



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())











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)






