# Copyright © 2024 Apple Inc.
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Union

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

from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
from .rope_utils import SuScaledRoPE
from .switch_layers import SwitchGLU


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str = "phimoe"
    vocab_size: int = 32064
    hidden_size: int = 4096
    intermediate_size: int = 6400
    num_hidden_layers: int = 32
    num_attention_heads: int = 32
    num_key_value_heads: int = 8
    max_position_embeddings: int = 131072
    original_max_position_embeddings: int = 4096
    rms_norm_eps: float = 1e-6
    rope_scaling: Dict[str, Union[float, List[float]]] = None
    num_local_experts: int = 16
    num_experts_per_tok: int = 2
    rope_theta: float = 10000.0


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.hidden_size // n_heads
        self.scale = head_dim**-0.5

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

        self.rope = SuScaledRoPE(
            head_dim,
            base=args.rope_theta,
            max_position_embeddings=args.max_position_embeddings,
            original_max_position_embeddings=args.original_max_position_embeddings,
            short_factor=args.rope_scaling["short_factor"],
            long_factor=args.rope_scaling["long_factor"],
            short_mscale=args.rope_scaling["short_mscale"],
            long_mscale=args.rope_scaling["long_mscale"],
        )

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache=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 PhiMoESparseMoeBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.hidden_dim = args.hidden_size
        self.ffn_dim = args.intermediate_size
        self.num_experts = args.num_local_experts
        self.top_k = args.num_experts_per_tok

        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
        self.switch_mlp = SwitchGLU(self.hidden_dim, self.ffn_dim, self.num_experts)

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

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

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

        return y


class PhiMoEDecoderLayer(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.hidden_size = args.hidden_size

        self.self_attn = Attention(args)
        self.block_sparse_moe = PhiMoESparseMoeBlock(args)
        self.input_layernorm = nn.LayerNorm(args.hidden_size, eps=args.rms_norm_eps)
        self.post_attention_layernorm = nn.LayerNorm(
            args.hidden_size, eps=args.rms_norm_eps
        )

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache=None,
    ) -> mx.array:
        residual = x
        hidden_states = self.input_layernorm(x)
        hidden_states = self.self_attn(hidden_states, mask=mask, cache=cache)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.block_sparse_moe(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states


class PhiMoEModel(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 = [PhiMoEDecoderLayer(args) for _ in range(args.num_hidden_layers)]
        self.norm = nn.LayerNorm(args.hidden_size, eps=args.rms_norm_eps)

    def __call__(
        self,
        inputs: mx.array,
        cache=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.model_type = args.model_type
        self.args = args
        self.model = PhiMoEModel(args)
        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=True)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        out = self.model(inputs, cache)
        return self.lm_head(out)

    def sanitize(self, weights):
        if "model.layers.0.block_sparse_moe.experts.0.w1.weight" not in weights:
            return weights
        for l in range(self.args.num_hidden_layers):
            prefix = f"model.layers.{l}"
            for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]:
                for k in ["weight", "scales", "biases"]:
                    if f"{prefix}.block_sparse_moe.experts.0.{n}.{k}" in weights:
                        to_join = [
                            weights.pop(
                                f"{prefix}.block_sparse_moe.experts.{e}.{n}.{k}"
                            )
                            for e in range(self.args.num_local_experts)
                        ]
                        weights[f"{prefix}.block_sparse_moe.switch_mlp.{m}.{k}"] = (
                            mx.stack(to_join)
                        )

        return weights

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