





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 						=					False
__UpperCAmelCase 						=					Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__UpperCAmelCase 						=					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, 20_48), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)

class 						__a							:



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

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



       def        A							(   self		:      Dict    ,	UpperCAmelCase		:      Any	):
           lowerCAmelCase_    :		int 						=      int(file.split("""."""	)[0].split("""_"""	)[-1]	)
           lowerCAmelCase_    :		int 						=      os.path.join(self.inputdir    ,	UpperCAmelCase	)
           return (img_id, filepath)



       @property
       def        A							(   self		:      List[str]	):
           lowerCAmelCase_    :		Optional[int] 						=      []
           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_    :		Any 						=      batch
                   lowerCAmelCase_    :		int 						=      []
                   yield list(map(UpperCAmelCase    ,	zip(*UpperCAmelCase	)	)	)

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



       def __call__(   self		:      int	):
           # make writer
           if not TEST:
               lowerCAmelCase_    :		List[str] 						=      datasets.ArrowWriter(features=self.schema    ,	path=self.outputfile	)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator	):
               lowerCAmelCase_    :		Union[str, Any] 						=      self.preprocess(UpperCAmelCase	)
               lowerCAmelCase_    :		Tuple 						=      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_    :		int 						=      output_dict.pop("""normalized_boxes"""	)
               if not TEST:
                   lowerCAmelCase_    :		int 						=      np.array(UpperCAmelCase	)
                   lowerCAmelCase_    :		Dict 						=      self.schema.encode_batch(UpperCAmelCase	)
                   writer.write_batch(UpperCAmelCase	)
               if TEST:
                   break
               # finalizer the writer
           if not TEST:
               lowerCAmelCase_    :		Dict 						=      writer.finalize()
               print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)






def 	__UpperCamelCase (	lowercase__		:				Optional[int]						)		->					Union[str, Any]:
    '''simple docstring'''





    try:
        lowerCAmelCase_    :		int 						=      json.load(lowercase__						)
        try:
            lowerCAmelCase_    :		List[str] 						=      list(data.keys()						)
        except Exception:
            lowerCAmelCase_    :		Optional[Any] 						=      [d['img_id'] for d in data]
    except Exception:
        try:
            lowerCAmelCase_    :		List[str] 						=      eval(stream.read()						)
        except Exception:
            lowerCAmelCase_    :		List[str] 						=      stream.read().split("""\n"""						)
    return data


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






'''simple docstring'''







from __future__ import annotations






def       lowerCAmelCase__       (				lowerCamelCase     :			str  ,lowerCamelCase     :			dict[str, str]   ):
       _A  , _A  , _A     :	int      =					'', '', ''
       _A     :	Optional[int]      =					[]

       for character in message_part:
              tmp.append(character_to_number[character]   )

       for each in tmp:
              one += each[0]
              two += each[1]
              three += each[2]

       return one + two + three






def       lowerCAmelCase__       (				lowerCamelCase     :			str  ,lowerCamelCase     :			dict[str, str]   ):
       _A  , _A     :	Tuple      =					'', ''
       _A     :	Tuple      =					[]

       for character in message_part:
              this_part += character_to_number[character]

       for digit in this_part:
              tmp += digit
              if len(lowerCamelCase   ) == len(lowerCamelCase   ):
                     result.append(lowerCamelCase   )
                     _A     :	str      =					''

       return result[0], result[1], result[2]






def       lowerCAmelCase__       (				lowerCamelCase     :			str  ,lowerCamelCase     :			str   ):
       # Validate message and alphabet, set to upper and remove spaces
       _A     :	str      =					alphabet.replace(' '  ,''   ).upper()
       _A     :	Optional[Any]      =					message.replace(' '  ,''   ).upper()

       # Check length and characters
       if len(lowerCamelCase   ) != 27:
              raise KeyError('Length of alphabet has to be 27.'   )
       for each in message:
              if each not in alphabet:
                     raise ValueError('Each message character has to be included in alphabet!'   )

    # Generate dictionares
       _A     :	int      =					(
           '111',
           '112',
           '113',
           '121',
           '122',
           '123',
           '131',
           '132',
           '133',
           '211',
           '212',
           '213',
           '221',
           '222',
           '223',
           '231',
           '232',
           '233',
           '311',
           '312',
           '313',
           '321',
           '322',
           '323',
           '331',
           '332',
           '333',
       )
       _A     :	Optional[int]      =					{}
       _A     :	Any      =					{}
       for letter, number in zip(lowerCamelCase  ,lowerCamelCase   ):
              _A     :	str      =					number
              _A     :	List[str]      =					letter

       return message, alphabet, character_to_number, number_to_character






def       lowerCAmelCase__       (				lowerCamelCase     :			str  ,lowerCamelCase     :			str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,lowerCamelCase     :			int = 5   ):
       _A  , _A  , _A  , _A     :	Union[str, Any]      =					__prepare(
           lowerCamelCase  ,lowerCamelCase   )
       _A  , _A     :	Any      =					'', ''

       for i in range(0  ,len(lowerCamelCase   ) + 1  ,lowerCamelCase   ):
              encrypted_numeric += __encrypt_part(
                  message[i : i + period]  ,lowerCamelCase   )

       for i in range(0  ,len(lowerCamelCase   )  ,3   ):
              encrypted += number_to_character[encrypted_numeric[i : i + 3]]

       return encrypted






def       lowerCAmelCase__       (				lowerCamelCase     :			str  ,lowerCamelCase     :			str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,lowerCamelCase     :			int = 5   ):
       _A  , _A  , _A  , _A     :	Optional[Any]      =					__prepare(
           lowerCamelCase  ,lowerCamelCase   )
       _A     :	Tuple      =					[]
       _A     :	List[str]      =					''

       for i in range(0  ,len(lowerCamelCase   ) + 1  ,lowerCamelCase   ):
              _A  , _A  , _A     :	Tuple      =					__decrypt_part(message[i : i + period]  ,lowerCamelCase   )

              for j in range(0  ,len(lowerCamelCase   )   ):
                     decrypted_numeric.append(a[j] + b[j] + c[j]   )

       for each in decrypted_numeric:
              decrypted += number_to_character[each]

       return decrypted


if __name__ == "__main__":
   A      :     Dict										= '''DEFEND THE EAST WALL OF THE CASTLE.'''
   A      :     Any										= encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
   A      :     List[Any]										= decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
   print(f"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")






