


"""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, 20_48), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)
class 							lowerCamelCase       :






							def __init__(						self :     Optional[Any]		, lowerCamelCase_ :     List[str]=sys.argv[1:]				):
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    1
													UpperCAmelCase__	,				UpperCAmelCase__		=    getopt.getopt(lowerCamelCase_		, '''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(lowerCamelCase_				)
																			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(lowerCamelCase_				), f"""{outputfile}"""
													if subset_list is not None:
																			with open(os.path.realpath(lowerCamelCase_				)				) as f:
																									UpperCAmelCase__		=    {self._vqa_file_split()[0] for x in tryload(lowerCamelCase_				)}
													else:
																			UpperCAmelCase__		=    None

													UpperCAmelCase__		=    CONFIG
													if torch.cuda.is_available():
																			UpperCAmelCase__		=    '''cuda'''
													UpperCAmelCase__		=    os.path.realpath(lowerCamelCase_				)
													UpperCAmelCase__		=    os.path.realpath(lowerCamelCase_				)
													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 					lowerCAmelCase     (						self :     str		, lowerCamelCase_ :     Any				):
													UpperCAmelCase__		=    int(file.split('''.'''				)[0].split('''_'''				)[-1]				)
													UpperCAmelCase__		=    os.path.join(self.inputdir		, lowerCamelCase_				)
													return (img_id, filepath)






							@property
							def 					lowerCAmelCase     (						self :     int				):
													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(lowerCamelCase_				)				)
																			if len(lowerCamelCase_				) == self.batch:
																									UpperCAmelCase__		=    batch
																									UpperCAmelCase__		=    []
																									yield list(map(lowerCamelCase_		, zip(*lowerCamelCase_				)				)				)

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






							def __call__(						self :     int				):
													# make writer
													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(lowerCamelCase_				)
																			UpperCAmelCase__		=    self.model(
																			    lowerCamelCase_		, lowerCamelCase_		, scales_yx=lowerCamelCase_		, 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(lowerCamelCase_				)
																									UpperCAmelCase__		=    self.schema.encode_batch(lowerCamelCase_				)
																									writer.write_batch(lowerCamelCase_				)
																			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__ (       _UpperCamelCase ):





						"""simple docstring"""

						try:
												UpperCAmelCase__		=    json.load(_UpperCamelCase )
												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)



"""simple docstring"""







import safs
class 							lowerCamelCase       (			safs.SaFileSystem ):


							"""simple docstring"""