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
  __snake_case							      =						False
  __snake_case							      =						Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
  __snake_case							      =						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_		:
  
  
  
  						"""simple docstring"""
  						def __init__(       self					,       SCREAMING_SNAKE_CASE_=sys.argv[1:]				)       ->   List[Any]:
  										UpperCamelCase :Union[str, Any]										=     None
  										UpperCamelCase :Union[str, Any]										=     None
  										UpperCamelCase :List[str]										=     None
  										UpperCamelCase :Any										=     1
  										UpperCamelCase :Optional[Any]										=     getopt.getopt(SCREAMING_SNAKE_CASE_					,       '''i:o:b:s'''					,       ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']				)
  										for opt, arg in opts:
  														if opt in ("-i", "--inputdir"):
  																		UpperCamelCase :str										=     arg
  														elif opt in ("-o", "--outfile"):
  																		UpperCamelCase :int										=     arg
  														elif opt in ("-b", "--batch_size"):
  																		UpperCamelCase :Optional[Any]										=     int(SCREAMING_SNAKE_CASE_				)
  														elif opt in ("-s", "--subset_list"):
  																		UpperCamelCase :Optional[int]										=     arg
  
  										assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
  										assert outputfile is not None and not os.path.isfile(SCREAMING_SNAKE_CASE_				), F'''{outputfile}'''
  										if subset_list is not None:
  														with open(os.path.realpath(SCREAMING_SNAKE_CASE_				)				) as f:
  																		UpperCamelCase :int										=     {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE_				)}
  										else:
  														UpperCamelCase :Optional[int]										=     None
  
  										UpperCamelCase :List[str]										=     CONFIG
  										if torch.cuda.is_available():
  														UpperCamelCase :int										=     '''cuda'''
  										UpperCamelCase :Union[str, Any]										=     os.path.realpath(SCREAMING_SNAKE_CASE_				)
  										UpperCamelCase :int										=     os.path.realpath(SCREAMING_SNAKE_CASE_				)
  										UpperCamelCase :str										=     Preprocess(self.config				)
  										UpperCamelCase :Optional[int]										=     GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''					,       config=self.config				)
  										UpperCamelCase :Tuple										=     batch_size if batch_size != 0 else 1
  										UpperCamelCase :Any										=     DEFAULT_SCHEMA
  						def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Any:
  										UpperCamelCase :Union[str, Any]										=     int(file.split('''.'''				)[0].split('''_'''				)[-1]				)
  										UpperCamelCase :Any										=     os.path.join(self.inputdir					,       SCREAMING_SNAKE_CASE_				)
  										return (img_id, filepath)
  						@property
  						def 					UpperCAmelCase		(       self				)       ->   int:
  										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(SCREAMING_SNAKE_CASE_				)				)
  														if len(SCREAMING_SNAKE_CASE_				) == self.batch:
  																		UpperCamelCase :Optional[int]										=     batch
  																		UpperCamelCase :Optional[int]										=     []
  																		yield list(map(SCREAMING_SNAKE_CASE_					,       zip(*SCREAMING_SNAKE_CASE_				)				)				)
  
  										for i in range(1				):
  														yield list(map(SCREAMING_SNAKE_CASE_					,       zip(*SCREAMING_SNAKE_CASE_				)				)				)
  
  
  
  
  						def __call__(       self				)       ->   Optional[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 :Optional[int]										=     self.preprocess(SCREAMING_SNAKE_CASE_				)
  														UpperCamelCase :str										=     self.model(
  														    SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       scales_yx=SCREAMING_SNAKE_CASE_					,       padding='''max_detections'''					,       max_detections=self.config.MAX_DETECTIONS					,       pad_value=0					,       return_tensors='''np'''					,       location='''cpu'''					,       )
  														UpperCamelCase :Any										=     output_dict.pop('''normalized_boxes'''				)
  														if not TEST:
  																		UpperCamelCase :List[str]										=     np.array(SCREAMING_SNAKE_CASE_				)
  																		UpperCamelCase :List[Any]										=     self.schema.encode_batch(SCREAMING_SNAKE_CASE_				)
  																		writer.write_batch(SCREAMING_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 						_A     (       SCREAMING_SNAKE_CASE__						:			Tuple ):
  				try:
  								UpperCamelCase :Any										=     json.load(SCREAMING_SNAKE_CASE__ )
  								try:
  												UpperCamelCase :Optional[Any]										=     list(data.keys() )
  								except Exception:
  												UpperCamelCase :Union[str, Any]										=     [d['''img_id'''] for d in data]
  				except Exception:
  								try:
  												UpperCamelCase :Union[str, Any]										=     eval(stream.read() )
  								except Exception:
  												UpperCamelCase :Optional[int]										=     stream.read().split('''\n''' )
  				return data
  
  
  if __name__ == "__main__":
  	__snake_case							      =						Extract(sys.argv[1:])
  	extract()
  	if not TEST:
  		__snake_case							      =						datasets.Dataset.from_file(extract.outputfile)
  		# wala!
  		# print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  # as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
  
  import os
  import sys
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow
  
  from .utils import load_json
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        @classmethod
        def 					UpperCAmelCase		(       cls				)       ->   List[Any]:
            return cls
  
  
  
  
        @slow
        @require_torch_gpu
        def 					UpperCAmelCase		(       self				)       ->   int:
            UpperCamelCase :int										=     self.get_auto_remove_tmp_dir()
            UpperCamelCase :List[str]										=     F'''
              --model_name Helsinki-NLP/opus-mt-en-ro
              --save_dir {output_dir}
              --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
              --num_beams 2
              --task translation
          '''.split()
  
            # we want this test to run even if there is only one GPU, but if there are more we use them all
            UpperCamelCase :List[str]										=     get_gpu_count()
            UpperCamelCase :Optional[Any]										=     F'''
              -m torch.distributed.launch
              --nproc_per_node={n_gpu}
              {self.test_file_dir}/run_distributed_eval.py
          '''.split()
            UpperCamelCase :List[Any]										=     [sys.executable] + distributed_args + args
            execute_subprocess_async(SCREAMING_SNAKE_CASE_					,       env=self.get_env()				)
  
            UpperCamelCase :Tuple										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''test_bleu.json'''				)
            UpperCamelCase :str										=     load_json(SCREAMING_SNAKE_CASE_				)
            # print(metrics)
            self.assertGreaterEqual(metrics['''bleu''']					,       25				)
  
  
  
  
  
  