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
  snake_case   :   Optional[int]    		=		False
  snake_case   :   List[str]    		=		Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  snake_case   :   Union[str, Any]    		=		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 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Any							,						UpperCamelCase_   :  List[str]=sys.argv[1:]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						None
             __magic_name__										=						None
             __magic_name__										=						None
             __magic_name__										=						1
             __magic_name__							,     __magic_name__										=						getopt.getopt(UpperCamelCase_							,						'i:o:b:s'							,						['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']   )
             for opt, arg in opts:
                    if opt in ("-i", "--inputdir"):
                           __magic_name__										=						arg
                    elif opt in ("-o", "--outfile"):
                           __magic_name__										=						arg
                    elif opt in ("-b", "--batch_size"):
                           __magic_name__										=						int(UpperCamelCase_   )
                    elif opt in ("-s", "--subset_list"):
                           __magic_name__										=						arg
  
             assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
             assert outputfile is not None and not os.path.isfile(UpperCamelCase_   ), f"""{outputfile}"""
             if subset_list is not None:
                    with open(os.path.realpath(UpperCamelCase_   )   ) as f:
                           __magic_name__										=						{self._vqa_file_split()[0] for x in tryload(UpperCamelCase_   )}
             else:
                    __magic_name__										=						None
  
             __magic_name__										=						CONFIG
             if torch.cuda.is_available():
                    __magic_name__										=						'cuda'
             __magic_name__										=						os.path.realpath(UpperCamelCase_   )
             __magic_name__										=						os.path.realpath(UpperCamelCase_   )
             __magic_name__										=						Preprocess(self.config   )
             __magic_name__										=						GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'							,						config=self.config   )
             __magic_name__										=						batch_size if batch_size != 0 else 1
             __magic_name__										=						DEFAULT_SCHEMA
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						int(file.split('.'   )[0].split('_'   )[-1]   )
             __magic_name__										=						os.path.join(self.inputdir							,						UpperCamelCase_   )
             return (img_id, filepath)
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[]
             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(UpperCamelCase_   )   )
                    if len(UpperCamelCase_   ) == self.batch:
                           __magic_name__										=						batch
                           __magic_name__										=						[]
                           yield list(map(UpperCamelCase_							,						zip(*UpperCamelCase_   )   )   )
  
             for i in range(1   ):
                    yield list(map(UpperCamelCase_							,						zip(*UpperCamelCase_   )   )   )
  
  
  
  
  
  
  
      def __call__(		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             if not TEST:
                    __magic_name__										=						datasets.ArrowWriter(features=self.schema							,						path=self.outputfile   )
             # do file generator
             for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                    __magic_name__							,     __magic_name__							,     __magic_name__										=						self.preprocess(UpperCamelCase_   )
                    __magic_name__										=						self.model(
                        UpperCamelCase_							,						UpperCamelCase_							,						scales_yx=UpperCamelCase_							,						padding='max_detections'							,						max_detections=self.config.MAX_DETECTIONS							,						pad_value=0							,						return_tensors='np'							,						location='cpu'							,						)
                    __magic_name__										=						output_dict.pop('normalized_boxes'   )
                    if not TEST:
                           __magic_name__										=						np.array(UpperCamelCase_   )
                           __magic_name__										=						self.schema.encode_batch(UpperCamelCase_   )
                           writer.write_batch(UpperCamelCase_   )
                    if TEST:
                           break
                    # finalizer the writer
             if not TEST:
                    __magic_name__							,     __magic_name__										=						writer.finalize()
                    print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""   )
  
  
  def        A	(							__snake_case:   str     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         try:
                __magic_name__										=						json.load(__snake_case     )
                try:
                       __magic_name__										=						list(data.keys()     )
                except Exception:
                       __magic_name__										=						[d['img_id'] for d in data]
         except Exception:
                try:
                       __magic_name__										=						eval(stream.read()     )
                except Exception:
                       __magic_name__										=						stream.read().split('\n'     )
         return data
  
  
  if __name__ == "__main__":
   snake_case   :   List[str]    		=		Extract(sys.argv[1:])
   extract()
   if not TEST:
    snake_case   :   int    		=		datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         sequence.append(__snake_case     )
  
         __magic_name__										=						0
         while sequence[index] != target:
                index += 1
  
         sequence.pop()
  
         if index == len(__snake_case     ):
                return None
  
         return index
  
  
  if __name__ == "__main__":
   snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
   snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]
  
   snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
   snake_case   :   str    		=		int(target_input)
   snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
   if result is not None:
    print(f"""{target} found at positions: {result}""")
   else:
    print("""Not found""")