






"""simple docstring"""


import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
_UpperCamelCase							:       Union[str, Any]       					=		{
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}




def      snake_case    (A_ :str ):







  '''simple docstring'''

  a	:     int							=  search.split()
  a	:     Optional[Any]							=  dict((g.split('=' ) for g in groups) )
  a	:     str							=  list(entries.keys() )
  a	:     Optional[int]							=  [[f'''--{k} {v}''' for v in vs.split(':' )] for k, vs in entries.items()]
  a	:     List[str]							=  [list(A_ ) for x in itertools.product(*A_ )]
  return matrix, entry_names




def      snake_case    ():







  '''simple docstring'''

  a	:     int							=  sys.argv[0]

  a	:     Tuple							=  argparse.ArgumentParser(
      usage=(
          '\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'
          ' refer to `run_eval.py -h` for the complete list.'
      ) )
  parser.add_argument(
      '--search'      ,     type=A_      ,     required=A_      ,     help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'      ,     )
  parser.add_argument(
      '--bs'      ,     type=A_      ,     default=8      ,     required=A_      ,     help='initial batch size (may get reduced if it\'s too big)' )
  parser.add_argument('--task'      ,     type=A_      ,     help='used for task_specific_params + metrics' )
  parser.add_argument(
      '--info'      ,     nargs='?'      ,     type=A_      ,     const=datetime_now()      ,     help=(
          'add custom notes to be printed before the results table. If no value is passed, the current datetime'
          ' string will be used.'
      )      ,     )
  a,	a	:     Tuple							=  parser.parse_known_args()
  # we share some of the args
  args_main.extend(['--task', args.task] )
  a	:     Optional[Any]							=  [prog] + args_main

  # to support variations like translation_en_to_de"
  a	:     Dict							=  'translation' if 'translation' in args.task else 'summarization'

  a,	a	:     List[Any]							=  parse_search_arg(args.search )
  a	:     Any							=  task_score_names[task]  # score cols first
  a	:     int							=  {col: len(str(A_ ) ) for col in col_names}
  a	:     int							=  []
  for r in matrix:
    a	:     List[str]							=  dict((x.replace('--'      ,     '' ).split() for x in r) )
    a	:     Optional[Any]							=  ' '.join(A_ ).split()
    args_exp.extend(['--bs', str(args.bs )] )  # in case we need to reduce its size due to CUDA OOM
    a	:     List[str]							=  args_normal + args_exp

    # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

    a	:     Tuple							=  run_generate(verbose=A_ )
    # make sure scores are first in the table
    a	:     List[Any]							=  OrderedDict()
    for score in task_score_names[task]:
      a	:     List[Any]							=  scores[score]
    result.update(A_ )
    results.append(A_ )

    # find widest entries
    for k, v in result.items():
      a	:     Dict							=  len(str(A_ ) )
      if l > col_widths[k]:
        a	:     Dict							=  l

  a	:     str							=  sorted(A_      ,     key=operator.itemgetter(*task_score_names[task] )      ,     reverse=A_ )
  print(' | '.join([f'''{col:{col_widths[col]}}''' for col in col_names] ) )
  print(' | '.join([f'''{'-'*col_widths[col]}''' for col in col_names] ) )
  for row in results_sorted:
    print(' | '.join([f'''{row[col]:{col_widths[col]}}''' for col in col_names] ) )

  a	:     Union[str, Any]							=  results_sorted[0]
  for score in task_score_names[task]:
    del best[score]
  a	:     Union[str, Any]							=  [f'''--{k} {v}''' for k, v in best.items()]
  a	:     Union[str, Any]							=  ['--bs', str(args.bs )]
  if args.info:
    print(f'''\nInfo: {args.info}''' )
  print('\nBest score args:' )
  print(' '.join(args_main + best_args + dyn_args ) )

  return results_sorted


if __name__ == "__main__":
   # Usage:
   # [normal-run_eval_search.py cmd plus] \
   # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
   #
   # Example:
   # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
   # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
   # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
   # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
   run_search()












"""simple docstring"""


_UpperCamelCase							:       str       					=		10**8




def      snake_case    (A_ :int ):







  '''simple docstring'''

  if n % 1_0 == 0:
    return False
  a	:     Optional[int]							=  str(A_ )
  return s == s[::-1]




def      snake_case    ():







  '''simple docstring'''

  a	:     Tuple							=  set()
  a	:     Optional[Any]							=  1
  a	:     List[str]							=  5
  while sum_squares < LIMIT:
    a	:     int							=  first_square + 1
    while sum_squares < LIMIT:
      if is_palindrome(A_ ):
        answer.add(A_ )
      last_square += 1
      sum_squares += last_square**2
    first_square += 1
    a	:     List[str]							=  first_square**2 + (first_square + 1) ** 2

  return sum(A_ )


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




