code: 
  
  
  
  
  '''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							:		Any										=						False
  _lowerCamelCase							:		Dict										=						Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
  _lowerCamelCase							:		Optional[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, 2_048), 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:]				):
  							_snake_case			 = None
  							_snake_case			 = None
  							_snake_case			 = None
  							_snake_case			 = 1
  							_snake_case ,					_snake_case			 = getopt.getopt(__UpperCamelCase,						"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(__UpperCamelCase				)
  									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(__UpperCamelCase				), f'''{outputfile}'''
  							if subset_list is not None:
  									with open(os.path.realpath(__UpperCamelCase				)				) as f:
  											_snake_case			 = {self._vqa_file_split()[0] for x in tryload(__UpperCamelCase				)}
  							else:
  									_snake_case			 = None
  
  							_snake_case			 = CONFIG
  							if torch.cuda.is_available():
  									_snake_case			 = "cuda"
  							_snake_case			 = os.path.realpath(__UpperCamelCase				)
  							_snake_case			 = os.path.realpath(__UpperCamelCase				)
  							_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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = int(file.split("."				)[0].split("_"				)[-1]				)
  							_snake_case			 = os.path.join(self.inputdir,						__UpperCamelCase				)
  							return (img_id, filepath)
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_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(__UpperCamelCase				)				)
  									if len(__UpperCamelCase				) == self.batch:
  											_snake_case			 = batch
  											_snake_case			 = []
  											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:
  									_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 ,					_snake_case ,					_snake_case			 = self.preprocess(__UpperCamelCase				)
  									_snake_case			 = self.model(
  									    __UpperCamelCase,						__UpperCamelCase,						scales_yx=__UpperCamelCase,						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(__UpperCamelCase				)
  											_snake_case			 = self.schema.encode_batch(__UpperCamelCase				)
  											writer.write_batch(__UpperCamelCase				)
  									if TEST:
  											break
  									# finalizer the writer
  							if not TEST:
  									_snake_case ,					_snake_case			 = writer.finalize()
  									print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''				)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		try:
  				_snake_case			 = json.load(lowercase_						)
  				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							:		Dict										=						Extract(sys.argv[1:])
  						extract()
  						if not TEST:
  												_lowerCamelCase							:		Any										=						datasets.Dataset.from_file(extract.outputfile)
  												# wala!
  												# print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import unittest
  import warnings
  from dataclasses import dataclass
  
  from transformers.convert_slow_tokenizer import SpmConverter
  from transformers.testing_utils import get_tests_dir
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  
  
  
  
  
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece.model"				)
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						0				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						1				)
  
  							self.assertIn(
  							    "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
  							    " which is not implemented in the fast tokenizers.",						str(w[0].message				),						)
  
  
  
  
  
  