





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   :							Optional[Any]     =	False
_UpperCamelCase   :							Dict     =	Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_UpperCamelCase   :							List[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, 2_0_4_8), 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  ,    UpperCAmelCase=sys.argv[1:]     )-> Dict:
     __A				 =       None
     __A				 =       None
     __A				 =       None
     __A				 =       1
     __A ,						__A				 =       getopt.getopt(UpperCAmelCase  ,    '''i:o:b:s'''  ,    ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']     )
     for opt, arg in opts:
        if opt in ("-i", "--inputdir"):
           __A				 =       arg
        elif opt in ("-o", "--outfile"):
           __A				 =       arg
        elif opt in ("-b", "--batch_size"):
           __A				 =       int(UpperCAmelCase     )
        elif opt in ("-s", "--subset_list"):
           __A				 =       arg

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

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

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> str:
     __A				 =       int(file.split('''.'''     )[0].split('''_'''     )[-1]     )
     __A				 =       os.path.join(self.inputdir  ,    UpperCAmelCase     )
     return (img_id, filepath)

  @property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     __A				 =       []
     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(UpperCAmelCase     )     )
        if len(UpperCAmelCase     ) == self.batch:
           __A				 =       batch
           __A				 =       []
           yield list(map(UpperCAmelCase  ,    zip(*UpperCAmelCase     )     )     )

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





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


   '''simple docstring'''







   try:
      __A				 =       json.load(snake_case     )
      try:
         __A				 =       list(data.keys()     )
      except Exception:
         __A				 =       [d['''img_id'''] for d in data]
   except Exception:
      try:
         __A				 =       eval(stream.read()     )
      except Exception:
         __A				 =       stream.read().split('''\n'''     )
   return data


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








from __future__ import annotations

import sys
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						tuple[int, int]:


   '''simple docstring'''







   if abs(snake_case     ) == 1:
      return a, 0
   elif abs(snake_case     ) == 1:
      return 0, b

   __A ,						__A				 =       a, b
   __A ,						__A				 =       1, 0
   __A ,						__A				 =       0, 1

   while remainder != 0:
      __A				 =       old_remainder // remainder
      __A ,						__A				 =       remainder, old_remainder - quotient * remainder
      __A ,						__A				 =       coeff_a, old_coeff_a - quotient * coeff_a
      __A ,						__A				 =       coeff_b, old_coeff_b - quotient * coeff_b

   # sign correction for negative numbers
   if a < 0:
      __A				 =       -old_coeff_a
   if b < 0:
      __A				 =       -old_coeff_b

   return old_coeff_a, old_coeff_b
def 				__UpperCamelCase  (		)  ->						Tuple:


   '''simple docstring'''







   if len(sys.argv     ) < 3:
      print('''2 integer arguments required'''     )
      return 1
   __A				 =       int(sys.argv[1]     )
   __A				 =       int(sys.argv[2]     )
   print(extended_euclidean_algorithm(snake_case    ,	snake_case     )     )
   return 0


if __name__ == "__main__":
    raise SystemExit(main())

