



"""simple docstring"""






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_lowercase					:					Any 		=		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def 	snake_case__  (						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			if "token" in model_name_or_path:
						return "rag_token"
			if "sequence" in model_name_or_path:
						return "rag_sequence"
			if "bart" in model_name_or_path:
						return "bart"
			return None






def 	snake_case__  (						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       str      ):
			"""simple docstring"""
			return max(metric_fn(__lowerCamelCase				,						__lowerCamelCase      ) for gt in ground_truths      )






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =[line.strip() for line in open(__lowerCamelCase				,						'''r'''      ).readlines()]
			lowerCamelCase__						:						Dict						      =[]

			if args.gold_data_mode == "qa":
						lowerCamelCase__						:						List[Any]						      =pd.read_csv(__lowerCamelCase				,						sep='''\t'''				,						header=__lowerCamelCase      )
						for answer_list in data[1]:
									lowerCamelCase__						:						Optional[int]						      =ast.literal_eval(__lowerCamelCase      )
									answers.append(__lowerCamelCase      )
			else:
						lowerCamelCase__						:						str						      =[line.strip() for line in open(__lowerCamelCase				,						'''r'''      ).readlines()]
						lowerCamelCase__						:						Tuple						      =[[reference] for reference in references]

			lowerCamelCase__						:						Union[str, Any]						      =0
			for prediction, ground_truths in zip(__lowerCamelCase				,						__lowerCamelCase      ):
						total += 1
						em += metric_max_over_ground_truths(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )
						fa += metric_max_over_ground_truths(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

			lowerCamelCase__						:						Dict						      =1_00.0 * em / total
			lowerCamelCase__						:						Optional[int]						      =1_00.0 * fa / total

			logger.info(f'''F1: {fa:.2f}'''      )
			logger.info(f'''EM: {em:.2f}'''      )






def 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       str				,						__lowerCamelCase							:       Optional[int]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Optional[int]						      =args.k
			lowerCamelCase__						:						str						      =[line.strip() for line in open(__lowerCamelCase				,						'''r'''      ).readlines()]
			lowerCamelCase__						:						str						      =[line.strip() for line in open(__lowerCamelCase				,						'''r'''      ).readlines()]

			lowerCamelCase__						:						Optional[Any]						      =0
			for hypo, reference in zip(__lowerCamelCase				,						__lowerCamelCase      ):
						lowerCamelCase__						:						Optional[int]						      =set(hypo.split('''\t'''      )[:k]      )
						lowerCamelCase__						:						Tuple						      =set(reference.split('''\t'''      )      )
						total += 1
						em += len(hypo_provenance & ref_provenance      ) / k

			lowerCamelCase__						:						Tuple						      =1_00.0 * em / total
			logger.info(f'''Precision@{k}: {em: .2f}'''      )






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       str      ):
			"""simple docstring"""
			def strip_title(__lowerCamelCase							:       Optional[Any]      ):
						if title.startswith('''"'''      ):
									lowerCamelCase__						:						Any						      =title[1:]
						if title.endswith('''"'''      ):
									lowerCamelCase__						:						str						      =title[:-1]
						return title

			lowerCamelCase__						:						List[Any]						      =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
			    __lowerCamelCase				,						return_tensors='''pt'''				,						padding=__lowerCamelCase				,						truncation=__lowerCamelCase				,						)['''input_ids'''].to(args.device      )

			lowerCamelCase__						:						Optional[int]						      =rag_model.rag.question_encoder(__lowerCamelCase      )
			lowerCamelCase__						:						int						      =question_enc_outputs[0]

			lowerCamelCase__						:						Any						      =rag_model.retriever(
			    __lowerCamelCase				,						question_enc_pool_output.cpu().detach().to(torch.floataa      ).numpy()				,						prefix=rag_model.rag.generator.config.prefix				,						n_docs=rag_model.config.n_docs				,						return_tensors='''pt'''				,						)
			lowerCamelCase__						:						Optional[Any]						      =rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
			lowerCamelCase__						:						Optional[int]						      =[]
			for docs in all_docs:
						lowerCamelCase__						:						Optional[Any]						      =[strip_title(__lowerCamelCase      ) for title in docs['''title''']]
						provenance_strings.append('''\t'''.join(__lowerCamelCase      )      )
			return provenance_strings






def 	snake_case__  (						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       Union[str, Any]				,						__lowerCamelCase							:       List[Any]      ):
			"""simple docstring"""
			with torch.no_grad():
						lowerCamelCase__						:						str						      =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    __lowerCamelCase				,						return_tensors='''pt'''				,						padding=__lowerCamelCase				,						truncation=__lowerCamelCase      )

						lowerCamelCase__						:						Union[str, Any]						      =inputs_dict.input_ids.to(args.device      )
						lowerCamelCase__						:						Any						      =inputs_dict.attention_mask.to(args.device      )
						lowerCamelCase__						:						Any						      =rag_model.generate(  # rag_model overwrites generate
						    __lowerCamelCase				,						attention_mask=__lowerCamelCase				,						num_beams=args.num_beams				,						min_length=args.min_length				,						max_length=args.max_length				,						early_stopping=__lowerCamelCase				,						num_return_sequences=1				,						bad_words_ids=[[0, 0]]				,						)
						lowerCamelCase__						:						Optional[int]						      =rag_model.retriever.generator_tokenizer.batch_decode(__lowerCamelCase				,						skip_special_tokens=__lowerCamelCase      )

						if args.print_predictions:
									for q, a in zip(__lowerCamelCase				,						__lowerCamelCase      ):
												logger.info('''Q: {} - A: {}'''.format(__lowerCamelCase				,						__lowerCamelCase      )      )

						return answers






def 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						str						      =argparse.ArgumentParser()
			parser.add_argument(
			    '''--model_type'''				,						choices=['''rag_sequence''', '''rag_token''', '''bart''']				,						type=__lowerCamelCase				,						help=(
			        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
			        ''' model_name_or_path'''
			    )				,						)
			parser.add_argument(
			    '''--index_name'''				,						default=__lowerCamelCase				,						choices=['''exact''', '''compressed''', '''legacy''']				,						type=__lowerCamelCase				,						help='''RAG model retriever type'''				,						)
			parser.add_argument(
			    '''--index_path'''				,						default=__lowerCamelCase				,						type=__lowerCamelCase				,						help='''Path to the retrieval index'''				,						)
			parser.add_argument('''--n_docs'''				,						default=5				,						type=__lowerCamelCase				,						help='''Number of retrieved docs'''      )
			parser.add_argument(
			    '''--model_name_or_path'''				,						default=__lowerCamelCase				,						type=__lowerCamelCase				,						required=__lowerCamelCase				,						help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''				,						)
			parser.add_argument(
			    '''--eval_mode'''				,						choices=['''e2e''', '''retrieval''']				,						default='''e2e'''				,						type=__lowerCamelCase				,						help=(
			        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
			        ''' precision@k.'''
			    )				,						)
			parser.add_argument('''--k'''				,						default=1				,						type=__lowerCamelCase				,						help='''k for the precision@k calculation'''      )
			parser.add_argument(
			    '''--evaluation_set'''				,						default=__lowerCamelCase				,						type=__lowerCamelCase				,						required=__lowerCamelCase				,						help='''Path to a file containing evaluation samples'''				,						)
			parser.add_argument(
			    '''--gold_data_path'''				,						default=__lowerCamelCase				,						type=__lowerCamelCase				,						required=__lowerCamelCase				,						help='''Path to a tab-separated file with gold samples'''				,						)
			parser.add_argument(
			    '''--gold_data_mode'''				,						default='''qa'''				,						type=__lowerCamelCase				,						choices=['''qa''', '''ans''']				,						help=(
			        '''Format of the gold data file'''
			        '''qa - a single line in the following format: question [tab] answer_list'''
			        '''ans - a single line of the gold file contains the expected answer string'''
			    )				,						)
			parser.add_argument(
			    '''--predictions_path'''				,						type=__lowerCamelCase				,						default='''predictions.txt'''				,						help='''Name of the predictions file, to be stored in the checkpoints directory'''				,						)
			parser.add_argument(
			    '''--eval_all_checkpoints'''				,						action='''store_true'''				,						help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''				,						)
			parser.add_argument(
			    '''--eval_batch_size'''				,						default=8				,						type=__lowerCamelCase				,						help='''Batch size per GPU/CPU for evaluation.'''				,						)
			parser.add_argument(
			    '''--recalculate'''				,						help='''Recalculate predictions even if the prediction file exists'''				,						action='''store_true'''				,						)
			parser.add_argument(
			    '''--num_beams'''				,						default=4				,						type=__lowerCamelCase				,						help='''Number of beams to be used when generating answers'''				,						)
			parser.add_argument('''--min_length'''				,						default=1				,						type=__lowerCamelCase				,						help='''Min length of the generated answers'''      )
			parser.add_argument('''--max_length'''				,						default=50				,						type=__lowerCamelCase				,						help='''Max length of the generated answers'''      )

			parser.add_argument(
			    '''--print_predictions'''				,						action='''store_true'''				,						help='''If True, prints predictions while evaluating.'''				,						)
			parser.add_argument(
			    '''--print_docs'''				,						action='''store_true'''				,						help='''If True, prints docs retried while generating.'''				,						)
			lowerCamelCase__						:						Optional[Any]						      =parser.parse_args()
			lowerCamelCase__						:						Optional[Any]						      =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''      )
			return args






def 	snake_case__  (						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						str						      ={}
			if args.model_type is None:
						lowerCamelCase__						:						List[Any]						      =infer_model_type(args.model_name_or_path      )
						assert args.model_type is not None
			if args.model_type.startswith('''rag'''      ):
						lowerCamelCase__						:						int						      =RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
						lowerCamelCase__						:						Union[str, Any]						      =args.n_docs
						if args.index_name is not None:
									lowerCamelCase__						:						Optional[int]						      =args.index_name
						if args.index_path is not None:
									lowerCamelCase__						:						Union[str, Any]						      =args.index_path
			else:
						lowerCamelCase__						:						List[Any]						      =BartForConditionalGeneration

			lowerCamelCase__						:						Union[str, Any]						      =(
			    [f.path for f in os.scandir(args.model_name_or_path      ) if f.is_dir()]
			    if args.eval_all_checkpoints
			    else [args.model_name_or_path]
			)

			logger.info('''Evaluate the following checkpoints: %s'''				,						__lowerCamelCase      )

			lowerCamelCase__						:						int						      =get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
			lowerCamelCase__						:						Any						      =evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

			for checkpoint in checkpoints:
						if os.path.exists(args.predictions_path      ) and (not args.recalculate):
									logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path      )      )
									score_fn(__lowerCamelCase				,						args.predictions_path				,						args.gold_data_path      )
									continue

						logger.info('''***** Running evaluation for {} *****'''.format(__lowerCamelCase      )      )
						logger.info('''  Batch size = %d'''				,						args.eval_batch_size      )
						logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path      )      )

						if args.model_type.startswith('''rag'''      ):
									lowerCamelCase__						:						List[str]						      =RagRetriever.from_pretrained(__lowerCamelCase				,						**__lowerCamelCase      )
									lowerCamelCase__						:						Dict						      =model_class.from_pretrained(__lowerCamelCase				,						retriever=__lowerCamelCase				,						**__lowerCamelCase      )
									model.retriever.init_retrieval()
						else:
									lowerCamelCase__						:						List[str]						      =model_class.from_pretrained(__lowerCamelCase				,						**__lowerCamelCase      )
						model.to(args.device      )

						with open(args.evaluation_set				,						'''r'''      ) as eval_file, open(args.predictions_path				,						'''w'''      ) as preds_file:
									lowerCamelCase__						:						List[Any]						      =[]
									for line in tqdm(__lowerCamelCase      ):
												questions.append(line.strip()      )
												if len(__lowerCamelCase      ) == args.eval_batch_size:
															lowerCamelCase__						:						Tuple						      =evaluate_batch_fn(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )
															preds_file.write('''\n'''.join(__lowerCamelCase      ) + '''\n'''      )
															preds_file.flush()
															lowerCamelCase__						:						Optional[int]						      =[]
									if len(__lowerCamelCase      ) > 0:
												lowerCamelCase__						:						Union[str, Any]						      =evaluate_batch_fn(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )
												preds_file.write('''\n'''.join(__lowerCamelCase      )      )
												preds_file.flush()

									score_fn(__lowerCamelCase				,						args.predictions_path				,						args.gold_data_path      )


if __name__ == "__main__":
		_lowercase					:					str 		=		get_args()
		main(args)






"""simple docstring"""

import math
from itertools import permutations






def 				A		(							snake_case :int						) ->      bool:

 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(snake_case						) + 1						)							,   6						):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 				A		(							snake_case :int							,   snake_case :list						) ->      bool:
 __UpperCamelCase    ,      __UpperCamelCase							    =			0, len(snake_case						) - 1
 while left <= right:
  __UpperCamelCase							    =			(left + right) // 2
  if prime_list[middle] == target:
   return True
  elif prime_list[middle] < target:
   __UpperCamelCase							    =			middle + 1
  else:
   __UpperCamelCase							    =			middle - 1

 return False






def 				A		(							) ->      int:
 __UpperCamelCase							    =			[n for n in range(1_0_0_1							,   1_0_0_0_0							,   2						) if is_prime(snake_case						)]
 __UpperCamelCase							    =			[]

 for number in prime_list:
  __UpperCamelCase							    =			[]

  for prime_member in permutations(list(str(snake_case						)						)						):
   __UpperCamelCase							    =			int(''.join(snake_case						)						)

   if prime % 2 == 0:
    continue

   if search(snake_case							,   snake_case						):
    tmp_numbers.append(snake_case						)

  tmp_numbers.sort()
  if len(snake_case						) >= 3:
   candidates.append(snake_case						)

 __UpperCamelCase							    =			[]
 for candidate in candidates:
  __UpperCamelCase							    =			len(snake_case						)
  __UpperCamelCase							    =			False

  for i in range(snake_case						):
   for j in range(i + 1							,   snake_case						):
    for k in range(j + 1							,   snake_case						):
     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]]						)						)
      __UpperCamelCase							    =			True

     if found:
      break
    if found:
     break
   if found:
    break

 __UpperCamelCase							    =			set()
 for seq in passed:
  answer.add(''.join([str(snake_case						) for i in seq]						)						)

 return max(int(snake_case						) for x in answer						)


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



