# Copyright © 2023-2025 Apple Inc.

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

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

from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    vocab_size: int
    hidden_size: int
    num_hidden_layers: int
    intermediate_size: int
    num_attention_heads: int
    num_key_value_heads: int
    rms_norm_eps: float
    rope_theta: float = 10000
    max_position_embeddings: int = 32768
    attention_bias: bool = False
    use_qk_norm: bool = True
    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
    tie_word_embeddings: bool = False
    head_dim: Optional[int] = None

    def __post_init__(self):
        if self.rope_scaling:
            required_keys = {"alpha", "factor", "type"}
            if not all(key in self.rope_scaling for key in required_keys):
                raise ValueError(f"rope_scaling must contain keys {required_keys}")


class DynamicNTKAlphaRoPE(nn.Module):
    def __init__(
        self,
        dims: int,
        base: float = 10000,
        scaling_alpha: float = 1.0,
    ):
        super().__init__()
        self.dims = dims
        base = base * scaling_alpha ** (dims / (dims - 2))
        self._freqs = base ** (mx.arange(0, self.dims, 2) / self.dims)

    def __call__(self, x, offset: int = 0):
        return mx.fast.rope(
            x,
            self.dims,
            traditional=False,
            base=None,
            scale=1.0,
            offset=offset,
            freqs=self._freqs,
        )


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

        head_dim = (
            args.head_dim if args.head_dim is not None else args.hidden_size // n_heads
        )
        self.head_dim = head_dim
        self.scale = head_dim**-0.5

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

        self.use_qk_norm = args.use_qk_norm
        if self.use_qk_norm:
            self.query_layernorm = nn.RMSNorm(head_dim, args.rms_norm_eps)
            self.key_layernorm = nn.RMSNorm(head_dim, args.rms_norm_eps)

        scaling_alpha = 1.0
        if args.rope_scaling and "alpha" in args.rope_scaling:
            scaling_alpha = args.rope_scaling["alpha"]

        self.rope = DynamicNTKAlphaRoPE(
            head_dim,
            base=args.rope_theta,
            scaling_alpha=scaling_alpha,
        )

    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, self.head_dim).transpose(
            0, 2, 1, 3
        )
        keys = keys.reshape(B, L, self.n_kv_heads, self.head_dim).transpose(0, 2, 1, 3)
        values = values.reshape(B, L, self.n_kv_heads, self.head_dim).transpose(
            0, 2, 1, 3
        )

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

        if self.use_qk_norm:
            queries = self.query_layernorm(queries)
            keys = self.key_layernorm(keys)

        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, args: ModelArgs):
        super().__init__()

        dim = args.hidden_size
        hidden_dim = args.intermediate_size

        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)

    def __call__(self, x) -> mx.array:
        return self.down_proj(nn.silu(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)
        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
        )
        self.args = args

    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 HunyuanV1DenseModel(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.vocab_size = args.vocab_size
        self.num_hidden_layers = args.num_hidden_layers
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [TransformerBlock(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, 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 = HunyuanV1DenseModel(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:
            return self.model.embed_tokens.as_linear(out)
        else:
            return self.lm_head(out)

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

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