





"""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
A_     :      List[Any]								=False
A_     :      Any								=Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
A_     :      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_0_4_8), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)







class 	__a   :



     def __init__(       self					,     a__=sys.argv[1:]   ):
            _lowerCamelCase    	=					None
            _lowerCamelCase    	=					None
            _lowerCamelCase    	=					None
            _lowerCamelCase    	=					1
            _lowerCamelCase	,	_lowerCamelCase    	=					getopt.getopt(a__					,     'i:o:b:s'					,     ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']   )
            for opt, arg in opts:
                   if opt in ("-i", "--inputdir"):
                          _lowerCamelCase    	=					arg
                   elif opt in ("-o", "--outfile"):
                          _lowerCamelCase    	=					arg
                   elif opt in ("-b", "--batch_size"):
                          _lowerCamelCase    	=					int(a__   )
                   elif opt in ("-s", "--subset_list"):
                          _lowerCamelCase    	=					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:
                          _lowerCamelCase    	=					{self._vqa_file_split()[0] for x in tryload(a__   )}
            else:
                   _lowerCamelCase    	=					None

            _lowerCamelCase    	=					CONFIG
            if torch.cuda.is_available():
                   _lowerCamelCase    	=					'cuda'
            _lowerCamelCase    	=					os.path.realpath(a__   )
            _lowerCamelCase    	=					os.path.realpath(a__   )
            _lowerCamelCase    	=					Preprocess(self.config   )
            _lowerCamelCase    	=					GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'					,     config=self.config   )
            _lowerCamelCase    	=					batch_size if batch_size != 0 else 1
            _lowerCamelCase    	=					DEFAULT_SCHEMA



     def 						snake_case_			(       self					,     a__   ):
            _lowerCamelCase    	=					int(file.split('.'   )[0].split('_'   )[-1]   )
            _lowerCamelCase    	=					os.path.join(self.inputdir					,     a__   )
            return (img_id, filepath)



     @property
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					[]
            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:
                          _lowerCamelCase    	=					batch
                          _lowerCamelCase    	=					[]
                          yield list(map(a__					,     zip(*a__   )   )   )

            for i in range(1   ):
                   yield list(map(a__					,     zip(*a__   )   )   )



     def __call__(       self   ):
            # make writer
            if not TEST:
                   _lowerCamelCase    	=					datasets.ArrowWriter(features=self.schema					,     path=self.outputfile   )
            # do file generator
            for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                   _lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase    	=					self.preprocess(a__   )
                   _lowerCamelCase    	=					self.model(
                       a__					,     a__					,     scales_yx=a__					,     padding='max_detections'					,     max_detections=self.config.MAX_DETECTIONS					,     pad_value=0					,     return_tensors='np'					,     location='cpu'					,     )
                   _lowerCamelCase    	=					output_dict.pop('normalized_boxes'   )
                   if not TEST:
                          _lowerCamelCase    	=					np.array(a__   )
                          _lowerCamelCase    	=					self.schema.encode_batch(a__   )
                          writer.write_batch(a__   )
                   if TEST:
                          break
                   # finalizer the writer
            if not TEST:
                   _lowerCamelCase	,	_lowerCamelCase    	=					writer.finalize()
                   print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'   )





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[str]		)->		Optional[Any]:
       try:
              _lowerCamelCase    	=					json.load(snake_case		)
              try:
                     _lowerCamelCase    	=					list(data.keys()		)
              except Exception:
                     _lowerCamelCase    	=					[d['img_id'] for d in data]
       except Exception:
              try:
                     _lowerCamelCase    	=					eval(stream.read()		)
              except Exception:
                     _lowerCamelCase    	=					stream.read().split('\n'		)
       return data


if __name__ == "__main__":
      A_     :      Optional[int]								=Extract(sys.argv[1:])
      extract()
      if not TEST:
            A_     :      str								=datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)













"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       )->		Tuple:
       _lowerCamelCase    	=					1

       while True:
              if (
                  sorted(str(snake_case		)		)
                  == sorted(str(2 * i		)		)
                  == sorted(str(3 * i		)		)
                  == sorted(str(4 * i		)		)
                  == sorted(str(5 * i		)		)
                  == sorted(str(6 * i		)		)
              ):
                     return i

              i += 1


if __name__ == "__main__":
      print(solution())






