


'''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
__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, 2_048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)




class 		a__				:





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_=sys.argv[1:]   )       ->							List[str]:
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	getopt.getopt(snake_case_					,    '''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(snake_case_   )
																					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(snake_case_   ), F"""{outputfile}"""
														if subset_list is not None:
																					with open(os.path.realpath(snake_case_   )   ) as f:
																												lowerCAmelCase__          =	{self._vqa_file_split()[0] for x in tryload(snake_case_   )}
														else:
																					lowerCAmelCase__          =	None

														lowerCAmelCase__          =	CONFIG
														if torch.cuda.is_available():
																					lowerCAmelCase__          =	"""cuda"""
														lowerCAmelCase__          =	os.path.realpath(snake_case_   )
														lowerCAmelCase__          =	os.path.realpath(snake_case_   )
														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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Dict:
														lowerCAmelCase__          =	int(file.split('''.'''   )[0].split('''_'''   )[-1]   )
														lowerCAmelCase__          =	os.path.join(self.inputdir					,    snake_case_   )
														return (img_id, filepath)







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														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(snake_case_   )   )
																					if len(snake_case_   ) == self.batch:
																												lowerCAmelCase__          =	batch
																												lowerCAmelCase__          =	[]
																												yield list(map(snake_case_					,    zip(*snake_case_   )   )   )

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







							def __call__(		self   )       ->							Optional[int]:
														# make writer
														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__          =	self.preprocess(snake_case_   )
																					lowerCAmelCase__          =	self.model(
																					    snake_case_					,    snake_case_					,    scales_yx=snake_case_					,    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(snake_case_   )
																												lowerCAmelCase__          =	self.schema.encode_batch(snake_case_   )
																												writer.write_batch(snake_case_   )
																					if TEST:
																												break
																					# finalizer the writer
														if not TEST:
																					lowerCAmelCase__          =	writer.finalize()
																					print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""   )


def 					_snake_case   (     A       )						->    Tuple:
							try:
														lowerCAmelCase__          =	json.load(A       )
														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    				=							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)

def 				A__    (  lowerCamelCase = 20  )     ->		int:

				try:
								UpperCamelCase_:							int		=  int(lowerCamelCase  )
				except (TypeError, ValueError):
								raise TypeError("""Parameter n must be int or castable to int."""  )
				if n <= 0:
								raise ValueError("""Parameter n must be greater than or equal to one."""  )
				UpperCamelCase_:							Any		=  0
				while 1:
								i += n * (n - 1)
								UpperCamelCase_:							List[Any]		=  0
								for j in range(2   ,			lowerCamelCase  ):
												if i % j != 0:
																UpperCamelCase_:							Union[str, Any]		=  1
																break
								if nfound == 0:
												if i == 0:
																UpperCamelCase_:							List[str]		=  1
												return i
				return None


if __name__ == "__main__":
							print(F"""{solution() = }""")






