




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
UpperCAmelCase__						:       Dict							=    logging.getLogger(__name__)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  np.random.seed(args.seed							)
  torch.manual_seed(args.seed							)
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed							)


def 		_lowerCamelCase				(					)			->		Any:





  """simple docstring"""





  _lowercase: List[Any]						     =    argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'					,     default=UpperCamelCase					,     type=UpperCamelCase					,     required=UpperCamelCase					,     )
  parser.add_argument('--prompt'					,     type=UpperCamelCase					,     default=''							)
  parser.add_argument('--length'					,     type=UpperCamelCase					,     default=20							)
  parser.add_argument('--stop_token'					,     type=UpperCamelCase					,     default=UpperCamelCase					,     help='Token at which text generation is stopped'							)
  parser.add_argument(
      '--temperature'					,     type=UpperCamelCase					,     default=1.0					,     help='temperature of 1.0 has no effect, lower tend toward greedy sampling'					,     )
  parser.add_argument(
      '--repetition_penalty'					,     type=UpperCamelCase					,     default=1.0					,     help='primarily useful for CTRL model; in that case, use 1.2'							)
  parser.add_argument('--k'					,     type=UpperCamelCase					,     default=0							)
  parser.add_argument('--penalty_alpha'					,     type=UpperCamelCase					,     default=0.0							)
  parser.add_argument('--p'					,     type=UpperCamelCase					,     default=0.9							)

  parser.add_argument('--prefix'					,     type=UpperCamelCase					,     default=''					,     help='Text added prior to input.'							)
  parser.add_argument('--padding_text'					,     type=UpperCamelCase					,     default=''					,     help='Deprecated, the use of `--prefix` is preferred.'							)
  parser.add_argument('--xlm_language'					,     type=UpperCamelCase					,     default=''					,     help='Optional language when used with the XLM model.'							)

  parser.add_argument('--seed'					,     type=UpperCamelCase					,     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'					,     )
  _lowercase: int						     =    parser.parse_args()

  _lowercase: Dict						     =    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
  _lowercase: Union[str, Any]						     =    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.fpaa}'							)

  set_seed(UpperCamelCase							)

  # Initialize the model and tokenizer
  _lowercase: Union[str, Any]						     =    AutoTokenizer.from_pretrained(args.model_name_or_path							)
  _lowercase: List[str]						     =    AutoModelForCausalLM.from_pretrained(args.model_name_or_path							)

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device							)

  if args.fpaa:
    model.half()

  logger.info(UpperCamelCase							)
  _lowercase: Optional[int]						     =    args.prompt if args.prompt else input('Model prompt >>> '							)

  _lowercase: Union[str, Any]						     =    tokenizer(UpperCamelCase					,     return_tensors='pt'					,     add_special_tokens=UpperCamelCase							)
  _lowercase: str						     =    {key: value.to(args.device							) for key, value in inputs.items()}

  _lowercase: Union[str, Any]						     =    model.generate(
      **UpperCamelCase					,     max_length=args.length + len(inputs['input_ids'][0]							)					,     penalty_alpha=args.penalty_alpha					,     top_k=args.k					,     )

  _lowercase: Dict						     =    []
  for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase							):
    print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
    _lowercase: Optional[Any]						     =    generated_sequence.tolist()

    # Decode text
    _lowercase: Optional[Any]						     =    tokenizer.decode(UpperCamelCase					,     clean_up_tokenization_spaces=UpperCamelCase					,     add_special_tokens=UpperCamelCase							)

    # Remove all text after the stop token
    _lowercase: int						     =    text[: text.find(args.stop_token							) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    _lowercase: int						     =    (
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]					,     clean_up_tokenization_spaces=UpperCamelCase							)							) :]
    )

    generated_sequences.append(UpperCamelCase							)
    print(UpperCamelCase							)

  return generated_sequences


if __name__ == "__main__":
   main()






import math


def 		_lowerCamelCase				(					UpperCamelCase							)			->		int:





  """simple docstring"""





  return sum(int(UpperCamelCase							) for digit in str(UpperCamelCase							)							)


def 		_lowerCamelCase				(					UpperCamelCase = 30							)			->		int:





  """simple docstring"""





  _lowercase: int						     =    []
  for digit in range(2					,     100							):
    for power in range(2					,     100							):
      _lowercase: int						     =    int(math.pow(UpperCamelCase					,     UpperCamelCase							)							)
      if digit == digit_sum(UpperCamelCase							):
        digit_to_powers.append(UpperCamelCase							)

  digit_to_powers.sort()
  return digit_to_powers[n - 1]


if __name__ == "__main__":
   print(solution())
