# Copyright © 2026 Apple Inc.

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Union

import mlx.core as mx
import mlx.nn as nn
from mlx.nn.layers.distributed import shard_inplace, shard_linear, sum_gradients

from .activations import swiglu
from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
from .pipeline import PipelineMixin
from .switch_layers import SwitchGLU


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    hidden_size: int
    num_hidden_layers: int
    intermediate_size: int
    num_attention_heads: int
    num_experts: int
    num_experts_per_tok: int
    decoder_sparse_step: int
    mlp_only_layers: List[int]
    moe_intermediate_size: int
    rms_norm_eps: float
    vocab_size: int
    num_key_value_heads: int
    head_dim: int
    rope_theta: float
    tie_word_embeddings: bool
    max_position_embeddings: int
    norm_topk_prob: bool
    rope_scaling: Optional[Dict[str, Union[float, str]]] = None


class Attention(nn.Module):
    def __init__(self, args: ModelArgs, layer_idx: int):
        super().__init__()

        dim = args.hidden_size
        self.n_heads = n_heads = args.num_attention_heads
        assert args.num_key_value_heads is not None
        self.n_kv_heads = n_kv_heads = args.num_key_value_heads

        head_dim = getattr(
            args, "head_dim", args.hidden_size // args.num_attention_heads
        )
        self.scale = head_dim**-0.5

        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)

        self.q_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
        self.k_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)

        self.rope = nn.RoPE(
            head_dim,
            traditional=False,
            base=args.rope_theta,
        )

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Any] = None,
    ) -> mx.array:
        B, L, D = x.shape

        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)

        # Prepare the queries, keys and values for the attention computation
        queries = self.q_norm(queries.reshape(B, L, self.n_heads, -1)).transpose(
            0, 2, 1, 3
        )
        keys = self.k_norm(keys.reshape(B, L, self.n_kv_heads, -1)).transpose(
            0, 2, 1, 3
        )
        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)

        if cache is not None:
            queries = self.rope(queries, offset=cache.offset)
            keys = self.rope(keys, offset=cache.offset)
            keys, values = cache.update_and_fetch(keys, values)
        else:
            queries = self.rope(queries)
            keys = self.rope(keys)

        output = scaled_dot_product_attention(
            queries, keys, values, cache=cache, scale=self.scale, mask=mask
        )
        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
        return self.o_proj(output)


class MLP(nn.Module):
    def __init__(self, dim, hidden_dim):
        super().__init__()
        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)

    def __call__(self, x) -> mx.array:
        return self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x)))


class Qwen3MoeSparseMoeBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        dim = args.hidden_size
        intermediate_size = args.moe_intermediate_size

        self.num_experts = num_experts = args.num_experts
        self.top_k = args.num_experts_per_tok
        self.norm_topk_prob = args.norm_topk_prob

        self.gate = nn.Linear(dim, num_experts, bias=False)
        self.switch_mlp = SwitchGLU(dim, intermediate_size, num_experts)

        self.sharding_group = None

    def __call__(
        self,
        x: mx.array,
    ) -> mx.array:
        if self.sharding_group is not None:
            x = sum_gradients(self.sharding_group)(x)

        gates = self.gate(x)
        gates = mx.softmax(gates, axis=-1, precise=True)

        k = self.top_k
        inds = mx.argpartition(gates, kth=-k, axis=-1)[..., -k:]
        inds = mx.stop_gradient(inds)
        scores = mx.take_along_axis(gates, inds, axis=-1)
        if self.norm_topk_prob:
            scores /= mx.sum(scores, axis=-1, keepdims=True)

        y = self.switch_mlp(x, inds)
        y = (y * scores[..., None]).sum(axis=-2).astype(y.dtype)

        if self.sharding_group is not None:
            y = mx.distributed.all_sum(y, group=self.sharding_group)

        return y


class Qwen3MoeDecoderLayer(nn.Module):
    def __init__(self, args: ModelArgs, layer_idx: int):
        super().__init__()
        self.hidden_size = args.hidden_size
        self.self_attn = Attention(args, layer_idx)

        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
        self.post_attention_layernorm = nn.RMSNorm(
            args.hidden_size, eps=args.rms_norm_eps
        )
        self.args = args

        if (layer_idx not in args.mlp_only_layers) and (
            args.num_experts > 0 and (layer_idx + 1) % args.decoder_sparse_step == 0
        ):
            self.mlp = Qwen3MoeSparseMoeBlock(args)
        else:
            self.mlp = MLP(args.hidden_size, args.intermediate_size)

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Any] = None,
    ) -> mx.array:
        r = self.self_attn(self.input_layernorm(x), mask, cache)
        h = x + r
        r = self.mlp(self.post_attention_layernorm(h))
        out = h + r
        return out


class Qwen3MoeModel(PipelineMixin, nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.vocab_size = args.vocab_size
        self.num_hidden_layers = args.num_hidden_layers
        assert self.vocab_size > 0
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [
            Qwen3MoeDecoderLayer(args=args, layer_idx=i)
            for i in range(args.num_hidden_layers)
        ]
        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
        input_embeddings: Optional[mx.array] = None,
    ) -> mx.array:
        if input_embeddings is not None:
            h = input_embeddings
        else:
            h = self.embed_tokens(inputs)

        pipeline_rank = self.pipeline_rank
        pipeline_size = self.pipeline_size

        if cache is None:
            cache = [None] * len(self.pipeline_layers)

        mask = create_attention_mask(h, cache[0])

        # Receive from the previous process in the pipeline
        if pipeline_rank < pipeline_size - 1:
            h = mx.distributed.recv_like(h, (pipeline_rank + 1))

        for layer, c in zip(self.pipeline_layers, cache):
            h = layer(h, mask, c)

        # Send to the next process in the pipeline
        if pipeline_rank != 0:
            h = mx.distributed.send(h, (pipeline_rank - 1) % pipeline_size)
            if cache[-1] is not None:
                cache[-1].keys = mx.depends(cache[-1].keys, h)

        # Broadcast h while keeping it in the graph
        if pipeline_size > 1:
            h = mx.distributed.all_gather(h)[: h.shape[0]]

        return self.norm(h)


class Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.model_type = args.model_type
        self.model = Qwen3MoeModel(args)
        if not args.tie_word_embeddings:
            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
        input_embeddings: Optional[mx.array] = None,
    ) -> mx.array:
        out = self.model(inputs, cache, input_embeddings)
        if self.args.tie_word_embeddings:
            out = self.model.embed_tokens.as_linear(out)
        else:
            out = self.lm_head(out)
        return out

    def sanitize(self, weights):
        if self.args.tie_word_embeddings:
            weights.pop("lm_head.weight", None)
        moe_layers = sorted(
            int(k.split(".")[2])
            for k in weights
            if k.startswith("model.layers.")
            and k.endswith(".mlp.experts.0.up_proj.weight")
        )
        for l in moe_layers:
            prefix = f"model.layers.{l}"
            for n in ["up_proj", "down_proj", "gate_proj"]:
                to_join = [
                    weights.pop(f"{prefix}.mlp.experts.{e}.{n}.weight")
                    for e in range(self.args.num_experts)
                ]
                weights[f"{prefix}.mlp.switch_mlp.{n}.weight"] = mx.stack(to_join)
        return weights

    def shard(self, group: Optional[mx.distributed.Group] = None):
        group = group or mx.distributed.init()
        N = group.size()
        for layer in self.model.layers:
            # Shard the self attention
            layer.self_attn.q_proj = shard_linear(
                layer.self_attn.q_proj, "all-to-sharded", group=group
            )
            layer.self_attn.k_proj = shard_linear(
                layer.self_attn.k_proj, "all-to-sharded", group=group
            )
            layer.self_attn.v_proj = shard_linear(
                layer.self_attn.v_proj, "all-to-sharded", group=group
            )
            layer.self_attn.o_proj = shard_linear(
                layer.self_attn.o_proj, "sharded-to-all", group=group
            )
            layer.self_attn.n_heads //= N
            layer.self_attn.n_kv_heads //= N

            # Shard the dense MLP layers
            if isinstance(layer.mlp, MLP):
                layer.mlp.gate_proj = shard_linear(
                    layer.mlp.gate_proj, "all-to-sharded", group=group
                )
                layer.mlp.down_proj = shard_linear(
                    layer.mlp.down_proj, "sharded-to-all", group=group
                )
                layer.mlp.up_proj = shard_linear(
                    layer.mlp.up_proj, "all-to-sharded", group=group
                )

            # Shard the MoE. Shard in place since the MoE should be responsible
            # for aggregating the results. The router (gate) stays replicated.
            else:
                layer.mlp.sharding_group = group
                shard_inplace(
                    layer.mlp.switch_mlp.gate_proj, "all-to-sharded", group=group
                )
                shard_inplace(
                    layer.mlp.switch_mlp.down_proj, "sharded-to-all", group=group
                )
                shard_inplace(
                    layer.mlp.switch_mlp.up_proj, "all-to-sharded", group=group
                )

    @property
    def quant_predicate(self):
        def predicate(path, _):
            if path.endswith("mlp.gate"):
                return {"group_size": 64, "bits": 8}
            return True

        return predicate

    @property
    def layers(self):
        return self.model.pipeline_layers