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
  lowercase_			       =				False
  lowercase_			       =				Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  lowercase_			       =				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_048), 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				,	A=sys.argv[1:] )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    1
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    getopt.getopt(A				,	"""i:o:b:s"""				,	["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""] )
          for opt, arg in opts:
              if opt in ("-i", "--inputdir"):
                  _SCREAMING_SNAKE_CASE             =    arg
              elif opt in ("-o", "--outfile"):
                  _SCREAMING_SNAKE_CASE             =    arg
              elif opt in ("-b", "--batch_size"):
                  _SCREAMING_SNAKE_CASE             =    int(A )
              elif opt in ("-s", "--subset_list"):
                  _SCREAMING_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(A ), f'{outputfile}'
          if subset_list is not None:
              with open(os.path.realpath(A ) ) as f:
                  _SCREAMING_SNAKE_CASE             =    {self._vqa_file_split()[0] for x in tryload(A )}
          else:
              _SCREAMING_SNAKE_CASE             =    None
  
          _SCREAMING_SNAKE_CASE             =    CONFIG
          if torch.cuda.is_available():
              _SCREAMING_SNAKE_CASE             =    """cuda"""
          _SCREAMING_SNAKE_CASE             =    os.path.realpath(A )
          _SCREAMING_SNAKE_CASE             =    os.path.realpath(A )
          _SCREAMING_SNAKE_CASE             =    Preprocess(self.config )
          _SCREAMING_SNAKE_CASE             =    GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""				,	config=self.config )
          _SCREAMING_SNAKE_CASE             =    batch_size if batch_size != 0 else 1
          _SCREAMING_SNAKE_CASE             =    DEFAULT_SCHEMA
  
  
      def     snake_case_(	self				,	A )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    int(file.split(""".""" )[0].split("""_""" )[-1] )
          _SCREAMING_SNAKE_CASE             =    os.path.join(self.inputdir				,	A )
          return (img_id, filepath)
  
  
      @property
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_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(A ) )
              if len(A ) == self.batch:
                  _SCREAMING_SNAKE_CASE             =    batch
                  _SCREAMING_SNAKE_CASE             =    []
                  yield list(map(A				,	zip(*A ) ) )
  
          for i in range(1 ):
              yield list(map(A				,	zip(*A ) ) )
  
  
  
  
  
      def __call__(	self )	->     Union[str, Any]:
          # make writer
          if not TEST:
              _SCREAMING_SNAKE_CASE             =    datasets.ArrowWriter(features=self.schema				,	path=self.outputfile )
          # do file generator
          for i, (img_ids, filepaths) in enumerate(self.file_generator ):
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.preprocess(A )
              _SCREAMING_SNAKE_CASE             =    self.model(
                  A				,	A				,	scales_yx=A				,	padding="""max_detections"""				,	max_detections=self.config.MAX_DETECTIONS				,	pad_value=0				,	return_tensors="""np"""				,	location="""cpu"""				,	)
              _SCREAMING_SNAKE_CASE             =    output_dict.pop("""normalized_boxes""" )
              if not TEST:
                  _SCREAMING_SNAKE_CASE             =    np.array(A )
                  _SCREAMING_SNAKE_CASE             =    self.schema.encode_batch(A )
                  writer.write_batch(A )
              if TEST:
                  break
              # finalizer the writer
          if not TEST:
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    writer.finalize()
              print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb' )
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any					)		->List[Any]:
      try:
          _SCREAMING_SNAKE_CASE             =    json.load(__lowerCamelCase					)
          try:
              _SCREAMING_SNAKE_CASE             =    list(data.keys()					)
          except Exception:
              _SCREAMING_SNAKE_CASE             =    [d["""img_id"""] for d in data]
      except Exception:
          try:
              _SCREAMING_SNAKE_CASE             =    eval(stream.read()					)
          except Exception:
              _SCREAMING_SNAKE_CASE             =    stream.read().split("""\n"""					)
      return data
  
  
  if __name__ == "__main__":
        lowercase_			       =				Extract(sys.argv[1:])
        extract()
        if not TEST:
              lowercase_			       =				datasets.Dataset.from_file(extract.outputfile)
              # wala!
              # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					str					)		->str:
      _SCREAMING_SNAKE_CASE             =    word.upper()
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    {}
      _SCREAMING_SNAKE_CASE             =    []
  
      for letter in word:
          if letter not in letter_nums:
              _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
              next_num += 1
          word_pattern.append(letter_nums[letter]					)
      return ".".join(__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        import pprint
        import time
  
        lowercase_			       =				time.time()
        with open("""dictionary.txt""") as in_file:
              lowercase_			       =				in_file.read().splitlines()
  
        lowercase_			       =				{}
        for word in word_list:
              lowercase_			       =				get_word_pattern(word)
              if pattern in all_patterns:
                    all_patterns[pattern].append(word)
              else:
                    lowercase_			       =				[word]
  
        with open("""word_patterns.txt""", """w""") as out_file:
              out_file.write(pprint.pformat(all_patterns))
  
        lowercase_			       =				round(time.time() - start_time, 2)
        print(f"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
        # Done!  9,581 word patterns found in 0.58 seconds.
  
  
  
  