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
  _UpperCAmelCase   :      Union[str, Any] 						=  False
  _UpperCAmelCase   :      int 						=  Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  _UpperCAmelCase   :      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, 2048), 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  ,					_snake_case=sys.argv[1:]     )				->    Any:
  				_UpperCamelCase       : int    =      None
  				_UpperCamelCase       : Any    =      None
  				_UpperCamelCase       : Any    =      None
  				_UpperCamelCase       : Union[str, Any]    =      1
  				_UpperCamelCase,							_UpperCamelCase       : Optional[int]    =      getopt.getopt(_snake_case  ,					'''i:o:b:s'''  ,					['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']     )
  				for opt, arg in opts:
  						if opt in ("-i", "--inputdir"):
  								_UpperCamelCase       : Union[str, Any]    =      arg
  						elif opt in ("-o", "--outfile"):
  								_UpperCamelCase       : Union[str, Any]    =      arg
  						elif opt in ("-b", "--batch_size"):
  								_UpperCamelCase       : str    =      int(_snake_case     )
  						elif opt in ("-s", "--subset_list"):
  								_UpperCamelCase       : str    =      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:
  								_UpperCamelCase       : Union[str, Any]    =      {self._vqa_file_split()[0] for x in tryload(_snake_case     )}
  				else:
  						_UpperCamelCase       : Optional[Any]    =      None
  
  				_UpperCamelCase       : Tuple    =      CONFIG
  				if torch.cuda.is_available():
  						_UpperCamelCase       : Tuple    =      '''cuda'''
  				_UpperCamelCase       : List[Any]    =      os.path.realpath(_snake_case     )
  				_UpperCamelCase       : int    =      os.path.realpath(_snake_case     )
  				_UpperCamelCase       : List[str]    =      Preprocess(self.config     )
  				_UpperCamelCase       : int    =      GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''  ,					config=self.config     )
  				_UpperCamelCase       : Optional[int]    =      batch_size if batch_size != 0 else 1
  				_UpperCamelCase       : Dict    =      DEFAULT_SCHEMA
  		def _lowercase  (    self  ,					_snake_case     )				->    Optional[Any]:
  				_UpperCamelCase       : Tuple    =      int(file.split('''.'''     )[0].split('''_'''     )[-1]     )
  				_UpperCamelCase       : str    =      os.path.join(self.inputdir  ,					_snake_case     )
  				return (img_id, filepath)
  		@property
  		def _lowercase  (    self     )				->    List[str]:
  				_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(_snake_case     )     )
  						if len(_snake_case     ) == self.batch:
  								_UpperCamelCase       : int    =      batch
  								_UpperCamelCase       : Dict    =      []
  								yield list(map(_snake_case  ,					zip(*_snake_case     )     )     )
  
  				for i in range(1     ):
  						yield list(map(_snake_case  ,					zip(*_snake_case     )     )     )
  
  
  
  
  		def __call__(    self     )				->    str:
  				# make writer
  				if not TEST:
  						_UpperCamelCase       : List[str]    =      datasets.ArrowWriter(features=self.schema  ,					path=self.outputfile     )
  				# do file generator
  				for i, (img_ids, filepaths) in enumerate(self.file_generator     ):
  						_UpperCamelCase,							_UpperCamelCase,							_UpperCamelCase       : Union[str, Any]    =      self.preprocess(_snake_case     )
  						_UpperCamelCase       : List[str]    =      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'''  ,					)
  						_UpperCamelCase       : Optional[int]    =      output_dict.pop('''normalized_boxes'''     )
  						if not TEST:
  								_UpperCamelCase       : List[Any]    =      np.array(_snake_case     )
  								_UpperCamelCase       : str    =      self.schema.encode_batch(_snake_case     )
  								writer.write_batch(_snake_case     )
  						if TEST:
  								break
  						# finalizer the writer
  				if not TEST:
  						_UpperCamelCase,							_UpperCamelCase       : str    =      writer.finalize()
  						print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''     )
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      Any:
  		try:
  				_UpperCamelCase       : int    =      json.load(UpperCamelCase	)
  				try:
  						_UpperCamelCase       : str    =      list(data.keys()	)
  				except Exception:
  						_UpperCamelCase       : str    =      [d['''img_id'''] for d in data]
  		except Exception:
  				try:
  						_UpperCamelCase       : Tuple    =      eval(stream.read()	)
  				except Exception:
  						_UpperCamelCase       : List[Any]    =      stream.read().split('''\n'''	)
  		return data
  
  
  if __name__ == "__main__":
  	_UpperCAmelCase   :      Any 						=  Extract(sys.argv[1:])
  	extract()
  	if not TEST:
  		_UpperCAmelCase   :      Dict 						=  datasets.Dataset.from_file(extract.outputfile)
  		# wala!
  		# print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _UpperCAmelCase   :      int 						=  {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	_UpperCAmelCase   :      int 						=  [
  	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """GPTSanJapaneseForConditionalGeneration""",
  	    """GPTSanJapaneseModel""",
  	    """GPTSanJapanesePreTrainedModel""",
  	]
  	_UpperCAmelCase   :      str 						=  [
  	    """GPTSanJapaneseTokenizer""",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_gptsan_japanese import (
  		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    GPTSanJapaneseForConditionalGeneration,
  		    GPTSanJapaneseModel,
  		    GPTSanJapanesePreTrainedModel,
  		)
  		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
  	import sys
  
  	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  