


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     :			Optional[int]      =False
__lowerCAmelCase     :			Any      =Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__lowerCAmelCase     :			Optional[int]      =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_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)
class 	_lowercase				:






				'''simple docstring'''



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

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


				def 		__magic_name__(	self      :List[str]      ,      lowerCAmelCase__      :int     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		int(file.split('''.'''     )[0].split('''_'''     )[-1]     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		os.path.join(self.inputdir      ,      lowerCAmelCase__     )
										return (img_id, filepath)


				@property
				def 		__magic_name__(	self      :str     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		[]
										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:
																						__SCREAMING_SNAKE_CASE						:					Dict		      =		batch
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		[]
																						yield list(map(lowerCAmelCase__      ,      zip(*lowerCAmelCase__     )     )     )

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



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




def 	_UpperCamelCase							(				lowercase__				):
						try:
												__SCREAMING_SNAKE_CASE						:					int		      =		json.load(lowercase__				)
												try:
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		list(data.keys()				)
												except Exception:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		[d['''img_id'''] for d in data]
						except Exception:
												try:
																		__SCREAMING_SNAKE_CASE						:					List[str]		      =		eval(stream.read()				)
												except Exception:
																		__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		stream.read().split('''\n'''				)
						return data


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




from __future__ import annotations


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		sorted(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					str		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)


def 	_UpperCamelCase							(				):
						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()
