import struct
import numpy as np
import torch

def writeString(fo, s):
    fo.write(struct.pack('i', len(s)))
    fo.write(s.encode())

def writeKeyValue(fo, key, value):
    writeString(fo, key)
    writeString(fo, value)

fastllm_data_type_dict = {
    "int4": 8,
    "int8": 3,
    "float16": 7,
    "float32": 0,
}
fastllm_weight_type_dict = {
    "linear": 1,
    "embedding": 2
}

v = np.random.randint(-127, 127, [10, 20]);
temp = v;
c_max = np.expand_dims(np.abs(v).max(axis = -1), -1)
c_scale = c_max / 127.0
v = (v / c_scale + 128.5).clip(1, 255).astype(np.uint8)

def write_int8(fo, v):
    c_max = np.expand_dims(np.abs(v).max(axis = -1), -1).clip(0.1, 1e100)
    c_scale = c_max / 127.0
    v = (v / c_scale + 128.5).clip(1, 255).astype(np.uint8)
    fo.write(struct.pack('i', 3))
    fo.write(struct.pack('i', 0))
    for i in range(c_max.shape[0]):
        fo.write(struct.pack('f', -c_max[i][0]));
        fo.write(struct.pack('f', c_max[i][0]));
    fo.write(v.data)

def write_int4(fo, v):
    # c_min = np.expand_dims(-np.abs(v).max(axis = -1), -1)
    # c_max = np.expand_dims(np.abs(v).max(axis = -1), -1)
    # c_scale = c_max / 7.0
    # c_min = c_scale * -8.0

    c_min = np.expand_dims(v.min(axis = -1), -1)
    c_max = np.expand_dims(v.max(axis = -1), -1)
    c_scale = (c_max - c_min) / 15.0
    c_zero = np.round(0.0 - c_min / c_scale)
    c_zero = c_zero.clip(0, 15)
    c_min = -c_scale * c_zero

    v = (v - c_min) / c_scale
    v = (v + 0.5).astype(np.int8).clip(0, 15).astype(np.uint8)
    v = v[:, 0::2] * 16 + v[:, 1::2]
    fo.write(struct.pack('i', 8))
    fo.write(struct.pack('i', 0))
    for i in range(c_min.shape[0]):
        fo.write(struct.pack('f', c_min[i][0]));
        fo.write(struct.pack('f', c_max[i][0]));
    fo.write(v.data)

def tofile(exportPath,
           model,
           tokenizer = None,
           pre_prompt = None,
           user_role = None,
           bot_role = None,
           history_sep = None,
           dtype = "float16"):
    if (dtype not in fastllm_data_type_dict):
        print("dtype should in ", list(fastllm_data_type_dict.keys()))
        exit(0)

    dict = model.state_dict()
    fo = open(exportPath, "wb")

    # 0. version id
    fo.write(struct.pack('i', 2))

    # 0.1 model info
    modelInfo = model.config.__dict__
    if model.generation_config is not None:
        modelInfo.update(model.generation_config.__dict__)
    if ("model_type" not in modelInfo):
        print("unknown model_type.")
        exit(0)

    if (pre_prompt):
        modelInfo["pre_prompt"] = pre_prompt
    if (user_role):
        modelInfo["user_role"] = user_role
    if (bot_role):
        modelInfo["bot_role"] = bot_role
    if (history_sep):
        modelInfo["history_sep"] = history_sep
    if (modelInfo["model_type"] == "baichuan" and hasattr(model, "model") and hasattr(model.model, "get_alibi_mask")):
        # Baichuan 2代
        modelInfo["use_alibi"] = "1"
        modelInfo["pre_prompt"] = ""
        modelInfo["user_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.user_token_id) + ">") if hasattr(model.generation_config, "user_token_id") else "";
        modelInfo["bot_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.assistant_token_id) + ">") if hasattr(model.generation_config, "assistant_token_id") else "";
        modelInfo["history_sep"] = ""
    if (modelInfo["model_type"] == "baichuan" and modelInfo["vocab_size"] == 125696):
        # Baichuan 2代 7B
        modelInfo["pre_prompt"] = ""
        modelInfo["user_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.user_token_id) + ">") if hasattr(model.generation_config, "user_token_id") else "";
        modelInfo["bot_role"] = ("<FLM_FIX_TOKEN_" + str(model.generation_config.assistant_token_id) + ">") if hasattr(model.generation_config, "assistant_token_id") else "";
        modelInfo["history_sep"] = ""
    if modelInfo["model_type"] == "qwen":
        if modelInfo["chat_format"] == "chatml":
            modelInfo["im_end_id"] = tokenizer.im_end_id
            modelInfo["im_start_id"] = tokenizer.im_start_id

    modelInfo["tokenizer_use_score"] = "1" # 分词带分数

    if hasattr(model, "peft_config"):
        adapter_size = len(model.peft_config)
        modelInfo["peft_size"] = adapter_size

    fo.write(struct.pack('i', len(modelInfo)))
    for it in modelInfo.keys():
        writeKeyValue(fo, str(it), str(modelInfo[it]))

    if hasattr(model, "peft_config"):
        for adapter_name in model.peft_config.keys():
            adapter_dict = model.peft_config[adapter_name].__dict__
            writeString(fo, adapter_name)
            fo.write(struct.pack('i', len(adapter_dict)))
            for it in adapter_dict.keys():
                writeKeyValue(fo, str(it), str(adapter_dict[it]))

    # 1. vocab
    if (tokenizer):
        if (hasattr(tokenizer, "tokenizer")):
            if (modelInfo['model_type'] == "qwen"):
                pass
            else:
                tokenizer = tokenizer.tokenizer
        if (hasattr(tokenizer, "sp_model")):
            piece_size = tokenizer.sp_model.piece_size()
            fo.write(struct.pack('i', piece_size))
            for i in range(piece_size):
                s = tokenizer.sp_model.id_to_piece(i).encode()
                fo.write(struct.pack('i', len(s)))
                for c in s:
                    fo.write(struct.pack('i', c))
                fo.write(struct.pack('i', i))
                fo.write(struct.pack('f', float(tokenizer.sp_model.get_score(i))))
        else:
            vocab = tokenizer.get_vocab()
            fo.write(struct.pack('i', len(vocab)))
            for v in vocab.keys():
                if (modelInfo['model_type'] == "qwen"):
                    s = v
                elif (modelInfo["model_type"] == "moss"):
                    s = [(ord(c) if c not in tokenizer.byte_decoder else tokenizer.byte_decoder[c]) for c in v]
                else:
                    s = v.encode()
                fo.write(struct.pack('i', len(s)))
                for c in s:
                    fo.write(struct.pack('i', c))
                fo.write(struct.pack('i', vocab[v]))
                fo.write(struct.pack('f', 1.0))
    else:
        fo.write(struct.pack('i', 0))

    weight_type_dict = {}
    module_dict = {}
    for key, m in model.named_modules():
        if (isinstance(m, torch.nn.Linear)):
            weight_type_dict[key + ".weight"] = "linear"
            module_dict[key + ".weight"] = m
        if (isinstance(m, torch.nn.Embedding)):
            weight_type_dict[key] = "embedding"

    # 2. weight
    fo.write(struct.pack('i', len(dict)))
    tot = 0
    for key in dict:
        ori_data_type = 0
        ori_np_data_type = np.float32
        cur_weight_type = 0
        if (key in weight_type_dict and weight_type_dict[key] in fastllm_weight_type_dict):
            cur_weight_type = fastllm_weight_type_dict[weight_type_dict[key]]
        to_data_type = 0
        if (cur_weight_type == 1):
            to_data_type = fastllm_data_type_dict[dtype]
            if (to_data_type == 7):
                ori_data_type = 7
                ori_np_data_type = np.float16

        cur = dict[key].numpy().astype(ori_np_data_type)
        
        if hasattr(model, "peft_config"):
            weight_name = key.replace('base_model.model.', '')
            fo.write(struct.pack('i', len(weight_name)))
            fo.write(weight_name.encode())
        else:
            fo.write(struct.pack('i', len(key)))
            fo.write(key.encode())
        fo.write(struct.pack('i', len(cur.shape)))
        for i in cur.shape:
            fo.write(struct.pack('i', i))
        if (to_data_type == 3):
            write_int8(fo, cur)
        elif (to_data_type == 8):
            write_int4(fo, cur)
        else:
            fo.write(struct.pack('i', to_data_type))
            fo.write(cur.data)
        tot += 1
        print("output (", tot, "/", len(dict), end = " )\r")
    print("\nfinish.")
    fo.close()