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
  lowerCamelCase_     :     Any    		=						False
  lowerCamelCase_     :     str    		=						Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  lowerCamelCase_     :     int    		=						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    __A       :
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  	def __init__(	self				,					__A=sys.argv[1:]		)      ->  Optional[int]:
  					a					=None
  					a					=None
  					a					=None
  					a					=1
  					a					=getopt.getopt(_lowerCAmelCase				,					'''i:o:b:s'''				,					['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']		)
  					for opt, arg in opts:
  									if opt in ("-i", "--inputdir"):
  													a					=arg
  									elif opt in ("-o", "--outfile"):
  													a					=arg
  									elif opt in ("-b", "--batch_size"):
  													a					=int(_lowerCAmelCase		)
  									elif opt in ("-s", "--subset_list"):
  													a					=arg
  
  					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
  					assert outputfile is not None and not os.path.isfile(_lowerCAmelCase		), f'''{outputfile}'''
  					if subset_list is not None:
  									with open(os.path.realpath(_lowerCAmelCase		)		) as f:
  													a					={self._vqa_file_split()[0] for x in tryload(_lowerCAmelCase		)}
  					else:
  									a					=None
  
  					a					=CONFIG
  					if torch.cuda.is_available():
  									a					="""cuda"""
  					a					=os.path.realpath(_lowerCAmelCase		)
  					a					=os.path.realpath(_lowerCAmelCase		)
  					a					=Preprocess(self.config		)
  					a					=GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''				,					config=self.config		)
  					a					=batch_size if batch_size != 0 else 1
  					a					=DEFAULT_SCHEMA
  
  
  	def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Any:
  					a					=int(file.split('''.'''		)[0].split('''_'''		)[-1]		)
  					a					=os.path.join(self.inputdir				,					_lowerCAmelCase		)
  					return (img_id, filepath)
  
  
  	@property
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
  					a					=[]
  					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(_lowerCAmelCase		)		)
  									if len(_lowerCAmelCase		) == self.batch:
  													a					=batch
  													a					=[]
  													yield list(map(_lowerCAmelCase				,					zip(*_lowerCAmelCase		)		)		)
  
  					for i in range(1		):
  									yield list(map(_lowerCAmelCase				,					zip(*_lowerCAmelCase		)		)		)
  
  
  
  
  
  	def __call__(	self		)      ->  Union[str, Any]:
  					# make writer
  					if not TEST:
  									a					=datasets.ArrowWriter(features=self.schema				,					path=self.outputfile		)
  					# do file generator
  					for i, (img_ids, filepaths) in enumerate(self.file_generator		):
  									a					=self.preprocess(_lowerCAmelCase		)
  									a					=self.model(
  									    _lowerCAmelCase				,					_lowerCAmelCase				,					scales_yx=_lowerCAmelCase				,					padding='''max_detections'''				,					max_detections=self.config.MAX_DETECTIONS				,					pad_value=0				,					return_tensors='''np'''				,					location='''cpu'''				,					)
  									a					=output_dict.pop('''normalized_boxes'''		)
  									if not TEST:
  													a					=np.array(_lowerCAmelCase		)
  													a					=self.schema.encode_batch(_lowerCAmelCase		)
  													writer.write_batch(_lowerCAmelCase		)
  									if TEST:
  													break
  									# finalizer the writer
  					if not TEST:
  									a					=writer.finalize()
  									print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''		)
  
  def _A		(		lowercase	):
  
  
  
  
  
  
  
  				"""simple docstring"""
  				try:
  								a					=json.load(__lowerCamelCase	)
  								try:
  												a					=list(data.keys()	)
  								except Exception:
  												a					=[d["""img_id"""] for d in data]
  				except Exception:
  								try:
  												a					=eval(stream.read()	)
  								except Exception:
  												a					=stream.read().split('''\n'''	)
  				return data
  
  
  if __name__ == "__main__":
  							lowerCamelCase_     :     Optional[int]    		=						Extract(sys.argv[1:])
  							extract()
  							if not TEST:
  														lowerCamelCase_     :     Optional[int]    		=						datasets.Dataset.from_file(extract.outputfile)
  														# wala!
  														# print(np.array(dataset[0:2]["roi_features"]).shape)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a	,   a					=img.shape[0], img.shape[1]
  
      def to_grayscale(lowercase ,					lowercase ,					lowercase	):
          return 0.2126 * red + 0.587 * green + 0.114 * blue
  
      def normalize(lowercase	):
          return min(lowercase ,					2_55	)
  
      for i in range(lowercase	):
          for j in range(lowercase	):
              a					=int(to_grayscale(*img[i][j]	)	)
              a					=[
                  normalize(lowercase	),
                  normalize(greyscale + factor	),
                  normalize(greyscale + 2 * factor	),
              ]
  
      return img
  
  
  if __name__ == "__main__":
         # read original image
         lowerCamelCase_     :     Any    		=						{
             percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
         }
  
         for percentage, img in images.items():
                make_sepia(img, percentage)
  
         for percentage, img in images.items():
                imshow(F'Original image with sepia (factor: {percentage})', img)
  
         waitKey(0)
         destroyAllWindows()