code: 
  
  
  
  
  
  '''simple docstring'''
  
  import random
  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, prepare_image_inputs
  
  
  if is_torch_available():
    import torch
  
  if is_vision_available():
    import PIL
  
    from transformers import FlavaImageProcessor
    from transformers.image_utils import PILImageResampling
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )
  else:
    __lowerCAmelCase							:			List[str]				=None
  
  
  
  
  
  
  class      UpperCAmelCase			( unittest.TestCase       ):
   def __init__(       self   :List[Any]		, lowercase_   :Optional[Any]		, lowercase_   :Optional[Any]=7		, lowercase_   :Tuple=3		, lowercase_   :List[str]=30		, lowercase_   :Union[str, Any]=4_00		, lowercase_   :Dict=True		, lowercase_   :Tuple=None		, lowercase_   :Dict=True		, lowercase_   :List[str]=None		, lowercase_   :Optional[int]=None		, lowercase_   :Any=True		, lowercase_   :Union[str, Any]=1 / 2_55		, lowercase_   :Optional[int]=True		, lowercase_   :Any=FLAVA_IMAGE_MEAN		, lowercase_   :Union[str, Any]=FLAVA_IMAGE_STD		, lowercase_   :Any=14		, lowercase_   :str=75		, lowercase_   :Optional[int]=None		, lowercase_   :Tuple=16		, lowercase_   :str=0.3		, lowercase_   :Optional[Any]=None		, lowercase_   :str=True		, lowercase_   :Tuple=None		, lowercase_   :List[Any]=None		, lowercase_   :str=True		, lowercase_   :List[Any]=None		, lowercase_   :str=True		, lowercase_   :Optional[int]=True		, lowercase_   :int=FLAVA_CODEBOOK_MEAN		, lowercase_   :Optional[Any]=FLAVA_CODEBOOK_STD		, )->							Union[str, Any]:
       A__       =    size if size is not None else {"height": 2_24, "width": 2_24}
       A__       =    crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       A__       =    codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
       A__       =    codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}
  
       A__       =    parent
       A__       =    batch_size
       A__       =    num_channels
       A__       =    do_resize
       A__       =    do_rescale
       A__       =    rescale_factor
       A__       =    min_resolution
       A__       =    max_resolution
       A__       =    size
       A__       =    resample if resample is not None else PILImageResampling.BICUBIC
       A__       =    do_normalize
       A__       =    image_mean
       A__       =    image_std
       A__       =    do_center_crop
       A__       =    crop_size
  
       A__       =    input_size_patches
       A__       =    total_mask_patches
       A__       =    mask_group_max_patches
       A__       =    mask_group_min_patches
       A__       =    mask_group_min_aspect_ratio
       A__       =    mask_group_max_aspect_ratio
  
       A__       =    codebook_do_resize
       A__       =    codebook_size
       A__       =    codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
       A__       =    codebook_do_center_crop
       A__       =    codebook_crop_size
       A__       =    codebook_do_map_pixels
       A__       =    codebook_do_normalize
       A__       =    codebook_image_mean
       A__       =    codebook_image_std
   def   UpperCAmelCase_							(       self   :int							)->							Optional[int]:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "resample": self.resample,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "input_size_patches": self.input_size_patches,
           "total_mask_patches": self.total_mask_patches,
           "mask_group_max_patches": self.mask_group_max_patches,
           "mask_group_min_patches": self.mask_group_min_patches,
           "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
           "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
           "codebook_do_resize": self.codebook_do_resize,
           "codebook_size": self.codebook_size,
           "codebook_resample": self.codebook_resample,
           "codebook_do_center_crop": self.codebook_do_center_crop,
           "codebook_crop_size": self.codebook_crop_size,
           "codebook_do_map_pixels": self.codebook_do_map_pixels,
           "codebook_do_normalize": self.codebook_do_normalize,
           "codebook_image_mean": self.codebook_image_mean,
           "codebook_image_std": self.codebook_image_std,
       }
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[int]:
       return (self.size["height"], self.size["width"])
   def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
       return (
           (self.input_size_patches, self.input_size_patches)
           if not isinstance(self.input_size_patches		, lowercase_							)
           else self.input_size_patches
       )
  
  
  
   def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
       return (self.codebook_size["height"], self.codebook_size["width"])
  
  
  
  
  
  
  @require_torch
  @require_vision
  class      UpperCAmelCase			( UpperCamelCase__    , unittest.TestCase       ):
   __lowercase     	=       FlavaImageProcessor if is_vision_available() else None
   __lowercase     	=       None
   def   UpperCAmelCase_							(       self   :Any							)->							int:
       A__       =    FlavaImageProcessingTester(self							)
   @property
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							int:
       return self.image_processor_tester.prepare_image_processor_dict()
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[Any]:
       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_		, "resample"							)							)
       self.assertTrue(hasattr(lowercase_		, "crop_size"							)							)
       self.assertTrue(hasattr(lowercase_		, "do_center_crop"							)							)
       self.assertTrue(hasattr(lowercase_		, "do_rescale"							)							)
       self.assertTrue(hasattr(lowercase_		, "rescale_factor"							)							)
       self.assertTrue(hasattr(lowercase_		, "masking_generator"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_do_resize"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_size"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_resample"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_do_center_crop"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_crop_size"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_do_map_pixels"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_do_normalize"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_image_mean"							)							)
       self.assertTrue(hasattr(lowercase_		, "codebook_image_std"							)							)
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Tuple:
       A__       =    self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size		, {"height": 2_24, "width": 2_24}							)
       self.assertEqual(image_processor.crop_size		, {"height": 2_24, "width": 2_24}							)
       self.assertEqual(image_processor.codebook_size		, {"height": 1_12, "width": 1_12}							)
       self.assertEqual(image_processor.codebook_crop_size		, {"height": 1_12, "width": 1_12}							)
  
       A__       =    self.image_processing_class.from_dict(
           self.image_processor_dict		, size=42		, crop_size=84		, codebook_size=33		, codebook_crop_size=66							)
       self.assertEqual(image_processor.size		, {"height": 42, "width": 42}							)
       self.assertEqual(image_processor.crop_size		, {"height": 84, "width": 84}							)
       self.assertEqual(image_processor.codebook_size		, {"height": 33, "width": 33}							)
       self.assertEqual(image_processor.codebook_crop_size		, {"height": 66, "width": 66}							)
   def   UpperCAmelCase_							(       self   :List[str]							)->							List[Any]:
       pass
   def   UpperCAmelCase_							(       self   :Dict							)->							Dict:
       # Initialize image_processing
       A__       =    self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_							)
       for image in image_inputs:
           self.assertIsInstance(lowercase_		, PIL.Image.Image							)
  
       # Test not batched input
       A__       =    image_processing(image_inputs[0]		, return_tensors="pt"							)
  
       # Test no bool masked pos
       self.assertFalse("bool_masked_pos" in encoded_images							)
  
       A__,			A__       =    self.image_processor_tester.get_expected_image_size()
  
       self.assertEqual(
           encoded_images.pixel_values.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )
  
       # Test batched
       A__       =    image_processing(lowercase_		, return_tensors="pt"							)
       A__,			A__       =    self.image_processor_tester.get_expected_image_size()
  
       # Test no bool masked pos
       self.assertFalse("bool_masked_pos" in encoded_images							)
  
       self.assertEqual(
           encoded_images.pixel_values.shape		, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		, )
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Dict		, lowercase_   :int							)->							Union[str, Any]:
       # Initialize image_processing
       A__       =    self.image_processing_class(**self.image_processor_dict							)
       # create random tensors
       A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, **lowercase_							)
       for image in image_inputs:
           self.assertIsInstance(lowercase_		, lowercase_							)
  
       # Test not batched input
       A__       =    image_processing(image_inputs[0]		, return_tensors="pt"							)
  
       A__,			A__       =    self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )
  
       A__       =    image_processing(lowercase_		, return_image_mask=lowercase_		, return_tensors="pt"							)
  
       A__,			A__       =    self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape		, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		, )
  
       A__,			A__       =    self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape		, (
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )		, )
  
       # Test batched
       A__       =    image_processing(lowercase_		, return_tensors="pt"							).pixel_values
  
       A__,			A__       =    self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.shape		, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		, )
  
       # Test masking
       A__       =    image_processing(lowercase_		, return_image_mask=lowercase_		, return_tensors="pt"							)
  
       A__,			A__       =    self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape		, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		, )
  
       A__,			A__       =    self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape		, (
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )		, )
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Union[str, Any]:
       self._test_call_framework(np.ndarray		, prepare_kwargs={"numpify": True}							)
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
       self._test_call_framework(torch.Tensor		, prepare_kwargs={"torchify": True}							)
   def   UpperCAmelCase_							(       self   :str							)->							Tuple:
       # Initialize image_processing
       random.seed(12_34							)
       A__       =    self.image_processing_class(**self.image_processor_dict							)
       A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, torchify=lowercase_							)
  
       # Test not batched input
       A__       =    image_processing(image_inputs[0]		, return_image_mask=lowercase_		, return_tensors="pt"							)
       self.assertEqual(encoded_images.bool_masked_pos.sum().item()		, 75							)
  
  
  
   def   UpperCAmelCase_							(       self   :Any							)->							List[str]:
       # Initialize image_processing
       A__       =    self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_							)
       for image in image_inputs:
           self.assertIsInstance(lowercase_		, PIL.Image.Image							)
  
       # Test not batched input
       A__       =    image_processing(image_inputs[0]		, return_codebook_pixels=lowercase_		, return_tensors="pt"							)
       A__,			A__       =    self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )
  
       # Test batched
       A__       =    image_processing(lowercase_		, return_codebook_pixels=lowercase_		, return_tensors="pt"							)
       A__,			A__       =    self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape		, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_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)
  
  
  
  