

from __future__ import annotations







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   list[int]:

	"""simple docstring"""






	if not list_of_ints:
		return []

	if min(SCREAMING_SNAKE_CASE		) < 0:
		raise ValueError('All numbers must be positive'		)

	__lowerCAmelCase:       Tuple            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
	return _msd_radix_sort(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int		)   ->   list[int]:

	"""simple docstring"""






	if bit_position == 0 or len(SCREAMING_SNAKE_CASE		) in [0, 1]:
		return list_of_ints

	__lowerCAmelCase:       List[str]            =				[]
	__lowerCAmelCase:       Any            =				[]
	# Split numbers based on bit at bit_position from the right
	for number in list_of_ints:
		if (number >> (bit_position - 1)) & 1:
			# number has a one at bit bit_position
			ones.append(SCREAMING_SNAKE_CASE		)
		else:
			# number has a zero at bit bit_position
			zeros.append(SCREAMING_SNAKE_CASE		)

			 # recursively split both lists further
	__lowerCAmelCase:       Dict            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)
	__lowerCAmelCase:       Union[str, Any]            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)

	# recombine lists
	__lowerCAmelCase:       List[str]            =				zeros
	res.extend(SCREAMING_SNAKE_CASE		)

	return res







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   List[str]:

	"""simple docstring"""






	__lowerCAmelCase:       Optional[int]            =				len(SCREAMING_SNAKE_CASE		)
	if not list_of_ints or length == 1:
		return

	if min(SCREAMING_SNAKE_CASE		) < 0:
		raise ValueError('All numbers must be positive'		)

	__lowerCAmelCase:       Optional[int]            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
	_msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , 0   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int		)   ->   str:

	"""simple docstring"""






	if bit_position == 0 or end_index - begin_index <= 1:
		return

	bit_position -= 1

	__lowerCAmelCase:       Optional[int]            =				begin_index
	__lowerCAmelCase:       Optional[Any]            =				end_index - 1
	while i <= j:
		__lowerCAmelCase:       Any            =				False
		if not (list_of_ints[i] >> bit_position) & 1:
			# found zero at the beginning
			i += 1
			__lowerCAmelCase:       Optional[Any]            =				True
		if (list_of_ints[j] >> bit_position) & 1:
			# found one at the end
			j -= 1
			__lowerCAmelCase:       List[str]            =				True

		if changed:
			continue

		__lowerCAmelCase:       Union[str, Any]            =				list_of_ints[j], list_of_ints[i]
		j -= 1
		if j != i:
			i += 1

	_msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
	_msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()








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, 2_0_4_8), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)






class 							A_			:


     def __init__(							self      :		Optional[int]      ,  UpperCAmelCase      :		str=sys.argv[1:] )							->     Optional[int]:
      __lowerCAmelCase:       List[Any]            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       Any            =				1
      __lowerCAmelCase     ,							__lowerCAmelCase:       int            =				getopt.getopt(UpperCAmelCase      ,  'i:o:b:s'      ,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
      for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
        __lowerCAmelCase:       Any            =				arg
       elif opt in ("-o", "--outfile"):
        __lowerCAmelCase:       List[Any]            =				arg
       elif opt in ("-b", "--batch_size"):
        __lowerCAmelCase:       List[Any]            =				int(UpperCAmelCase )
       elif opt in ("-s", "--subset_list"):
        __lowerCAmelCase:       Union[str, Any]            =				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:
        __lowerCAmelCase:       Any            =				{self._vqa_file_split()[0] for x in tryload(UpperCAmelCase )}
      else:
       __lowerCAmelCase:       Union[str, Any]            =				None

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


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


     @property
     def   UpperCAmelCase  (							self      :		int )							->     Dict:
      __lowerCAmelCase:       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(UpperCAmelCase ) )
       if len(UpperCAmelCase ) == self.batch:
        __lowerCAmelCase:       Optional[int]            =				batch
        __lowerCAmelCase:       Tuple            =				[]
        yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )

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


     def __call__(							self      :		Tuple )							->     Dict:
      # make writer
      if not TEST:
       __lowerCAmelCase:       Tuple            =				datasets.ArrowWriter(features=self.schema      ,  path=self.outputfile )
      # do file generator
      for i, (img_ids, filepaths) in enumerate(self.file_generator ):
       __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       List[Any]            =				self.preprocess(UpperCAmelCase )
       __lowerCAmelCase:       str            =				self.model(
           UpperCAmelCase      ,  UpperCAmelCase      ,  scales_yx=UpperCAmelCase      ,  padding='max_detections'      ,  max_detections=self.config.MAX_DETECTIONS      ,  pad_value=0      ,  return_tensors='np'      ,  location='cpu'      ,  )
       __lowerCAmelCase:       str            =				output_dict.pop('normalized_boxes' )
       if not TEST:
        __lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase )
        __lowerCAmelCase:       Optional[int]            =				self.schema.encode_batch(UpperCAmelCase )
        writer.write_batch(UpperCAmelCase )
       if TEST:
        break
       # finalizer the writer
      if not TEST:
       __lowerCAmelCase     ,							__lowerCAmelCase:       Optional[int]            =				writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb''' )







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Any:

 """simple docstring"""






 try:
  __lowerCAmelCase:       Optional[Any]            =				json.load(SCREAMING_SNAKE_CASE		)
  try:
   __lowerCAmelCase:       Tuple            =				list(data.keys()		)
  except Exception:
   __lowerCAmelCase:       int            =				[d['img_id'] for d in data]
 except Exception:
  try:
   __lowerCAmelCase:       Dict            =				eval(stream.read()		)
  except Exception:
   __lowerCAmelCase:       Dict            =				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)





