
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      :					int   	=       False
__lowerCAmelCase      :					Any   	=       Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__lowerCAmelCase      :					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, 2048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)




class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Any      ,							UpperCamelCase__      :			Optional[Any]=sys.argv[1:]   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	None
    __magic_name__    					=	None
    __magic_name__    					=	None
    __magic_name__    					=	1
    __magic_name__					,   __magic_name__    					=	getopt.getopt(__UpperCamelCase      ,							"""i:o:b:s"""      ,							["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
    for opt, arg in opts:
      if opt in ("-i", "--inputdir"):
        __magic_name__    					=	arg
      elif opt in ("-o", "--outfile"):
        __magic_name__    					=	arg
      elif opt in ("-b", "--batch_size"):
        __magic_name__    					=	int(__UpperCamelCase   )
      elif opt in ("-s", "--subset_list"):
        __magic_name__    					=	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:
        __magic_name__    					=	{self._vqa_file_split()[0] for x in tryload(__UpperCamelCase   )}
    else:
      __magic_name__    					=	None

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







    """simple docstring"""






    __magic_name__    					=	int(file.split("""."""   )[0].split("""_"""   )[-1]   )
    __magic_name__    					=	os.path.join(self.inputdir      ,							__UpperCamelCase   )
    return (img_id, filepath)
  @property
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	[]
    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:
        __magic_name__    					=	batch
        __magic_name__    					=	[]
        yield list(map(__UpperCamelCase      ,							zip(*__UpperCamelCase   )   )   )

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





  def __call__(			self      :			Dict   )      ->     Optional[Any]:







    """simple docstring"""






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







def a__   (	UpperCAmelCase__ ):






  '''simple docstring'''





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


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



from itertools import compress, repeat
from math import ceil, sqrt

def a__   (	A_ ):






  '''simple docstring'''





  if num <= 2:
    return []
  if num == 3:
    return [2]

  # Odd sieve for numbers in range [3, num - 1]
  __magic_name__    					=	bytearray(b"""\x01""" ) * ((num >> 1) - 1)

  for i in range(3,					int(sqrt(A_ ) ) + 1,					2 ):
    if sieve[(i >> 1) - 1]:
      __magic_name__    					=	i**2
      __magic_name__    					=	repeat(
          0,					ceil((num - i_squared) / (i << 1) ) )

  return [2] + list(compress(range(3,					A_,					2 ),					A_ ) )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

