# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.

from typing import Tuple
import os
import sys
import torch
import fire
import time
import json
from pathlib import Path
from llama import ModelArgs, Transformer, Tokenizer, LLaMA


def load(
        ckpt_dir: str,
        tokenizer_path: str,
        max_seq_len: int,
        max_batch_size: int,
) -> LLaMA:
    print("Creating model...")
    start_time = time.time()
    checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))

    with open(Path(ckpt_dir) / "params.json", "r") as f:
        params = json.loads(f.read())

    model_args: ModelArgs = ModelArgs(
        max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
    )

    tokenizer = Tokenizer(model_path=tokenizer_path)
    model_args.vocab_size = tokenizer.n_words

    model = Transformer(model_args)

    # Original copyright by tloen
    # https://github.com/tloen/llama-int8/blob/main/example.py
    key_to_dim = {
        "w1": 0,
        "w2": -1,
        "w3": 0,
        "wo": -1,
        "wq": 0,
        "wk": 0,
        "wv": 0,
        "output": 0,
        "tok_embeddings": -1,
        "ffn_norm": None,
        "attention_norm": None,
        "norm": None,
        "rope": None,
    }
    
    for i, ckpt in enumerate(checkpoints):
        print(f"Loading checkpoint {i}")
        checkpoint = torch.load(ckpt, map_location="cpu")
        for parameter_name, parameter in model.named_parameters():
            short_name = parameter_name.split(".")[-2]
            if key_to_dim[short_name] is None and i == 0:
                parameter.data = checkpoint[parameter_name]
            elif key_to_dim[short_name] == 0:
                size = checkpoint[parameter_name].size(0)
                parameter.data[size * i: size * (i + 1), :] = checkpoint[
                    parameter_name
                ]
            elif key_to_dim[short_name] == -1:
                size = checkpoint[parameter_name].size(-1)
                parameter.data[:, size * i: size * (i + 1)] = checkpoint[
                    parameter_name
                ]
            del checkpoint[parameter_name]
        del checkpoint

    model.to("cpu")

    generator = LLaMA(model, tokenizer)
    print(f"Loaded model in {time.time() - start_time:.2f} seconds")
    return generator


def main(
        ckpt_dir: str = './model',
        tokenizer_path: str = './tokenizer/tokenizer.model',
        temperature: float = 0.8,
        top_p: float = 0.95,
        max_seq_len: int = 512,
        max_batch_size: int = 32,
):
    # torch.manual_seed(1)
    # torch.set_default_dtype(torch.bfloat16)

    generator = load(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)

    while True:
        prompt = input(f'prompt> ')
        if len(prompt.strip()) > 0:
            prompts = [prompt]
            results = generator.generate(
                prompts, max_gen_len=256, temperature=temperature, top_p=top_p
            )

            for result in results:
                print(result)


if __name__ == "__main__":
    fire.Fire(main)