# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

import math

import torch
from internvl.model.internvl_chat import InternVLChatConfig, InternVLChatModel
from transformers import AutoTokenizer


def split_model(num_layers, vit_alpha=0.5):
    device_map = {}
    world_size = torch.cuda.device_count()
    # Since the first GPU will be used for ViT, treat it as half a GPU.
    num_layers_per_gpu = math.ceil(num_layers / (world_size - vit_alpha))
    num_layers_per_gpu = [num_layers_per_gpu] * world_size
    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * (1 - vit_alpha))
    layer_cnt = 0
    for i, num_layer in enumerate(num_layers_per_gpu):
        for j in range(num_layer):
            device_map[f"language_model.model.layers.{layer_cnt}"] = i
            layer_cnt += 1
    device_map["vision_model"] = 0
    device_map["mlp1"] = 0
    device_map["language_model.model.tok_embeddings"] = 0
    device_map["language_model.model.embed_tokens"] = 0
    device_map["language_model.output"] = 0
    device_map["language_model.model.norm"] = 0
    device_map["language_model.lm_head"] = 0
    device_map[f"language_model.model.layers.{num_layers - 1}"] = 0
    device_map["language_model.model.rotary_emb"] = 0

    return device_map


def load_model_and_tokenizer(args):
    if args.auto:
        config = InternVLChatConfig.from_pretrained(args.checkpoint)
        num_hidden_layers = config.llm_config.num_hidden_layers
        device_map = split_model(num_hidden_layers)
    kwargs = {"device_map": device_map} if args.auto else {}
    tokenizer = AutoTokenizer.from_pretrained(
        args.checkpoint, trust_remote_code=True, use_fast=False
    )
    model = InternVLChatModel.from_pretrained(
        args.checkpoint,
        low_cpu_mem_usage=True,
        torch_dtype=torch.bfloat16,
        load_in_8bit=args.load_in_8bit,
        load_in_4bit=args.load_in_4bit,
        **kwargs,
    ).eval()
    if not args.load_in_8bit and not args.load_in_4bit and not args.auto:
        model = model.cuda()
    return model, tokenizer
