code: 
  
  
  
  import getopt
  import json
  import os
  
  # import numpy as np
  import sys
  from collections import OrderedDict
  
  import datasets
  import numpy as np
  import torch
  from modeling_frcnn import GeneralizedRCNN
  from processing_image import Preprocess
  
  from utils import Config
  __A       					=		False
  __A       					=		Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
  __A       					=		datasets.Features(
      OrderedDict(
          {
              '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
              '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
              '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
              '''img_id''': datasets.Value('''int32'''),
              '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
              '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
              '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype='''float32'''),
              '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
              '''preds_per_image''': datasets.Value(dtype='''int32'''),
          }
      )
  )
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		Optional[Any]		,						__UpperCAmelCase			:		Optional[Any]=sys.argv[1:]  )    ->				Optional[Any]:
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     1
  							UpperCAmelCase_=     getopt.getopt(__a		,						"""i:o:b:s"""		,						["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]  )
  							for opt, arg in opts:
  													if opt in ("-i", "--inputdir"):
  																			UpperCAmelCase_=     arg
  													elif opt in ("-o", "--outfile"):
  																			UpperCAmelCase_=     arg
  													elif opt in ("-b", "--batch_size"):
  																			UpperCAmelCase_=     int(__a  )
  													elif opt in ("-s", "--subset_list"):
  																			UpperCAmelCase_=     arg
  
  							assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
  							assert outputfile is not None and not os.path.isfile(__a  ), F"""{outputfile}"""
  							if subset_list is not None:
  													with open(os.path.realpath(__a  )  ) as f:
  																			UpperCAmelCase_=     {self._vqa_file_split()[0] for x in tryload(__a  )}
  							else:
  													UpperCAmelCase_=     None
  
  							UpperCAmelCase_=     CONFIG
  							if torch.cuda.is_available():
  													UpperCAmelCase_=     'cuda'
  							UpperCAmelCase_=     os.path.realpath(__a  )
  							UpperCAmelCase_=     os.path.realpath(__a  )
  							UpperCAmelCase_=     Preprocess(self.config  )
  							UpperCAmelCase_=     GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""		,						config=self.config  )
  							UpperCAmelCase_=     batch_size if batch_size != 0 else 1
  							UpperCAmelCase_=     DEFAULT_SCHEMA
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		List[str]  )    ->				Dict:
  							UpperCAmelCase_=     int(file.split("""."""  )[0].split("""_"""  )[-1]  )
  							UpperCAmelCase_=     os.path.join(self.inputdir		,						__a  )
  							return (img_id, filepath)
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Optional[Any]:
  							UpperCAmelCase_=     []
  							for i, file in enumerate(os.listdir(self.inputdir  )  ):
  													if self.subset_list is not None and i not in self.subset_list:
  																			continue
  													batch.append(self._vqa_file_split(__a  )  )
  													if len(__a  ) == self.batch:
  																			UpperCAmelCase_=     batch
  																			UpperCAmelCase_=     []
  																			yield list(map(__a		,						zip(*__a  )  )  )
  
  							for i in range(1  ):
  													yield list(map(__a		,						zip(*__a  )  )  )
  
  
  
  
  	def __call__(			self			:		Any  )    ->				List[str]:
  
  
  
  
  
  							if not TEST:
  													UpperCAmelCase_=     datasets.ArrowWriter(features=self.schema		,						path=self.outputfile  )
  							# do file generator
  							for i, (img_ids, filepaths) in enumerate(self.file_generator  ):
  													UpperCAmelCase_=     self.preprocess(__a  )
  													UpperCAmelCase_=     self.model(
  													    __a		,						__a		,						scales_yx=__a		,						padding="""max_detections"""		,						max_detections=self.config.MAX_DETECTIONS		,						pad_value=0		,						return_tensors="""np"""		,						location="""cpu"""		,						)
  													UpperCAmelCase_=     output_dict.pop("""normalized_boxes"""  )
  													if not TEST:
  																			UpperCAmelCase_=     np.array(__a  )
  																			UpperCAmelCase_=     self.schema.encode_batch(__a  )
  																			writer.write_batch(__a  )
  													if TEST:
  																			break
  													# finalizer the writer
  							if not TEST:
  													UpperCAmelCase_=     writer.finalize()
  													print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""  )
  
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[Any]       )   ->				Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						try:
  												UpperCAmelCase_=     json.load(_SCREAMING_SNAKE_CASE       )
  												try:
  																		UpperCAmelCase_=     list(data.keys()       )
  												except Exception:
  																		UpperCAmelCase_=     [d['img_id'] for d in data]
  						except Exception:
  												try:
  																		UpperCAmelCase_=     eval(stream.read()       )
  												except Exception:
  																		UpperCAmelCase_=     stream.read().split("""\n"""       )
  						return data
  
  
  if __name__ == "__main__":
  	__A       					=		Extract(sys.argv[1:])
  	extract()
  	if not TEST:
  		__A       					=		datasets.Dataset.from_file(extract.outputfile)
  		# wala!
  		# print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
style_context: 
  
  
  
  from .prime_check import Test
  
  Test()
  
  
  
  
  