





"""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 			a       :



 def __init__(    self							,					_snake_case=sys.argv[1:]    ):







  """simple docstring"""






  lowerCAmelCase													=   None
  lowerCAmelCase													=   None
  lowerCAmelCase													=   None
  lowerCAmelCase													=   1
  lowerCAmelCase													=   getopt.getopt(_snake_case							,					'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(_snake_case    )
   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(_snake_case    ), F'{outputfile}'
  if subset_list is not None:
   with open(os.path.realpath(_snake_case    )    ) as f:
    lowerCAmelCase													=   {self._vqa_file_split()[0] for x in tryload(_snake_case    )}
  else:
   lowerCAmelCase													=   None

  lowerCAmelCase													=   CONFIG
  if torch.cuda.is_available():
   lowerCAmelCase													=   '''cuda'''
  lowerCAmelCase													=   os.path.realpath(_snake_case    )
  lowerCAmelCase													=   os.path.realpath(_snake_case    )
  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       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   int(file.split('.'    )[0].split('_'    )[-1]    )
  lowerCAmelCase													=   os.path.join(self.inputdir							,					_snake_case    )
  return (img_id, filepath)



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  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(_snake_case    )    )
   if len(_snake_case    ) == self.batch:
    lowerCAmelCase													=   batch
    lowerCAmelCase													=   []
    yield list(map(_snake_case							,					zip(*_snake_case    )    )    )

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



 def __call__(    self    ):







  """simple docstring"""






  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													=   self.preprocess(_snake_case    )
   lowerCAmelCase													=   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'							,					)
   lowerCAmelCase													=   output_dict.pop('normalized_boxes'    )
   if not TEST:
    lowerCAmelCase													=   np.array(_snake_case    )
    lowerCAmelCase													=   self.schema.encode_batch(_snake_case    )
    writer.write_batch(_snake_case    )
   if TEST:
    break
   # finalizer the writer
  if not TEST:
   lowerCAmelCase													=   writer.finalize()
   print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'    )


def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						List[str] ):
 try:
  lowerCAmelCase													=   json.load(_UpperCAmelCase )
  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__":
       __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)












"""simple docstring"""
import math

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						int ):

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5     ,    int(math.sqrt(_UpperCAmelCase ) + 1 )     ,    6 ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						int = 200_0000 ):
 return sum(num for num in range(3     ,    _UpperCAmelCase     ,    2 ) if is_prime(_UpperCAmelCase ) ) + 2 if n > 2 else 0


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





