


'''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      :     Optional[Any]										= False
A      :     Tuple										= Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
A      :     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  __lowerCamelCase :
    """simple docstring"""


    def __init__(  self			:							Any      ,							SCREAMING_SNAKE_CASE			:							Tuple=sys.argv[1:]):
           _A     :	Optional[int]      =					None
           _A     :	List[str]      =					None
           _A     :	Tuple      =					None
           _A     :	Optional[Any]      =					1
           _A  , _A     :	Union[str, 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"):
                         _A     :	Dict      =					arg
                  elif opt in ("-o", "--outfile"):
                         _A     :	Optional[int]      =					arg
                  elif opt in ("-b", "--batch_size"):
                         _A     :	int      =					int(SCREAMING_SNAKE_CASE)
                  elif opt in ("-s", "--subset_list"):
                         _A     :	List[Any]      =					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:
                         _A     :	Optional[Any]      =					{self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE)}
           else:
                  _A     :	Dict      =					None

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


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Any):
           _A     :	int      =					int(file.split('.')[0].split('_')[-1])
           _A     :	int      =					os.path.join(self.inputdir      ,							SCREAMING_SNAKE_CASE)
           return (img_id, filepath)


    @property
    def    A    (  self			:							List[str]):
           _A     :	Optional[int]      =					[]
           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:
                         _A     :	Any      =					batch
                         _A     :	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			:							int):
           # make writer
           if not TEST:
                  _A     :	List[str]      =					datasets.ArrowWriter(features=self.schema      ,							path=self.outputfile)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator):
                  _A  , _A  , _A     :	Union[str, Any]      =					self.preprocess(SCREAMING_SNAKE_CASE)
                  _A     :	Tuple      =					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'      ,							)
                  _A     :	int      =					output_dict.pop('normalized_boxes')
                  if not TEST:
                         _A     :	int      =					np.array(SCREAMING_SNAKE_CASE)
                         _A     :	Dict      =					self.schema.encode_batch(SCREAMING_SNAKE_CASE)
                         writer.write_batch(SCREAMING_SNAKE_CASE)
                  if TEST:
                         break
                  # finalizer the writer
           if not TEST:
                  _A  , _A     :	Dict      =					writer.finalize()
                  print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb')






def       lowerCAmelCase__       (				lowerCamelCase     :			Optional[int]   ):
       try:
              _A     :	int      =					json.load(lowerCamelCase   )
              try:
                     _A     :	List[str]      =					list(data.keys()   )
              except Exception:
                     _A     :	Optional[Any]      =					[d['img_id'] for d in data]
       except Exception:
              try:
                     _A     :	List[str]      =					eval(stream.read()   )
              except Exception:
                     _A     :	List[str]      =					stream.read().split('\n'   )
       return data


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










'''simple docstring'''







import math






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	int      =					math.log(2  ,10   ) * number
       _A     :	List[Any]      =					round((log_number - int(lowerCamelCase   ))  ,15   )

       return difference






def       lowerCAmelCase__       (				lowerCamelCase     :			int = 678910   ):
       _A     :	List[str]      =					90
       _A     :	List[Any]      =					0

       _A     :	List[str]      =					math.log(1.23  ,10   )
       _A     :	Any      =					math.log(1.24  ,10   )
       _A     :	Union[str, Any]      =					0

       while position < number:
              _A     :	Dict      =					log_difference(lowerCamelCase   )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     _A     :	str      =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






