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, 20_48), dtype='''float32'''),
              '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
              '''preds_per_image''': datasets.Value(dtype='''int32'''),
          }
      )
  )
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__=sys.argv[1:]		):
              lowercase			:   List[Any]					   =		None
              lowercase			:   Union[str, Any]					   =		None
              lowercase			:   Union[str, Any]					   =		None
              lowercase			:   List[Any]					   =		1
              lowercase			:   str					   =		getopt.getopt(__a							,						'''i:o:b:s'''							,						['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']		)
              for opt, arg in opts:
                    if opt in ("-i", "--inputdir"):
                          lowercase			:   List[str]					   =		arg
                    elif opt in ("-o", "--outfile"):
                          lowercase			:   Any					   =		arg
                    elif opt in ("-b", "--batch_size"):
                          lowercase			:   Optional[Any]					   =		int(__a		)
                    elif opt in ("-s", "--subset_list"):
                          lowercase			:   Any					   =		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:
                          lowercase			:   int					   =		{self._vqa_file_split()[0] for x in tryload(__a		)}
              else:
                    lowercase			:   Tuple					   =		None
  
              lowercase			:   str					   =		CONFIG
              if torch.cuda.is_available():
                    lowercase			:   Optional[int]					   =		'cuda'
              lowercase			:   Union[str, Any]					   =		os.path.realpath(__a		)
              lowercase			:   int					   =		os.path.realpath(__a		)
              lowercase			:   Optional[int]					   =		Preprocess(self.config		)
              lowercase			:   List[Any]					   =		GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''							,						config=self.config		)
              lowercase			:   Optional[Any]					   =		batch_size if batch_size != 0 else 1
              lowercase			:   Any					   =		DEFAULT_SCHEMA
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Optional[int]					   =		int(file.split('''.'''		)[0].split('''_'''		)[-1]		)
              lowercase			:   int					   =		os.path.join(self.inputdir							,						__a		)
              return (img_id, filepath)
  
  
        @property
        def        __lowerCamelCase			(							self		):
              lowercase			:   Optional[Any]					   =		[]
              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:
                          lowercase			:   List[Any]					   =		batch
                          lowercase			:   str					   =		[]
                          yield list(map(__a							,						zip(*__a		)		)		)
  
              for i in range(1		):
                    yield list(map(__a							,						zip(*__a		)		)		)
  
  
  
  
  
  
        def __call__(							self		):
  
  
  
  
  
              if not TEST:
                    lowercase			:   Any					   =		datasets.ArrowWriter(features=self.schema							,						path=self.outputfile		)
              # do file generator
              for i, (img_ids, filepaths) in enumerate(self.file_generator		):
                    lowercase			:   Tuple					   =		self.preprocess(__a		)
                    lowercase			:   Union[str, Any]					   =		self.model(
                        __a							,						__a							,						scales_yx=__a							,						padding='''max_detections'''							,						max_detections=self.config.MAX_DETECTIONS							,						pad_value=0							,						return_tensors='''np'''							,						location='''cpu'''							,						)
                    lowercase			:   str					   =		output_dict.pop('''normalized_boxes'''		)
                    if not TEST:
                          lowercase			:   List[str]					   =		np.array(__a		)
                          lowercase			:   List[Any]					   =		self.schema.encode_batch(__a		)
                          writer.write_batch(__a		)
                    if TEST:
                          break
                    # finalizer the writer
              if not TEST:
                    lowercase			:   Union[str, Any]					   =		writer.finalize()
                    print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""		)
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        try:
              lowercase			:   List[Any]					   =		json.load(_SCREAMING_SNAKE_CASE     )
              try:
                    lowercase			:   Optional[Any]					   =		list(data.keys()     )
              except Exception:
                    lowercase			:   List[Any]					   =		[d['img_id'] for d in data]
        except Exception:
              try:
                    lowercase			:   List[str]					   =		eval(stream.read()     )
              except Exception:
                    lowercase			:   Union[str, Any]					   =		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 typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __a   				=      {
      '''configuration_decision_transformer''': [
          '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''DecisionTransformerConfig''',
      ],
  }
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __a   				=      [
        '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DecisionTransformerGPT2Model''',
        '''DecisionTransformerGPT2PreTrainedModel''',
        '''DecisionTransformerModel''',
        '''DecisionTransformerPreTrainedModel''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DecisionTransformerConfig,
    )
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  