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    :				List[Any]       =					False
  _SCREAMING_SNAKE_CASE    :				Any       =					Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
  _SCREAMING_SNAKE_CASE    :				Tuple       =					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__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case=sys.argv[1:]					):
           snake_case					=       None
           snake_case					=       None
           snake_case					=       None
           snake_case					=       1
           snake_case				,						snake_case					=       getopt.getopt(__snake_case    ,      '''i:o:b:s'''    ,      ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']					)
           for opt, arg in opts:
                if opt in ("-i", "--inputdir"):
                     snake_case					=       arg
                elif opt in ("-o", "--outfile"):
                     snake_case					=       arg
                elif opt in ("-b", "--batch_size"):
                     snake_case					=       int(__snake_case					)
                elif opt in ("-s", "--subset_list"):
                     snake_case					=       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:
                     snake_case					=       {self._vqa_file_split()[0] for x in tryload(__snake_case					)}
           else:
                snake_case					=       None
  
           snake_case					=       CONFIG
           if torch.cuda.is_available():
                snake_case					=       '''cuda'''
           snake_case					=       os.path.realpath(__snake_case					)
           snake_case					=       os.path.realpath(__snake_case					)
           snake_case					=       Preprocess(self.config					)
           snake_case					=       GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''    ,      config=self.config					)
           snake_case					=       batch_size if batch_size != 0 else 1
           snake_case					=       DEFAULT_SCHEMA
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       int(file.split('''.'''					)[0].split('''_'''					)[-1]					)
           snake_case					=       os.path.join(self.inputdir    ,      __snake_case					)
           return (img_id, filepath)
      @property
      def 	a_    ( self					):
           snake_case					=       []
           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:
                     snake_case					=       batch
                     snake_case					=       []
                     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:
                snake_case					=       datasets.ArrowWriter(features=self.schema    ,      path=self.outputfile					)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator					):
                snake_case				,						snake_case				,						snake_case					=       self.preprocess(__snake_case					)
                snake_case					=       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'''    ,      )
                snake_case					=       output_dict.pop('''normalized_boxes'''					)
                if not TEST:
                     snake_case					=       np.array(__snake_case					)
                     snake_case					=       self.schema.encode_batch(__snake_case					)
                     writer.write_batch(__snake_case					)
                if TEST:
                     break
                # finalizer the writer
           if not TEST:
                snake_case				,						snake_case					=       writer.finalize()
                print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''					)
  
  
  
  
  
  
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       try:
            snake_case					=       json.load(UpperCamelCase_       )
            try:
                 snake_case					=       list(data.keys()       )
            except Exception:
                 snake_case					=       [d['''img_id'''] for d in data]
       except Exception:
            try:
                 snake_case					=       eval(stream.read()       )
            except Exception:
                 snake_case					=       stream.read().split('''\n'''       )
       return data
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					Extract(sys.argv[1:])
   extract()
   if not TEST:
    _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
  
  
style_context: 
  
  
  from manim import *
  
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       Rectangle(height=0.5    ,      width=0.5					)
           snake_case					=       Rectangle(height=0.46    ,      width=0.46					).set_stroke(width=0					)
           snake_case					=       Rectangle(height=0.25    ,      width=0.25					)
  
           snake_case					=       [mem.copy() for i in range(6					)]
           snake_case					=       [mem.copy() for i in range(6					)]
           snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
           snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
           snake_case					=       VGroup(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0					)
           snake_case					=       Text('''CPU'''    ,      font_size=2_4					)
           snake_case					=       Group(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0.5    ,      aligned_edge=__snake_case					)
           cpu.move_to([-2.5, -0.5, 0]					)
           self.add(__snake_case					)
  
           snake_case					=       [mem.copy() for i in range(4					)]
           snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
           snake_case					=       Text('''GPU'''    ,      font_size=2_4					)
           snake_case					=       Group(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0.5    ,      aligned_edge=__snake_case					)
           gpu.move_to([-1, -1, 0]					)
           self.add(__snake_case					)
  
           snake_case					=       [mem.copy() for i in range(6					)]
           snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
  
           snake_case					=       Text('''Model'''    ,      font_size=2_4					)
           snake_case					=       Group(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0.5    ,      aligned_edge=__snake_case					)
           model.move_to([3, -1.0, 0]					)
           self.add(__snake_case					)
  
           snake_case					=       []
           snake_case					=       []
  
           for i, rect in enumerate(__snake_case					):
                rect.set_stroke(__snake_case					)
  
                snake_case					=       Rectangle(height=0.46 / 4    ,      width=0.46 / 3					).set_stroke(width=0.0					).set_fill(__snake_case    ,      opacity=0.7					)
  
                if i == 0:
                     cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT					)    ,      buff=0.02    ,      direction=__snake_case					)
                     cpu_target.set_x(cpu_target.get_x() + 0.1					)
                elif i == 3:
                     cpu_target.next_to(model_cpu_arr[0]    ,      direction=__snake_case    ,      buff=0.0					)
                else:
                     cpu_target.next_to(model_cpu_arr[i - 1]    ,      direction=__snake_case    ,      buff=0.0					)
                self.add(__snake_case					)
                model_cpu_arr.append(__snake_case					)
  
           self.add(*__snake_case    ,      *__snake_case					)
  
           snake_case					=       [meta_mem.copy() for i in range(6					)]
           snake_case					=       [meta_mem.copy() for i in range(6					)]
           snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
           snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
           snake_case					=       VGroup(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0					)
           snake_case					=       Text('''Disk'''    ,      font_size=2_4					)
           snake_case					=       Group(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0.5    ,      aligned_edge=__snake_case					)
           disk.move_to([-4.0, -1.25, 0]					)
           self.add(__snake_case    ,      __snake_case					)
  
           snake_case					=       []
  
           for i in range(6					):
                snake_case					=       fill.copy().set_fill(__snake_case    ,      opacity=0.8					)
                target.move_to(disk_left_col_base[i]					).scale(0.5					)
                cpu_disk_arr.append(__snake_case					)
  
           self.add(*__snake_case					)
  
           snake_case					=       Square(side_length=2.2					)
           key.move_to([-5, 2, 0]					)
  
           snake_case					=       MarkupText(
               F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''    ,      font_size=1_8    ,      )
  
           key_text.move_to([-5, 2.4, 0]					)
  
           self.add(__snake_case    ,      __snake_case					)
  
           snake_case					=       MarkupText(
               F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''    ,      font_size=1_8    ,      )
  
           blue_text.next_to(__snake_case    ,      DOWN * 2.4    ,      aligned_edge=key_text.get_left()					)
           self.add(__snake_case					)
  
           snake_case					=       MarkupText(F'''The offloaded weights are all sent to the CPU.'''    ,      font_size=2_4					)
           step_a.move_to([2, 2, 0]					)
  
           self.play(Write(__snake_case    ,      run_time=3					)					)
  
           for i in range(6					):
                snake_case					=       cpu_disk_arr[i]
                snake_case					=       rect.copy().set_fill(__snake_case    ,      opacity=0.8					).scale(2.0					)
                cpa.generate_target()
                cpa.target.move_to(model_base[i]					)
  
                if i == 0:
                     rect.set_fill(__snake_case    ,      opacity=0.8					)
                     rect.generate_target()
                     rect.target.move_to(cpu_left_col_base[0]					).scale(2.0					)
  
                     self.remove(
                         *__snake_case    ,      *__snake_case    ,      )
  
                else:
                     rect.generate_target()
                     rect.target.move_to(cpu_left_col_base[i]					).scale(2.0					)
                self.play(MoveToTarget(__snake_case					)    ,      MoveToTarget(__snake_case					)    ,      model_base[i].animate.set_stroke(__snake_case					)					)
           self.play(FadeOut(__snake_case					)					)
  
           snake_case					=       MarkupText(
               F'''Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.'''    ,      font_size=2_4    ,      )
           step_a.move_to([2, 2, 0]					)
  
           self.play(Write(__snake_case    ,      run_time=3					)					)
  
           snake_case					=       []
           snake_case					=       []
           for i in range(6					):
                snake_case					=       Arrow(start=__snake_case    ,      end=__snake_case    ,      color=__snake_case    ,      buff=0.5					)
                a.next_to(model_base[i].get_left()    ,      __snake_case    ,      buff=0.2					)
                arrows.append(__snake_case					)
                animations.append(Write(__snake_case					)					)
           self.play(*__snake_case					)
           self.wait()
  
  
  
  
  