# 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 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,
    max_seq_len: int,
    max_batch_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=max_seq_len, max_batch_size=max_batch_size, **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 main(
    ckpt_dir: str,
    tokenizer_path: str,
    temperature: float = 0.9,
    top_p: float = 0.982,
    max_seq_len: int = 768,
    max_batch_size: int = 16,
):
    local_rank, world_size = setup_model_parallel()
    if local_rank > 0:
        sys.stdout = open(os.devnull, "w")

    generator = load(
        ckpt_dir, tokenizer_path, local_rank, world_size, max_seq_len, max_batch_size
    )
    #     results = generator.generate(
    #     prompts, max_gen_len=256, temperature=temperature, top_p=top_p
    # )
    seamlessmode = False
    historymode = False
    is_first_prompt = True
    #Interactive mode
    last_result = ""
    while True:
        prompt = input("Enter prompt: ")
        print("\n")
        # Re-encode the prompt into:
        # Me: <prompt>
        # AI: <empty>
        if seamlessmode:
            if is_first_prompt:
                prompt = "Question: " + prompt + "\nAnswer: "
                is_first_prompt = False
            else:
                prompt = prompt
        else:
            prompt = "Question: " + prompt + "\nAnswer: "

        #concatenate the last result with the prompt
        prompt = last_result + prompt

        #trim the prompt to the max sequence length 512
        prompt = prompt[-512:]

        results = generator.generate(
            [prompt], max_gen_len=768, temperature=temperature, top_p=top_p
        )
        print(results[0])
        #concatenate the last result with the new result
        if seamlessmode:
            last_result = last_result + results[0]
        else:
            last_result = last_result + results[0] + "\n"
        
        if not historymode:
            last_result = ""

        print("\n")
        #a dark blue seperator
        print("\033[1;34;40m" + "-" * 20 + "\033[0m")
        print("\n")

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