# Copyright © 2025 Apple Inc.

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

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


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    hidden_size: int
    num_hidden_layers: int
    intermediate_size: int
    num_attention_heads: int
    rms_norm_eps: float
    vocab_size: int
    num_key_value_heads: int
    head_dim: int
    max_position_embeddings: Optional[int] = None
    attention_bias: bool = False
    attention_out_bias: bool = False
    mlp_bias: bool = False
    rope_theta: float = 10000
    rope_traditional: bool = False
    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
    tie_word_embeddings: bool = True


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

        self.scale = head_dim**-0.5

        input_bias = args.attention_bias
        output_bias = args.attention_out_bias

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

        self.rope = initialize_rope(
            self.head_dim,
            args.rope_theta,
            args.rope_traditional,
            args.rope_scaling,
            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)

        # Prepare the queries, keys and values for the attention computation
        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 MLP(nn.Module):
    def __init__(self, dim, hidden_dim, bias=False):
        super().__init__()
        self.gate_proj = nn.Linear(dim, hidden_dim, bias=bias)
        self.down_proj = nn.Linear(hidden_dim, dim, bias=bias)
        self.up_proj = nn.Linear(dim, hidden_dim, bias=bias)

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


class TransformerBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.num_attention_heads = args.num_attention_heads
        self.hidden_size = args.hidden_size
        self.self_attn = Attention(args)
        self.mlp = MLP(args.hidden_size, args.intermediate_size, args.mlp_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))
        out = h + r
        return out


class SeedModel(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [
            TransformerBlock(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,
        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, cache=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 = SeedModel(args)
        self.tie_word_embeddings = args.tie_word_embeddings
        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,
    ):
        h = self.model(inputs, cache=cache)
        if self.tie_word_embeddings:
            return h @ self.model.embed_tokens.weight.T
        else:
            return self.lm_head(h)

    def sanitize(self, weights):
        if self.tie_word_embeddings:
            weights.pop("lm_head.weight", None)

        return weights

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