# Copyright © 2025 Apple Inc.

from dataclasses import dataclass
from functools import partial
from typing import Any, List, Optional, Tuple

import mlx.core as mx
import mlx.nn as nn

from .base import (
    BaseModelArgs,
    create_attention_mask,
    create_ssm_mask,
    scaled_dot_product_attention,
)
from .cache import KVCache, MambaCache
from .ssm import ssm_update
from .switch_layers import SwitchMLP


@dataclass()
class ModelArgs(BaseModelArgs):
    model_type: str
    vocab_size: int
    hidden_size: int
    intermediate_size: int
    num_hidden_layers: int
    max_position_embeddings: int
    num_attention_heads: int
    num_key_value_heads: int
    attention_bias: bool
    mamba_num_heads: int
    mamba_head_dim: int
    mamba_proj_bias: bool
    ssm_state_size: int
    conv_kernel: int
    n_groups: int
    time_step_limit: Tuple[float, float]
    mlp_bias: bool
    layer_norm_epsilon: float
    use_bias: bool
    use_conv_bias: bool
    hybrid_override_pattern: List[str]
    head_dim: Optional[int] = None
    moe_intermediate_size: Optional[int] = None
    moe_shared_expert_intermediate_size: Optional[int] = None
    n_group: Optional[int] = None
    n_routed_experts: Optional[int] = None
    n_shared_experts: Optional[int] = None
    topk_group: Optional[int] = None
    num_experts_per_tok: Optional[int] = None
    norm_topk_prob: Optional[bool] = None
    routed_scaling_factor: Optional[float] = None


class MambaRMSNormGated(nn.Module):
    def __init__(self, hidden_size: int, eps: float, group_size: int):
        super().__init__()
        self.eps = eps
        self.weight = mx.ones(hidden_size)
        self.group_size = group_size

    def __call__(self, x: mx.array, gate: mx.array = None) -> mx.array:
        if gate is not None:
            x = x * nn.silu(gate)
        x = mx.unflatten(x, axis=-1, shape=(-1, self.group_size))
        x = mx.fast.rms_norm(x, weight=None, eps=self.eps)
        return self.weight * x.flatten(-2)


class NemotronHMamba2Mixer(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.num_heads = args.mamba_num_heads
        self.hidden_size = args.hidden_size
        self.ssm_state_size = args.ssm_state_size
        self.conv_kernel_size = args.conv_kernel
        self.intermediate_size = args.mamba_num_heads * args.mamba_head_dim
        self.n_groups = args.n_groups
        self.head_dim = args.mamba_head_dim
        self.time_step_limit = args.time_step_limit
        self.heads_per_group = self.num_heads // self.n_groups

        self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size

        self.conv1d = nn.Conv1d(
            in_channels=self.conv_dim,
            out_channels=self.conv_dim,
            kernel_size=args.conv_kernel,
            padding=0,
            groups=self.conv_dim,
            bias=args.use_conv_bias,
        )

        projection_size = self.intermediate_size + self.conv_dim + self.num_heads
        self.in_proj = nn.Linear(
            self.hidden_size, projection_size, bias=args.mamba_proj_bias
        )

        self.dt_bias = mx.ones(self.num_heads)
        self.A_log = mx.log(mx.arange(1, self.num_heads + 1, dtype=mx.float32))
        self.D = mx.ones(self.num_heads)

        group_size = self.intermediate_size // self.n_groups
        self.norm = MambaRMSNormGated(
            self.intermediate_size,
            eps=args.layer_norm_epsilon,
            group_size=group_size,
        )
        self.out_proj = nn.Linear(
            self.intermediate_size, self.hidden_size, bias=args.mamba_proj_bias
        )

    def _apply_conv(
        self, conv_input: mx.array, cache: Optional[MambaCache] = None
    ) -> mx.array:
        if cache is not None:
            if cache[0] is None:
                conv_state = mx.zeros(
                    (conv_input.shape[0], self.conv_kernel_size - 1, self.conv_dim),
                    dtype=conv_input.dtype,
                )
            else:
                conv_state = cache[0]
            padded_input = mx.concatenate([conv_state, conv_input], axis=1)
            cache[0] = padded_input[:, -(self.conv_kernel_size - 1) :, :]
        else:
            padded_input = mx.pad(
                conv_input, [(0, 0), (self.conv_kernel_size - 1, 0), (0, 0)]
            )
        conv_output = self.conv1d(padded_input)
        return nn.silu(conv_output)

    def _ssm(
        self,
        hidden_states: mx.array,
        B: mx.array,
        C: mx.array,
        dt: mx.array,
        state: Optional[mx.array],
        mask: Optional[mx.array] = None,
    ) -> mx.array:
        batch_size, seq_len, _ = hidden_states.shape

        hidden_states = hidden_states.reshape(
            batch_size, seq_len, self.num_heads, self.head_dim
        )
        B = B.reshape(batch_size, seq_len, self.n_groups, self.ssm_state_size)
        C = C.reshape(batch_size, seq_len, self.n_groups, self.ssm_state_size)

        y, state = ssm_update(
            hidden_states,
            self.A_log,
            B,
            C,
            self.D.astype(hidden_states.dtype),
            dt,
            self.dt_bias,
            state,
            self.time_step_limit,
            mask,
        )

        return y.reshape(batch_size, seq_len, self.intermediate_size), state

    def __call__(
        self,
        hidden_states: mx.array,
        mask: Optional[mx.array],
        cache: Optional[MambaCache] = None,
    ) -> mx.array:

        projected = self.in_proj(hidden_states)

        gate, conv_input, dt = mx.split(
            projected,
            [self.intermediate_size, self.intermediate_size + self.conv_dim],
            axis=-1,
        )
        if mask is not None:
            conv_input = mx.where(mask[..., None], conv_input, 0)

        conv_output = self._apply_conv(conv_input, cache)

        hidden_states_ssm, B, C = mx.split(
            conv_output,
            [
                self.intermediate_size,
                self.intermediate_size + self.n_groups * self.ssm_state_size,
            ],
            axis=-1,
        )
        state = cache[1] if cache else None
        y, state = self._ssm(hidden_states_ssm, B, C, dt, state, mask)
        if cache:
            cache[1] = state
        y = self.norm(y, gate)
        return self.out_proj(y)


class NemotronHAttention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.hidden_size = args.hidden_size
        self.num_heads = args.num_attention_heads
        self.head_dim = (
            args.head_dim
            if args.head_dim is not None
            else (args.hidden_size // args.num_attention_heads)
        )
        self.num_key_value_heads = args.num_key_value_heads
        self.scale = self.head_dim**-0.5

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

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

        queries = self.q_proj(x).reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3)
        keys = (
            self.k_proj(x)
            .reshape(B, L, self.num_key_value_heads, -1)
            .transpose(0, 2, 1, 3)
        )
        values = (
            self.v_proj(x)
            .reshape(B, L, self.num_key_value_heads, -1)
            .transpose(0, 2, 1, 3)
        )

        if cache is not None:
            keys, values = cache.update_and_fetch(keys, values)

        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 NemotronHMLP(nn.Module):
    def __init__(self, args: ModelArgs, intermediate_size=None):
        super().__init__()
        intermediate_size = intermediate_size or args.intermediate_size

        self.up_proj = nn.Linear(
            args.hidden_size, intermediate_size, bias=args.mlp_bias
        )
        self.down_proj = nn.Linear(
            intermediate_size, args.hidden_size, bias=args.mlp_bias
        )

    def __call__(self, x):
        return self.down_proj(nn.relu2(self.up_proj(x)))


@mx.compile
def group_expert_select(
    gates,
    e_score_correction_bias,
    top_k,
    n_group,
    topk_group,
    routed_scaling_factor,
    norm_topk_prob,
):

    orig_scores = scores = mx.sigmoid(gates.astype(mx.float32))
    scores = scores + e_score_correction_bias
    if n_group > 1:
        scores = mx.unflatten(scores, axis=-1, shape=(n_group, -1))
        group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1, keepdims=True)
        k = n_group - topk_group
        group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :]
        scores = mx.put_along_axis(
            scores, mx.stop_gradient(group_idx), mx.array(0.0), axis=-2
        )
        scores = mx.flatten(scores, -2, -1)

    k = top_k
    inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
    scores = mx.take_along_axis(orig_scores, inds, axis=-1)
    if top_k > 1 and norm_topk_prob:
        denominator = scores.sum(axis=-1, keepdims=True)
        scores = scores / (denominator + 1e-20)
    scores = scores * routed_scaling_factor

    return inds, scores


class MoEGate(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.norm_topk_prob = config.norm_topk_prob
        self.n_routed_experts = config.n_routed_experts
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_group = config.n_group
        self.topk_group = config.topk_group
        self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))
        self.e_score_correction_bias = mx.zeros((self.n_routed_experts,))

    def __call__(self, x):
        return group_expert_select(
            x @ self.weight.T,
            self.e_score_correction_bias,
            self.top_k,
            self.n_group,
            self.topk_group,
            self.routed_scaling_factor,
            self.norm_topk_prob,
        )


class NemotronHMoE(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.config = config
        self.num_experts_per_tok = config.num_experts_per_tok
        self.switch_mlp = SwitchMLP(
            config.hidden_size,
            config.moe_intermediate_size,
            config.n_routed_experts,
            activation=nn.ReLU2(),
        )

        self.gate = MoEGate(config)
        if config.n_shared_experts is not None:
            intermediate_size = config.moe_shared_expert_intermediate_size
            self.shared_experts = NemotronHMLP(
                config, intermediate_size=intermediate_size
            )

    def __call__(self, x):
        inds, scores = self.gate(x)
        y = self.switch_mlp(x, inds)
        y = (y * scores[..., None]).sum(axis=-2).astype(y.dtype)
        if self.config.n_shared_experts is not None:
            y = y + self.shared_experts(x)

        return y


class NemotronHBlock(nn.Module):
    def __init__(self, args: ModelArgs, block_type: str):
        super().__init__()
        self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)

        self.block_type = block_type

        if self.block_type == "M":
            self.mixer = NemotronHMamba2Mixer(args)
        elif self.block_type == "*":
            self.mixer = NemotronHAttention(args)
        elif self.block_type == "-":
            self.mixer = NemotronHMLP(args)
        elif self.block_type == "E":
            self.mixer = NemotronHMoE(args)

    def __call__(
        self,
        x,
        mask: Optional[mx.array] = None,
        cache: Optional[Any] = None,
    ):
        hidden_states = self.norm(x)
        if self.block_type == "M" or self.block_type == "*":
            hidden_states = self.mixer(hidden_states, mask=mask, cache=cache)
        else:
            hidden_states = self.mixer(hidden_states)

        return x + hidden_states


class NemotronHModel(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [
            NemotronHBlock(args, block_type)
            for block_type in args.hybrid_override_pattern
        ]
        self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
        self.fa_idx = 0
        self.ssm_idx = 0
        for b in args.hybrid_override_pattern:
            if b == "*":
                break
            elif b == "M":
                self.fa_idx += 1
        for b in args.hybrid_override_pattern:
            if b == "*":
                self.ssm_idx += 1
            elif b == "M":
                break

    def __call__(
        self,
        inputs,
        cache: Optional[Any] = None,
    ):
        hidden_states = self.embeddings(inputs)

        if cache is None:
            cache = [None] * len(self.layers)
        attn_mask = create_attention_mask(hidden_states, cache[self.fa_idx])
        ssm_mask = create_ssm_mask(hidden_states, cache[self.ssm_idx])

        cache_counter = 0
        for layer in self.layers:
            if layer.block_type == "M" or layer.block_type == "*":
                c = cache[cache_counter]
                cache_counter += 1
            else:
                c = None

            if layer.block_type == "*":
                mask = attn_mask
            else:
                mask = ssm_mask
            hidden_states = layer(hidden_states, mask=mask, cache=c)

        return self.norm_f(hidden_states)


class Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.backbone = NemotronHModel(args)
        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
        self.model_type = args.model_type

    def __call__(
        self,
        inputs: mx.array,
        cache: Optional[Any] = None,
    ):
        out = self.backbone(inputs, cache=cache)
        return self.lm_head(out)

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

    def make_cache(self):
        caches = []
        for l in self.layers:
            if l.block_type == "M":
                caches.append(MambaCache())
            elif l.block_type == "*":
                caches.append(KVCache())
        return caches

    def sanitize(self, weights):
        for k, v in weights.items():
            if "conv1d.weight" in k and v.shape[-1] != 1:
                weights[k] = v.moveaxis(2, 1)

        # Stack experts
        for l in range(self.args.num_hidden_layers):
            prefix = f"backbone.layers.{l}.mixer"
            for m, n in [("down_proj", "fc2"), ("up_proj", "fc1")]:
                if f"{prefix}.experts.0.{m}.weight" in weights:
                    to_join = [
                        weights.pop(f"{prefix}.experts.{e}.{m}.weight")
                        for e in range(self.args.n_routed_experts)
                    ]
                    weights[f"{prefix}.switch_mlp.{n}.weight"] = mx.stack(to_join)

        return weights

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

        return predicate