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
  lowerCamelCase    =      {
      '''translation''': ['''bleu'''],
      '''summarization''': ROUGE_KEYS,
  }
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Tuple							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    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(snake_case__							) for x in itertools.product(*snake_case__							)]
    return matrix, entry_names
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    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=snake_case__			,   required=snake_case__			,   help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''			,   )
    parser.add_argument(
        '''--bs'''			,   type=snake_case__			,   default=8			,   required=snake_case__			,   help='''initial batch size (may get reduced if it\'s too big)'''							)
    parser.add_argument('''--task'''			,   type=snake_case__			,   help='''used for task_specific_params + metrics'''							)
    parser.add_argument(
        '''--info'''			,   nargs='''?'''			,   type=snake_case__			,   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(snake_case__							)							) for col in col_names}
    a__						=[]
    for r in matrix:
      a__						=dict((x.replace('''--'''			,   ''''''							).split() for x in r)							)
      a__						=''' '''.join(snake_case__							).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=snake_case__							)
      # make sure scores are first in the table
      a__						=OrderedDict()
      for score in task_score_names[task]:
        a__						=scores[score]
      result.update(snake_case__							)
      results.append(snake_case__							)
  
      # find widest entries
      for k, v in result.items():
        a__						=len(str(snake_case__							)							)
        if l > col_widths[k]:
          a__						=l
  
    a__						=sorted(snake_case__			,   key=operator.itemgetter(*task_score_names[task]							)			,   reverse=snake_case__							)
    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()
  
style_context: 
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
         from .tokenization_xglm import XGLMTokenizer
  else:
         lowerCamelCase     =      None
  
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  lowerCamelCase     =      {
      '''vocab_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
      },
      '''tokenizer_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
      },
  }
  
  lowerCamelCase     =      {
      '''facebook/xglm-564M''': 2048,
  }
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
     lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
     lowerCamelCase__				:			Any												=     XGLMTokenizer
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=7
       a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
  
       a__						=kwargs.get('''additional_special_tokens''',					[] )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]
  
       super().__init__(
           lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)
  
       a__						=vocab_file
       a__						=False if not self.vocab_file else True
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       a__						=[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[self.sep_token_id]
  
       if token_ids_a is None:
         return len(sep + token_ids_a ) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.''' )
  
       if not os.path.isdir(lowercase_ ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
         return
       a__						=os.path.join(
           lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
  
       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
         copyfile(self.vocab_file,					lowercase_ )
  
       return (out_vocab_file,)
  