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_			  =    False
  A_			  =    Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
  A_			  =    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   :
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Any				,			UpperCamelCase_:		Any=sys.argv[1:]					):
       UpperCamelCase_     =None
       UpperCamelCase_     =None
       UpperCamelCase_     =None
       UpperCamelCase_     =1
       UpperCamelCase_    ,							UpperCamelCase_     =getopt.getopt(UpperCamelCase_				,			"i:o:b:s"				,			["inputdir=", "outfile=", "batch_size=", "subset_list="]					)
       for opt, arg in opts:
          if opt in ("-i", "--inputdir"):
             UpperCamelCase_     =arg
          elif opt in ("-o", "--outfile"):
             UpperCamelCase_     =arg
          elif opt in ("-b", "--batch_size"):
             UpperCamelCase_     =int(UpperCamelCase_					)
          elif opt in ("-s", "--subset_list"):
             UpperCamelCase_     =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:
             UpperCamelCase_     ={self._vqa_file_split()[0] for x in tryload(UpperCamelCase_					)}
       else:
          UpperCamelCase_     =None
  
       UpperCamelCase_     =CONFIG
       if torch.cuda.is_available():
          UpperCamelCase_     ="cuda"
       UpperCamelCase_     =os.path.realpath(UpperCamelCase_					)
       UpperCamelCase_     =os.path.realpath(UpperCamelCase_					)
       UpperCamelCase_     =Preprocess(self.config					)
       UpperCamelCase_     =GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"				,			config=self.config					)
       UpperCamelCase_     =batch_size if batch_size != 0 else 1
       UpperCamelCase_     =DEFAULT_SCHEMA
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Dict					):
       UpperCamelCase_     =int(file.split("."					)[0].split("_"					)[-1]					)
       UpperCamelCase_     =os.path.join(self.inputdir				,			UpperCamelCase_					)
       return (img_id, filepath)
    @property
    def 					UpperCamelCase__   (      self:		Optional[int]					):
       UpperCamelCase_     =[]
       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:
             UpperCamelCase_     =batch
             UpperCamelCase_     =[]
             yield list(map(UpperCamelCase_				,			zip(*UpperCamelCase_					)					)					)
  
       for i in range(1					):
          yield list(map(UpperCamelCase_				,			zip(*UpperCamelCase_					)					)					)
  
  
  
  
  
    def __call__(      self:		Dict					):
       # make writer
       if not TEST:
          UpperCamelCase_     =datasets.ArrowWriter(features=self.schema				,			path=self.outputfile					)
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator					):
          UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =self.preprocess(UpperCamelCase_					)
          UpperCamelCase_     =self.model(
              UpperCamelCase_				,			UpperCamelCase_				,			scales_yx=UpperCamelCase_				,			padding="max_detections"				,			max_detections=self.config.MAX_DETECTIONS				,			pad_value=0				,			return_tensors="np"				,			location="cpu"				,			)
          UpperCamelCase_     =output_dict.pop("normalized_boxes"					)
          if not TEST:
             UpperCamelCase_     =np.array(UpperCamelCase_					)
             UpperCamelCase_     =self.schema.encode_batch(UpperCamelCase_					)
             writer.write_batch(UpperCamelCase_					)
          if TEST:
             break
          # finalizer the writer
       if not TEST:
          UpperCamelCase_    ,							UpperCamelCase_     =writer.finalize()
          print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""					)
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     try:
        UpperCamelCase_     =json.load(A			)
        try:
           UpperCamelCase_     =list(data.keys()			)
        except Exception:
           UpperCamelCase_     =[d["img_id"] for d in data]
     except Exception:
        try:
           UpperCamelCase_     =eval(stream.read()			)
        except Exception:
           UpperCamelCase_     =stream.read().split("\n"			)
     return data
  
  
  if __name__ == "__main__":
      A_			  =    Extract(sys.argv[1:])
      extract()
      if not TEST:
          A_			  =    datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from __future__ import annotations
  
  from pathlib import Path
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A			):
     return pointa[0] * pointa[1] - pointa[1] * pointa[0]
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A    ,       A    ,       A			):
     UpperCamelCase_     =(xa, ya)
     UpperCamelCase_     =(xa - xa, ya - ya)
     UpperCamelCase_     =(xa - xa, ya - ya)
     UpperCamelCase_     =-vector_product(A    ,       A			) / vector_product(
         A    ,       A			)
     UpperCamelCase_     =+vector_product(A    ,       A			) / vector_product(
         A    ,       A			)
  
     return a > 0 and b > 0 and a + b < 1
  
  
  
  
  def 						_UpperCamelCase			(  A = "p102_triangles.txt"			):
     UpperCamelCase_     =Path(A			).parent.joinpath(A			).read_text(encoding="utf-8"			)
  
     UpperCamelCase_     =[]
     for line in data.strip().split("\n"			):
        triangles.append([int(A			) for number in line.split(","			)]			)
  
     UpperCamelCase_     =0
     UpperCamelCase_     =42
  
     for triangle in triangles:
        ret += contains_origin(*A			)
  
     return ret
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  