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       a			(   a  )    ->Union[str, Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   search.split()
  			SCREAMING_SNAKE_CASE    	=   dict((g.split('''='''  ) for g in groups)  )
  			SCREAMING_SNAKE_CASE    	=   list(entries.keys()  )
  			SCREAMING_SNAKE_CASE    	=   [[F"""--{k} {v}""" for v in vs.split(''':'''  )] for k, vs in entries.items()]
  			SCREAMING_SNAKE_CASE    	=   [list(a  ) for x in itertools.product(*a  )]
  			return matrix, entry_names
  
  
  def       a			(   )    ->Union[str, Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   sys.argv[0]
  
  			SCREAMING_SNAKE_CASE    	=   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=a		,	required=a		,	help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''		,	)
  			parser.add_argument(
  			    '''--bs'''		,	type=a		,	default=8		,	required=a		,	help='''initial batch size (may get reduced if it\'s too big)'''  )
  			parser.add_argument('''--task'''		,	type=a		,	help='''used for task_specific_params + metrics'''  )
  			parser.add_argument(
  			    '''--info'''		,	nargs='''?'''		,	type=a		,	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.'''
  			    )		,	)
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   parser.parse_known_args()
  			# we share some of the args
  			args_main.extend(['''--task''', args.task]  )
  			SCREAMING_SNAKE_CASE    	=   [prog] + args_main
  
  			# to support variations like translation_en_to_de"
  			SCREAMING_SNAKE_CASE    	=   '''translation''' if '''translation''' in args.task else '''summarization'''
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   parse_search_arg(args.search  )
  			SCREAMING_SNAKE_CASE    	=   task_score_names[task]  # score cols first
  			SCREAMING_SNAKE_CASE    	=   {col: len(str(a  )  ) for col in col_names}
  			SCREAMING_SNAKE_CASE    	=   []
  			for r in matrix:
  						SCREAMING_SNAKE_CASE    	=   dict((x.replace('''--'''		,	''''''  ).split() for x in r)  )
  						SCREAMING_SNAKE_CASE    	=   ''' '''.join(a  ).split()
  						args_exp.extend(['''--bs''', str(args.bs  )]  )  # in case we need to reduce its size due to CUDA OOM
  						SCREAMING_SNAKE_CASE    	=   args_normal + args_exp
  
  						# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry
  
  						SCREAMING_SNAKE_CASE    	=   run_generate(verbose=a  )
  						# make sure scores are first in the table
  						SCREAMING_SNAKE_CASE    	=   OrderedDict()
  						for score in task_score_names[task]:
  									SCREAMING_SNAKE_CASE    	=   scores[score]
  						result.update(a  )
  						results.append(a  )
  
  						# find widest entries
  						for k, v in result.items():
  									SCREAMING_SNAKE_CASE    	=   len(str(a  )  )
  									if l > col_widths[k]:
  												SCREAMING_SNAKE_CASE    	=   l
  
  			SCREAMING_SNAKE_CASE    	=   sorted(a		,	key=operator.itemgetter(*task_score_names[task]  )		,	reverse=a  )
  			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]  )  )
  
  			SCREAMING_SNAKE_CASE    	=   results_sorted[0]
  			for score in task_score_names[task]:
  						del best[score]
  			SCREAMING_SNAKE_CASE    	=   [F"""--{k} {v}""" for k, v in best.items()]
  			SCREAMING_SNAKE_CASE    	=   ['''--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 copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import AutoConfig
  
  
  __lowerCAmelCase				      =					logging.get_logger(__name__)
  
  __lowerCAmelCase				      =					{
      'facebook/musicgen-small': 'https://huggingface.co/facebook/musicgen-small/resolve/main/config.json',
      # See all Musicgen models at https://huggingface.co/models?filter=musicgen
  }
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  	UpperCamelCase_     :				int								=  'musicgen_decoder'
  	UpperCamelCase_     :				Optional[int]								=  ['past_key_values']
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:Tuple=2_0_4_8      ,    lowercase			:Dict=2_0_4_8      ,    lowercase			:Tuple=2_4      ,    lowercase			:Any=4_0_9_6      ,    lowercase			:Optional[Any]=1_6      ,    lowercase			:List[Any]=0.0      ,    lowercase			:Union[str, Any]=True      ,    lowercase			:Optional[Any]="gelu"      ,    lowercase			:Any=1_0_2_4      ,    lowercase			:str=0.1      ,    lowercase			:List[Any]=0.0      ,    lowercase			:List[Any]=0.0      ,    lowercase			:List[Any]=0.02      ,    lowercase			:Optional[int]=False      ,    lowercase			:List[Any]=4      ,    lowercase			:Any=2_0_4_8      ,    lowercase			:Any=2_0_4_8      ,    lowercase			:Optional[int]=None      ,    lowercase			:Tuple=False      ,    **lowercase			:List[Any]      ,    )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   vocab_size
  				SCREAMING_SNAKE_CASE    	=   max_position_embeddings
  				SCREAMING_SNAKE_CASE    	=   hidden_size
  				SCREAMING_SNAKE_CASE    	=   ffn_dim
  				SCREAMING_SNAKE_CASE    	=   num_hidden_layers
  				SCREAMING_SNAKE_CASE    	=   num_attention_heads
  				SCREAMING_SNAKE_CASE    	=   dropout
  				SCREAMING_SNAKE_CASE    	=   attention_dropout
  				SCREAMING_SNAKE_CASE    	=   activation_dropout
  				SCREAMING_SNAKE_CASE    	=   activation_function
  				SCREAMING_SNAKE_CASE    	=   initializer_factor
  				SCREAMING_SNAKE_CASE    	=   layerdrop
  				SCREAMING_SNAKE_CASE    	=   use_cache
  				SCREAMING_SNAKE_CASE    	=   scale_embedding  # scale factor will be sqrt(d_model) if True
  				SCREAMING_SNAKE_CASE    	=   num_codebooks
  				super().__init__(
  				    pad_token_id=lowercase      ,    bos_token_id=lowercase      ,    eos_token_id=lowercase      ,    tie_word_embeddings=lowercase      ,    **lowercase      ,    )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  	UpperCamelCase_     :				str								=  'musicgen'
  	UpperCamelCase_     :				Union[str, Any]								=  True
  
  
  
  
  
  
  
  	def __init__(    self			:str      ,    **lowercase			:int )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__(**lowercase )
  				if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
  							raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config''' )
  
  				SCREAMING_SNAKE_CASE    	=   kwargs.pop('''text_encoder''' )
  				SCREAMING_SNAKE_CASE    	=   text_encoder_config.pop('''model_type''' )
  
  				SCREAMING_SNAKE_CASE    	=   kwargs.pop('''audio_encoder''' )
  				SCREAMING_SNAKE_CASE    	=   audio_encoder_config.pop('''model_type''' )
  
  				SCREAMING_SNAKE_CASE    	=   kwargs.pop('''decoder''' )
  
  				SCREAMING_SNAKE_CASE    	=   AutoConfig.for_model(lowercase      ,    **lowercase )
  				SCREAMING_SNAKE_CASE    	=   AutoConfig.for_model(lowercase      ,    **lowercase )
  				SCREAMING_SNAKE_CASE    	=   MusicgenDecoderConfig(**lowercase )
  				SCREAMING_SNAKE_CASE    	=   True
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:Tuple      ,    lowercase			:PretrainedConfig      ,    lowercase			:PretrainedConfig      ,    lowercase			:MusicgenDecoderConfig      ,    **lowercase			:Tuple      ,    )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return cls(
  				    text_encoder=text_encoder_config.to_dict()      ,    audio_encoder=audio_encoder_config.to_dict()      ,    decoder=decoder_config.to_dict()      ,    **lowercase      ,    )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   copy.deepcopy(self.__dict__ )
  				SCREAMING_SNAKE_CASE    	=   self.text_encoder.to_dict()
  				SCREAMING_SNAKE_CASE    	=   self.audio_encoder.to_dict()
  				SCREAMING_SNAKE_CASE    	=   self.decoder.to_dict()
  				SCREAMING_SNAKE_CASE    	=   self.__class__.model_type
  				return output