



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
UpperCAmelCase							=       False
UpperCAmelCase							=       Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
UpperCAmelCase							=       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_048), 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	,	a_=sys.argv[1:]		):
											a__					   =		None
											a__					   =		None
											a__					   =		None
											a__					   =		1
											a__   ,      a__					   =		getopt.getopt(a_	,	"""i:o:b:s"""	,	["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]		)
											for opt, arg in opts:
															if opt in ("-i", "--inputdir"):
																			a__					   =		arg
															elif opt in ("-o", "--outfile"):
																			a__					   =		arg
															elif opt in ("-b", "--batch_size"):
																			a__					   =		int(a_		)
															elif opt in ("-s", "--subset_list"):
																			a__					   =		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:
																			a__					   =		{self._vqa_file_split()[0] for x in tryload(a_		)}
											else:
															a__					   =		None

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


							def _a   (				self	,	a_		):
											a__					   =		int(file.split("""."""		)[0].split("""_"""		)[-1]		)
											a__					   =		os.path.join(self.inputdir	,	a_		)
											return (img_id, filepath)


							@property
							def _a   (				self		):
											a__					   =		[]
											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:
																			a__					   =		batch
																			a__					   =		[]
																			yield list(map(a_	,	zip(*a_		)		)		)

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







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




def       A_   (						__a  :					str		):

				"""simple docstring"""
				try:
								a__					   =		json.load(__a		)
								try:
												a__					   =		list(data.keys()		)
								except Exception:
												a__					   =		[d["""img_id"""] for d in data]
				except Exception:
								try:
												a__					   =		eval(stream.read()		)
								except Exception:
												a__					   =		stream.read().split("""\n"""		)
				return data


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






UpperCAmelCase							=       (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
UpperCAmelCase							=       (-153, -1, 0, 1.2, 200, "A", [], {}, None)

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Initialization of sum and number of digits.
				a__					   =		0
				a__					   =		0
				a__					   =		n
				# Calculation of digits of the number
				while temp > 0:
								number_of_digits += 1
								temp //= 10
				# Dividing number into separate digits and find Armstrong number
				a__					   =		n
				while temp > 0:
								a__					   =		temp % 10
								total += rem**number_of_digits
								temp //= 10
				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Init a "histogram" of the digits
				a__					   =		[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
				a__					   =		0
				a__					   =		0
				a__					   =		n
				while temp > 0:
								a__   ,      a__					   =		divmod(__a			,		10		)
								digit_histogram[rem] += 1
								digit_total += 1

				for cnt, i in zip(__a			,		range(len(__a		)		)		):
								total += cnt * i**digit_total

				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False
				a__					   =		len(str(__a		)		)  # the power that all digits will be raised to
				# check if sum of each digit multiplied expo times is equal to number
				return n == sum(int(__a		) ** expo for i in str(__a		)		)

def       A_   (						):

				"""simple docstring"""
				a__					   =		int(input("""Enter an integer to see if it is an Armstrong number: """		).strip()		)
				print(F'''{num} is {"" if armstrong_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if narcissistic_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if pluperfect_number(__a		) else "not "}an Armstrong number.'''		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

