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					:Any  =      Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
  _lowerCAmelCase					: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_048), dtype='float32'),
              'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
              'preds_per_image': datasets.Value(dtype='int32'),
          }
      )
  )
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A=sys.argv[1:]	)							->      List[Any]:
           _UpperCAmelCase							:						Any       							=					None
           _UpperCAmelCase							:						str       							=					None
           _UpperCAmelCase							:						str       							=					None
           _UpperCAmelCase							:						Optional[Any]       							=					1
           _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[Any]       							=					getopt.getopt(A ,			'''i:o:b:s''' ,			['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
           for opt, arg in opts:
               if opt in ("-i", "--inputdir"):
                   _UpperCAmelCase							:						Optional[Any]       							=					arg
               elif opt in ("-o", "--outfile"):
                   _UpperCAmelCase							:						Dict       							=					arg
               elif opt in ("-b", "--batch_size"):
                   _UpperCAmelCase							:						Dict       							=					int(A	)
               elif opt in ("-s", "--subset_list"):
                   _UpperCAmelCase							:						List[Any]       							=					arg
  
           assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
           assert outputfile is not None and not os.path.isfile(A	), f'{outputfile}'
           if subset_list is not None:
               with open(os.path.realpath(A	)	) as f:
                   _UpperCAmelCase							:						str       							=					{self._vqa_file_split()[0] for x in tryload(A	)}
           else:
               _UpperCAmelCase							:						Optional[Any]       							=					None
  
           _UpperCAmelCase							:						List[str]       							=					CONFIG
           if torch.cuda.is_available():
               _UpperCAmelCase							:						List[str]       							=					'''cuda'''
           _UpperCAmelCase							:						Union[str, Any]       							=					os.path.realpath(A	)
           _UpperCAmelCase							:						Optional[Any]       							=					os.path.realpath(A	)
           _UpperCAmelCase							:						List[str]       							=					Preprocess(self.config	)
           _UpperCAmelCase							:						Tuple       							=					GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned''' ,			config=self.config	)
           _UpperCAmelCase							:						Dict       							=					batch_size if batch_size != 0 else 1
           _UpperCAmelCase							:						Optional[int]       							=					DEFAULT_SCHEMA
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Union[str, Any]:
           _UpperCAmelCase							:						Tuple       							=					int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
           _UpperCAmelCase							:						Optional[Any]       							=					os.path.join(self.inputdir ,			A	)
           return (img_id, filepath)
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      str:
           _UpperCAmelCase							:						int       							=					[]
           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(A	)	)
               if len(A	) == self.batch:
                   _UpperCAmelCase							:						List[Any]       							=					batch
                   _UpperCAmelCase							:						Optional[int]       							=					[]
                   yield list(map(A ,			zip(*A	)	)	)
  
           for i in range(1	):
               yield list(map(A ,			zip(*A	)	)	)
  
  
  
  
  
       def __call__(      self	)							->      List[Any]:
           # make writer
           if not TEST:
               _UpperCAmelCase							:						List[Any]       							=					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(A	)
               _UpperCAmelCase							:						Union[str, Any]       							=					self.model(
                   A ,			A ,			scales_yx=A ,			padding='''max_detections''' ,			max_detections=self.config.MAX_DETECTIONS ,			pad_value=0 ,			return_tensors='''np''' ,			location='''cpu''' ,			)
               _UpperCAmelCase							:						List[str]       							=					output_dict.pop('''normalized_boxes'''	)
               if not TEST:
                   _UpperCAmelCase							:						Optional[Any]       							=					np.array(A	)
                   _UpperCAmelCase							:						Optional[int]       							=					self.schema.encode_batch(A	)
                   writer.write_batch(A	)
               if TEST:
                   break
               # finalizer the writer
           if not TEST:
               _UpperCAmelCase							,       _UpperCAmelCase							:						Dict       							=					writer.finalize()
               print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'	)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[Any]							):
      try:
          _UpperCAmelCase							:						Dict       							=					json.load(UpperCamelCase__							)
          try:
              _UpperCAmelCase							:						List[Any]       							=					list(data.keys()							)
          except Exception:
              _UpperCAmelCase							:						Dict       							=					[d['''img_id'''] for d in data]
      except Exception:
          try:
              _UpperCAmelCase							:						List[str]       							=					eval(stream.read()							)
          except Exception:
              _UpperCAmelCase							:						List[Any]       							=					stream.read().split('''\n'''							)
      return data
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:Optional[Any]  =      Extract(sys.argv[1:])
    extract()
    if not TEST:
      _lowerCAmelCase					:Optional[int]  =      datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_mpnet import MPNetTokenizer
  
  
  _lowerCAmelCase					:Dict  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Tuple  =      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
  
  _lowerCAmelCase					:Any  =      {
      'vocab_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
      },
      'tokenizer_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
      },
  }
  
  _lowerCAmelCase					:str  =      {
      'microsoft/mpnet-base': 512,
  }
  
  _lowerCAmelCase					:Union[str, Any]  =      {
      'microsoft/mpnet-base': {'do_lower_case': True},
  }
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =VOCAB_FILES_NAMES
       a__             =PRETRAINED_VOCAB_FILES_MAP
       a__             =PRETRAINED_INIT_CONFIGURATION
       a__             =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a__             =MPNetTokenizer
       a__             =['''input_ids''', '''attention_mask''']
  
  
  
  
  
       def __init__(      self ,			A=None ,			A=None ,			A=True ,			A="<s>" ,			A="</s>" ,			A="</s>" ,			A="<s>" ,			A="[UNK]" ,			A="<pad>" ,			A="<mask>" ,			A=True ,			A=None ,			**A ,			)							->      Dict:
           super().__init__(
               A ,			tokenizer_file=A ,			do_lower_case=A ,			bos_token=A ,			eos_token=A ,			sep_token=A ,			cls_token=A ,			unk_token=A ,			pad_token=A ,			mask_token=A ,			tokenize_chinese_chars=A ,			strip_accents=A ,			**A ,			)
  
           _UpperCAmelCase							:						List[str]       							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
           if (
               pre_tok_state.get('''lowercase''' ,			A	) != do_lower_case
               or pre_tok_state.get('''strip_accents''' ,			A	) != strip_accents
           ):
               _UpperCAmelCase							:						Optional[int]       							=					getattr(A ,			pre_tok_state.pop('''type'''	)	)
               _UpperCAmelCase							:						List[Any]       							=					do_lower_case
               _UpperCAmelCase							:						List[Any]       							=					strip_accents
               _UpperCAmelCase							:						Tuple       							=					pre_tok_class(**A	)
  
           _UpperCAmelCase							:						Union[str, Any]       							=					do_lower_case
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      str:
           if self._mask_token is None:
               if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''	)
               return None
           return str(self._mask_token	)
  
  
  
  
  
       @mask_token.setter
       def 				__lowerCAmelCase			(      self ,			A	)							->      str:
           _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else value
           _UpperCAmelCase							:						List[Any]       							=					value
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      str:
           _UpperCAmelCase							:						List[str]       							=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
               return output
  
           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
           _UpperCAmelCase							:						int       							=					[self.sep_token_id]
           _UpperCAmelCase							:						int       							=					[self.cls_token_id]
  
           if token_ids_a is None:
               return len(cls + token_ids_a + sep	) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Tuple[str]:
           _UpperCAmelCase							:						str       							=					self._tokenizer.model.save(A ,			name=A	)
           return tuple(A	)
  
  
  