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
  __A :		str      							=    False
  __A :		Dict      							=    Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
  __A :		List[str]      							=    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 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=sys.argv[1:] ):
         UpperCamelCase				:   List[Any]          =							None
         UpperCamelCase				:   Tuple          =							None
         UpperCamelCase				:   List[Any]          =							None
         UpperCamelCase				:   List[str]          =							1
         UpperCamelCase       ,							UpperCamelCase				:   Any          =							getopt.getopt(SCREAMING_SNAKE_CASE_    ,      """i:o:b:s"""    ,      ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""] )
         for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                 UpperCamelCase				:   Any          =							arg
             elif opt in ("-o", "--outfile"):
                 UpperCamelCase				:   List[str]          =							arg
             elif opt in ("-b", "--batch_size"):
                 UpperCamelCase				:   int          =							int(SCREAMING_SNAKE_CASE_ )
             elif opt in ("-s", "--subset_list"):
                 UpperCamelCase				:   Tuple          =							arg
  
         assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
         assert outputfile is not None and not os.path.isfile(SCREAMING_SNAKE_CASE_ ), f'{outputfile}'
         if subset_list is not None:
             with open(os.path.realpath(SCREAMING_SNAKE_CASE_ ) ) as f:
                 UpperCamelCase				:   Optional[Any]          =							{self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE_ )}
         else:
             UpperCamelCase				:   Tuple          =							None
  
         UpperCamelCase				:   List[str]          =							CONFIG
         if torch.cuda.is_available():
             UpperCamelCase				:   Tuple          =							"""cuda"""
         UpperCamelCase				:   int          =							os.path.realpath(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   int          =							os.path.realpath(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Any          =							Preprocess(self.config )
         UpperCamelCase				:   int          =							GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""    ,      config=self.config )
         UpperCamelCase				:   List[Any]          =							batch_size if batch_size != 0 else 1
         UpperCamelCase				:   Union[str, Any]          =							DEFAULT_SCHEMA
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							int(file.split(""".""" )[0].split("""_""" )[-1] )
         UpperCamelCase				:   int          =							os.path.join(self.inputdir    ,      SCREAMING_SNAKE_CASE_ )
         return (img_id, filepath)
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         UpperCamelCase				:   List[str]          =							[]
         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(SCREAMING_SNAKE_CASE_ ) )
             if len(SCREAMING_SNAKE_CASE_ ) == self.batch:
                 UpperCamelCase				:   Dict          =							batch
                 UpperCamelCase				:   List[str]          =							[]
                 yield list(map(SCREAMING_SNAKE_CASE_    ,      zip(*SCREAMING_SNAKE_CASE_ ) ) )
  
         for i in range(1 ):
             yield list(map(SCREAMING_SNAKE_CASE_    ,      zip(*SCREAMING_SNAKE_CASE_ ) ) )
  
  
  
  
  
  
  
     def __call__(							self ):
         # make writer
         if not TEST:
             UpperCamelCase				:   int          =							datasets.ArrowWriter(features=self.schema    ,      path=self.outputfile )
         # do file generator
         for i, (img_ids, filepaths) in enumerate(self.file_generator ):
             UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Any          =							self.preprocess(SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   int          =							self.model(
                 SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      scales_yx=SCREAMING_SNAKE_CASE_    ,      padding="""max_detections"""    ,      max_detections=self.config.MAX_DETECTIONS    ,      pad_value=0    ,      return_tensors="""np"""    ,      location="""cpu"""    ,      )
             UpperCamelCase				:   str          =							output_dict.pop("""normalized_boxes""" )
             if not TEST:
                 UpperCamelCase				:   Dict          =							np.array(SCREAMING_SNAKE_CASE_ )
                 UpperCamelCase				:   Optional[Any]          =							self.schema.encode_batch(SCREAMING_SNAKE_CASE_ )
                 writer.write_batch(SCREAMING_SNAKE_CASE_ )
             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 					A_			(		snake_case_     :       str    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      try:
          UpperCamelCase				:   List[Any]          =							json.load(snake_case_    )
          try:
              UpperCamelCase				:   Tuple          =							list(data.keys()    )
          except Exception:
              UpperCamelCase				:   str          =							[d["""img_id"""] for d in data]
      except Exception:
          try:
              UpperCamelCase				:   Dict          =							eval(stream.read()    )
          except Exception:
              UpperCamelCase				:   str          =							stream.read().split("""\n"""    )
      return data
  
  
  if __name__ == "__main__":
    __A :		Optional[int]      							=    Extract(sys.argv[1:])
    extract()
    if not TEST:
      __A :		Tuple      							=    datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      assert b != 0, "This cannot accept modulo that is == 0"
      if n == 0:
          return 1
  
      if n % 2 == 1:
          return (bin_exp_mod(snake_case_						,n - 1						,snake_case_    ) * a) % b
  
      UpperCamelCase				:   List[Any]          =							bin_exp_mod(snake_case_						,n / 2						,snake_case_    )
      return (r * r) % b
  
  
  if __name__ == "__main__":
    try:
      __A :		Any      							=    int(input('''Enter Base : ''').strip())
      __A :		Any      							=    int(input('''Enter Power : ''').strip())
      __A :		List[Any]      							=    int(input('''Enter Modulo : ''').strip())
    except ValueError:
      print('''Invalid literal for integer''')
  
    print(bin_exp_mod(BASE, POWER, MODULO))
  
  
  
  
  