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
  lowerCAmelCase					 =	False
  lowerCAmelCase					 =	Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
  lowerCAmelCase					 =	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, 2_0_4_8), dtype='''float32'''),
              '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
              '''preds_per_image''': datasets.Value(dtype='''int32'''),
          }
      )
  )
  
  
  
  
  
  class    A					:
  
  
  							def __init__(self   ,       lowerCAmelCase=sys.argv[1:]      ):
  									__lowercase=    None
  									__lowercase=    None
  									__lowercase=    None
  									__lowercase=    1
  									__lowercase,							__lowercase=    getopt.getopt(__snake_case   ,       'i:o:b:s'   ,       ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
  									for opt, arg in opts:
  											if opt in ("-i", "--inputdir"):
  													__lowercase=    arg
  											elif opt in ("-o", "--outfile"):
  													__lowercase=    arg
  											elif opt in ("-b", "--batch_size"):
  													__lowercase=    int(__snake_case      )
  											elif opt in ("-s", "--subset_list"):
  													__lowercase=    arg
  
  									assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
  									assert outputfile is not None and not os.path.isfile(__snake_case      ), f'{outputfile}'
  									if subset_list is not None:
  											with open(os.path.realpath(__snake_case      )      ) as f:
  													__lowercase=    {self._vqa_file_split()[0] for x in tryload(__snake_case      )}
  									else:
  											__lowercase=    None
  
  									__lowercase=    CONFIG
  									if torch.cuda.is_available():
  											__lowercase=    'cuda'
  									__lowercase=    os.path.realpath(__snake_case      )
  									__lowercase=    os.path.realpath(__snake_case      )
  									__lowercase=    Preprocess(self.config      )
  									__lowercase=    GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'   ,       config=self.config      )
  									__lowercase=    batch_size if batch_size != 0 else 1
  									__lowercase=    DEFAULT_SCHEMA
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    int(file.split('.'      )[0].split('_'      )[-1]      )
  									__lowercase=    os.path.join(self.inputdir   ,       __snake_case      )
  									return (img_id, filepath)
  
  
  							@property
  							def     _A (self      ):
  									__lowercase=    []
  									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(__snake_case      )      )
  											if len(__snake_case      ) == self.batch:
  													__lowercase=    batch
  													__lowercase=    []
  													yield list(map(__snake_case   ,       zip(*__snake_case      )      )      )
  
  									for i in range(1      ):
  											yield list(map(__snake_case   ,       zip(*__snake_case      )      )      )
  
  
  
  
  
  
  
  							def __call__(self      ):
  									# make writer
  									if not TEST:
  											__lowercase=    datasets.ArrowWriter(features=self.schema   ,       path=self.outputfile      )
  									# do file generator
  									for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
  											__lowercase,							__lowercase,							__lowercase=    self.preprocess(__snake_case      )
  											__lowercase=    self.model(
  											    __snake_case   ,       __snake_case   ,       scales_yx=__snake_case   ,       padding='max_detections'   ,       max_detections=self.config.MAX_DETECTIONS   ,       pad_value=0   ,       return_tensors='np'   ,       location='cpu'   ,       )
  											__lowercase=    output_dict.pop('normalized_boxes'      )
  											if not TEST:
  													__lowercase=    np.array(__snake_case      )
  													__lowercase=    self.schema.encode_batch(__snake_case      )
  													writer.write_batch(__snake_case      )
  											if TEST:
  													break
  											# finalizer the writer
  									if not TEST:
  											__lowercase,							__lowercase=    writer.finalize()
  											print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'      )
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			Optional[int]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		try:
  				__lowercase=    json.load(UpperCamelCase_			)
  				try:
  						__lowercase=    list(data.keys()			)
  				except Exception:
  						__lowercase=    [d['img_id'] for d in data]
  		except Exception:
  				try:
  						__lowercase=    eval(stream.read()			)
  				except Exception:
  						__lowercase=    stream.read().split('\n'			)
  		return data
  
  
  if __name__ == "__main__":
  	lowerCAmelCase					 =	Extract(sys.argv[1:])
  	extract()
  	if not TEST:
  		lowerCAmelCase					 =	datasets.Dataset.from_file(extract.outputfile)
  		# wala!
  		# print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
  
  
style_context: 
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return " ".join([word[::-1] for word in input_str.split()]			)
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  
  