code: def main():
      parser = argparse.ArgumentParser()
  
      parser.add_argument("--model_arch", type=str, default="llama")
      parser.add_argument(
          "--model_name", type=str, default="huggyllama/llama-13b"
      )
      parser.add_argument(
          "--cache_dir", type=str, default="../../checkpoint/"
      )
  
      parser.add_argument("--heavy_ratio", type=float, default=0.1)
      parser.add_argument("--recent_ratio", type=float, default=0.1)
  
      parser.add_argument("--length", type=int, default=64)
  
      parser.add_argument(
          "--seed",
          type=int,
          default=42,
          help="random seed for initialization",
      )
      parser.add_argument(
          "--no_cuda",
          action="store_true",
          help="Avoid using CUDA when available",
      )
      parser.add_argument(
          "--fp16",
          action="store_true",
          help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
      )
      args = parser.parse_args()
  
      args.device = torch.device(
          "cuda"
          if torch.cuda.is_available() and not args.no_cuda
          else "cpu"
      )
      args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
  
      logger.warning(
          f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fp16}"
      )
      set_seed(args)
  
      # Change to your custom prompt text
      # prompt_text = 'In the year 2087, humanity has achieved remarkable technological advancements and established colonies on multiple planets within the Milky Way galaxy. Interstellar travel has become commonplace, with faster-than-light spacecraft enabling people to explore distant star systems. Earth has undergone significant changes due to sustainable development efforts, such as harnessing renewable energy sources and implementing widespread ecological restoration projects. However, alongside these triumphs, new challenges have emerged, including the rise of artificial intelligence, ethical dilemmas surrounding genetic engineering, and interplanetary political tensions. Against this backdrop, a team of intrepid scientists embarks on a mission to uncover the secrets of an ancient alien civilization, hidden deep within an uncharted exoplanet. As they navigate treacherous terrains and encounter otherworldly phenomena, they must confront their own fears and reconcile humanity\'s thirst for knowledge with the potential consequences of uncovering secrets that were better left buried. The fate of both their mission and the future of humanity hang in the balance.'
      prompt_text = "In a small, bustling cafe nestled in the heart of a vibrant city, a serendipitous event unfolded, leaving a lasting impression on all who witnessed it. As the patrons sat sipping their coffees and engaging in animated conversations, a talented street musician entered the cafe, carrying a weathered guitar and radiating an aura of creativity."
  
      model_name = args.model_name
      config = AutoConfig.from_pretrained(
          model_name, cache_dir=args.cache_dir
      )
      config.heavy_ratio = args.heavy_ratio
      config.recent_ratio = args.recent_ratio
  
      tokenizer = AutoTokenizer.from_pretrained(
          model_name, use_fast=True, cache_dir=args.cache_dir
      )
  
      ######## Generate with Full Cache
      model = AutoModelForCausalLM.from_pretrained(
          model_name, cache_dir=args.cache_dir
      )
      model.half().eval().cuda()
  
      # input_ids = tokenizer(prompt_text, return_tensors='pt').input_ids.to(model.device)
      input_ids = tokenizer(
          prompt_text, add_special_tokens=False, return_tensors="pt"
      ).input_ids.to(model.device)
  
      generate_ids = model.generate(
          input_ids, max_new_tokens=args.length
      )
      result = tokenizer.batch_decode(
          generate_ids,
          skip_special_tokens=True,
          clean_up_tokenization_spaces=False,
      )[0]
      print(
          "################## Generated Context with Full Cache ###################"
      )
      print(result)
  
      ######### Enable HH
      checkpoint = copy.deepcopy(model.state_dict())
      model = ENABLE_Heavy_Hitter_FUNCTIONS[args.model_arch](
          model, config
      )
      model.load_state_dict(checkpoint)
      model.half().eval().cuda()
  
      generate_ids_hh = model.generate(
          input_ids, max_new_tokens=args.length
      )
      result_hh = tokenizer.batch_decode(
          generate_ids_hh,
          skip_special_tokens=True,
          clean_up_tokenization_spaces=False,
      )[0]
      print(
          "################## Generated Context with Heavy Hitter Oracle ###################"
      )
      print(result_hh)
  
file_path: H2O/h2o_hf/run_text_generation.py
imports: import argparse
  import logging
  import numpy
  import torch
  import json
  import tqdm
  import copy
  from transformers import CTRLLMHeadModel, CTRLTokenizer, GPT2LMHeadModel, GPT2Tokenizer, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, TransfoXLLMHeadModel, TransfoXLTokenizer, XLMTokenizer, XLMWithLMHeadModel, XLNetLMHeadModel, XLNetTokenizer
  from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
  from utils_hh.modify_llama import convert_kvcache_llama_heavy_recent, LlamaAttention_heavy_hitter
  from utils_hh.modify_gptneox import convert_kvcache_gpt_neox_heavy_recent, GPTNeoXAttention_Mask
  from utils_hh.modify_opt import convert_kvcache_opt_heavy_recent, OPTAttention_Mask
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types