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   						=    False
  _SCREAMING_SNAKE_CASE   						=    Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  _SCREAMING_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, 2048), dtype="""float32"""),
              """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
              """preds_per_image""": datasets.Value(dtype="""int32"""),
          }
      )
  )
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
      '''simple docstring'''
  
  
  
  
  
      def __init__(					self  :   int      ,      lowerCAmelCase  :   List[str]=sys.argv[1:]   )	-> Any:
        a__     		=     None
        a__     		=     None
        a__     		=     None
        a__     		=     1
        a__		,a__     		=     getopt.getopt(lowerCAmelCase      ,      "i:o:b:s"      ,      ["inputdir=", "outfile=", "batch_size=", "subset_list="]   )
        for opt, arg in opts:
          if opt in ("-i", "--inputdir"):
            a__     		=     arg
          elif opt in ("-o", "--outfile"):
            a__     		=     arg
          elif opt in ("-b", "--batch_size"):
            a__     		=     int(lowerCAmelCase   )
          elif opt in ("-s", "--subset_list"):
            a__     		=     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:
            a__     		=     {self._vqa_file_split()[0] for x in tryload(lowerCAmelCase   )}
        else:
          a__     		=     None
  
        a__     		=     CONFIG
        if torch.cuda.is_available():
          a__     		=     "cuda"
        a__     		=     os.path.realpath(lowerCAmelCase   )
        a__     		=     os.path.realpath(lowerCAmelCase   )
        a__     		=     Preprocess(self.config   )
        a__     		=     GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"      ,      config=self.config   )
        a__     		=     batch_size if batch_size != 0 else 1
        a__     		=     DEFAULT_SCHEMA
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any      ,      lowerCAmelCase  :   Optional[Any]   )	-> str:
        a__     		=     int(file.split("."   )[0].split("_"   )[-1]   )
        a__     		=     os.path.join(self.inputdir      ,      lowerCAmelCase   )
        return (img_id, filepath)
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   Any   )	-> Optional[Any]:
        a__     		=     []
        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:
            a__     		=     batch
            a__     		=     []
            yield list(map(lowerCAmelCase      ,      zip(*lowerCAmelCase   )   )   )
  
        for i in range(1   ):
          yield list(map(lowerCAmelCase      ,      zip(*lowerCAmelCase   )   )   )
  
  
  
  
      def __call__(					self  :   List[Any]   )	-> Tuple:
        # make writer
        if not TEST:
          a__     		=     datasets.ArrowWriter(features=self.schema      ,      path=self.outputfile   )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
          a__		,a__		,a__     		=     self.preprocess(lowerCAmelCase   )
          a__     		=     self.model(
              lowerCAmelCase      ,      lowerCAmelCase      ,      scales_yx=lowerCAmelCase      ,      padding="max_detections"      ,      max_detections=self.config.MAX_DETECTIONS      ,      pad_value=0      ,      return_tensors="np"      ,      location="cpu"      ,      )
          a__     		=     output_dict.pop("normalized_boxes"   )
          if not TEST:
            a__     		=     np.array(lowerCAmelCase   )
            a__     		=     self.schema.encode_batch(lowerCAmelCase   )
            writer.write_batch(lowerCAmelCase   )
          if TEST:
            break
          # finalizer the writer
        if not TEST:
          a__		,a__     		=     writer.finalize()
          print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"   )
  
  def 				__lowercase	(						lowerCamelCase__					)				->Tuple:
  
    '''simple docstring'''
  
  
  
  
    try:
      a__     		=     json.load(lowerCamelCase__					)
      try:
        a__     		=     list(data.keys()					)
      except Exception:
        a__     		=     [d["img_id"] for d in data]
    except Exception:
      try:
        a__     		=     eval(stream.read()					)
      except Exception:
        a__     		=     stream.read().split("\n"					)
    return data
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    Extract(sys.argv[1:])
       extract()
       if not TEST:
            _SCREAMING_SNAKE_CASE   						=    datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
style_context: from unittest.mock import patch
  
  import datasets
  from datasets import Dataset
  def 				__lowercase	(						)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     Dataset.from_dict({"col_1": [3, 2, 0, 1]}					)
  
    with patch("tqdm.auto.tqdm"					) as mock_tqdm:
      datasets.disable_progress_bar()
      dset.map(lambda lowerCamelCase__					: {"col_2": x["col_1"] + 1}					)
      mock_tqdm.assert_not_called()
  
      mock_tqdm.reset_mock()
  
      datasets.enable_progress_bar()
      dset.map(lambda lowerCamelCase__					: {"col_2": x["col_1"] + 1}					)
      mock_tqdm.assert_called()
  
  