# Copyright © 2023-2024 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
from .rope_utils import initialize_rope
from .switch_layers import SwitchGLU


@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
    logits_scaling: float
    attention_multiplier: float
    embedding_multiplier: float
    residual_multiplier: float
    max_position_embeddings: int
    num_key_value_heads: int
    attention_bias: bool
    rope_theta: float
    num_local_experts: int
    num_experts_per_tok: int
    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
    tie_word_embeddings: bool = True


class GraniteMoeAttention(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.hidden_size // n_heads

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

        self.rope = initialize_rope(
            self.head_dim,
            args.rope_theta,
            False,
            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)

        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 GraniteMoeTopKGating(nn.Module):
    def __init__(self, input_size: int, num_experts: int, top_k: int):
        super().__init__()
        self.num_experts = num_experts
        self.input_size = input_size
        self.top_k = top_k
        self.layer = nn.Linear(input_size, num_experts, bias=False)

    def __call__(self, hidden_states: mx.array):
        logits = self.layer(hidden_states)
        top_k_idx = mx.argpartition(logits, kth=-self.top_k, axis=-1)[
            ..., -self.top_k :
        ]
        top_k_logits = mx.take_along_axis(logits, top_k_idx, axis=-1)
        top_k_gates = mx.softmax(top_k_logits.astype(mx.float32), axis=-1)
        return top_k_idx, top_k_gates


class GraniteMoeMoE(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        self.input_size = args.hidden_size
        self.hidden_size = args.intermediate_size
        self.switch_mlp = SwitchGLU(
            self.input_size, self.hidden_size, args.num_local_experts
        )
        self.router = GraniteMoeTopKGating(
            input_size=self.input_size,
            num_experts=args.num_local_experts,
            top_k=args.num_experts_per_tok,
        )

    def __call__(self, x: mx.array) -> mx.array:
        token_ids, gates = self.router(x)
        y = self.switch_mlp(x, token_ids)
        return (y * gates[..., None]).sum(axis=-2).astype(y.dtype)


class GraniteMoeDecoderLayer(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.self_attn = GraniteMoeAttention(args)
        self.block_sparse_moe = GraniteMoeMoE(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.residual_multiplier = args.residual_multiplier

    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 * self.residual_multiplier
        r = self.block_sparse_moe(self.post_attention_layernorm(h))
        out = h + r * self.residual_multiplier
        return out


class GraniteMoEModel(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [
            GraniteMoeDecoderLayer(args=args) for _ in range(args.num_hidden_layers)
        ]
        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
        self.embedding_multiplier = args.embedding_multiplier

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

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

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        out = self.model(inputs, cache)
        if self.args.tie_word_embeddings:
            out = self.model.embed_tokens.as_linear(out)
        else:
            out = self.lm_head(out)
        return out / self.logits_scaling

    def sanitize(self, weights):
        if "model.layers.0.block_sparse_moe.input_linear.weight" not in weights:
            return weights
        for l in range(self.args.num_hidden_layers):
            prefix = f"model.layers.{l}.block_sparse_moe"
            key = f"{prefix}.input_linear.weight"
            value = weights.pop(key)
            gate_proj, up_proj = mx.split(value, 2, axis=1)
            weights[key.replace("input_linear", "switch_mlp.gate_proj")] = gate_proj
            weights[key.replace("input_linear", "switch_mlp.up_proj")] = up_proj
            key = f"{prefix}.output_linear.weight"
            weights[key.replace("output_linear", "switch_mlp.down_proj")] = weights.pop(
                key
            )
        if self.args.tie_word_embeddings:
            weights.pop("lm_head.weight", None)
        return weights

    @property
    def quant_predicate(self):
        def predicate(path, _):
            if path.endswith("block_sparse_moe.router.layer"):
                return {"group_size": 64, "bits": 8}
            return True

        return predicate

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