





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__    :      Optional[int]       					=False
UpperCAmelCase__    :      Optional[int]       					=Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase__    :      Optional[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, 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 ,			UpperCAmelCase_=sys.argv[1:]				):
								lowerCamelCase									=None
								lowerCamelCase									=None
								lowerCamelCase									=None
								lowerCamelCase									=1
								lowerCamelCase			,	lowerCamelCase									=getopt.getopt(UpperCAmelCase_ ,			"""i:o:b:s""" ,			["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]				)
								for opt, arg in opts:
												if opt in ("-i", "--inputdir"):
																lowerCamelCase									=arg
												elif opt in ("-o", "--outfile"):
																lowerCamelCase									=arg
												elif opt in ("-b", "--batch_size"):
																lowerCamelCase									=int(UpperCAmelCase_				)
												elif opt in ("-s", "--subset_list"):
																lowerCamelCase									=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									={self._vqa_file_split()[0] for x in tryload(UpperCAmelCase_				)}
								else:
												lowerCamelCase									=None

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


				def _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=int(file.split("""."""				)[0].split("""_"""				)[-1]				)
								lowerCamelCase									=os.path.join(self.inputdir ,			UpperCAmelCase_				)
								return (img_id, filepath)


				@property
				def _snake_case							(		self				):
								lowerCamelCase									=[]
								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									=batch
																lowerCamelCase									=[]
																yield list(map(UpperCAmelCase_ ,			zip(*UpperCAmelCase_				)				)				)

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


				def __call__(		self				):




								if not TEST:
												lowerCamelCase									=datasets.ArrowWriter(features=self.schema ,			path=self.outputfile				)
								# do file generator
								for i, (img_ids, filepaths) in enumerate(self.file_generator				):
												lowerCamelCase			,	lowerCamelCase			,	lowerCamelCase									=self.preprocess(UpperCAmelCase_				)
												lowerCamelCase									=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									=output_dict.pop("""normalized_boxes"""				)
												if not TEST:
																lowerCamelCase									=np.array(UpperCAmelCase_				)
																lowerCamelCase									=self.schema.encode_batch(UpperCAmelCase_				)
																writer.write_batch(UpperCAmelCase_				)
												if TEST:
																break
												# finalizer the writer
								if not TEST:
												lowerCamelCase			,	lowerCamelCase									=writer.finalize()
												print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""				)





def   _lowercase       (    _UpperCAmelCase					)    ->	Optional[Any]:





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


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













from __future__ import annotations

from string import ascii_letters

def 			__lowerCamelCase     (						snake_case__			,snake_case__			,snake_case__ = None						)				->    str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    alphabet or ascii_letters

						# The final result string
						_SCREAMING_SNAKE_CASE       =    """"""

						for character in input_string:
												if character not in alpha:
																		# Append without encryption if character is not in the alphabet
																		result += character
												else:
																		# Get the index of the new key and make sure it isn't too large
																		_SCREAMING_SNAKE_CASE       =    (alpha.index(snake_case__						) + key) % len(snake_case__						)

																		# Append the encoded character to the alphabet
																		result += alpha[new_key]

						return result

def 			__lowerCamelCase     (						snake_case__			,snake_case__			,snake_case__ = None						)				->    str:






						"""simple docstring"""





						key *= -1

						return encrypt(snake_case__			,snake_case__			,snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__ = None						)				->    dict[int, str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    alphabet or ascii_letters

						# To store data on all the combinations
						_SCREAMING_SNAKE_CASE       =    {}

						# Cycle through each combination
						for key in range(1			,len(snake_case__						) + 1						):
												# Decrypt the message and store the result in the data
												_SCREAMING_SNAKE_CASE       =    decrypt(snake_case__			,snake_case__			,snake_case__						)

						return brute_force_data


if __name__ == "__main__":
		while True:
				print(f"\n{'-' * 10}\n Menu\n{'-' * 10}")
				print(*['''1.Encrypt''', '''2.Decrypt''', '''3.BruteForce''', '''4.Quit'''], sep='''\n''')

				# get user input
				UpperCamelCase												=      input('''\nWhat would you like to do?: ''').strip() or '''4'''

				# run functions based on what the user chose
				if choice not in ("1", "2", "3", "4"):
						print('''Invalid choice, please enter a valid choice''')
				elif choice == "1":
						UpperCamelCase												=      input('''Please enter the string to be encrypted: ''')
						UpperCamelCase												=      int(input('''Please enter off-set: ''').strip())

						print(encrypt(input_string, key))
				elif choice == "2":
						UpperCamelCase												=      input('''Please enter the string to be decrypted: ''')
						UpperCamelCase												=      int(input('''Please enter off-set: ''').strip())

						print(decrypt(input_string, key))
				elif choice == "3":
						UpperCamelCase												=      input('''Please enter the string to be decrypted: ''')
						UpperCamelCase												=      brute_force(input_string)

						for key, value in brute_force_data.items():
								print(f"Key: {key} | Message: {value}")

				elif choice == "4":
						print('''Goodbye.''')
						break

