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_    :					Dict             ={
      """translation""": ["""bleu"""],
      """summarization""": ROUGE_KEYS,
  }
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""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   lowerCamelCase_       (					):
  
  
  
  						"""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: 
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
  	import torch
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		['''image_processor''', '''tokenizer''']
  							snake_case_        =		'''OneFormerImageProcessor'''
  							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
  							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if image_processor is None:
  																			raise ValueError("""You need to specify an `image_processor`."""		)
  													if tokenizer is None:
  																			raise ValueError("""You need to specify a `tokenizer`."""		)
  
  													a_					=		max_seq_length
  													a_					=		task_seq_length
  
  													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if text_list is None:
  																			raise ValueError("""tokens cannot be None."""		)
  
  													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
  													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]
  
  													a_					=		[]
  													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
  																			token_inputs.append(token.unsqueeze(0		)		)
  
  													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  													return token_inputs
  
  
  							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)