code: 
  '''simple docstring'''
  
  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
  UpperCamelCase_     			=       False
  UpperCamelCase_     			=       Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  UpperCamelCase_     			=       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   a_  :
  
  
  
  
         def __init__(     self   ,   snake_case_=sys.argv[1:]  ):
           _lowerCAmelCase       :  Dict   				=					None
           _lowerCAmelCase       :  Optional[Any]   				=					None
           _lowerCAmelCase       :  Optional[int]   				=					None
           _lowerCAmelCase       :  Optional[int]   				=					1
           _lowerCAmelCase				,					_lowerCAmelCase       :  str   				=					getopt.getopt(snake_case_   ,   """i:o:b:s"""   ,   ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]  )
           for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
               _lowerCAmelCase       :  int   				=					arg
             elif opt in ("-o", "--outfile"):
               _lowerCAmelCase       :  Tuple   				=					arg
             elif opt in ("-b", "--batch_size"):
               _lowerCAmelCase       :  Any   				=					int(snake_case_  )
             elif opt in ("-s", "--subset_list"):
               _lowerCAmelCase       :  List[str]   				=					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:
               _lowerCAmelCase       :  List[Any]   				=					{self._vqa_file_split()[0] for x in tryload(snake_case_  )}
           else:
             _lowerCAmelCase       :  Optional[int]   				=					None
  
           _lowerCAmelCase       :  Optional[int]   				=					CONFIG
           if torch.cuda.is_available():
             _lowerCAmelCase       :  List[str]   				=					"""cuda"""
           _lowerCAmelCase       :  Tuple   				=					os.path.realpath(snake_case_  )
           _lowerCAmelCase       :  Optional[int]   				=					os.path.realpath(snake_case_  )
           _lowerCAmelCase       :  Optional[Any]   				=					Preprocess(self.config  )
           _lowerCAmelCase       :  List[str]   				=					GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""   ,   config=self.config  )
           _lowerCAmelCase       :  Optional[Any]   				=					batch_size if batch_size != 0 else 1
           _lowerCAmelCase       :  int   				=					DEFAULT_SCHEMA
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           _lowerCAmelCase       :  Any   				=					int(file.split("""."""  )[0].split("""_"""  )[-1]  )
           _lowerCAmelCase       :  Dict   				=					os.path.join(self.inputdir   ,   snake_case_  )
           return (img_id, filepath)
  
  
  
  
         @property
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  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(snake_case_  )  )
             if len(snake_case_  ) == self.batch:
               _lowerCAmelCase       :  Union[str, Any]   				=					batch
               _lowerCAmelCase       :  str   				=					[]
               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:
             _lowerCAmelCase       :  Optional[Any]   				=					datasets.ArrowWriter(features=self.schema   ,   path=self.outputfile  )
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator  ):
             _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase       :  Any   				=					self.preprocess(snake_case_  )
             _lowerCAmelCase       :  Dict   				=					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"""   ,   )
             _lowerCAmelCase       :  Any   				=					output_dict.pop("""normalized_boxes"""  )
             if not TEST:
               _lowerCAmelCase       :  Tuple   				=					np.array(snake_case_  )
               _lowerCAmelCase       :  Tuple   				=					self.schema.encode_batch(snake_case_  )
               writer.write_batch(snake_case_  )
             if TEST:
               break
             # finalizer the writer
           if not TEST:
             _lowerCAmelCase				,					_lowerCAmelCase       :  List[Any]   				=					writer.finalize()
             print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'  )
  
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Optional[int]		)    ->						List[str]:
    try:
      _lowerCAmelCase       :  str   				=					json.load(_lowerCamelCase		)
      try:
        _lowerCAmelCase       :  List[Any]   				=					list(data.keys()		)
      except Exception:
        _lowerCAmelCase       :  List[Any]   				=					[d["""img_id"""] for d in data]
    except Exception:
      try:
        _lowerCAmelCase       :  Tuple   				=					eval(stream.read()		)
      except Exception:
        _lowerCAmelCase       :  Optional[Any]   				=					stream.read().split("""\n"""		)
    return data
  
  
  if __name__ == "__main__":
       UpperCamelCase_     			=       Extract(sys.argv[1:])
       extract()
       if not TEST:
            UpperCamelCase_     			=       datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_     			=       {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTSanJapaneseForConditionalGeneration""",
           """GPTSanJapaneseModel""",
           """GPTSanJapanesePreTrainedModel""",
       ]
       UpperCamelCase_     			=       [
           """GPTSanJapaneseTokenizer""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  