






"""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
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 				__SCREAMING_SNAKE_CASE    :


							def __init__(      self   :    int      ,     snake_case   :    Any=sys.argv[1:]				):
													'''simple docstring'''





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

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


							def     _UpperCamelCase							(      self   :    int      ,     snake_case   :    List[str]				):
													'''simple docstring'''





													A__    :	Optional[int]			=	int(file.split("""."""				)[0].split("""_"""				)[-1]				)
													A__    :	Optional[Any]			=	os.path.join(self.inputdir      ,     snake_case				)
													return (img_id, filepath)


							@property
							def     _UpperCamelCase							(      self   :    List[Any]				):
													'''simple docstring'''





													A__    :	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(snake_case				)				)
																			if len(snake_case				) == self.batch:
																									A__    :	Any			=	batch
																									A__    :	Optional[int]			=	[]
																									yield list(map(snake_case      ,     zip(*snake_case				)				)				)

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


							def __call__(      self   :    int				):
													'''simple docstring'''





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




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Optional[Any]   )		->Dict:
						try:
												A__    :	str			=	json.load(UpperCAmelCase__   )
												try:
																		A__    :	Any			=	list(data.keys()   )
												except Exception:
																		A__    :	Optional[Any]			=	[d["""img_id"""] for d in data]
						except Exception:
												try:
																		A__    :	List[str]			=	eval(stream.read()   )
												except Exception:
																		A__    :	Dict			=	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)














"""simple docstring"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       int = 4_0_0_0_0_0_0   )		->int:
						A__    :	int			=	1
						A__    :	List[str]			=	2
						A__    :	Union[str, Any]			=	0
						while j <= n:
												if j % 2 == 0:
																		total += j
												A__	,	A__    :	str			=	j, i + j

						return total


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






