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"""
  
  
  
  
  
  
  import unittest
  from dataclasses import dataclass
  from typing import List, Union
  
  import numpy as np
  import PIL.Image
  
  from diffusers.utils.outputs import BaseOutput
  @dataclass
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Union[List[PIL.Image.Image], np.ndarray]
  
  
  
  
  class 						__lowerCAmelCase   (			unittest.TestCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def 					UpperCamelCase__   (      self:		Tuple					):
       UpperCamelCase_     =CustomOutput(images=np.random.rand(1				,			3				,			4				,			4					)					)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,			np.ndarray					)
       assert outputs.images.shape == (1, 3, 4, 4)
       assert isinstance(outputs["images"]				,			np.ndarray					)
       assert outputs["images"].shape == (1, 3, 4, 4)
       assert isinstance(outputs[0]				,			np.ndarray					)
       assert outputs[0].shape == (1, 3, 4, 4)
  
       # test with a non-tensor attribute
       UpperCamelCase_     =CustomOutput(images=[PIL.Image.new("RGB"				,			(4, 4)					)]					)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,			UpperCamelCase_					)
       assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
       assert isinstance(outputs["images"]				,			UpperCamelCase_					)
       assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
       assert isinstance(outputs[0]				,			UpperCamelCase_					)
       assert isinstance(outputs[0][0]				,			PIL.Image.Image					)
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Optional[int]					):
       # test output reinitialization with a `dict` for compatibility with `accelerate`
       UpperCamelCase_     =CustomOutput({"images": np.random.rand(1				,			3				,			4				,			4					)}					)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,			np.ndarray					)
       assert outputs.images.shape == (1, 3, 4, 4)
       assert isinstance(outputs["images"]				,			np.ndarray					)
       assert outputs["images"].shape == (1, 3, 4, 4)
       assert isinstance(outputs[0]				,			np.ndarray					)
       assert outputs[0].shape == (1, 3, 4, 4)
  
       # test with a non-tensor attribute
       UpperCamelCase_     =CustomOutput({"images": [PIL.Image.new("RGB"				,			(4, 4)					)]}					)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,			UpperCamelCase_					)
       assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
       assert isinstance(outputs["images"]				,			UpperCamelCase_					)
       assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
       assert isinstance(outputs[0]				,			UpperCamelCase_					)
       assert isinstance(outputs[0][0]				,			PIL.Image.Image					)
  