
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():
     from PIL import Image

     from transformers import DeiTImageProcessor


class 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=7						,    __UpperCamelCase=3						,    __UpperCamelCase=18						,    __UpperCamelCase=30						,    __UpperCamelCase=400						,    __UpperCamelCase=True						,    __UpperCamelCase=None						,    __UpperCamelCase=True						,    __UpperCamelCase=None						,    __UpperCamelCase=True						,    __UpperCamelCase=[0.5, 0.5, 0.5]						,    __UpperCamelCase=[0.5, 0.5, 0.5]						,    ):
      A_								=					size if size is not None else {"height": 20, "width": 20}
      A_								=					crop_size if crop_size is not None else {"height": 18, "width": 18}

      A_								=					parent
      A_								=					batch_size
      A_								=					num_channels
      A_								=					image_size
      A_								=					min_resolution
      A_								=					max_resolution
      A_								=					do_resize
      A_								=					size
      A_								=					do_center_crop
      A_								=					crop_size
      A_								=					do_normalize
      A_								=					image_mean
      A_								=					image_std

  def 					lowercase_						(					self					):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }



@require_torch
@require_vision
class 							lowerCamelCase       (		__snake_case			,  unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		DeiTImageProcessor if is_vision_available() else None
  lowerCAmelCase_								=		True

  def 					lowercase_						(					self					):
      A_								=					DeiTImageProcessingTester(self					)

  @property
  def 					lowercase_						(					self					):
      return self.image_processor_tester.prepare_image_processor_dict()

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__UpperCamelCase						,    "do_resize"					)					)
      self.assertTrue(hasattr(__UpperCamelCase						,    "size"					)					)
      self.assertTrue(hasattr(__UpperCamelCase						,    "do_center_crop"					)					)
      self.assertTrue(hasattr(__UpperCamelCase						,    "center_crop"					)					)
      self.assertTrue(hasattr(__UpperCamelCase						,    "do_normalize"					)					)
      self.assertTrue(hasattr(__UpperCamelCase						,    "image_mean"					)					)
      self.assertTrue(hasattr(__UpperCamelCase						,    "image_std"					)					)

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size						,    {"height": 20, "width": 20}					)
      self.assertEqual(image_processor.crop_size						,    {"height": 18, "width": 18}					)

      A_								=					self.image_processing_class.from_dict(self.image_processor_dict						,    size=42						,    crop_size=84					)
      self.assertEqual(image_processor.size						,    {"height": 42, "width": 42}					)
      self.assertEqual(image_processor.crop_size						,    {"height": 84, "width": 84}					)

  def 					lowercase_						(					self					):
      pass

  def 					lowercase_						(					self					):
      # 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=__UpperCamelCase					)
      for image in image_inputs:
          self.assertIsInstance(__UpperCamelCase						,    Image.Image					)

      # Test not batched input
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )

      # Test batched
      A_								=					image_processing(__UpperCamelCase						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )

  def 					lowercase_						(					self					):
      # Initialize image_processing
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      A_								=					prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__UpperCamelCase						,    numpify=__UpperCamelCase					)
      for image in image_inputs:
          self.assertIsInstance(__UpperCamelCase						,    np.ndarray					)

      # Test not batched input
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )

      # Test batched
      A_								=					image_processing(__UpperCamelCase						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )

  def 					lowercase_						(					self					):
      # Initialize image_processing
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      A_								=					prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__UpperCamelCase						,    torchify=__UpperCamelCase					)
      for image in image_inputs:
          self.assertIsInstance(__UpperCamelCase						,    torch.Tensor					)

      # Test not batched input
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )

      # Test batched
      A_								=					image_processing(__UpperCamelCase						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )








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
__magic_name__  :  Dict     			=   False
__magic_name__  :  Union[str, Any]     			=   Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__magic_name__  :  Optional[Any]     			=   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 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase=sys.argv[1:]					):
      A_								=					None
      A_								=					None
      A_								=					None
      A_								=					1
      A_	,	A_								=					getopt.getopt(__UpperCamelCase						,    "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(__UpperCamelCase					)
          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(__UpperCamelCase					), f'{outputfile}'
      if subset_list is not None:
          with open(os.path.realpath(__UpperCamelCase					)					) as f:
              A_								=					{self._vqa_file_split()[0] for x in tryload(__UpperCamelCase					)}
      else:
          A_								=					None

      A_								=					CONFIG
      if torch.cuda.is_available():
          A_								=					"cuda"
      A_								=					os.path.realpath(__UpperCamelCase					)
      A_								=					os.path.realpath(__UpperCamelCase					)
      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 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					int(file.split("."					)[0].split("_"					)[-1]					)
      A_								=					os.path.join(self.inputdir						,    __UpperCamelCase					)
      return (img_id, filepath)

  @property
  def 					lowercase_						(					self					):
      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(__UpperCamelCase					)					)
          if len(__UpperCamelCase					) == self.batch:
              A_								=					batch
              A_								=					[]
              yield list(map(__UpperCamelCase						,    zip(*__UpperCamelCase					)					)					)

      for i in range(1					):
          yield list(map(__UpperCamelCase						,    zip(*__UpperCamelCase					)					)					)

  def __call__(					self					):
      # 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(__UpperCamelCase					)
          A_								=					self.model(
              __UpperCamelCase						,    __UpperCamelCase						,    scales_yx=__UpperCamelCase						,    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(__UpperCamelCase					)
              A_								=					self.schema.encode_batch(__UpperCamelCase					)
              writer.write_batch(__UpperCamelCase					)
          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        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    try:
        A_								=					json.load(snake_case__ )
        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__":
     __magic_name__  :  List[str]     			=   Extract(sys.argv[1:])
     extract()
     if not TEST:
          __magic_name__  :  List[Any]     			=   datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)






