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				:	Any				    =       {
      'translation': ['bleu'],
      'summarization': ROUGE_KEYS,
  }
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->int:
  
  
  
  	"""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(UpperCAmelCase    ) for x in itertools.product(*UpperCAmelCase    )]
  	return matrix, entry_names
  
  
  
  
  def    __a (     )      ->Union[str, Any]:
  
  
  
  	"""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=UpperCAmelCase       ,			required=UpperCAmelCase       ,			help="""param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""""       ,			)
  	parser.add_argument(
  	    """--bs"""       ,			type=UpperCAmelCase       ,			default=8       ,			required=UpperCAmelCase       ,			help="""initial batch size (may get reduced if it's too big)"""    )
  	parser.add_argument("""--task"""       ,			type=UpperCAmelCase       ,			help="""used for task_specific_params + metrics"""    )
  	parser.add_argument(
  	    """--info"""       ,			nargs="""?"""       ,			type=UpperCAmelCase       ,			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(UpperCAmelCase    )    ) for col in col_names}
  	A							  =      []
  	for r in matrix:
  		A							  =      dict((x.replace("""--"""       ,			""""""    ).split() for x in r)    )
  		A							  =      """ """.join(UpperCAmelCase    ).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=UpperCAmelCase    )
  		# make sure scores are first in the table
  		A							  =      OrderedDict()
  		for score in task_score_names[task]:
  			A							  =      scores[score]
  		result.update(UpperCAmelCase    )
  		results.append(UpperCAmelCase    )
  
  		# find widest entries
  		for k, v in result.items():
  			A							  =      len(str(UpperCAmelCase    )    )
  			if l > col_widths[k]:
  				A							  =      l
  
  	A							  =      sorted(UpperCAmelCase       ,			key=operator.itemgetter(*task_score_names[task]    )       ,			reverse=UpperCAmelCase    )
  	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: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  _lowerCamelCase				:	Tuple				    =       logging.getLogger(__name__)
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Union[str, Any]      ):
  			A							  =      False
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Any      ):
  			if not self.initialized:
  				A							  =      RagRetriever(
  				    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
  				A							  =      True
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			self.retriever.index.init_index()
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			# delete the old self.retriever object before assigning the new index
  			del self.retriever
  			A							  =      False
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ):
  			A  ,    A							  =      self.retriever._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			A							  =      self.retriever.index.get_doc_dicts(_lowerCAmelCase      )
  			return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str=None      ):
  			if index is not None and index.is_initialized() and len(_lowerCAmelCase      ) > 0:
  				raise ValueError(
  				    """When using Ray for distributed fine-tuning, """
  				    """you'll need to provide the paths instead, """
  				    """as the dataset and the index are loaded """
  				    """separately. More info in examples/rag/use_own_knowledge_dataset.py """      )
  
  			super().__init__(
  			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
  
  			A							  =      retrieval_workers
  			A							  =      question_encoder_tokenizer
  			A							  =      generator_tokenizer
  			if len(self.retrieval_workers      ) > 0:
  				ray.get(
  				    [
  				        worker.create_rag_retriever.remote(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
  				        for worker in self.retrieval_workers
  				    ]      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ):
  			logger.info("""initializing retrieval"""      )
  
  			if len(self.retrieval_workers      ) > 0:
  				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
  			else:
  				# Non-distributed training. Load index into this same process.
  				self.index.init_index()
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ):
  			if len(self.retrieval_workers      ) > 0:
  				# Select a random retrieval actor.
  				A							  =      self.retrieval_workers[random.randint(0      ,      len(self.retrieval_workers      ) - 1      )]
  				A  ,    A  ,    A							  =      ray.get(
  				    random_worker.retrieve.remote(_lowerCAmelCase      ,      _lowerCAmelCase      )      )
  			else:
  				A  ,    A							  =      self._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				A							  =      self.index.get_doc_dicts(_lowerCAmelCase      )
  			return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (cls						:      Union[str, Any]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      **_lowerCAmelCase						:      Union[str, Any]      ):
  			return super(_lowerCAmelCase      ,      cls      ).get_tokenizers(_lowerCAmelCase      ,      _lowerCAmelCase      ,      **_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (cls						:      List[Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str=None      ,      **_lowerCAmelCase						:      List[str]      ):
  			A							  =      kwargs.pop("""config"""      ,      _lowerCAmelCase      ) or RagConfig.from_pretrained(_lowerCAmelCase      ,      **_lowerCAmelCase      )
  			A							  =      RagTokenizer.from_pretrained(_lowerCAmelCase      ,      config=_lowerCAmelCase      )
  			A							  =      rag_tokenizer.question_encoder
  			A							  =      rag_tokenizer.generator
  
  			if indexed_dataset is not None:
  				A							  =      """custom"""
  				A							  =      CustomHFIndex(config.retrieval_vector_size      ,      _lowerCAmelCase      )
  			else:
  				A							  =      cls._build_index(_lowerCAmelCase      )
  
  			return cls(
  			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      retrieval_workers=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ):
  			logger.info("""re-loading the new dataset with embeddings"""      )
  			# access from the training loop
  
  			ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )
  
  			# build the index object again
  			A							  =      self._build_index(self.config      )
  
  			ray.get(
  			    [
  			        worker.create_rag_retriever.remote(
  			            self.config      ,      self.question_encoder_tokenizer      ,      self.generator_tokenizer      ,      _lowerCAmelCase      )
  			        for worker in self.retrieval_workers
  			    ]      )
  
  