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
  _lowercase		:     int 					={
      """translation""": ["""bleu"""],
      """summarization""": ROUGE_KEYS,
  }
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    Any   	=			search.split()
  		lowerCamelCase_  :    List[str]   	=			dict((g.split('='	) for g in groups)	)
  		lowerCamelCase_  :    List[Any]   	=			list(entries.keys()	)
  		lowerCamelCase_  :    Optional[Any]   	=			[[F"--{k} {v}" for v in vs.split(':'	)] for k, vs in entries.items()]
  		lowerCamelCase_  :    Union[str, Any]   	=			[list(lowerCAmelCase__	) for x in itertools.product(*lowerCAmelCase__	)]
  		return matrix, entry_names
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    int   	=			sys.argv[0]
  
  		lowerCamelCase_  :    str   	=			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=lowerCAmelCase__       ,required=lowerCAmelCase__       ,help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'       ,)
  		parser.add_argument(
  		    '--bs'       ,type=lowerCAmelCase__       ,default=8       ,required=lowerCAmelCase__       ,help='initial batch size (may get reduced if it\'s too big)'	)
  		parser.add_argument('--task'       ,type=lowerCAmelCase__       ,help='used for task_specific_params + metrics'	)
  		parser.add_argument(
  		    '--info'       ,nargs='?'       ,type=lowerCAmelCase__       ,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.'
  		    )       ,)
  		lowerCamelCase_	,			lowerCamelCase_  :    int   	=			parser.parse_known_args()
  		# we share some of the args
  		args_main.extend(['--task', args.task]	)
  		lowerCamelCase_  :    List[Any]   	=			[prog] + args_main
  
  		# to support variations like translation_en_to_de"
  		lowerCamelCase_  :    Optional[int]   	=			'translation' if 'translation' in args.task else 'summarization'
  
  		lowerCamelCase_	,			lowerCamelCase_  :    Dict   	=			parse_search_arg(args.search	)
  		lowerCamelCase_  :    Tuple   	=			task_score_names[task]  # score cols first
  		lowerCamelCase_  :    Dict   	=			{col: len(str(lowerCAmelCase__	)	) for col in col_names}
  		lowerCamelCase_  :    Union[str, Any]   	=			[]
  		for r in matrix:
  				lowerCamelCase_  :    int   	=			dict((x.replace('--'       ,''	).split() for x in r)	)
  				lowerCamelCase_  :    Optional[int]   	=			' '.join(lowerCAmelCase__	).split()
  				args_exp.extend(['--bs', str(args.bs	)]	)  # in case we need to reduce its size due to CUDA OOM
  				lowerCamelCase_  :    Dict   	=			args_normal + args_exp
  
  				# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry
  
  				lowerCamelCase_  :    str   	=			run_generate(verbose=lowerCAmelCase__	)
  				# make sure scores are first in the table
  				lowerCamelCase_  :    int   	=			OrderedDict()
  				for score in task_score_names[task]:
  						lowerCamelCase_  :    Any   	=			scores[score]
  				result.update(lowerCAmelCase__	)
  				results.append(lowerCAmelCase__	)
  
  				# find widest entries
  				for k, v in result.items():
  						lowerCamelCase_  :    int   	=			len(str(lowerCAmelCase__	)	)
  						if l > col_widths[k]:
  								lowerCamelCase_  :    Tuple   	=			l
  
  		lowerCamelCase_  :    Any   	=			sorted(lowerCAmelCase__       ,key=operator.itemgetter(*task_score_names[task]	)       ,reverse=lowerCAmelCase__	)
  		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]	)	)
  
  		lowerCamelCase_  :    Union[str, Any]   	=			results_sorted[0]
  		for score in task_score_names[task]:
  				del best[score]
  		lowerCamelCase_  :    Optional[Any]   	=			[F"--{k} {v}" for k, v in best.items()]
  		lowerCamelCase_  :    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 ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _lowercase		:     Dict 					=logging.get_logger(__name__)
  
  _lowercase		:     Any 					={
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  					_a			:	List[str]							=				'vilt'
  
  
  
  					def __init__(		self    :    int					,			lowerCamelCase    :    Any=3_05_22					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    str=40					,			lowerCamelCase    :    List[str]=7_68					,			lowerCamelCase    :    Optional[int]=12					,			lowerCamelCase    :    List[Any]=12					,			lowerCamelCase    :    List[str]=30_72					,			lowerCamelCase    :    Union[str, Any]="gelu"					,			lowerCamelCase    :    int=0.0					,			lowerCamelCase    :    str=0.0					,			lowerCamelCase    :    int=0.02					,			lowerCamelCase    :    Union[str, Any]=1E-12					,			lowerCamelCase    :    Dict=3_84					,			lowerCamelCase    :    List[str]=32					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=True					,			lowerCamelCase    :    int=-1					,			lowerCamelCase    :    List[Any]=False					,			lowerCamelCase    :    Any=-1					,			**lowerCamelCase    :    str					,			):
  							super().__init__(tie_word_embeddings=lowerCamelCase					,			**lowerCamelCase	)
  
  							lowerCamelCase_  :    str   	=			vocab_size
  							lowerCamelCase_  :    Dict   	=			type_vocab_size
  							lowerCamelCase_  :    str   	=			modality_type_vocab_size
  							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings
  
  							lowerCamelCase_  :    List[Any]   	=			hidden_size
  							lowerCamelCase_  :    Union[str, Any]   	=			num_hidden_layers
  							lowerCamelCase_  :    Any   	=			num_attention_heads
  							lowerCamelCase_  :    List[Any]   	=			intermediate_size
  							lowerCamelCase_  :    Union[str, Any]   	=			hidden_act
  							lowerCamelCase_  :    Any   	=			hidden_dropout_prob
  							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
  							lowerCamelCase_  :    Any   	=			initializer_range
  							lowerCamelCase_  :    str   	=			layer_norm_eps
  
  							lowerCamelCase_  :    Optional[Any]   	=			image_size
  							lowerCamelCase_  :    List[Any]   	=			patch_size
  							lowerCamelCase_  :    Tuple   	=			num_channels
  							lowerCamelCase_  :    Optional[Any]   	=			qkv_bias
  							lowerCamelCase_  :    Optional[int]   	=			max_image_length
  							lowerCamelCase_  :    Optional[Any]   	=			num_images
  