import argparse
import os
from pathlib import Path

from transformers import AutoTokenizer

import tensorrt_llm
from tensorrt_llm import BuildConfig, build
from tensorrt_llm.executor import GenerationExecutor
from tensorrt_llm.llmapi import SamplingParams
from tensorrt_llm.models import LLaMAForCausalLM


def parse_args():
    parser = argparse.ArgumentParser(description="Llama single model example")
    parser.add_argument(
        "--engine_dir",
        type=Path,
        required=True,
        help=
        "Directory to save and load the engine. When -c is specified, always rebuild and save to this dir. When -c is not specified, load engine when the engine_dir exists, rebuild otherwise"
    )
    parser.add_argument(
        "--hf_model_dir",
        type=str,
        required=True,
        help="Read the model data and tokenizer from this directory")
    parser.add_argument(
        "-c",
        "--clean_build",
        default=False,
        action="store_true",
        help=
        "Clean build the engine even if the engine_dir exists, be careful, this overwrites the engine_dir!!"
    )
    return parser.parse_args()


def main():
    tensorrt_llm.logger.set_level('verbose')
    args = parse_args()

    build_config = BuildConfig(max_input_len=256,
                               max_seq_len=276,
                               max_batch_size=1)
    build_config.plugin_config.gemm_plugin = 'auto'

    if args.clean_build or not args.engine_dir.exists():
        args.engine_dir.mkdir(exist_ok=True, parents=True)
        os.makedirs(args.engine_dir, exist_ok=True)
        llama = LLaMAForCausalLM.from_hugging_face(args.hf_model_dir)
        engine = build(llama, build_config)
        engine.save(args.engine_dir)

    tokenizer = AutoTokenizer.from_pretrained(args.hf_model_dir)
    with GenerationExecutor.create(args.engine_dir) as executor:
        sampling_params = SamplingParams(max_tokens=5)

        input_str = "What should you say when someone gives you a gift? You should say:"
        output = executor.generate(tokenizer.encode(input_str),
                                   sampling_params=sampling_params)
        output_str = tokenizer.decode(output.outputs[0].token_ids)
        print(f"{input_str} {output_str}")


if __name__ == "__main__":
    main()