

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
lowercase_     =       False
lowercase_     =       Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
lowercase_     =       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					:




  def __init__(    self							,       lowerCAmelCase=sys.argv[1:] )  ->			Optional[Any]:
        '''simple docstring'''






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

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

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




  def   A__					(    self							,       lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=int(file.split('.' )[0].split('_' )[-1] )
        _lowercase     	=os.path.join(self.inputdir							,       lowerCAmelCase )
        return (img_id, filepath)




  @property
  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=[]
        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(lowerCAmelCase ) )
              if len(lowerCAmelCase ) == self.batch:
                    _lowercase     	=batch
                    _lowercase     	=[]
                    yield list(map(lowerCAmelCase							,       zip(*lowerCAmelCase ) ) )

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




  def __call__(    self )  ->			Any:
        '''simple docstring'''






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





      """simple docstring"""



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


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





from __future__ import annotations

import random
import string





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase = None )  ->			None:
        '''simple docstring'''






        _lowercase     	=passcode or self.__passcode_creator()
        _lowercase     	=self.__make_key_list()
        _lowercase     	=self.__make_shift_key()




  def __str__(    self )  ->			str:
        '''simple docstring'''






        return "".join(self.__passcode )




  def   A__					(    self							,       lowerCAmelCase )  ->			list[int]:
        '''simple docstring'''






        for i in range(1							,       len(lowerCAmelCase )							,       2 ):
              iterlist[i] *= -1
        return iterlist




  def   A__					(    self )  ->			list[str]:
        '''simple docstring'''






        _lowercase     	=string.ascii_letters + string.digits
        _lowercase     	=[random.choice(lowerCAmelCase ) for _ in range(random.randint(10							,       20 ) )]
        return password




  def   A__					(    self )  ->			list[str]:
        '''simple docstring'''






        _lowercase     	=(
            string.ascii_letters + string.digits + string.punctuation + ' \t\n'
        )

        _lowercase     	=[]

        # creates points known as breakpoints to break the key_list_options at those
        # points and pivot each substring
        _lowercase     	=sorted(set(self.__passcode ) )
        _lowercase     	=[]

        # algorithm for creating a new shuffled list, keys_l, out of key_list_options
        for i in key_list_options:
              temp_list.extend(lowerCAmelCase )

              # checking breakpoints at which to pivot temporary sublist and add it into
              # keys_l
              if i in breakpoints or i == key_list_options[-1]:
                    keys_l.extend(temp_list[::-1] )
                    temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
        return keys_l




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=sum(self.__neg_pos([ord(lowerCAmelCase ) for x in self.__passcode] ) )
        return num if num > 0 else len(self.__passcode )




  def   A__					(    self							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=''

        # decoding shift like Caesar cipher algorithm implementing negative shift or
        # reverse shift or left shift
        for i in encoded_message:
              _lowercase     	=self.__key_list.index(lowerCAmelCase )
              decoded_message += self.__key_list[
                  (position - self.__shift_key) % -len(self.__key_list )
              ]

        return decoded_message




  def   A__					(    self							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=''

        # encoding shift like Caesar cipher algorithm implementing positive shift or
        # forward shift or right shift
        for i in plaintext:
              _lowercase     	=self.__key_list.index(lowerCAmelCase )
              encoded_message += self.__key_list[
                  (position + self.__shift_key) % len(self.__key_list )
              ]

        return encoded_message
def 					a				(      A__     :       str = "Hello, this is a modified Caesar cipher"					)   ->				str:





      """simple docstring"""



      _lowercase     	=ShuffledShiftCipher()
      return cipa.decrypt(cipa.encrypt(A__					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


