

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







			'''simple docstring'''

			def __init__(  self				:       List[str]							,     _UpperCamelCase				:       List[Any]=sys.argv[1:]							)   ->str:
									snake_case_     =			None
									snake_case_     =			None
									snake_case_     =			None
									snake_case_     =			1
									snake_case_,					snake_case_     =			getopt.getopt(_UpperCamelCase							,     '''i:o:b:s'''							,     ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']							)
									for opt, arg in opts:
															if opt in ("-i", "--inputdir"):
																					snake_case_     =			arg
															elif opt in ("-o", "--outfile"):
																					snake_case_     =			arg
															elif opt in ("-b", "--batch_size"):
																					snake_case_     =			int(_UpperCamelCase							)
															elif opt in ("-s", "--subset_list"):
																					snake_case_     =			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:
																					snake_case_     =			{self._vqa_file_split()[0] for x in tryload(_UpperCamelCase							)}
									else:
															snake_case_     =			None

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







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       int							)   ->Union[str, Any]:
									snake_case_     =			int(file.split('''.'''							)[0].split('''_'''							)[-1]							)
									snake_case_     =			os.path.join(self.inputdir							,     _UpperCamelCase							)
									return (img_id, filepath)







			@property
			def        snake_case__(  self				:       Optional[Any]							)   ->int:
									snake_case_     =			[]
									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:
																					snake_case_     =			batch
																					snake_case_     =			[]
																					yield list(map(_UpperCamelCase							,     zip(*_UpperCamelCase							)							)							)

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







			def __call__(  self				:       Union[str, Any]							)   ->int:
									# make writer
									if not TEST:
															snake_case_     =			datasets.ArrowWriter(features=self.schema							,     path=self.outputfile							)
									# do file generator
									for i, (img_ids, filepaths) in enumerate(self.file_generator							):
															snake_case_,					snake_case_,					snake_case_     =			self.preprocess(_UpperCamelCase							)
															snake_case_     =			self.model(
															    _UpperCamelCase							,     _UpperCamelCase							,     scales_yx=_UpperCamelCase							,     padding='''max_detections'''							,     max_detections=self.config.MAX_DETECTIONS							,     pad_value=0							,     return_tensors='''np'''							,     location='''cpu'''							,     )
															snake_case_     =			output_dict.pop('''normalized_boxes'''							)
															if not TEST:
																					snake_case_     =			np.array(_UpperCamelCase							)
																					snake_case_     =			self.schema.encode_batch(_UpperCamelCase							)
																					writer.write_batch(_UpperCamelCase							)
															if TEST:
																					break
															# finalizer the writer
									if not TEST:
															snake_case_,					snake_case_     =			writer.finalize()
															print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''							)






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


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


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return (input_a, input_a).count(0 ) % 2


def 						__SCREAMING_SNAKE_CASE      ():
						assert xor_gate(0						,	0 ) == 0
						assert xor_gate(0						,	1 ) == 1
						assert xor_gate(1						,	0 ) == 1
						assert xor_gate(1						,	1 ) == 0


if __name__ == "__main__":
					print(xor_gate(0, 0))
					print(xor_gate(0, 1))