content: import argparse
  import os
  import torch
  from pathlib import Path
  from tqdm import tqdm
  from transformers import AutoTokenizer, AutoModelForCausalLM
  import logging
  from evalplus.data import get_human_eval_plus,get_mbpp_plus, write_jsonl
  
  def build_humaneval_instruction(languge: str, question: str):
      return '''You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
  
  @@ Instruction
  Here is the given code to do completion:
  ```{}
  {}
  ```
  Please continue to complete the function with {} programming language. You are not allowed to modify the given code and do the completion only. 
  
  Please return all completed codes in one code block. 
  This code block should be in the following format:
  ```{}
  # Your codes here
  ```
  
  @@ Response
  '''.strip().format(languge.lower(), question.strip(),languge.lower(),languge.lower())
  
  
  build_mbpp_instruction='''You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
  
  @@ Instruction
  Here is the given problem and test examples:
  {}
  
  Please use the {} programming language to solve this problem.
  Please make sure that your code includes the functions from the test samples and that the input and output formats of these functions match the test samples.
  
  Please return all completed codes in one code block. 
  This code block should be in the following format:
  ```{}
  # Your codes here
  ```
  
  @@ Response
  '''
  
  
  def generate_one(example, lang, tokenizer, model, name, flags):
      if flags.dataset=="humaneval":
          prompt = build_humaneval_instruction(lang, example['prompt'])
      else:
          prompt = example['prompt']
          prompt = build_mbpp_instruction.strip().format(prompt,lang,lang)
      inputs = tokenizer.apply_chat_template(
          [{'role': 'user', 'content': prompt }],
          return_tensors="pt"
      ).to(model.device)
      stop_id = tokenizer.convert_tokens_to_ids("<|EOT|>")
      assert isinstance(stop_id, int), "Invalid tokenizer, EOT id not found"
      max_new_tokens=1024
      outputs = model.generate(
          inputs, 
          max_new_tokens=max_new_tokens,
          do_sample=False,
          pad_token_id=tokenizer.eos_token_id,
          eos_token_id=tokenizer.eos_token_id,
          temperature=0,
      )
      output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
      return output
  
  def gen_solution(args):
      os.environ["TOKENIZERS_PARALLELISM"] = "false"
  
      model_path = args.model
      logging.info(f"model:{model_path}")
      model_name = model_path.replace("/", "_")
      
      lang = "python"
      os.makedirs(os.path.join(args.output_path,model_name),exist_ok=True)
      output_file = os.path.join(args.output_path,model_name,f"single_{args.dataset}_plus_solutions.jsonl")
      if os.path.exists(output_file):
          logging.info(f"Old sample jsonl file exists, remove it. {output_file}")
          os.remove(output_file)
  
      tokenizer = AutoTokenizer.from_pretrained(model_path)
      logging.info("load tokenizer {} from {} over.".format(tokenizer.__class__, model_path))
      model = AutoModelForCausalLM.from_pretrained(
          model_path,
          torch_dtype=torch.bfloat16,
          device_map="auto",
      )
      model.eval()
  
      model_name = model_path.replace("/", "_")
      if args.dataset=="humaneval":
          examples = get_human_eval_plus().items()
      else:
          examples = get_mbpp_plus().items()
      logging.info("Read {} examples for evaluation over.".format(len(examples)))
      for task_id,example in tqdm(examples, desc='Generating'):
          code = generate_one(example, lang, tokenizer, model,model_name,args)
          gen_sample=[dict(task_id=task_id, solution=code)]
          write_jsonl(output_file, gen_sample ,append=True)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--model', type=str, help="model path")
      parser.add_argument('--output_path', type=str, help="output path", default="./output")
      parser.add_argument('--log_file', type=str, help="log file name")
      parser.add_argument(
          "--dataset", required=True, type=str, choices=["humaneval", "mbpp"]
      )
      args = parser.parse_args()
      model_name = args.model.replace("/", "_")
      os.makedirs(os.path.join(args.output_path,model_name),exist_ok=True)
      logfile=os.path.join(args.output_path,model_name,args.log_file)
      logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - \n%(message)s')
      file_handler = logging.FileHandler(logfile)
      file_handler.setLevel(logging.INFO)
      formatter = logging.Formatter('%(asctime)s - %(levelname)s - \n%(message)s')
      file_handler.setFormatter(formatter)  
      logging.getLogger().addHandler(file_handler)
  
      gen_solution(args)
  