code: 
  
  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
  snake_case_    						=				{
      '''translation''': ['''bleu'''],
      '''summarization''': ROUGE_KEYS,
  }
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Tuple   = search.split()
       lowercase__					:     Optional[int]   = dict((g.split('='    ) for g in groups)    )
       lowercase__					:     Tuple   = list(entries.keys()    )
       lowercase__					:     Tuple   = [[f"""--{k} {v}""" for v in vs.split(':'    )] for k, vs in entries.items()]
       lowercase__					:     Any   = [list(lowercase_    ) for x in itertools.product(*lowercase_    )]
       return matrix, entry_names
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[Any]   = sys.argv[0]
  
       lowercase__					:     Optional[int]   = 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=lowercase_			,  required=lowercase_			,  help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'			,  )
       parser.add_argument(
           '--bs'			,  type=lowercase_			,  default=8			,  required=lowercase_			,  help='initial batch size (may get reduced if it\'s too big)'    )
       parser.add_argument('--task'			,  type=lowercase_			,  help='used for task_specific_params + metrics'    )
       parser.add_argument(
           '--info'			,  nargs='?'			,  type=lowercase_			,  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.'
           )			,  )
       lowercase__					:     List[str]   = parser.parse_known_args()
       # we share some of the args
       args_main.extend(['--task', args.task]    )
       lowercase__					:     List[str]   = [prog] + args_main
  
       # to support variations like translation_en_to_de"
       lowercase__					:     str   = '''translation''' if '''translation''' in args.task else '''summarization'''
  
       lowercase__					:     Optional[Any]   = parse_search_arg(args.search    )
       lowercase__					:     List[Any]   = task_score_names[task]  # score cols first
       lowercase__					:     Dict   = {col: len(str(lowercase_    )    ) for col in col_names}
       lowercase__					:     Optional[int]   = []
       for r in matrix:
            lowercase__					:     Optional[int]   = dict((x.replace('--'			,  ''    ).split() for x in r)    )
            lowercase__					:     List[str]   = ''' '''.join(lowercase_    ).split()
            args_exp.extend(['--bs', str(args.bs    )]    )  # in case we need to reduce its size due to CUDA OOM
            lowercase__					:     Optional[Any]   = args_normal + args_exp
  
            # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry
  
            lowercase__					:     List[str]   = run_generate(verbose=lowercase_    )
            # make sure scores are first in the table
            lowercase__					:     Tuple   = OrderedDict()
            for score in task_score_names[task]:
                 lowercase__					:     str   = scores[score]
            result.update(lowercase_    )
            results.append(lowercase_    )
  
            # find widest entries
            for k, v in result.items():
                 lowercase__					:     List[Any]   = len(str(lowercase_    )    )
                 if l > col_widths[k]:
                      lowercase__					:     Optional[Any]   = l
  
       lowercase__					:     Tuple   = sorted(lowercase_			,  key=operator.itemgetter(*task_score_names[task]    )			,  reverse=lowercase_    )
       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]    )    )
  
       lowercase__					:     Any   = results_sorted[0]
       for score in task_score_names[task]:
            del best[score]
       lowercase__					:     Union[str, Any]   = [f"""--{k} {v}""" for k, v in best.items()]
       lowercase__					:     Dict   = ['''--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()
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  snake_case_    						=				{
      '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
      '''processing_donut''': ['''DonutProcessor'''],
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DonutSwinModel''',
             '''DonutSwinPreTrainedModel''',
         ]
  
  try:
         if not is_vision_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				['''DonutFeatureExtractor''']
         snake_case_    						=				['''DonutImageProcessor''']
  
  
  if TYPE_CHECKING:
         from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
         from .processing_donut import DonutProcessor
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_donut_swin import (
                    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DonutSwinModel,
                    DonutSwinPreTrainedModel,
                )
  
         try:
                if not is_vision_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .feature_extraction_donut import DonutFeatureExtractor
                from .image_processing_donut import DonutImageProcessor
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  