# Copyright © 2025 Apple Inc.

from dataclasses import dataclass
from functools import lru_cache
from typing import Any, List, Optional

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

from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
from .switch_layers import SwitchGLU


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    hidden_size: int
    intermediate_size: int
    num_attention_heads: int
    num_key_value_heads: int
    max_position_embeddings: int
    num_experts_per_tok: int
    num_local_experts: int
    shared_intermediate_size: int
    num_hidden_layers: int
    rms_norm_eps: float
    rope_theta: float
    rotary_dim: int
    vocab_size: int
    tie_word_embeddings: bool = False
    scoring_func: str = "sigmoid"
    head_dim: Optional[int] = None
    use_qk_norm: bool = True


@lru_cache
def sharded_rms_norm(group):
    @mx.compile
    def _cast_square_sum(x):
        return x.astype(mx.float32).square().sum(-1, keepdims=True)

    @mx.compile
    def _normalize(x, norm2, w, eps):
        norm2 = mx.distributed.all_sum(norm2, group=group)
        norm = mx.rsqrt(norm2 / (x.shape[-1] * group.size()) + eps)
        return (x.astype(mx.float32) * norm * w).astype(x.dtype)

    # Split the compile so that x upcasting doesn't break the compile and we
    # have 2 kernels generated 1 for f(x) = square(upcast(x)) and another
    # g(x) = downcast(upcast(x) * norm * w)
    def _inner_sharded_rms_norm(x, w, eps):
        return _normalize(x, _cast_square_sum(x), w, eps)

    return _inner_sharded_rms_norm


class ShardedRMSNorm(nn.Module):
    def __init__(
        self, dims: int, eps: float = 1e-5, group: Optional[mx.distributed.Group] = None
    ):
        super().__init__()
        group = group or mx.distributed.init()
        self.weight = mx.ones((dims // group.size(),))
        self.group = group
        self.eps = eps

    def _extra_repr(self):
        return f"{self.weight.shape[0] * self.group.size()}, eps={self.eps}"

    def __call__(self, x):
        return sharded_rms_norm(self.group)(x, self["weight"], self.eps)

    @classmethod
    def from_rms_norm(
        cls, norm_module, *, group: Optional[mx.distributed.Group] = None
    ):
        sn = cls(norm_module.weight.shape[0], norm_module.eps, group=group)
        sn.weight = mx.contiguous(
            mx.split(norm_module.weight, group.size(), axis=-1)[group.rank()]
        )

        return sn


class MiniMaxAttention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        self.hidden_dim = hidden_size = args.hidden_size

        self.num_attention_heads = args.num_attention_heads
        self.num_key_value_heads = args.num_key_value_heads
        self.head_dim = head_dim = (
            args.head_dim or hidden_size // args.num_attention_heads
        )
        self.scale = head_dim**-0.5

        self.q_proj = nn.Linear(
            args.hidden_size, self.num_attention_heads * head_dim, bias=False
        )
        self.k_proj = nn.Linear(
            args.hidden_size, self.num_key_value_heads * head_dim, bias=False
        )
        self.v_proj = nn.Linear(
            args.hidden_size, self.num_key_value_heads * head_dim, bias=False
        )
        self.o_proj = nn.Linear(
            self.num_attention_heads * head_dim, args.hidden_size, bias=False
        )

        self.use_qk_norm = args.use_qk_norm if hasattr(args, "use_qk_norm") else False
        if self.use_qk_norm:
            self.q_norm = nn.RMSNorm(
                head_dim * self.num_attention_heads, eps=args.rms_norm_eps
            )
            self.k_norm = nn.RMSNorm(
                head_dim * self.num_key_value_heads, eps=args.rms_norm_eps
            )

        self.rope = nn.RoPE(args.rotary_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)

        if self.use_qk_norm:
            queries = self.q_norm(queries)
            keys = self.k_norm(keys)

        queries = queries.reshape(B, L, self.num_attention_heads, -1).transpose(
            0, 2, 1, 3
        )
        keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3)
        values = values.reshape(B, L, self.num_key_value_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 MiniMaxSparseMoeBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.num_experts_per_tok = args.num_experts_per_tok

        self.gate = nn.Linear(args.hidden_size, args.num_local_experts, bias=False)
        self.switch_mlp = SwitchGLU(
            args.hidden_size, args.intermediate_size, args.num_local_experts
        )
        self.e_score_correction_bias = mx.zeros((args.num_local_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.astype(mx.float32))

        scores = mx.sigmoid(gates)
        orig_scores = scores
        scores = scores + self.e_score_correction_bias

        k = self.num_experts_per_tok
        inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
        scores = mx.take_along_axis(orig_scores, inds, axis=-1)

        scores = scores / (mx.sum(scores, axis=-1, keepdims=True) + 1e-20)
        scores = scores.astype(x.dtype)

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

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

        return y


class MiniMaxDecoderLayer(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        self.self_attn = MiniMaxAttention(args)

        self.block_sparse_moe = MiniMaxSparseMoeBlock(args)

        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
        )

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


class MiniMaxModel(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)

        self.layers = [
            MiniMaxDecoderLayer(args=args) for _ in range(args.num_hidden_layers)
        ]

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

    def __call__(
        self,
        inputs: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Any] = None,
    ) -> mx.array:
        h = self.embed_tokens(inputs)

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

        mask = create_attention_mask(h, cache[0])

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

        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 = MiniMaxModel(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,
        mask: Optional[mx.array] = None,
        cache: Optional[Any] = None,
    ):
        out = self.model(inputs=inputs, mask=mask, cache=cache)
        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):
        """Dequantize FP8 weights and restructure MoE experts."""

        def dequant(weight, scale_inv):
            dtype = mx.bfloat16
            weight = mx.from_fp8(weight, dtype=mx.bfloat16)
            bs = 128  # block size
            m, n = weight.shape
            pad_bottom = (-m) % bs
            pad_side = (-n) % bs
            weight = mx.pad(weight, ((0, pad_bottom), (0, pad_side)))
            weight = weight.reshape(
                ((m + pad_bottom) // bs, bs, (n + pad_side) // bs, bs)
            )
            weight = (weight * scale_inv[:, None, :, None]).reshape(
                m + pad_bottom, n + pad_side
            )
            return weight[:m, :n].astype(dtype)

        # Dequantize
        new_weights = {}
        for k, v in weights.items():
            if "weight_scale_inv" in k:
                scale_inv = v
                wk = k.replace("_scale_inv", "")
                weight = weights[wk]
                weight = dequant(weight, scale_inv)
                new_weights[wk] = weight
            elif k not in new_weights:
                new_weights[k] = v
        weights = new_weights

        # Step 2: Handle MoE expert weights restructuring
        if "model.layers.0.block_sparse_moe.experts.0.w1.weight" not in weights:
            return weights

        for l in range(self.args.num_hidden_layers):
            prefix = f"model.layers.{l}"
            mapping = {"w1": "gate_proj", "w2": "down_proj", "w3": "up_proj"}
            for orig_name, new_name in mapping.items():
                if f"{prefix}.block_sparse_moe.experts.0.{orig_name}.weight" in weights:
                    to_join = [
                        weights.pop(
                            f"{prefix}.block_sparse_moe.experts.{e}.{orig_name}.weight"
                        )
                        for e in range(self.args.num_local_experts)
                    ]
                    weights[
                        f"{prefix}.block_sparse_moe.switch_mlp.{new_name}.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()
        rank = group.rank()
        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
            )
            if layer.self_attn.use_qk_norm:
                layer.self_attn.q_norm = ShardedRMSNorm.from_rms_norm(
                    layer.self_attn.q_norm, group=group
                )
                layer.self_attn.k_norm = ShardedRMSNorm.from_rms_norm(
                    layer.self_attn.k_norm, group=group
                )

            layer.self_attn.num_attention_heads //= N
            layer.self_attn.num_key_value_heads //= N

            # Shard the MLP
            shard_inplace(
                layer.block_sparse_moe.switch_mlp.gate_proj,
                "all-to-sharded",
                group=group,
            )
            shard_inplace(
                layer.block_sparse_moe.switch_mlp.down_proj,
                "sharded-to-all",
                group=group,
            )
            shard_inplace(
                layer.block_sparse_moe.switch_mlp.up_proj,
                "all-to-sharded",
                group=group,
            )
            layer.block_sparse_moe.sharding_group = group

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

    @property
    def cast_predicate(self):
        def predicate(k):
            return "e_score_correction_bias" not in k

        return predicate

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

        return predicate