



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
__lowerCamelCase								=				{
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}




def       UpperCAmelCase__	(							__snake_case							)				->	Union[str, Any]:
  _A          =      search.split()
  _A          =      dict((g.split('''='''							) for g in groups)							)
  _A          =      list(entries.keys()							)
  _A          =      [[F'''--{k} {v}''' for v in vs.split(''':'''							)] for k, vs in entries.items()]
  _A          =      [list(_A							) for x in itertools.product(*_A							)]
  return matrix, entry_names




def       UpperCAmelCase__	(							)				->	List[Any]:
  _A          =      sys.argv[0]

  _A          =      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          =      parser.parse_known_args()
  # we share some of the args
  args_main.extend(['''--task''', args.task]							)
  _A          =      [prog] + args_main

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

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

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

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

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

  _A          =      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          =      results_sorted[0]
  for score in task_score_names[task]:
    del best[score]
  _A          =      [F'''--{k} {v}''' for k, v in best.items()]
  _A          =      ['''--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()




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	int:

  # Base Case
  if counter == 0 or capacity == 0:
    return 0

  # If weight of the nth item is more than Knapsack of capacity,
  #   then this item cannot be included in the optimal solution,
  # else return the maximum of two cases:
  #   (1) nth item included
  #   (2) not included
  if weights[counter - 1] > capacity:
    return knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
  else:
    _A          =      capacity - weights[counter - 1]
    _A          =      values[counter - 1] + knapsack(
        __snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    _A          =      knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    return max(__snake_case  ,			__snake_case							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()