code: 
  
  
  
  import math
  from itertools import permutations
  
  
  
  
  
  
  def 			__lowercase     (				a__     ):
  
  							if 1 < number < 4:
  														# 2 and 3 are primes
  														return True
  							elif number < 2 or number % 2 == 0 or number % 3 == 0:
  														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  														return False
  
  							# All primes number are in format of 6k +/- 1
  							for i in range(5							,     int(math.sqrt(a__     ) + 1     )							,     6     ):
  														if number % i == 0 or number % (i + 2) == 0:
  																					return False
  							return True
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__     ):
  							__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      0, len(a__     ) - 1
  							while left <= right:
  														__SCREAMING_SNAKE_CASE								=      (left + right) // 2
  														if prime_list[middle] == target:
  																					return True
  														elif prime_list[middle] < target:
  																					__SCREAMING_SNAKE_CASE								=      middle + 1
  														else:
  																					__SCREAMING_SNAKE_CASE								=      middle - 1
  
  							return False
  
  
  
  
  
  
  def 			__lowercase     (				):
  							__SCREAMING_SNAKE_CASE								=      [n for n in range(10_01							,     1_00_00							,     2     ) if is_prime(a__     )]
  							__SCREAMING_SNAKE_CASE								=      []
  
  							for number in prime_list:
  														__SCREAMING_SNAKE_CASE								=      []
  
  														for prime_member in permutations(list(str(a__     )     )     ):
  																					__SCREAMING_SNAKE_CASE								=      int(''.join(a__     )     )
  
  																					if prime % 2 == 0:
  																												continue
  
  																					if search(a__							,     a__     ):
  																												tmp_numbers.append(a__     )
  
  														tmp_numbers.sort()
  														if len(a__     ) >= 3:
  																					candidates.append(a__     )
  
  							__SCREAMING_SNAKE_CASE								=      []
  							for candidate in candidates:
  														__SCREAMING_SNAKE_CASE								=      len(a__     )
  														__SCREAMING_SNAKE_CASE								=      False
  
  														for i in range(a__     ):
  																					for j in range(i + 1							,     a__     ):
  																												for k in range(j + 1							,     a__     ):
  																																			if (
  																																			    abs(candidate[i] - candidate[j]     )
  																																			    == abs(candidate[j] - candidate[k]     )
  																																			    and len({candidate[i], candidate[j], candidate[k]}     ) == 3
  																																			):
  																																										passed.append(
  																																										    sorted([candidate[i], candidate[j], candidate[k]]     )     )
  																																										__SCREAMING_SNAKE_CASE								=      True
  
  																																			if found:
  																																										break
  																												if found:
  																																			break
  																					if found:
  																												break
  
  							__SCREAMING_SNAKE_CASE								=      set()
  							for seq in passed:
  														answer.add(''.join([str(a__     ) for i in seq]     )     )
  
  							return max(int(a__     ) for x in answer     )
  
  
  if __name__ == "__main__":
  			print(solution())
  
  
  
  
  
  
  
style_context: 
  
  
  
  import argparse
  import datetime
  import json
  import time
  import warnings
  from logging import getLogger
  from pathlib import Path
  from typing import Dict, List
  
  import torch
  from tqdm import tqdm
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
  
  
  lowerCAmelCase__	:  Union[str, Any] 		=getLogger(__name__)
  
  
  lowerCAmelCase__	:  Optional[int] 		='''cuda''' if torch.cuda.is_available() else '''cpu'''
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__							,     a__ = 8							,     a__ = DEFAULT_DEVICE							,     a__=False							,     a__="summarization"							,     a__=None							,     **a__							,     )       ->				Dict:
  							__SCREAMING_SNAKE_CASE								=      Path(a__     ).open('w'							,     encoding='utf-8'     )
  							__SCREAMING_SNAKE_CASE								=      str(a__     )
  							__SCREAMING_SNAKE_CASE								=      AutoModelForSeqaSeqLM.from_pretrained(a__     ).to(a__     )
  							if fpaa:
  														__SCREAMING_SNAKE_CASE								=      model.half()
  
  							__SCREAMING_SNAKE_CASE								=      AutoTokenizer.from_pretrained(a__     )
  							logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.
  
  							__SCREAMING_SNAKE_CASE								=      time.time()
  							# update config with task specific params
  							use_task_specific_params(a__							,     a__     )
  							if prefix is None:
  														__SCREAMING_SNAKE_CASE								=      prefix or getattr(model.config							,     'prefix'							,     ''     ) or ''
  							for examples_chunk in tqdm(list(chunks(a__							,     a__     )     )     ):
  														__SCREAMING_SNAKE_CASE								=      [prefix + text for text in examples_chunk]
  														__SCREAMING_SNAKE_CASE								=      tokenizer(a__							,     return_tensors='pt'							,     truncation=a__							,     padding='longest'     ).to(a__     )
  														__SCREAMING_SNAKE_CASE								=      model.generate(
  														    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **a__							,     )
  														__SCREAMING_SNAKE_CASE								=      tokenizer.batch_decode(a__							,     skip_special_tokens=a__							,     clean_up_tokenization_spaces=a__     )
  														for hypothesis in dec:
  																					fout.write(hypothesis + '\n'     )
  																					fout.flush()
  							fout.close()
  							__SCREAMING_SNAKE_CASE								=      int(time.time() - start_time     )  # seconds
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4     )}
  
  
  
  
  
  
  def 			__lowercase     (				)       ->				Dict:
  							return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )
  
  
  
  
  
  
  def 			__lowercase     (				a__=True     )       ->				Optional[Any]:
  							__SCREAMING_SNAKE_CASE								=      argparse.ArgumentParser()
  							parser.add_argument('model_name'							,     type=a__							,     help='like facebook/bart-large-cnn,t5-base, etc.'     )
  							parser.add_argument('input_path'							,     type=a__							,     help='like cnn_dm/test.source'     )
  							parser.add_argument('save_path'							,     type=a__							,     help='where to save summaries'     )
  							parser.add_argument('--reference_path'							,     type=a__							,     required=a__							,     help='like cnn_dm/test.target'     )
  							parser.add_argument('--score_path'							,     type=a__							,     required=a__							,     default='metrics.json'							,     help='where to save metrics'     )
  							parser.add_argument('--device'							,     type=a__							,     required=a__							,     default=a__							,     help='cuda, cuda:1, cpu etc.'     )
  							parser.add_argument(
  							    '--prefix'							,     type=a__							,     required=a__							,     default=a__							,     help='will be added to the begininng of src examples'     )
  							parser.add_argument('--task'							,     type=a__							,     default='summarization'							,     help='used for task_specific_params + metrics'     )
  							parser.add_argument('--bs'							,     type=a__							,     default=8							,     required=a__							,     help='batch size'     )
  							parser.add_argument(
  							    '--n_obs'							,     type=a__							,     default=-1							,     required=a__							,     help='How many observations. Defaults to all.'     )
  							parser.add_argument('--fp16'							,     action='store_true'     )
  							parser.add_argument('--dump-args'							,     action='store_true'							,     help='print the custom hparams with the results'     )
  							parser.add_argument(
  							    '--info'							,     nargs='?'							,     type=a__							,     const=datetime_now()							,     help=(
  							        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
  							        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
  							    )							,     )
  							# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
  							__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      parser.parse_known_args()
  							__SCREAMING_SNAKE_CASE								=      parse_numeric_n_bool_cl_kwargs(a__     )
  							if parsed_args and verbose:
  														print(f"""parsed the following generate kwargs: {parsed_args}"""     )
  							with open(args.input_path     ) as f:
  														__SCREAMING_SNAKE_CASE								=      [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  							if args.n_obs > 0:
  														__SCREAMING_SNAKE_CASE								=      examples[: args.n_obs]
  							Path(args.save_path     ).parent.mkdir(exist_ok=a__     )
  							if args.reference_path is None and Path(args.score_path     ).exists():
  														warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""     )
  							__SCREAMING_SNAKE_CASE								=      generate_summaries_or_translations(
  							    a__							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **a__							,     )
  
  							if args.reference_path is None:
  														return {}
  
  							# Compute scores
  							__SCREAMING_SNAKE_CASE								=      calculate_bleu if 'translation' in args.task else calculate_rouge
  							__SCREAMING_SNAKE_CASE								=      [x.rstrip() for x in open(args.save_path     ).readlines()]
  							__SCREAMING_SNAKE_CASE								=      [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(a__     )]
  							__SCREAMING_SNAKE_CASE								=      score_fn(a__							,     a__     )
  							scores.update(a__     )
  
  							if args.dump_args:
  														scores.update(a__     )
  							if args.info:
  														__SCREAMING_SNAKE_CASE								=      args.info
  
  							if verbose:
  														print(a__     )
  
  							if args.score_path is not None:
  														json.dump(a__							,     open(args.score_path							,     'w'     )     )
  
  							return scores
  
  
  if __name__ == "__main__":
  			# Usage for MT:
  			# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  			run_generate(verbose=True)
  
  
  
  
  
  
  