'''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 :				Dict														= False
_lowerCamelCase :				Union[str, Any]														= Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_lowerCamelCase :				Tuple														= 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    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__=sys.argv[1:]     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =None
        _UpperCamelCase  :List[Any]       =None
        _UpperCamelCase  :Optional[Any]       =None
        _UpperCamelCase  :Any       =1
        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =getopt.getopt(lowerCAmelCase__					,      """i:o:b:s"""					,      ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]     )
        for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                  _UpperCamelCase  :Optional[int]       =arg
             elif opt in ("-o", "--outfile"):
                  _UpperCamelCase  :List[str]       =arg
             elif opt in ("-b", "--batch_size"):
                  _UpperCamelCase  :str       =int(lowerCAmelCase__     )
             elif opt in ("-s", "--subset_list"):
                  _UpperCamelCase  :Union[str, Any]       =arg

        assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
        assert outputfile is not None and not os.path.isfile(lowerCAmelCase__     ), f'''{outputfile}'''
        if subset_list is not None:
             with open(os.path.realpath(lowerCAmelCase__     )     ) as f:
                  _UpperCamelCase  :Dict       ={self._vqa_file_split()[0] for x in tryload(lowerCAmelCase__     )}
        else:
             _UpperCamelCase  :Union[str, Any]       =None

        _UpperCamelCase  :int       =CONFIG
        if torch.cuda.is_available():
             _UpperCamelCase  :int       ="""cuda"""
        _UpperCamelCase  :List[Any]       =os.path.realpath(lowerCAmelCase__     )
        _UpperCamelCase  :int       =os.path.realpath(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =Preprocess(self.config     )
        _UpperCamelCase  :Union[str, Any]       =GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""					,      config=self.config     )
        _UpperCamelCase  :Optional[Any]       =batch_size if batch_size != 0 else 1
        _UpperCamelCase  :str       =DEFAULT_SCHEMA



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =int(file.split("""."""     )[0].split("""_"""     )[-1]     )
        _UpperCamelCase  :List[Any]       =os.path.join(self.inputdir					,      lowerCAmelCase__     )
        return (img_id, filepath)



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Any       =[]
        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(lowerCAmelCase__     )     )
             if len(lowerCAmelCase__     ) == self.batch:
                  _UpperCamelCase  :Union[str, Any]       =batch
                  _UpperCamelCase  :Any       =[]
                  yield list(map(lowerCAmelCase__					,      zip(*lowerCAmelCase__     )     )     )

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




   def __call__(    self     )				->     int:
        """simple docstring"""





        if not TEST:
             _UpperCamelCase  :Optional[int]       =datasets.ArrowWriter(features=self.schema					,      path=self.outputfile     )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator     ):
             _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self.preprocess(lowerCAmelCase__     )
             _UpperCamelCase  :str       =self.model(
                 lowerCAmelCase__					,      lowerCAmelCase__					,      scales_yx=lowerCAmelCase__					,      padding="""max_detections"""					,      max_detections=self.config.MAX_DETECTIONS					,      pad_value=0					,      return_tensors="""np"""					,      location="""cpu"""					,      )
             _UpperCamelCase  :str       =output_dict.pop("""normalized_boxes"""     )
             if not TEST:
                  _UpperCamelCase  :str       =np.array(lowerCAmelCase__     )
                  _UpperCamelCase  :Union[str, Any]       =self.schema.encode_batch(lowerCAmelCase__     )
                  writer.write_batch(lowerCAmelCase__     )
             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  _lowerCAmelCase				(				__a      )  ->      List[str]:
     '''simple docstring'''







     try:
          _UpperCamelCase  :int       =json.load(__a      )
          try:
               _UpperCamelCase  :int       =list(data.keys()      )
          except Exception:
               _UpperCamelCase  :List[Any]       =[d["""img_id"""] for d in data]
     except Exception:
          try:
               _UpperCamelCase  :Optional[Any]       =eval(stream.read()      )
          except Exception:
               _UpperCamelCase  :Tuple       =stream.read().split("""\n"""      )
     return data


if __name__ == "__main__":
      _lowerCamelCase :				Dict														= Extract(sys.argv[1:])
      extract()
      if not TEST:
            _lowerCamelCase :				int														= datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)
def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):
       return n * (2 * n - 1)


def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):
       _UpperCamelCase  :							str	    =	(1 + 24 * n) ** 0.5
       return ((1 + root) / 6) % 1 == 0


def SCREAMING_SNAKE_CASE					(  lowercase_:						int = 144					):
       _UpperCamelCase  :							Optional[Any]	    =	start
       _UpperCamelCase  :							List[str]	    =	hexagonal_num(lowercase_					)
       while not is_pentagonal(lowercase_					):
              n += 1
              _UpperCamelCase  :							Optional[int]	    =	hexagonal_num(lowercase_					)
       return num


if __name__ == "__main__":
 print(f'''{solution()} = ''')



