code: 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__           =				False
  lowerCamelCase__           =				Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
  lowerCamelCase__           =				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 					UpperCamelCase	:
  
  
  
  
  					def __init__( self :					List[str]      ,_lowerCAmelCase :					str=sys.argv[1:]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     None
  							__snake_case		       =     None
  							__snake_case		       =     None
  							__snake_case		       =     1
  							__snake_case		       =     getopt.getopt(_snake_case      ,"i:o:b:s"      ,["inputdir=", "outfile=", "batch_size=", "subset_list="]						)
  							for opt, arg in opts:
  									if opt in ("-i", "--inputdir"):
  											__snake_case		       =     arg
  									elif opt in ("-o", "--outfile"):
  											__snake_case		       =     arg
  									elif opt in ("-b", "--batch_size"):
  											__snake_case		       =     int(_snake_case						)
  									elif opt in ("-s", "--subset_list"):
  											__snake_case		       =     arg
  
  							assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
  							assert outputfile is not None and not os.path.isfile(_snake_case						), F"""{outputfile}"""
  							if subset_list is not None:
  									with open(os.path.realpath(_snake_case						)						) as f:
  											__snake_case		       =     {self._vqa_file_split()[0] for x in tryload(_snake_case						)}
  							else:
  									__snake_case		       =     None
  
  							__snake_case		       =     CONFIG
  							if torch.cuda.is_available():
  									__snake_case		       =     '''cuda'''
  							__snake_case		       =     os.path.realpath(_snake_case						)
  							__snake_case		       =     os.path.realpath(_snake_case						)
  							__snake_case		       =     Preprocess(self.config						)
  							__snake_case		       =     GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"      ,config=self.config						)
  							__snake_case		       =     batch_size if batch_size != 0 else 1
  							__snake_case		       =     DEFAULT_SCHEMA
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     int(file.split("."						)[0].split("_"						)[-1]						)
  							__snake_case		       =     os.path.join(self.inputdir      ,_snake_case						)
  							return (img_id, filepath)
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							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(_snake_case						)						)
  									if len(_snake_case						) == self.batch:
  											__snake_case		       =     batch
  											__snake_case		       =     []
  											yield list(map(_snake_case      ,zip(*_snake_case						)						)						)
  
  							for i in range(1						):
  									yield list(map(_snake_case      ,zip(*_snake_case						)						)						)
  
  
  
  
  
  
  
  					def __call__( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not TEST:
  									__snake_case		       =     datasets.ArrowWriter(features=self.schema      ,path=self.outputfile						)
  							# do file generator
  							for i, (img_ids, filepaths) in enumerate(self.file_generator						):
  									__snake_case		       =     self.preprocess(_snake_case						)
  									__snake_case		       =     self.model(
  									    _snake_case      ,_snake_case      ,scales_yx=_snake_case      ,padding="max_detections"      ,max_detections=self.config.MAX_DETECTIONS      ,pad_value=0      ,return_tensors="np"      ,location="cpu"      ,)
  									__snake_case		       =     output_dict.pop("normalized_boxes"						)
  									if not TEST:
  											__snake_case		       =     np.array(_snake_case						)
  											__snake_case		       =     self.schema.encode_batch(_snake_case						)
  											writer.write_batch(_snake_case						)
  									if TEST:
  											break
  									# finalizer the writer
  							if not TEST:
  									__snake_case		       =     writer.finalize()
  									print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""						)
  
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   Any:
  		try:
  				__snake_case		       =     json.load(__snake_case	)
  				try:
  						__snake_case		       =     list(data.keys()	)
  				except Exception:
  						__snake_case		       =     [d['''img_id'''] for d in data]
  		except Exception:
  				try:
  						__snake_case		       =     eval(stream.read()	)
  				except Exception:
  						__snake_case		       =     stream.read().split("\n"	)
  		return data
  
  
  if __name__ == "__main__":
  		lowerCamelCase__           =				Extract(sys.argv[1:])
  		extract()
  		if not TEST:
  				lowerCamelCase__           =				datasets.Dataset.from_file(extract.outputfile)
  				# wala!
  				# print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
  
style_context: import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  
  if is_torch_available():
  		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
  else:
  		lowerCamelCase__           =				False
  
  if is_vision_available():
  		from PIL import Image
  
  		from transformers import (
  		    AutoProcessor,
  		    PixaStructImageProcessor,
  		    PixaStructProcessor,
  		    PreTrainedTokenizerFast,
  		    TaTokenizer,
  		)
  
  
  
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_1_11			,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,      )
  @require_vision
  @require_torch
  class 					UpperCamelCase	(     unittest.TestCase      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     tempfile.mkdtemp()
  
  							__snake_case		       =     PixaStructImageProcessor()
  							__snake_case		       =     TaTokenizer.from_pretrained("t5-small"						)
  
  							__snake_case		       =     PixaStructProcessor(_lowerCAmelCase      ,_lowerCAmelCase						)
  
  							processor.save_pretrained(self.tmpdirname						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,**_lowerCAmelCase :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return AutoProcessor.from_pretrained(self.tmpdirname      ,**_lowerCAmelCase						).tokenizer
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any      ,**_lowerCAmelCase :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return AutoProcessor.from_pretrained(self.tmpdirname      ,**_lowerCAmelCase						).image_processor
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							shutil.rmtree(self.tmpdirname						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     [np.random.randint(255      ,size=(3, 30, 400)      ,dtype=np.uinta						)]
  
  							__snake_case		       =     [Image.fromarray(np.moveaxis(_lowerCAmelCase      ,0      ,-1						)						) for x in image_inputs]
  
  							return image_inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=self.get_tokenizer()      ,image_processor=self.get_image_processor()						)
  							processor.save_pretrained(self.tmpdirname						)
  
  							__snake_case		       =     self.get_tokenizer(bos_token="(BOS)"      ,eos_token="(EOS)"						)
  							__snake_case		       =     self.get_image_processor(do_normalize=_lowerCAmelCase      ,padding_value=1.0						)
  
  							__snake_case		       =     PixaStructProcessor.from_pretrained(
  							    self.tmpdirname      ,bos_token="(BOS)"      ,eos_token="(EOS)"      ,do_normalize=_lowerCAmelCase      ,padding_value=1.0						)
  
  							self.assertEqual(processor.tokenizer.get_vocab()      ,tokenizer_add_kwargs.get_vocab()						)
  							self.assertIsInstance(processor.tokenizer      ,_lowerCAmelCase						)
  
  							self.assertEqual(processor.image_processor.to_json_string()      ,image_processor_add_kwargs.to_json_string()						)
  							self.assertIsInstance(processor.image_processor      ,_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     self.prepare_image_inputs()
  
  							__snake_case		       =     image_processor(_lowerCAmelCase      ,return_tensors="np"						)
  							__snake_case		       =     processor(images=_lowerCAmelCase      ,return_tensors="np"						)
  
  							for key in input_feat_extract.keys():
  									self.assertAlmostEqual(input_feat_extract[key].sum()      ,input_processor[key].sum()      ,delta=1E-2						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     "lower newer"
  
  							__snake_case		       =     processor(text=_lowerCAmelCase						)
  
  							__snake_case		       =     tokenizer(_lowerCAmelCase      ,return_token_type_ids=_lowerCAmelCase      ,add_special_tokens=_lowerCAmelCase						)
  
  							for key in encoded_tok.keys():
  									self.assertListEqual(encoded_tok[key]      ,encoded_processor[key]						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     "lower newer"
  							__snake_case		       =     self.prepare_image_inputs()
  
  							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)
  
  							self.assertListEqual(
  							    list(inputs.keys()						)      ,["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)
  
  							# test if it raises when no input is passed
  							with pytest.raises(_lowerCAmelCase						):
  									processor()
  
  
  
  
  					def 							UpperCamelCase_						( self :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     "lower newer"
  							__snake_case		       =     self.prepare_image_inputs()
  
  							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)
  
  							__snake_case		       =     [512, 1_024, 2_048, 4_096]
  							__snake_case		       =     [770, 770, 770, 770]
  							# with text
  							for i, max_patch in enumerate(_lowerCAmelCase						):
  									__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase      ,max_patches=_lowerCAmelCase						)
  									self.assertEqual(inputs["flattened_patches"][0].shape[0]      ,_lowerCAmelCase						)
  									self.assertEqual(inputs["flattened_patches"][0].shape[1]      ,expected_hidden_size[i]						)
  
  							# without text input
  							for i, max_patch in enumerate(_lowerCAmelCase						):
  									__snake_case		       =     processor(images=_lowerCAmelCase      ,max_patches=_lowerCAmelCase						)
  									self.assertEqual(inputs["flattened_patches"][0].shape[0]      ,_lowerCAmelCase						)
  									self.assertEqual(inputs["flattened_patches"][0].shape[1]      ,expected_hidden_size[i]						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  							__snake_case		       =     processor.batch_decode(_lowerCAmelCase						)
  							__snake_case		       =     tokenizer.batch_decode(_lowerCAmelCase						)
  
  							self.assertListEqual(_lowerCAmelCase      ,_lowerCAmelCase						)
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_image_processor()
  							__snake_case		       =     self.get_tokenizer()
  
  							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)
  
  							__snake_case		       =     "lower newer"
  							__snake_case		       =     self.prepare_image_inputs()
  
  							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)
  
  							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
  							self.assertListEqual(
  							    list(inputs.keys()						)      ,["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)
  
  							__snake_case		       =     processor(text=_lowerCAmelCase						)
  
  							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
  							self.assertListEqual(list(inputs.keys()						)      ,["input_ids", "attention_mask"]						)
  
  
  
  
  
  