

'''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_      =							False
A_      =							Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
A_      =							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 							_snake_case	:
   def __init__(	self     : List[Any]       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=sys.argv[1:]							):
     SCREAMING_SNAKE_CASE:Any   =   None
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   None
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   None
     SCREAMING_SNAKE_CASE:Optional[int]   =   1
     SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE: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"):
         SCREAMING_SNAKE_CASE:Any   =   arg
       elif opt in ("-o", "--outfile"):
         SCREAMING_SNAKE_CASE:Optional[Any]   =   arg
       elif opt in ("-b", "--batch_size"):
         SCREAMING_SNAKE_CASE:Optional[Any]   =   int(__SCREAMING_SNAKE_CASE							)
       elif opt in ("-s", "--subset_list"):
         SCREAMING_SNAKE_CASE:Dict   =   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:
         SCREAMING_SNAKE_CASE:List[str]   =   {self._vqa_file_split()[0] for x in tryload(__SCREAMING_SNAKE_CASE							)}
     else:
       SCREAMING_SNAKE_CASE:List[Any]   =   None

     SCREAMING_SNAKE_CASE:List[str]   =   CONFIG
     if torch.cuda.is_available():
       SCREAMING_SNAKE_CASE:int   =   "cuda"
     SCREAMING_SNAKE_CASE:Tuple   =   os.path.realpath(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   os.path.realpath(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:List[str]   =   Preprocess(self.config							)
     SCREAMING_SNAKE_CASE:Any   =   GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"       ,config=self.config							)
     SCREAMING_SNAKE_CASE:Dict   =   batch_size if batch_size != 0 else 1
     SCREAMING_SNAKE_CASE:List[Any]   =   DEFAULT_SCHEMA
   def       __UpperCamelCase  (	self     : Union[str, Any]       ,SCREAMING_SNAKE_CASE__     : Any							):
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   int(file.split("."							)[0].split("_"							)[-1]							)
     SCREAMING_SNAKE_CASE:List[Any]   =   os.path.join(self.inputdir       ,__SCREAMING_SNAKE_CASE							)
     return (img_id, filepath)
   @property
   def       __UpperCamelCase  (	self     : Dict							):
     SCREAMING_SNAKE_CASE: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(__SCREAMING_SNAKE_CASE							)							)
       if len(__SCREAMING_SNAKE_CASE							) == self.batch:
         SCREAMING_SNAKE_CASE:Optional[int]   =   batch
         SCREAMING_SNAKE_CASE:Optional[Any]   =   []
         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     : Dict							):




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





def 				A_					(	snake_case	):





  try:
    SCREAMING_SNAKE_CASE:Tuple   =   json.load(_lowerCamelCase	)
    try:
      SCREAMING_SNAKE_CASE:Any   =   list(data.keys()	)
    except Exception:
      SCREAMING_SNAKE_CASE:int   =   [d["img_id"] for d in data]
  except Exception:
    try:
      SCREAMING_SNAKE_CASE:Any   =   eval(stream.read()	)
    except Exception:
      SCREAMING_SNAKE_CASE:Union[str, Any]   =   stream.read().split("\n"	)
  return data


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









'''simple docstring'''

from __future__ import annotations

from collections import Counter





def 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:int   =   [True] * n
  SCREAMING_SNAKE_CASE:List[str]   =   False
  SCREAMING_SNAKE_CASE:Tuple   =   False
  SCREAMING_SNAKE_CASE:int   =   True

  for i in range(3 ,    int(n**0.5 + 1	) ,    2	):
    SCREAMING_SNAKE_CASE:str   =   i * 2
    while index < n:
      SCREAMING_SNAKE_CASE:Any   =   False
      SCREAMING_SNAKE_CASE:List[Any]   =   index + i

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   [2]

  for i in range(3 ,    snake_case ,    2	):
    if is_prime[i]:
      primes.append(snake_case	)

  return primes





def 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:List[str]   =   str(snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   []
  SCREAMING_SNAKE_CASE:List[str]   =   ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]

  for duplicate in Counter(snake_case	) - Counter(set(snake_case	)	):
    SCREAMING_SNAKE_CASE:List[str]   =   [int(number_str.replace(snake_case ,    snake_case	)	) for digit in digits]
    replacements.append(snake_case	)

  return replacements





def 				A_					(	snake_case = 8	):
  SCREAMING_SNAKE_CASE:str   =   set()

  # Filter primes with less than 3 replaceable digits
  SCREAMING_SNAKE_CASE:Tuple   =   {
      x for x in set(prime_sieve(1000000	)	) if len(str(snake_case	)	) - len(set(str(snake_case	)	)	) >= 3
  }

  for prime in primes:
    if prime in numbers_checked:
      continue

    SCREAMING_SNAKE_CASE:List[Any]   =   digit_replacements(snake_case	)

    for family in replacements:
      numbers_checked.update(snake_case	)
      SCREAMING_SNAKE_CASE:Optional[Any]   =   primes.intersection(snake_case	)

      if len(snake_case	) != family_length:
        continue

      return min(snake_case	)

  return -1


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






