


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 			lowerCamelCase_					:





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

								a_     			=   CONFIG
								if torch.cuda.is_available():
									a_     			=   """cuda"""
								a_     			=   os.path.realpath(_SCREAMING_SNAKE_CASE    )
								a_     			=   os.path.realpath(_SCREAMING_SNAKE_CASE    )
								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   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   int(file.split("""."""    )[0].split("""_"""    )[-1]    )
								a_     			=   os.path.join(self.inputdir			,       _SCREAMING_SNAKE_CASE    )
								return (img_id, filepath)





							@property
							def   __magic_name__      (							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(_SCREAMING_SNAKE_CASE    )    )
									if len(_SCREAMING_SNAKE_CASE    ) == self.batch:
										a_     			=   batch
										a_     			=   []
										yield list(map(_SCREAMING_SNAKE_CASE			,       zip(*_SCREAMING_SNAKE_CASE    )    )    )

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





							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(_SCREAMING_SNAKE_CASE    )
									a_     			=   self.model(
									    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       scales_yx=_SCREAMING_SNAKE_CASE			,       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(_SCREAMING_SNAKE_CASE    )
										a_     			=   self.schema.encode_batch(_SCREAMING_SNAKE_CASE    )
										writer.write_batch(_SCREAMING_SNAKE_CASE    )
									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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Any	)     ->	str:



	"""simple docstring"""






	try:
		a_     			=   json.load(UpperCamelCase	)
		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__":
			_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)



def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      list[tuple[int, int, int]]	)     ->	list[tuple[int, int, int]]:



	"""simple docstring"""






	a_     			=   sorted(UpperCamelCase     ,					key=lambda UpperCamelCase	: edge[2]	)

	a_     			=   list(range(UpperCamelCase	)	)

	def find_parent(UpperCamelCase		:      Any	):
		if i != parent[i]:
			a_     			=   find_parent(parent[i]	)
		return parent[i]

	a_     			=   0
	a_     			=   []

	for edge in edges:
		a_     			=   find_parent(edge[0]	)
		a_     			=   find_parent(edge[1]	)
		if parent_a != parent_b:
			minimum_spanning_tree_cost += edge[2]
			minimum_spanning_tree.append(UpperCamelCase	)
			a_     			=   parent_b

	return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
			_A     ,				_A							     =       list(map(int, input().strip().split()))
			_A							     =       []

			for _ in range(num_edges):
						_A     ,				_A     ,				_A							     =       (int(x) for x in input().strip().split())
						edges.append((nodea, nodea, cost))

			kruskal(num_nodes, edges)