

"""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
lowerCamelCase__			:					int							  =					False
lowerCamelCase__			:					List[str]							  =					Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCamelCase__			:					List[str]							  =					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       _UpperCAmelCase     :
			def __init__(						self ,						_A=sys.argv[1:]       )      ->      int:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   None
				_UpperCAmelCase      :							str       =   None
				_UpperCAmelCase      :							Dict       =   None
				_UpperCAmelCase      :							Union[str, Any]       =   1
				_UpperCAmelCase      :							Union[str, Any]       =   getopt.getopt(_A ,						"""i:o:b:s""" ,						["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]       )
				for opt, arg in opts:
					if opt in ("-i", "--inputdir"):
						_UpperCAmelCase      :							Union[str, Any]       =   arg
					elif opt in ("-o", "--outfile"):
						_UpperCAmelCase      :							str       =   arg
					elif opt in ("-b", "--batch_size"):
						_UpperCAmelCase      :							Optional[Any]       =   int(_A       )
					elif opt in ("-s", "--subset_list"):
						_UpperCAmelCase      :							Any       =   arg

				assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
				assert outputfile is not None and not os.path.isfile(_A       ), f'''{outputfile}'''
				if subset_list is not None:
					with open(os.path.realpath(_A       )       ) as f:
						_UpperCAmelCase      :							Any       =   {self._vqa_file_split()[0] for x in tryload(_A       )}
				else:
					_UpperCAmelCase      :							List[str]       =   None

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






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   int(file.split("""."""       )[0].split("""_"""       )[-1]       )
				_UpperCAmelCase      :							Tuple       =   os.path.join(self.inputdir ,						_A       )
				return (img_id, filepath)
			@property
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							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(_A       )       )
					if len(_A       ) == self.batch:
						_UpperCAmelCase      :							List[str]       =   batch
						_UpperCAmelCase      :							Union[str, Any]       =   []
						yield list(map(_A ,						zip(*_A       )       )       )

				for i in range(1       ):
					yield list(map(_A ,						zip(*_A       )       )       )
			def __call__(						self       )      ->      Any:






				'''simple docstring'''







				if not TEST:
					_UpperCAmelCase      :							Union[str, Any]       =   datasets.ArrowWriter(features=self.schema ,						path=self.outputfile       )
				# do file generator
				for i, (img_ids, filepaths) in enumerate(self.file_generator       ):
					_UpperCAmelCase      :							Dict       =   self.preprocess(_A       )
					_UpperCAmelCase      :							str       =   self.model(
					    _A ,						_A ,						scales_yx=_A ,						padding="""max_detections""" ,						max_detections=self.config.MAX_DETECTIONS ,						pad_value=0 ,						return_tensors="""np""" ,						location="""cpu""" ,						)
					_UpperCAmelCase      :							Dict       =   output_dict.pop("""normalized_boxes"""       )
					if not TEST:
						_UpperCAmelCase      :							str       =   np.array(_A       )
						_UpperCAmelCase      :							Any       =   self.schema.encode_batch(_A       )
						writer.write_batch(_A       )
					if TEST:
						break
					# finalizer the writer
				if not TEST:
					_UpperCAmelCase      :							Optional[int]       =   writer.finalize()
					print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''       )
def  UpperCamelCase   (     _lowerCAmelCase       :  List[Any]	)			->     int:
	try:
		_UpperCAmelCase      :							Tuple       =   json.load(_lowerCAmelCase	)
		try:
			_UpperCAmelCase      :							List[str]       =   list(data.keys()	)
		except Exception:
			_UpperCAmelCase      :							Dict       =   [d["""img_id"""] for d in data]
	except Exception:
		try:
			_UpperCAmelCase      :							Tuple       =   eval(stream.read()	)
		except Exception:
			_UpperCAmelCase      :							Optional[Any]       =   stream.read().split("""\n"""	)
	return data


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








"""simple docstring"""






def  UpperCamelCase   (     _lowerCAmelCase       :  float,					_lowerCAmelCase       :  float,					_lowerCAmelCase       :  float	)			->     float:
	if mass < 0:
		raise ValueError("""The mass of the body cannot be negative"""	)
	if radius <= 0:
		raise ValueError("""The radius is always a positive non zero integer"""	)
	return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





