





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
SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      False
SCREAMING_SNAKE_CASE_      :			Dict		  =      Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
SCREAMING_SNAKE_CASE_      :			Union[str, 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, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)

class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    Union[str, Any]				,			__lowerCamelCase       :    List[Any]=sys.argv[1:]      )					->   Dict:

			'''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       UpperCAmelCase (	self       :    Tuple				,			__lowerCamelCase       :    Tuple      )					->   Tuple:

			'''simple docstring'''




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




	@property
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Tuple:

			'''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       :    List[str]      )					->   Optional[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    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Any:
		try:
				__lowercase					=    json.load(snake_case       )
				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__":
		SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      Extract(sys.argv[1:])
		extract()
		if not TEST:
				SCREAMING_SNAKE_CASE_      :			List[str]		  =      datasets.Dataset.from_file(extract.outputfile)
				# wala!
				# print(np.array(dataset[0:2]["roi_features"]).shape)











SCREAMING_SNAKE_CASE_      :			str		  =      [chr(i) for i in range(32, 126)]
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			List[str]		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Dict		  =      list(reversed(range(len(alphabets))))
SCREAMING_SNAKE_CASE_      :			int		  =      []
SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      0



def    SCREAMING_SNAKE_CASE							(			)		->						int:
		global gear_one_pos
		global gear_two_pos
		global gear_three_pos
		__lowercase					=    gear_one[0]
		gear_one.append(snake_case       )
		del gear_one[0]
		gear_one_pos += 1
		if gear_one_pos % int(len(snake_case       )       ) == 0:
				__lowercase					=    gear_two[0]
				gear_two.append(snake_case       )
				del gear_two[0]
				gear_two_pos += 1
				if gear_two_pos % int(len(snake_case       )       ) == 0:
						__lowercase					=    gear_three[0]
						gear_three.append(snake_case       )
						del gear_three[0]
						gear_three_pos += 1



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    alphabets.index(snake_case       )
		__lowercase					=    gear_one[target]
		__lowercase					=    gear_two[target]
		__lowercase					=    gear_three[target]
		__lowercase					=    reflector[target]
		__lowercase					=    gear_three.index(snake_case       )
		__lowercase					=    gear_two.index(snake_case       )
		__lowercase					=    gear_one.index(snake_case       )
		code.append(alphabets[target]       )
		rotator()


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Tuple		  =      list(input('''Type your message:\n'''))
		while True:
				try:
						SCREAMING_SNAKE_CASE_      :			List[str]		  =      int(input('''Please set token:(must be only digits)\n'''))
						break
				except Exception as error:
						print(error)
		for _ in range(token):
				rotator()
		for j in decode:
				engine(j)
		print('''\n''' + ''''''.join(code))
		print(
		    F"""\nYour Token is {token} please write it down.\nIf you want to decode """
		    '''this message again you should input same digits as token!'''
		)




