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
  A_:      int        =			{
      """translation""": ["""bleu"""],
      """summarization""": ROUGE_KEYS,
  }
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	search.split()
  							_lowercase				=	dict((g.split("""="""					) for g in groups)					)
  							_lowercase				=	list(entries.keys()					)
  							_lowercase				=	[[f'''--{k} {v}''' for v in vs.split(""":"""					)] for k, vs in entries.items()]
  							_lowercase				=	[list(lowerCAmelCase__					) for x in itertools.product(*lowerCAmelCase__					)]
  							return matrix, entry_names
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	sys.argv[0]
  
  							_lowercase				=	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."""
  							    ) ,)
  							_lowercase				=	parser.parse_known_args()
  							# we share some of the args
  							args_main.extend(["""--task""", args.task]					)
  							_lowercase				=	[prog] + args_main
  
  							# to support variations like translation_en_to_de"
  							_lowercase				=	'translation' if 'translation' in args.task else 'summarization'
  
  							_lowercase				=	parse_search_arg(args.search					)
  							_lowercase				=	task_score_names[task]  # score cols first
  							_lowercase				=	{col: len(str(lowerCAmelCase__					)					) for col in col_names}
  							_lowercase				=	[]
  							for r in matrix:
  														_lowercase				=	dict((x.replace("""--""" ,""""""					).split() for x in r)					)
  														_lowercase				=	' '.join(lowerCAmelCase__					).split()
  														args_exp.extend(["""--bs""", str(args.bs					)]					)  # in case we need to reduce its size due to CUDA OOM
  														_lowercase				=	args_normal + args_exp
  
  														# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry
  
  														_lowercase				=	run_generate(verbose=lowerCAmelCase__					)
  														# make sure scores are first in the table
  														_lowercase				=	OrderedDict()
  														for score in task_score_names[task]:
  																					_lowercase				=	scores[score]
  														result.update(lowerCAmelCase__					)
  														results.append(lowerCAmelCase__					)
  
  														# find widest entries
  														for k, v in result.items():
  																					_lowercase				=	len(str(lowerCAmelCase__					)					)
  																					if l > col_widths[k]:
  																												_lowercase				=	l
  
  							_lowercase				=	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]					)					)
  
  							_lowercase				=	results_sorted[0]
  							for score in task_score_names[task]:
  														del best[score]
  							_lowercase				=	[f'''--{k} {v}''' for k, v in best.items()]
  							_lowercase				=	['--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_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_:      int        =			{
      'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
      'tokenization_bart': ['BartTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[str]        =			['BartTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      int        =			[
  				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'BartForCausalLM',
  				    'BartForConditionalGeneration',
  				    'BartForQuestionAnswering',
  				    'BartForSequenceClassification',
  				    'BartModel',
  				    'BartPretrainedModel',
  				    'PretrainedBartModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Any        =			[
  				    'TFBartForConditionalGeneration',
  				    'TFBartForSequenceClassification',
  				    'TFBartModel',
  				    'TFBartPretrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      str        =			[
  				    'FlaxBartDecoderPreTrainedModel',
  				    'FlaxBartForCausalLM',
  				    'FlaxBartForConditionalGeneration',
  				    'FlaxBartForQuestionAnswering',
  				    'FlaxBartForSequenceClassification',
  				    'FlaxBartModel',
  				    'FlaxBartPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  				from .tokenization_bart import BartTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_bart_fast import BartTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_bart import (
  								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    BartForCausalLM,
  								    BartForConditionalGeneration,
  								    BartForQuestionAnswering,
  								    BartForSequenceClassification,
  								    BartModel,
  								    BartPretrainedModel,
  								    PretrainedBartModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_bart import (
  								    TFBartForConditionalGeneration,
  								    TFBartForSequenceClassification,
  								    TFBartModel,
  								    TFBartPretrainedModel,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flax_bart import (
  								    FlaxBartDecoderPreTrainedModel,
  								    FlaxBartForCausalLM,
  								    FlaxBartForConditionalGeneration,
  								    FlaxBartForQuestionAnswering,
  								    FlaxBartForSequenceClassification,
  								    FlaxBartModel,
  								    FlaxBartPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  