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       : 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       : 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       : 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'''
  
  import argparse
  import copy
  import json
  import multiprocessing
  import os
  import time
  
  from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
  from huggingface_hub import ModelFilter, hf_api
  
  import transformers
  from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
  from transformers.image_processing_utils import BaseImageProcessor
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      List[Any]:
  		_UpperCamelCase       : Union[str, Any]    =      set()
  		# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
  		for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
  				_UpperCamelCase       : List[Any]    =      getattr(transformers.models.auto			,UpperCamelCase			,UpperCamelCase	)
  				if module is None:
  						continue
  				# all mappings in a single auto modeling file
  				_UpperCamelCase       : Optional[Any]    =      [
  				    x
  				    for x in dir(UpperCamelCase	)
  				    if x.endswith('''_MAPPING_NAMES'''	)
  				    and (x.startswith('''MODEL_'''	) or x.startswith('''TF_MODEL_'''	) or x.startswith('''FLAX_MODEL_'''	))
  				]
  				for name in mapping_names:
  						_UpperCamelCase       : List[str]    =      getattr(UpperCamelCase			,UpperCamelCase	)
  						if mapping is not None:
  								for v in mapping.values():
  										if isinstance(UpperCamelCase			,(list, tuple)	):
  												model_names.update(UpperCamelCase	)
  										elif isinstance(UpperCamelCase			,UpperCamelCase	):
  												model_names.add(UpperCamelCase	)
  
  		return sorted(UpperCamelCase	)
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      Any:
  		# All model names defined in auto mappings
  		_UpperCamelCase       : Union[str, Any]    =      set(get_all_model_names()	)
  
  		with open('''tests/utils/tiny_model_summary.json'''	) as fp:
  				_UpperCamelCase       : Optional[Any]    =      json.load(UpperCamelCase	)
  		_UpperCamelCase       : Tuple    =      set()
  		for model_base_name in tiny_model_info:
  				tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']	)
  
  		# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
  		_UpperCamelCase       : List[Any]    =      model_names.difference(UpperCamelCase	)
  		for model_name in copy.copy(UpperCamelCase	):
  				if not model_name.startswith('''TF'''	) and f'''TF{model_name}''' in not_on_hub:
  						tiny_models_names.remove(UpperCamelCase	)
  				elif model_name.startswith('''TF'''	) and model_name[2:] in not_on_hub:
  						tiny_models_names.remove(UpperCamelCase	)
  
  		return sorted(UpperCamelCase	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      List[str]:
  		_UpperCamelCase       : Optional[Any]    =      COMPOSITE_MODELS.values()
  
  		# All tiny model base names on Hub
  		_UpperCamelCase       : Optional[Any]    =      get_all_model_names()
  		_UpperCamelCase       : Union[str, Any]    =      hf_api.list_models(
  		    filter=ModelFilter(
  		        author='''hf-internal-testing'''			,)	)
  		_UpperCamelCase       : Any    =      set()
  		for x in models:
  				_UpperCamelCase       : Optional[int]    =      x.modelId
  				_UpperCamelCase,							_UpperCamelCase       : str    =      model.split('''/'''	)
  				if not model.startswith('''tiny-random-'''	):
  						continue
  				_UpperCamelCase       : Optional[Any]    =      model.replace('''tiny-random-'''			,''''''	)
  				if not model[0].isupper():
  						continue
  				if model not in model_names and model not in special_models:
  						continue
  				_models.add(UpperCamelCase	)
  
  		_UpperCamelCase       : List[Any]    =      sorted(_models	)
  		# All tiny model names on Hub
  		_UpperCamelCase       : int    =      {}
  		for model in models:
  				_UpperCamelCase       : Optional[Any]    =      f'''hf-internal-testing/tiny-random-{model}'''
  				_UpperCamelCase       : Any    =      model.split('''-'''	)[0]
  				try:
  						_UpperCamelCase       : List[str]    =      hf_api.repo_info(UpperCamelCase	)
  						_UpperCamelCase       : List[str]    =      {
  						    '''tokenizer_classes''': set(),
  						    '''processor_classes''': set(),
  						    '''model_classes''': set(),
  						    '''sha''': repo_info.sha,
  						}
  				except Exception:
  						continue
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Union[str, Any]    =      AutoTokenizer.from_pretrained(UpperCamelCase	)
  						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : int    =      AutoTokenizer.from_pretrained(UpperCamelCase			,use_fast=UpperCamelCase	)
  						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : int    =      AutoImageProcessor.from_pretrained(UpperCamelCase	)
  						content["processor_classes"].add(img_p.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : List[Any]    =      AutoFeatureExtractor.from_pretrained(UpperCamelCase	)
  						if not isinstance(UpperCamelCase			,UpperCamelCase	):
  								content["processor_classes"].add(feat_p.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Tuple    =      getattr(UpperCamelCase			,UpperCamelCase	)
  						_UpperCamelCase       : Tuple    =      model_class.from_pretrained(UpperCamelCase	)
  						content["model_classes"].add(m.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Dict    =      getattr(UpperCamelCase			,f'''TF{model}'''	)
  						_UpperCamelCase       : Optional[Any]    =      model_class.from_pretrained(UpperCamelCase	)
  						content["model_classes"].add(m.__class__.__name__	)
  				except Exception:
  						pass
  
  				_UpperCamelCase       : Dict    =      sorted(content['''tokenizer_classes''']	)
  				_UpperCamelCase       : Tuple    =      sorted(content['''processor_classes''']	)
  				_UpperCamelCase       : Union[str, Any]    =      sorted(content['''model_classes''']	)
  
  				_UpperCamelCase       : List[Any]    =      content
  				with open(os.path.join(UpperCamelCase			,'''hub_tiny_model_summary.json'''	)			,'''w'''	) as fp:
  						json.dump(UpperCamelCase			,UpperCamelCase			,ensure_ascii=UpperCamelCase			,indent=4	)
  
  
  if __name__ == "__main__":
  	_UpperCAmelCase   :      List[str] 						=  argparse.ArgumentParser()
  	parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
  	_UpperCAmelCase   :      List[Any] 						=  parser.parse_args()
  
  	# This has to be `spawn` to avoid hanging forever!
  	multiprocessing.set_start_method("""spawn""")
  
  	_UpperCAmelCase   :      Optional[Any] 						=  """tiny_models"""
  	_UpperCAmelCase   :      Optional[Any] 						=  True
  	_UpperCAmelCase   :      str 						=  None
  	_UpperCAmelCase   :      int 						=  get_tiny_model_names_from_repo()
  	_UpperCAmelCase   :      Optional[Any] 						=  True
  	_UpperCAmelCase   :      List[str] 						=  True
  	_UpperCAmelCase   :      str 						=  """hf-internal-testing"""
  
  	create_tiny_models(
  	    output_path,
  	    all,
  	    model_types,
  	    models_to_skip,
  	    no_check,
  	    upload,
  	    organization,
  	    token=os.environ.get("""TOKEN""", None),
  	    num_workers=args.num_workers,
  	)
  
  
  
  
  
  