__(
        self,
        config: CohereConfig,
        params_dtype: torch.dtype | None = None,
        quant_config: QuantizationConfig | None = None,
        tp_size: int | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.tp_size = get_tensor_model_parallel_world_size()

        if self.tp_size > config.num_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {config.num_experts}."
            )

        if (
            hasattr(config, "expert_selection_fn")
            and config.expert_selection_fn == "sigmoid"
        ):
            self.custom_routing_function = token_choice_with_bias
        else:
            self.custom_routing_function = None

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.num_experts,
            bias=False,
            params_dtype=params_dtype,
            quant_config=None,
            prefix=f"{prefix}.gate",
        )

        if hasattr(config, "num_shared_experts") and config.num_shared_experts > 0:
            self.shared_experts = Cohere2MoeMLP(
                config=config,
                intermediate_size=config.intermediate_size * config.num_shared_experts,
                quant_config=quant_config,
                prefix=f"{prefix}.shared_experts",
            )
            self.shared_expert_combination_strategy = getattr(
                config, "shared_expert_combination_strategy", "sum"
            )
            assert self.shared_expert_combination_strategy in ("average", "sum"), (
                "shared_expert_combination_strategy must be one of ['average', 'sum']"
            )
        else:
            self.shared_experts = None
            self.shared_expert_combination_strategy = None

        self.experts = FusedMoE(
            num_experts=config.num_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            params_dtype=params_dtype,
            renormalize=getattr(config, "norm_topk_prob", True),
            quant_config=quant_config,
            tp_size=tp_size,
            prefix=f"{prefix}.experts",
            custom_routing_function=self.custom_routing_function,
            shared_experts=self.shared_experts,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_shape = hidden_states.shape
        hidden_states = hidden_states.view(-1, self.hidden_size)
        router_logits, _ = self.gate(hidden_states)
        # FusedMoE handles shared expert overlap internally and returns
        # shared_output + routed_output when shared_experts is set.
        final_hidden_states = self.experts(hidden_states, router_logits)
        if self.shared_expert_combination_strategy == "average":
            final_hidden_states = final_hidden_states / 2
        return final_hidden_states.view(orig_shape)


class Cohere2MoeDecoderLayer(nn.Module):
    def __init__(
        self,
        config: CohereConfig,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.layer_idx = extract_layer_index(prefix)

        self.self_attn = Cohere2MoeAttention(
            config,
            cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )

        if config.mlp_layer_types[self.layer_idx] == "dense":
            self.mlp = Cohere2MoeMLP(
                config=config,
                intermediate_size=getattr(
                    config, "prefix_dense_intermediate_size", config.intermediate_size
                ),
                quant_config=quant_config,
                reduce_results=True,
                prefix=f"{prefix}.mlp",
            )
        else:
            self.mlp = Cohere2Moe(
                config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
            )

        norm_cls, norm_eps = select_norm_impl(config)
        self.input_layernorm = norm_cls(param_shape=(config.hidden_size,), eps=norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        residual = hidden_states
        hidden_states, residual = self.input_layernorm(hidden_states, residual)
        hidden_states_attention = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )
        hidden_states_mlp = self.mlp(hidden_states)

        hidden_states = residual + hidden_states_attention + hidden_states_mlp
        return hidden_states, residual


@support_torch_compile
class Cohere2MoeModel(nn.Module):
    """Transformer decoder for Cohere2Moe."""

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        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
        self.org_vocab_size = config.vocab_size
        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size, config.hidden_size
        )

        # Decoder layers read per-layer MLP layout from config.mlp_layer_types
        # (dense MLP vs MoE) and use it for weight loading. Transformers >=5.10
        # populates this field; older versions only expose first_k_dense_replace.
        # Normalize here so layer construction below sees a consistent layout.
        if getattr(config, "mlp_layer_types", None) is None:
            first_k_dense_replace = getattr(config, "first_k_dense_replace", None)
            n = config.num_hidden_layers
            if first_k_dense_replace is not None:
                config.mlp_layer_types = ["dense"] * first_k_dense_replace + [
                    "sparse"
                ] * (n - first_k_dense_replace)
            else:
                config.mlp_layer_types = ["sparse"] * n

        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: Cohere2MoeDecoderLayer(
                config, cache_config, quant_config, prefix=prefix
            ),
            prefix=f"{prefix}.layers",
        )
        norm_cls, norm_eps = select_norm_impl(config)
        self.norm = norm_cls(param_shape=(config.hidden_size,), eps=norm_eps)
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )

    def get_input_embeddings(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 = 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.get_input_embeddings(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 load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        expert_params_mapping = 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.num_experts,
        )

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            for param_name, shard_name, shard_id in stacked_params_mapping:
                if shard_name not in name:
                    continue
                if "mlp.experts" in name:
                    continue
                name = name.replace(shard_name, param_name)
                if name.endswith(".bias") and name not in params_dict:
                    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:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
                    if is_pp_missing_parameter(name, self):
                        continue
                    if (
                        name.endswith(".bias") or name.endswith("_bias")
                    ) and name not in params_dict:
                        continue
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    break
                else:
                    if (
                        name.endswith(".bias") or name.endswith("_bias")
                    ) and name not in params_dict:
                        continue
                    if is_pp_missing_parameter(name, self):
                        continue
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        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 Cohere2MoeForCausalLM(nn.Module, SupportsPP, SupportsQuant):
    is_text_generation_model = True

    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 = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        assert getattr(config, "tie_word_embeddings", True)
        self.unpadded_vocab_size = config.vocab_size
        self.quant_config = quant_config
        self.logits_scale = config.logit_scale
        self.logits_processor = LogitsProcessor(
            self.unpadded_vocab_size, config.vocab_size, scale=self.logits_scale
        )
        self.model = Cohere2MoeModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        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.get_input_embeddings(input_ids)

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

    @torch.no_grad()
    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, positions, intermediate_tensors, inputs_embeds)

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        return self.logits_processor(self.model.embed_tokens, hidden_states)

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