code: 
  
  
  
  
  
  '''simple docstring'''
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin
  
  
  if is_torch_available():
    import torch
  
  if is_vision_available():
    from PIL import Image
  
    from transformers import TvltImageProcessor
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      str=10		, _lowerCamelCase      :      Optional[Any]=10		, _lowerCamelCase      :      List[Any]=False		, _lowerCamelCase      :      Tuple=False ):
      A__       =    []
      for i in range(image_processor_tester.num_frames ):
          video.append(np.random.randint(2_55		, size=(image_processor_tester.num_channels, width, height)		, dtype=np.uinta ) )
  
      if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          A__       =    [Image.fromarray(np.moveaxis(_lowerCamelCase		, 0		, -1 ) ) for frame in video]
  
      if torchify:
          A__       =    [torch.from_numpy(_lowerCamelCase ) for frame in video]
  
      return video
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int		, _lowerCamelCase      :      str=False		, _lowerCamelCase      :      List[Any]=False		, _lowerCamelCase      :      Optional[int]=False ):
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
      A__       =    []
      for i in range(image_processor_tester.batch_size ):
          if equal_resolution:
              A__       =    A__       =    image_processor_tester.max_resolution
          else:
              A__,			A__       =    np.random.choice(
                  np.arange(image_processor_tester.min_resolution		, image_processor_tester.max_resolution )		, 2 )
              A__       =    prepare_video(
                  image_processor_tester=_lowerCamelCase		, width=_lowerCamelCase		, height=_lowerCamelCase		, numpify=_lowerCamelCase		, torchify=_lowerCamelCase		, )
          video_inputs.append(_lowerCamelCase )
  
      return video_inputs
  
  
  
  
  
  
  class      UpperCAmelCase			( unittest.TestCase       ):
   def __init__(       self   :Tuple		, lowercase_   :Dict		, lowercase_   :Tuple=7		, lowercase_   :Optional[Any]=3		, lowercase_   :Optional[Any]=4		, lowercase_   :Any=18		, lowercase_   :Optional[Any]=30		, lowercase_   :Tuple=4_00		, lowercase_   :Tuple=True		, lowercase_   :str=None		, lowercase_   :Dict=True		, lowercase_   :int=[0.5, 0.5, 0.5]		, lowercase_   :Any=[0.5, 0.5, 0.5]		, lowercase_   :int=True		, lowercase_   :Dict=None		, )->							List[Any]:
       A__       =    size if size is not None else {"shortest_edge": 18}
       A__       =    crop_size if crop_size is not None else {"height": 18, "width": 18}
  
       A__       =    parent
       A__       =    batch_size
       A__       =    num_channels
       A__       =    num_frames
       A__       =    image_size
       A__       =    min_resolution
       A__       =    max_resolution
       A__       =    do_resize
       A__       =    size
       A__       =    do_normalize
       A__       =    image_mean
       A__       =    image_std
       A__       =    do_center_crop
       A__       =    crop_size
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Dict:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }
  
  
  
  
  
  
  @require_torch
  @require_vision
  class      UpperCAmelCase			( UpperCamelCase__    , unittest.TestCase       ):
   __lowercase     	=       TvltImageProcessor if is_vision_available() else None
   def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
       A__       =    TvltImageProcessorTester(self							)
   @property
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Any:
       return self.image_processor_tester.prepare_image_processor_dict()
   def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
       A__       =    self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(lowercase_		, "image_mean"							)							)
       self.assertTrue(hasattr(lowercase_		, "image_std"							)							)
       self.assertTrue(hasattr(lowercase_		, "do_normalize"							)							)
       self.assertTrue(hasattr(lowercase_		, "do_resize"							)							)
       self.assertTrue(hasattr(lowercase_		, "do_center_crop"							)							)
       self.assertTrue(hasattr(lowercase_		, "size"							)							)
   def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
       # Initialize image_processor
       A__       =    self.image_processing_class(**self.image_processor_dict							)
       # create random PIL videos
       A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_							)
       for video in video_inputs:
           self.assertIsInstance(lowercase_		, lowercase_							)
           self.assertIsInstance(video[0]		, Image.Image							)
  
       # Test not batched input
       A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
       self.assertEqual(
           encoded_videos.shape		, (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )		, )
  
       # Test batched
       A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
       self.assertEqual(
           encoded_videos.shape		, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )		, )
   def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
       # Initialize image_processor
       A__       =    self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, numpify=lowercase_							)
       for video in video_inputs:
           self.assertIsInstance(lowercase_		, lowercase_							)
           self.assertIsInstance(video[0]		, np.ndarray							)
  
       # Test not batched input
       A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
       self.assertEqual(
           encoded_videos.shape		, (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )		, )
  
       # Test batched
       A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
       self.assertEqual(
           encoded_videos.shape		, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )		, )
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Union[str, Any]:
       # Initialize image_processor
       A__       =    self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, torchify=lowercase_							)
       for video in video_inputs:
           self.assertIsInstance(lowercase_		, lowercase_							)
           self.assertIsInstance(video[0]		, torch.Tensor							)
  
       # Test not batched input
       A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
       self.assertEqual(
           encoded_videos.shape		, (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )		, )
  
       # Test batched
       A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
       self.assertEqual(
           encoded_videos.shape		, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )		, )
  
  
  
  
style_context: 
  
  
  
  
  
  '''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							:			str				=False
  __lowerCAmelCase							:			Union[str, Any]				=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, 2048), dtype="float32"),
              "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
              "preds_per_image": datasets.Value(dtype="int32"),
          }
      )
  )
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :List[str]		, lowercase_   :List[Any]=sys.argv[1:]							)->							Dict:
       A__       =    None
       A__       =    None
       A__       =    None
       A__       =    1
       A__,			A__       =    getopt.getopt(lowercase_		, "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(lowercase_							)
           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(lowercase_							), F"{outputfile}"
       if subset_list is not None:
           with open(os.path.realpath(lowercase_							)							) as f:
               A__       =    {self._vqa_file_split()[0] for x in tryload(lowercase_							)}
       else:
           A__       =    None
  
       A__       =    CONFIG
       if torch.cuda.is_available():
           A__       =    "cuda"
       A__       =    os.path.realpath(lowercase_							)
       A__       =    os.path.realpath(lowercase_							)
       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   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]							)->							Dict:
       A__       =    int(file.split("."							)[0].split("_"							)[-1]							)
       A__       =    os.path.join(self.inputdir		, lowercase_							)
       return (img_id, filepath)
   @property
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
       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(lowercase_							)							)
           if len(lowercase_							) == self.batch:
               A__       =    batch
               A__       =    []
               yield list(map(lowercase_		, zip(*lowercase_							)							)							)
  
       for i in range(1							):
           yield list(map(lowercase_		, zip(*lowercase_							)							)							)
  
  
  
   def __call__(       self   :Optional[int]							)->							List[str]:
       # 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__,			A__,			A__       =    self.preprocess(lowercase_							)
           A__       =    self.model(
               lowercase_		, lowercase_		, scales_yx=lowercase_		, 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(lowercase_							)
               A__       =    self.schema.encode_batch(lowercase_							)
               writer.write_batch(lowercase_							)
           if TEST:
               break
           # finalizer the writer
       if not TEST:
           A__,			A__       =    writer.finalize()
           print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"							)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
      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							:			int				=Extract(sys.argv[1:])
    extract()
    if not TEST:
      __lowerCAmelCase							:			Optional[Any]				=datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  