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
  __snake_case             =	False
  __snake_case             =	Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
  __snake_case             =	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   __lowerCamelCase :
  
  
  
  
  
  
  
         def __init__( self:   List[str],A_:   int=sys.argv[1:]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = None
              __UpperCamelCase   = None
              __UpperCamelCase   = None
              __UpperCamelCase   = 1
              __UpperCamelCase   = getopt.getopt(_lowerCamelCase,'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(_lowerCamelCase				)
                   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(_lowerCamelCase				), F'''{outputfile}'''
              if subset_list is not None:
                   with open(os.path.realpath(_lowerCamelCase				)				) as f:
                        __UpperCamelCase   = {self._vqa_file_split()[0] for x in tryload(_lowerCamelCase				)}
              else:
                   __UpperCamelCase   = None
  
              __UpperCamelCase   = CONFIG
              if torch.cuda.is_available():
                   __UpperCamelCase   = """cuda"""
              __UpperCamelCase   = os.path.realpath(_lowerCamelCase				)
              __UpperCamelCase   = os.path.realpath(_lowerCamelCase				)
              __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 		snake_case_  ( self:   Tuple,A_:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = int(file.split('.'				)[0].split('_'				)[-1]				)
              __UpperCamelCase   = os.path.join(self.inputdir,_lowerCamelCase				)
              return (img_id, filepath)
  
  
  
  
  
  
  
         @property
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __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(_lowerCamelCase				)				)
                   if len(_lowerCamelCase				) == self.batch:
                        __UpperCamelCase   = batch
                        __UpperCamelCase   = []
                        yield list(map(_lowerCamelCase,zip(*_lowerCamelCase				)				)				)
  
              for i in range(1				):
                   yield list(map(_lowerCamelCase,zip(*_lowerCamelCase				)				)				)
  
  
  
  
  
  
  
         def __call__( self:   int				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              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(_lowerCamelCase				)
                   __UpperCamelCase   = self.model(
                       _lowerCamelCase,_lowerCamelCase,scales_yx=_lowerCamelCase,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(_lowerCamelCase				)
                        __UpperCamelCase   = self.schema.encode_batch(_lowerCamelCase				)
                        writer.write_batch(_lowerCamelCase				)
                   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		(		_lowercase   ):
  
  
       """simple docstring"""
  
  
       try:
            __UpperCamelCase   = json.load(_lowercase   )
            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__":
      __snake_case             =	Extract(sys.argv[1:])
      extract()
      if not TEST:
          __snake_case             =	datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
style_context: 
  
  
  from ..utils import DummyObject, requires_backends
  
  
  
  class   __lowerCamelCase (metaclass=_a	):
         _lowercase       							=    ["""torch""", """transformers""", """k_diffusion"""]
  
  
  
  
  
  
  
         def __init__( self:   Tuple,*A_:   Union[str, Any],**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(self,['torch', 'transformers', 'k_diffusion']				)
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   int,*A_:   Optional[Any],**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   int,*A_:   Tuple,**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)
  
  
  