# Copyright © 2023-2024 Apple Inc.

from dataclasses import dataclass
from typing import Optional, Tuple

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

from .base import BaseModelArgs, create_attention_mask


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    hidden_size: int
    num_hidden_layers: int
    intermediate_size: int
    num_attention_heads: int
    head_dim: int
    rms_norm_eps: float
    vocab_size: int
    num_key_value_heads: int
    rope_theta: float = 10000
    rope_traditional: bool = False
    attn_logit_softcapping: float = 50.0
    final_logit_softcapping: float = 30.0
    query_pre_attn_scalar: float = 144.0


class RMSNorm(nn.Module):
    def __init__(self, dims: int, eps: float = 1e-5):
        super().__init__()
        self.weight = mx.ones((dims,))
        self.eps = eps

    def __call__(self, x):
        return mx.fast.rms_norm(x, 1.0 + self.weight, self.eps)


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.repeats = n_heads // n_kv_heads
        self.head_dim = head_dim = args.head_dim

        self.scale = 1.0 / (args.query_pre_attn_scalar**0.5)

        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
        self.attn_logit_softcapping = args.attn_logit_softcapping
        self.rope = nn.RoPE(
            head_dim,
            traditional=args.rope_traditional,
            base=args.rope_theta,
        )

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Tuple[mx.array, mx.array]] = 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)

        queries = queries * self.scale

        if self.repeats > 1:
            queries = queries.reshape(
                B, self.n_kv_heads, self.repeats, L, self.head_dim
            )
            keys = mx.expand_dims(keys, 2)
            values = mx.expand_dims(values, 2)

        scores = queries @ keys.swapaxes(-1, -2)
        scores = mx.tanh(scores / self.attn_logit_softcapping)
        scores *= self.attn_logit_softcapping

        if mask is not None:
            scores = scores + mask
        scores = mx.softmax(scores, precise=True, axis=-1)
        output = scores @ values
        if self.repeats > 1:
            output = output.reshape(B, self.n_heads, L, self.head_dim)
        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):
        super().__init__()
        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.gelu(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)
        self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
        self.pre_feedforward_layernorm = RMSNorm(
            args.hidden_size, eps=args.rms_norm_eps
        )
        self.post_feedforward_layernorm = RMSNorm(
            args.hidden_size, eps=args.rms_norm_eps
        )
        self.post_attention_layernorm = 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[Tuple[mx.array, mx.array]] = None,
    ) -> mx.array:
        r = self.self_attn(self.input_layernorm(x.astype(mx.float32)), mask, cache)
        h = x + self.post_attention_layernorm(r)
        r = self.mlp(self.pre_feedforward_layernorm(h).astype(mx.float16)).astype(
            mx.float32
        )
        out = h + self.post_feedforward_layernorm(r)
        return out


class GemmaModel(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
        assert self.vocab_size > 0
        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 = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        h = self.embed_tokens(inputs)
        h = h * (self.args.hidden_size**0.5)

        mask = create_attention_mask(h, cache)

        if cache is None:
            cache = [None] * len(self.layers)

        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.model_type = args.model_type
        self.final_logit_softcapping = args.final_logit_softcapping
        self.model = GemmaModel(args)
        self.args = args

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        out = self.model(inputs, cache)
        out = self.model.embed_tokens.as_linear(out)
        out = mx.tanh(out / self.final_logit_softcapping)
        out = out * self.final_logit_softcapping
        return out

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

    @property
    def head_dim(self):
        return self.args.head_dim

    @property
    def n_kv_heads(self):
        return self.args.num_key_value_heads