



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__			:   int      			=    Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
UpperCAmelCase__			:   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 							lowerCAmelCase_		:







		def __init__(    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   Union[str, Any]=sys.argv[1:]					)       ->		Tuple:


						'''simple docstring'''







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

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







		def 					a_  (    self  :   List[str]       ,		UpperCAmelCase_  :   Any					)       ->		str:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	int(file.split('''.'''					)[0].split('''_'''					)[-1]					)
						_UpperCAmelCase						:  List[Any]     					=	os.path.join(self.inputdir       ,		UpperCAmelCase_					)
						return (img_id, filepath)







		@property
		def 					a_  (    self  :   Any					)       ->		Any:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[Any]     					=	[]
						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:
														_UpperCAmelCase						:  Union[str, Any]     					=	batch
														_UpperCAmelCase						:  Tuple     					=	[]
														yield list(map(UpperCAmelCase_       ,		zip(*UpperCAmelCase_					)					)					)

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







		def __call__(    self  :   List[Any]					)       ->		Tuple:


						'''simple docstring'''







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


def 	_A	(   _UpperCamelCase			):






				try:
								_UpperCAmelCase						:  Any     					=	json.load(a__			)
								try:
												_UpperCAmelCase						:  Tuple     					=	list(data.keys()			)
								except Exception:
												_UpperCAmelCase						:  int     					=	[d['''img_id'''] for d in data]
				except Exception:
								try:
												_UpperCAmelCase						:  List[Any]     					=	eval(stream.read()			)
								except Exception:
												_UpperCAmelCase						:  Any     					=	stream.read().split('''\n'''			)
				return data


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


'''simple docstring'''







def   lowerCAmelCase(  a__    :  int = 1000      ):







       '''simple docstring'''






       lowerCamelCase__				   =      0
       for i in range(a__      ):
              if i % 3 == 0 or i % 5 == 0:
                     result += i
       return result


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

