






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
_a       =    False
_a       =    Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
_a       =    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					:





	"""simple docstring"""






	def __init__(				self			,       UpperCAmelCase=sys.argv[1:]       ):
							"""simple docstring"""







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

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

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







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

	@property
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     []
							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							   =     batch
																			_UpperCAmelCase							   =     []
																			yield list(map(UpperCAmelCase			,       zip(*UpperCAmelCase       )       )       )

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

	def __call__(				self       ):
							"""simple docstring"""







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



def __A       ( __lowerCAmelCase      )->    Any:





						"""simple docstring"""



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


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








def __A       ( __lowerCAmelCase = 100					,						__lowerCAmelCase = 100      )->    int:





						"""simple docstring"""



						return max(
						    sum(int(__lowerCAmelCase      ) for x in str(base**power      )      )
						    for base in range(__lowerCAmelCase      )
						    for power in range(__lowerCAmelCase      )      )


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
