

'''simple docstring'''




import numpy as np


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Optional[int]="utf-8"    ,				SCREAMING_SNAKE_CASE :				str="surrogatepass"  ):
  UpperCAmelCase  							=		bin(int.from_bytes(text.encode(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )    ,				'big'  )  )[2:]
  return bits.zfill(8 * ((len(SCREAMING_SNAKE_CASE  ) + 7) // 8)  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Optional[Any]="utf-8"    ,				SCREAMING_SNAKE_CASE :				List[Any]="surrogatepass"  ):
  UpperCAmelCase  							=		int(SCREAMING_SNAKE_CASE    ,				2  )
  return n.to_bytes((n.bit_length() + 7) // 8    ,				'big'  ).decode(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ) or "\0"


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Union[str, Any]    ,				SCREAMING_SNAKE_CASE :				Optional[Any]  ):
  if size_par + len(SCREAMING_SNAKE_CASE  ) <= 2**size_par - (len(SCREAMING_SNAKE_CASE  ) - 1):
    raise ValueError('size of parity don\'t match with size of data'  )

  UpperCAmelCase  							=		[]
  UpperCAmelCase  							=		[]
  UpperCAmelCase  							=		[bin(SCREAMING_SNAKE_CASE  )[2:] for x in range(1    ,				size_par + len(SCREAMING_SNAKE_CASE  ) + 1  )]

  # sorted information data for the size of the output data
  UpperCAmelCase  							=		[]
  # data position template + parity
  UpperCAmelCase  							=		[]
  # parity bit counter
  UpperCAmelCase  							=		0
  # counter position of data bits
  UpperCAmelCase  							=		0

  for x in range(1    ,				size_par + len(SCREAMING_SNAKE_CASE  ) + 1  ):
    # Performs a template of bit positions - who should be given,
    # and who should be parity
    if qtd_bp < size_par:
      if (np.log(SCREAMING_SNAKE_CASE  ) / np.log(2  )).is_integer():
        data_out_gab.append('P'  )
        UpperCAmelCase  							=		qtd_bp + 1
      else:
        data_out_gab.append('D'  )
    else:
      data_out_gab.append('D'  )

    # Sorts the data to the new output size
    if data_out_gab[-1] == "D":
      data_ord.append(data[cont_data]  )
      cont_data += 1
    else:
      data_ord.append(SCREAMING_SNAKE_CASE  )

    # Calculates parity
  UpperCAmelCase  							=		0  # parity bit counter
  for bp in range(1    ,				size_par + 1  ):
    # Bit counter one for a given parity
    UpperCAmelCase  							=		0
    # counter to control the loop reading
    UpperCAmelCase  							=		0
    for x in data_ord:
      if x is not None:
        try:
          UpperCAmelCase  							=		(bin_pos[cont_loop])[-1 * (bp)]
        except IndexError:
          UpperCAmelCase  							=		'0'
        if aux == "1" and x == "1":
          cont_bo += 1
      cont_loop += 1
    parity.append(cont_bo % 2  )

    qtd_bp += 1

  # Mount the message
  UpperCAmelCase  							=		0  # parity bit counter
  for x in range(0    ,				size_par + len(SCREAMING_SNAKE_CASE  )  ):
    if data_ord[x] is None:
      data_out.append(str(parity[cont_bp]  )  )
      cont_bp += 1
    else:
      data_out.append(data_ord[x]  )

  return data_out


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				str  ):
  UpperCAmelCase  							=		[]
  # Parity bit counter
  UpperCAmelCase  							=		0
  # Counter p data bit reading
  UpperCAmelCase  							=		0
  # list of parity received
  UpperCAmelCase  							=		[]
  UpperCAmelCase  							=		[]

  for x in range(1    ,				len(SCREAMING_SNAKE_CASE  ) + 1  ):
    # Performs a template of bit positions - who should be given,
    #  and who should be parity
    if qtd_bp < size_par and (np.log(SCREAMING_SNAKE_CASE  ) / np.log(2  )).is_integer():
      data_out_gab.append('P'  )
      UpperCAmelCase  							=		qtd_bp + 1
    else:
      data_out_gab.append('D'  )

    # Sorts the data to the new output size
    if data_out_gab[-1] == "D":
      data_output.append(data[cont_data]  )
    else:
      parity_received.append(data[cont_data]  )
    cont_data += 1

  # -----------calculates the parity with the data
  UpperCAmelCase  							=		[]
  UpperCAmelCase  							=		[]
  UpperCAmelCase  							=		[bin(SCREAMING_SNAKE_CASE  )[2:] for x in range(1    ,				size_par + len(SCREAMING_SNAKE_CASE  ) + 1  )]

  #  sorted information data for the size of the output data
  UpperCAmelCase  							=		[]
  # Data position feedback + parity
  UpperCAmelCase  							=		[]
  # Parity bit counter
  UpperCAmelCase  							=		0
  # Counter p data bit reading
  UpperCAmelCase  							=		0

  for x in range(1    ,				size_par + len(SCREAMING_SNAKE_CASE  ) + 1  ):
    # Performs a template position of bits - who should be given,
    # and who should be parity
    if qtd_bp < size_par and (np.log(SCREAMING_SNAKE_CASE  ) / np.log(2  )).is_integer():
      data_out_gab.append('P'  )
      UpperCAmelCase  							=		qtd_bp + 1
    else:
      data_out_gab.append('D'  )

    # Sorts the data to the new output size
    if data_out_gab[-1] == "D":
      data_ord.append(data_output[cont_data]  )
      cont_data += 1
    else:
      data_ord.append(SCREAMING_SNAKE_CASE  )

    # Calculates parity
  UpperCAmelCase  							=		0  # parity bit counter
  for bp in range(1    ,				size_par + 1  ):
    # Bit counter one for a certain parity
    UpperCAmelCase  							=		0
    # Counter to control loop reading
    UpperCAmelCase  							=		0
    for x in data_ord:
      if x is not None:
        try:
          UpperCAmelCase  							=		(bin_pos[cont_loop])[-1 * (bp)]
        except IndexError:
          UpperCAmelCase  							=		'0'
        if aux == "1" and x == "1":
          cont_bo += 1
      cont_loop += 1
    parity.append(str(cont_bo % 2  )  )

    qtd_bp += 1

  # Mount the message
  UpperCAmelCase  							=		0  # Parity bit counter
  for x in range(0    ,				size_par + len(SCREAMING_SNAKE_CASE  )  ):
    if data_ord[x] is None:
      data_out.append(str(parity[cont_bp]  )  )
      cont_bp += 1
    else:
      data_out.append(data_ord[x]  )

  UpperCAmelCase  							=		parity_received == parity
  return data_output, ack





'''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			:	Any								=  False
_a			:	Union[str, Any]								=  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a			:	Dict								=  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_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)



class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_=sys.argv[1:]       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		1
    UpperCAmelCase							, UpperCAmelCase  							=		getopt.getopt(a_				,	'i:o:b:s'				,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']       )
    for opt, arg in opts:
      if opt in ("-i", "--inputdir"):
        UpperCAmelCase  							=		arg
      elif opt in ("-o", "--outfile"):
        UpperCAmelCase  							=		arg
      elif opt in ("-b", "--batch_size"):
        UpperCAmelCase  							=		int(a_       )
      elif opt in ("-s", "--subset_list"):
        UpperCAmelCase  							=		arg

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

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


    """simple docstring"""
    UpperCAmelCase  							=		int(file.split('.'       )[0].split('_'       )[-1]       )
    UpperCAmelCase  							=		os.path.join(self.inputdir				,	a_       )
    return (img_id, filepath)
  @property
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    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(a_       )       )
      if len(a_       ) == self.batch:
        UpperCAmelCase  							=		batch
        UpperCAmelCase  							=		[]
        yield list(map(a_				,	zip(*a_       )       )       )

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





  def __call__(       self       )							->			str:


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




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int  ):
  try:
    UpperCAmelCase  							=		json.load(SCREAMING_SNAKE_CASE  )
    try:
      UpperCAmelCase  							=		list(data.keys()  )
    except Exception:
      UpperCAmelCase  							=		[d['img_id'] for d in data]
  except Exception:
    try:
      UpperCAmelCase  							=		eval(stream.read()  )
    except Exception:
      UpperCAmelCase  							=		stream.read().split('\n'  )
  return data


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


