


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,
)
snake_case__ :						Optional[int]     =			logging.getLogger(__name__)


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


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

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

  parser.add_argument("""--seed"""   ,     type=_lowerCAmelCase   ,     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"""   ,     )
  UpperCAmelCase__    						=   parser.parse_args()

  UpperCAmelCase__    						=   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""      )
  UpperCAmelCase__    						=   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(_lowerCAmelCase      )

  # Initialize the model and tokenizer
  UpperCAmelCase__    						=   AutoTokenizer.from_pretrained(args.model_name_or_path      )
  UpperCAmelCase__    						=   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(_lowerCAmelCase      )
  UpperCAmelCase__    						=   args.prompt if args.prompt else input("""Model prompt >>> """      )

  UpperCAmelCase__    						=   tokenizer(_lowerCAmelCase   ,     return_tensors="""pt"""   ,     add_special_tokens=_lowerCAmelCase      )
  UpperCAmelCase__    						=   {key: value.to(args.device      ) for key, value in inputs.items()}

  UpperCAmelCase__    						=   model.generate(
      **_lowerCAmelCase   ,     max_length=args.length + len(inputs["""input_ids"""][0]      )   ,     penalty_alpha=args.penalty_alpha   ,     top_k=args.k   ,     )

  UpperCAmelCase__    						=   []
  for generated_sequence_idx, generated_sequence in enumerate(_lowerCAmelCase      ):
    print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''      )
    UpperCAmelCase__    						=   generated_sequence.tolist()

    # Decode text
    UpperCAmelCase__    						=   tokenizer.decode(_lowerCAmelCase   ,     clean_up_tokenization_spaces=_lowerCAmelCase   ,     add_special_tokens=_lowerCAmelCase      )

    # Remove all text after the stop token
    UpperCAmelCase__    						=   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
    UpperCAmelCase__    						=   (
        prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]   ,     clean_up_tokenization_spaces=_lowerCAmelCase      )      ) :]
    )

    generated_sequences.append(_lowerCAmelCase      )
    print(_lowerCAmelCase      )

  return generated_sequences


if __name__ == "__main__":
   main()










from __future__ import annotations

from collections import Counter


def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   [True] * n
  UpperCAmelCase__    						=   False
  UpperCAmelCase__    						=   False
  UpperCAmelCase__    						=   True

  for i in range(3   ,     int(n**0.5 + 1      )   ,     2      ):
    UpperCAmelCase__    						=   i * 2
    while index < n:
      UpperCAmelCase__    						=   False
      UpperCAmelCase__    						=   index + i

  UpperCAmelCase__    						=   [2]

  for i in range(3   ,     _lowerCAmelCase   ,     2      ):
    if is_prime[i]:
      primes.append(_lowerCAmelCase      )

  return primes


def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   str(_lowerCAmelCase      )
  UpperCAmelCase__    						=   []
  UpperCAmelCase__    						=   ["""0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9"""]

  for duplicate in Counter(_lowerCAmelCase      ) - Counter(set(_lowerCAmelCase      )      ):
    UpperCAmelCase__    						=   [int(number_str.replace(_lowerCAmelCase   ,     _lowerCAmelCase      )      ) for digit in digits]
    replacements.append(_lowerCAmelCase      )

  return replacements


def 						lowercase		(     _lowerCAmelCase = 8      ):
  UpperCAmelCase__    						=   set()

  # Filter primes with less than 3 replaceable digits
  UpperCAmelCase__    						=   {
      x for x in set(prime_sieve(100_0000      )      ) if len(str(_lowerCAmelCase      )      ) - len(set(str(_lowerCAmelCase      )      )      ) >= 3
  }

  for prime in primes:
    if prime in numbers_checked:
      continue

    UpperCAmelCase__    						=   digit_replacements(_lowerCAmelCase      )

    for family in replacements:
      numbers_checked.update(_lowerCAmelCase      )
      UpperCAmelCase__    						=   primes.intersection(_lowerCAmelCase      )

      if len(_lowerCAmelCase      ) != family_length:
        continue

      return min(_lowerCAmelCase      )

  return -1


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




