# Copyright © 2025 Apple Inc.

from dataclasses import dataclass
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


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    hidden_size: int
    num_attention_heads: int
    num_hidden_layers: int
    vocab_size: int
    intermediate_size: int
    intermediate_size_mlp: int
    num_key_value_heads: int
    rms_norm_eps: float
    rope_theta: float
    head_dim: int
    tie_word_embeddings: bool
    no_rope_layers: list
    use_qk_norm: bool


class Attention(nn.Module):
    def __init__(self, args: ModelArgs, use_rope):
        super().__init__()
        self.args = args
        self.n_heads = args.num_attention_heads
        self.n_kv_heads = args.num_key_value_heads
        self.head_dim = args.head_dim
        self.scale = self.head_dim**-0.5

        self.q_proj = nn.Linear(
            args.hidden_size, self.n_heads * self.head_dim, bias=False
        )
        self.k_proj = nn.Linear(
            args.hidden_size, self.n_kv_heads * self.head_dim, bias=False
        )
        self.v_proj = nn.Linear(
            args.hidden_size, self.n_kv_heads * self.head_dim, bias=False
        )
        self.o_proj = nn.Linear(
            self.n_heads * self.head_dim, args.hidden_size, bias=False
        )
        self.use_rope = use_rope
        if use_rope:
            self.rope = nn.RoPE(self.head_dim, traditional=True, base=args.rope_theta)
        self.use_qk_norm = args.use_qk_norm
        self.rms_norm_eps = args.rms_norm_eps

    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)
        keys = keys.reshape(B, L, self.n_kv_heads, -1)
        if self.use_qk_norm:
            queries = mx.fast.rms_norm(queries, None, self.rms_norm_eps)
            keys = mx.fast.rms_norm(keys, None, self.rms_norm_eps)
        queries = queries.transpose(0, 2, 1, 3)
        keys = keys.transpose(0, 2, 1, 3)
        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)

        if self.use_rope:
            offset = cache.offset if cache is not None else 0
            queries = self.rope(queries, offset=offset)
            keys = self.rope(keys, offset=offset)

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

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


class TransformerBlock(nn.Module):
    def __init__(self, args: ModelArgs, use_rope):
        super().__init__()
        self.self_attn = Attention(args, use_rope)

        self.feed_forward = MLP(
            args.hidden_size,
            args.intermediate_size_mlp,
        )

        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.feed_forward(self.post_attention_layernorm(h))
        return h + r


class LanguageModel(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.vocab_size = args.vocab_size
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [
            TransformerBlock(args=args, use_rope=args.no_rope_layers[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: 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 = LanguageModel(args)

        self.tie_word_embeddings = args.tie_word_embeddings
        if not self.tie_word_embeddings:
            self.output = nn.Linear(args.hidden_size, args.vocab_size, bias=False)

    def __call__(
        self,
        inputs: mx.array,
        cache: Optional[Any] = None,
    ) -> mx.array:
        h = self.model(inputs, cache)
        if self.tie_word_embeddings:
            return h @ self.model.embed_tokens.weight.T
        else:
            return self.output(h)

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