# Copyright © 2023-2024 Apple Inc.

from dataclasses import dataclass, field
from typing import Any, Optional

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

from .activations import swiglu
from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
from .rope_utils import initialize_rope
from .switch_layers import SwitchGLU


@dataclass
class ModelArgs(BaseModelArgs):
    hidden_size: int
    intermediate_size: int
    model_type: str
    max_position_embeddings: int
    num_attention_heads: int
    num_key_value_heads: int
    num_hidden_layers: int
    rms_norm_eps: float
    vocab_size: int
    rope_theta: float
    use_bias: bool
    tie_word_embeddings: bool
    moe_num_experts: int
    moe_layer_start_index: int = 0
    moe_intermediate_size: int = 0
    moe_capacity: list[int] = field(default_factory=list)
    moe_k: int = 1
    moe_layer_interval: int = 1
    moe_use_aux_free: bool = False
    moe_num_shared_experts: int = 0
    moe_layer_end_index: Optional[int] = None
    head_dim: Optional[int] = None
    moe_gate_act: str = "softmax"


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

        dim = args.hidden_size
        self.n_heads = n_heads = args.num_attention_heads
        self.n_kv_heads = n_kv_heads = args.num_key_value_heads

        self.head_dim = head_dim = args.head_dim or dim // n_heads
        self.scale = head_dim**-0.5

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

        self.rope = initialize_rope(
            head_dim,
            base=args.rope_theta,
            traditional=True,
            max_position_embeddings=args.max_position_embeddings,
        )

    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)

        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
        keys = 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 Ernie4_5_MLP(nn.Module):
    def __init__(self, dim, hidden_dim, use_bias=False):
        super().__init__()
        self.gate_proj = nn.Linear(dim, hidden_dim, bias=use_bias)
        self.down_proj = nn.Linear(hidden_dim, dim, bias=use_bias)
        self.up_proj = nn.Linear(dim, hidden_dim, bias=use_bias)

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


class Ernie4_5_MoeMLP(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.k = args.moe_k
        self.moe_intermediate_size = (
            args.moe_intermediate_size
            if args.moe_intermediate_size
            else args.intermediate_size
        )

        self.gate = nn.Linear(args.hidden_size, args.moe_num_experts, bias=False)

        self.switch_mlp = SwitchGLU(
            args.hidden_size,
            self.moe_intermediate_size,
            args.moe_num_experts,
            bias=args.use_bias,
        )

        if getattr(args, "moe_num_shared_experts", 0) > 0:
            shared_intermediate_size = (
                args.moe_intermediate_size * args.moe_num_shared_experts
                if getattr(args, "moe_intermediate_size", None)
                else args.intermediate_size * args.moe_num_shared_experts
            )
            self.shared_experts = Ernie4_5_MLP(
                args.hidden_size, shared_intermediate_size, args.use_bias
            )
        else:
            self.shared_experts = None

        if args.moe_gate_act == "softmax":
            self.gate_act = nn.Softmax()
        elif args.moe_gate_act == "sigmoid":
            self.gate_act = nn.Sigmoid()
        else:
            raise ValueError(f"{args.moe_gate_act} is not supported.")

    def __call__(self, x: mx.array) -> mx.array:
        gates = self.gate(x)
        gates = self.gate_act(gates.astype(mx.float32))

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

        scores = scores / mx.maximum(scores.sum(axis=-1, keepdims=True), 1e-12)

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

        if self.shared_experts is not None:
            y = y + self.shared_experts(x)

        return y


class Ernie4_5_DecoderLayer(nn.Module):
    def __init__(self, args: ModelArgs, layer_idx: int):
        super().__init__()
        self.self_attn = Attention(args)

        moe_layer_start_index = (
            min(args.moe_layer_start_index)
            if isinstance(args.moe_layer_start_index, (tuple, list))
            else args.moe_layer_start_index
        )

        if args.moe_layer_end_index is None:
            moe_layer_end_index = args.num_hidden_layers - 1
        else:
            moe_layer_end_index = (
                max(args.moe_layer_end_index)
                if isinstance(args.moe_layer_end_index, (tuple, list))
                else args.moe_layer_end_index
            )

        if (
            ((layer_idx + 1) % args.moe_layer_interval == 0)
            and layer_idx >= moe_layer_start_index
            and layer_idx <= moe_layer_end_index
        ):
            self.mlp = Ernie4_5_MoeMLP(args)
        else:
            self.mlp = Ernie4_5_MLP(
                args.hidden_size, args.intermediate_size, args.use_bias
            )

        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 = self.self_attn(self.input_layernorm(x), mask, cache)
        h = x + r
        r = self.mlp(self.post_attention_layernorm(h))
        return h + r


class Ernie45Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [
            Ernie4_5_DecoderLayer(args, 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,
    ):
        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 = Ernie45Model(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,
    ):
        out = self.model(inputs, cache)
        if self.args.tie_word_embeddings:
            out = self.model.embed_tokens.as_linear(out)
        else:
            out = self.lm_head(out)
        return out

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

    def sanitize(self, weights):
        remove_patterns = [
            "mtp_block.",
            "mtp_linear_proj.",
            "mtp_hidden_norm.",
            "mtp_emb_norm.",
            "e_score_correction_bias",
        ]

        weights = {
            key: value
            for key, value in weights.items()
            if not any(pattern in key for pattern in remove_patterns)
        }

        # Stack experts
        for l in range(self.args.num_hidden_layers):
            prefix = f"model.layers.{l}"
            for m in ["gate_proj", "down_proj", "up_proj"]:
                if f"{prefix}.mlp.experts.0.{m}.weight" in weights:
                    to_join = [
                        weights.pop(f"{prefix}.mlp.experts.{e}.{m}.weight")
                        for e in range(self.args.moe_num_experts)
                    ]
                    weights[f"{prefix}.mlp.switch_mlp.{m}.weight"] = mx.stack(to_join)

        return weights
