# 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 tqdm import tqdm

from pathlib import Path

from fairscale.nn.model_parallel.initialize import initialize_model_parallel

from llama import ModelArgs, Transformer, Tokenizer, LLaMA


def setup_model_parallel() -> Tuple[int, int]:
    local_rank = int(os.environ.get("LOCAL_RANK", -1))
    world_size = int(os.environ.get("WORLD_SIZE", -1))

    torch.distributed.init_process_group("nccl")
    initialize_model_parallel(world_size)
    torch.cuda.set_device(local_rank)

    # seed must be the same in all processes
    torch.manual_seed(1)
    return local_rank, world_size


def load(ckpt_dir: str, tokenizer_path: str, local_rank: int, world_size: int) -> LLaMA:
    start_time = time.time()
    checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
    assert (
        world_size == len(checkpoints)
    ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
    ckpt_path = checkpoints[local_rank]
    print("Loading")
    checkpoint = torch.load(ckpt_path, map_location="cpu")
    with open(Path(ckpt_dir) / "params.json", "r") as f:
        params = json.loads(f.read())

    model_args: ModelArgs = ModelArgs(max_seq_len=1536, max_batch_size=32, **params)
    tokenizer = Tokenizer(model_path=tokenizer_path)
    model_args.vocab_size = tokenizer.n_words
    torch.set_default_tensor_type(torch.cuda.HalfTensor)
    model = Transformer(model_args)
    torch.set_default_tensor_type(torch.FloatTensor)
    model.load_state_dict(checkpoint, strict=False)

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


def read(lang):

    score_map = dict()

    f = open("/data/wyt/in-context/xglm/bleu_scores.txt", 'r')
    for line in f:
        idx = int(line.split("\t")[0])
        score = float(line.split("\t")[1])
        score_map[idx] = score

    new_map = sorted(score_map.items(), key=lambda x:x[1], reverse=True)
    top_k = [i[0] for i in new_map]

    example_src_list, example_tgt_list = [], []
    with open("/data/wyt/in-context/xglm/corpus/dev.%s" % (lang[:2]), 'r') as f:
        src_list = [line.strip() for line in f]
    with open("/data/wyt/in-context/xglm/corpus/dev.%s" % (lang[-2:]), 'r') as f:
        tgt_list = [line.strip() for line in f]

    f = open("/data/wyt/in-context/xglm/corpus/wmt19/test.%s.%s" % (lang, lang[:2]), 'r')
    test_src_list = [line.strip() for line in f if line != "\n"]

    demonstration = "Translate these sentences from German to English:\n" if lang == "de-en" else "Translate these sentences from English to German:\n"
    for idx in top_k[:16]:
        demonstration = demonstration + "%s\n%s\n" % (src_list[idx], tgt_list[idx])
    return demonstration, test_src_list


def main(ckpt_dir: str, tokenizer_path: str, lang: str, temperature: float = 0.8, top_p: float = 0.95):
    local_rank, world_size = setup_model_parallel()
    if local_rank > 0:
        sys.stdout = open(os.devnull, 'w')

    print("temperature: ", temperature)
    generator = load(ckpt_dir, tokenizer_path, local_rank, world_size)
    
    demonstration, test_src_list = read(lang)
    step_len = 8
    for i in tqdm(range(0, len(test_src_list), step_len)):
        prompt_list = [demonstration + test_src + "\n" for test_src in test_src_list[i:i+step_len]]
        results = generator.generate(prompt_list, max_gen_len=1536, temperature=temperature, top_p=top_p)

        if local_rank == 0:
            f = open("%s.%s.temp%02d.raw" % (lang, lang[-2:], temperature * 10), 'a')
            for res_idx, result in enumerate(results):
                start = result.find(test_src_list[i + res_idx])
                if start == -1:
                    tgt = ""
                else:
                    lines = result[start:].split("\n")
                    tgt = lines[1] if len(lines) > 1 else ""

                f.write(tgt + "\n")
    

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