






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 OwlViTImageProcessor
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def __init__(		self					:	Optional[int]  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	List[str]=7  ,  lowerCAmelCase__					:	Any=3  ,  lowerCAmelCase__					:	Optional[Any]=1_8  ,  lowerCAmelCase__					:	Dict=3_0  ,  lowerCAmelCase__					:	Optional[int]=4_0_0  ,  lowerCAmelCase__					:	int=True  ,  lowerCAmelCase__					:	str=None  ,  lowerCAmelCase__					:	List[Any]=True  ,  lowerCAmelCase__					:	Optional[int]=None  ,  lowerCAmelCase__					:	Optional[int]=True  ,  lowerCAmelCase__					:	str=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]  ,  lowerCAmelCase__					:	Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]  ,  lowerCAmelCase__					:	Optional[int]=True  ,  )							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		parent
      SCREAMING_SNAKE_CASE				    =		batch_size
      SCREAMING_SNAKE_CASE				    =		num_channels
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		min_resolution
      SCREAMING_SNAKE_CASE				    =		max_resolution
      SCREAMING_SNAKE_CASE				    =		do_resize
      SCREAMING_SNAKE_CASE				    =		size if size is not None else {'height': 1_8, 'width': 1_8}
      SCREAMING_SNAKE_CASE				    =		do_center_crop
      SCREAMING_SNAKE_CASE				    =		crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
      SCREAMING_SNAKE_CASE				    =		do_normalize
      SCREAMING_SNAKE_CASE				    =		image_mean
      SCREAMING_SNAKE_CASE				    =		image_std
      SCREAMING_SNAKE_CASE				    =		do_convert_rgb





     def     __UpperCamelCase						(		self					:	Dict)							->	Any:

      """simple docstring"""
      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,
          "do_convert_rgb": self.do_convert_rgb,
      }

@require_torch
@require_vision
class 	a_(		lowercase__			,							unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				Tuple  					=OwlViTImageProcessor if is_vision_available() else None





     def     __UpperCamelCase						(		self					:	Tuple)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		OwlViTImageProcessingTester(self)





     @property
     def     __UpperCamelCase						(		self					:	int)							->	Any:

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_resize'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'size'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_center_crop'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'center_crop'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_normalize'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'image_mean'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'image_std'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_convert_rgb'))





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size  ,  {'height': 1_8, 'width': 1_8})
      self.assertEqual(image_processor.crop_size  ,  {'height': 1_8, 'width': 1_8})

      SCREAMING_SNAKE_CASE				    =		self.image_processing_class.from_dict(self.image_processor_dict  ,  size=4_2  ,  crop_size=8_4)
      self.assertEqual(image_processor.size  ,  {'height': 4_2, 'width': 4_2})
      self.assertEqual(image_processor.crop_size  ,  {'height': 8_4, 'width': 8_4})





     def     __UpperCamelCase						(		self					:	Tuple)							->	Union[str, Any]:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__)

      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		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
      SCREAMING_SNAKE_CASE				    =		image_processing(lowerCAmelCase__  ,  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     __UpperCamelCase						(		self					:	Dict)							->	Optional[Any]:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__  ,  numpify=lowerCAmelCase__)
      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		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
      SCREAMING_SNAKE_CASE				    =		image_processing(lowerCAmelCase__  ,  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     __UpperCamelCase						(		self					:	Dict)							->	Dict:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__  ,  torchify=lowerCAmelCase__)
      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		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
      SCREAMING_SNAKE_CASE				    =		image_processing(lowerCAmelCase__  ,  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
UpperCAmelCase      		=					False
UpperCAmelCase      		=					Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase      		=					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, 20_48), 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							:		Optional[Any]							,      lowerCamelCase							:		int=sys.argv[1:]	)			->				int:
        _UpperCamelCase  :							Tuple	    =	None
        _UpperCamelCase  :							Dict	    =	None
        _UpperCamelCase  :							List[Any]	    =	None
        _UpperCamelCase  :							Dict	    =	1
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	getopt.getopt(lowerCamelCase							,      'i:o:b:s'							,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']	)
        for opt, arg in opts:
               if opt in ("-i", "--inputdir"):
                      _UpperCamelCase  :							Dict	    =	arg
               elif opt in ("-o", "--outfile"):
                      _UpperCamelCase  :							int	    =	arg
               elif opt in ("-b", "--batch_size"):
                      _UpperCamelCase  :							List[str]	    =	int(lowerCamelCase	)
               elif opt in ("-s", "--subset_list"):
                      _UpperCamelCase  :							Dict	    =	arg

        assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
        assert outputfile is not None and not os.path.isfile(lowerCamelCase	), f"{outputfile}"
        if subset_list is not None:
               with open(os.path.realpath(lowerCamelCase	)	) as f:
                      _UpperCamelCase  :							str	    =	{self._vqa_file_split()[0] for x in tryload(lowerCamelCase	)}
        else:
               _UpperCamelCase  :							Any	    =	None

        _UpperCamelCase  :							Any	    =	CONFIG
        if torch.cuda.is_available():
               _UpperCamelCase  :							List[str]	    =	'cuda'
        _UpperCamelCase  :							int	    =	os.path.realpath(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	os.path.realpath(lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	Preprocess(self.config	)
        _UpperCamelCase  :							Tuple	    =	GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'							,      config=self.config	)
        _UpperCamelCase  :							List[Any]	    =	batch_size if batch_size != 0 else 1
        _UpperCamelCase  :							Any	    =	DEFAULT_SCHEMA

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				int:
        _UpperCamelCase  :							Tuple	    =	int(file.split('.'	)[0].split('_'	)[-1]	)
        _UpperCamelCase  :							List[Any]	    =	os.path.join(self.inputdir							,      lowerCamelCase	)
        return (img_id, filepath)

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							str	    =	[]
        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(lowerCamelCase	)	)
               if len(lowerCamelCase	) == self.batch:
                      _UpperCamelCase  :							Dict	    =	batch
                      _UpperCamelCase  :							List[Any]	    =	[]
                      yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

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

 def __call__(      self							:		Optional[Any]	)			->				Any:
        # make writer
        if not TEST:
               _UpperCamelCase  :							int	    =	datasets.ArrowWriter(features=self.schema							,      path=self.outputfile	)
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator	):
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Any	    =	self.preprocess(lowerCamelCase	)
               _UpperCamelCase  :							Any	    =	self.model(
                   lowerCamelCase							,      lowerCamelCase							,      scales_yx=lowerCamelCase							,      padding='max_detections'							,      max_detections=self.config.MAX_DETECTIONS							,      pad_value=0							,      return_tensors='np'							,      location='cpu'							,      )
               _UpperCamelCase  :							Tuple	    =	output_dict.pop('normalized_boxes'	)
               if not TEST:
                      _UpperCamelCase  :							Dict	    =	np.array(lowerCamelCase	)
                      _UpperCamelCase  :							Optional[int]	    =	self.schema.encode_batch(lowerCamelCase	)
                      writer.write_batch(lowerCamelCase	)
               if TEST:
                      break
               # finalizer the writer
        if not TEST:
               _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	writer.finalize()
               print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"	)







def SCREAMING_SNAKE_CASE					(  lowercase_:						str					):
       try:
              _UpperCamelCase  :							Optional[Any]	    =	json.load(lowercase_					)
              try:
                     _UpperCamelCase  :							Union[str, Any]	    =	list(data.keys()					)
              except Exception:
                     _UpperCamelCase  :							Optional[Any]	    =	[d['img_id'] for d in data]
       except Exception:
              try:
                     _UpperCamelCase  :							Any	    =	eval(stream.read()					)
              except Exception:
                     _UpperCamelCase  :							Optional[int]	    =	stream.read().split('\n'					)
       return data


if __name__ == "__main__":
 UpperCAmelCase      		=					Extract(sys.argv[1:])
 extract()
 if not TEST:
  UpperCAmelCase      		=					datasets.Dataset.from_file(extract.outputfile)
  # wala!
  # print(np.array(dataset[0:2]["roi_features"]).shape)



