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
  SCREAMING_SNAKE_CASE__		:       Any   							=							False
  SCREAMING_SNAKE_CASE__		:       str   							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  SCREAMING_SNAKE_CASE__		:       int   							=							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, 20_48), dtype="""float32"""),
              """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
              """preds_per_image""": datasets.Value(dtype="""int32"""),
          }
      )
  )
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase=sys.argv[1:]		):
            UpperCAmelCase__			:     List[Any]										= None
            UpperCAmelCase__			:     int										= None
            UpperCAmelCase__			:     List[Any]										= None
            UpperCAmelCase__			:     str										= 1
            UpperCAmelCase__			,		UpperCAmelCase__			:     List[str]										= getopt.getopt(_lowerCAmelCase  ,	"""i:o:b:s"""  ,	["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]		)
            for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                      UpperCAmelCase__			:     str										= arg
                 elif opt in ("-o", "--outfile"):
                      UpperCAmelCase__			:     str										= arg
                 elif opt in ("-b", "--batch_size"):
                      UpperCAmelCase__			:     Dict										= int(_lowerCAmelCase		)
                 elif opt in ("-s", "--subset_list"):
                      UpperCAmelCase__			:     Any										= 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__			:     Optional[int]										= {self._vqa_file_split()[0] for x in tryload(_lowerCAmelCase		)}
            else:
                 UpperCAmelCase__			:     List[str]										= None
  
            UpperCAmelCase__			:     List[Any]										= CONFIG
            if torch.cuda.is_available():
                 UpperCAmelCase__			:     Dict										= """cuda"""
            UpperCAmelCase__			:     Tuple										= os.path.realpath(_lowerCAmelCase		)
            UpperCAmelCase__			:     List[Any]										= os.path.realpath(_lowerCAmelCase		)
            UpperCAmelCase__			:     int										= Preprocess(self.config		)
            UpperCAmelCase__			:     Optional[int]										= GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""  ,	config=self.config		)
            UpperCAmelCase__			:     str										= batch_size if batch_size != 0 else 1
            UpperCAmelCase__			:     Tuple										= DEFAULT_SCHEMA
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     int										= int(file.split("""."""		)[0].split("""_"""		)[-1]		)
            UpperCAmelCase__			:     str										= os.path.join(self.inputdir  ,	_lowerCAmelCase		)
            return (img_id, filepath)
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     int										= []
            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__			:     str										= batch
                      UpperCAmelCase__			:     Tuple										= []
                      yield list(map(_lowerCAmelCase  ,	zip(*_lowerCAmelCase		)		)		)
  
            for i in range(1		):
                 yield list(map(_lowerCAmelCase  ,	zip(*_lowerCAmelCase		)		)		)
  
  
  
  
       def __call__(							self		):
            # make writer
            if not TEST:
                 UpperCAmelCase__			:     Optional[Any]										= datasets.ArrowWriter(features=self.schema  ,	path=self.outputfile		)
            # do file generator
            for i, (img_ids, filepaths) in enumerate(self.file_generator		):
                 UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			:     Dict										= self.preprocess(_lowerCAmelCase		)
                 UpperCAmelCase__			:     int										= 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__			:     Union[str, Any]										= output_dict.pop("""normalized_boxes"""		)
                 if not TEST:
                      UpperCAmelCase__			:     Any										= np.array(_lowerCAmelCase		)
                      UpperCAmelCase__			:     str										= self.schema.encode_batch(_lowerCAmelCase		)
                      writer.write_batch(_lowerCAmelCase		)
                 if TEST:
                      break
                 # finalizer the writer
            if not TEST:
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Union[str, Any]										= writer.finalize()
                 print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:
  
       '''simple docstring'''
       try:
            UpperCAmelCase__			:     Union[str, Any]										= json.load(__lowerCamelCase					)
            try:
                 UpperCAmelCase__			:     List[Any]										= list(data.keys()					)
            except Exception:
                 UpperCAmelCase__			:     Optional[int]										= [d["""img_id"""] for d in data]
       except Exception:
            try:
                 UpperCAmelCase__			:     Dict										= eval(stream.read()					)
            except Exception:
                 UpperCAmelCase__			:     Union[str, Any]										= stream.read().split("""\n"""					)
       return data
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							Extract(sys.argv[1:])
        extract()
        if not TEST:
              SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							datasets.Dataset.from_file(extract.outputfile)
              # wala!
              # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     List[Any]										= value
            UpperCAmelCase__			:     Node | None										= None
            UpperCAmelCase__			:     Node | None										= None
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
       __lowerCamelCase										=     42
  
  
  
  
       def __init__(							self		):
            UpperCAmelCase__			:     Any										= 0
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if node is None:
                 return
  
            if path_sum == self.target:
                 self.paths += 1
  
            if node.left:
                 self.depth_first_search(node.left  ,	path_sum + node.left.value		)
            if node.right:
                 self.depth_first_search(node.right  ,	path_sum + node.right.value		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
            if node is None:
                 return 0
            if target is not None:
                 UpperCAmelCase__			:     List[Any]										= target
  
            self.depth_first_search(_lowerCAmelCase  ,	node.value		)
            self.path_sum(node.left		)
            self.path_sum(node.right		)
  
            return self.paths
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
  
  
  
  
  
  
  