






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_			:Optional[Any]	    =		False
A_			:str	    =		Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
A_			:Union[str, Any]	    =		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 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__=sys.argv[1:]       ):




									"""simple docstring"""

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

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


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Any									=int(file.split('.'       )[0].split('_'       )[-1]       )
									__UpperCamelCase :   Dict									=os.path.join(self.inputdir				,    lowerCamelCase__       )
									return (img_id, filepath)


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Tuple									=[]
									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 :   Optional[int]									=batch
																			__UpperCamelCase :   Dict									=[]
																			yield list(map(lowerCamelCase__				,    zip(*lowerCamelCase__       )       )       )

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







				def __call__(					self       ):




									"""simple docstring"""

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






def 				A	(			a_   )	->		int:
					try:
										__UpperCamelCase :   Any									=json.load(a_   )
										try:
															__UpperCamelCase :   Optional[Any]									=list(data.keys()   )
										except Exception:
															__UpperCamelCase :   List[str]									=[d['img_id'] for d in data]
					except Exception:
										try:
															__UpperCamelCase :   Tuple									=eval(stream.read()   )
										except Exception:
															__UpperCamelCase :   int									=stream.read().split('\n'   )
					return data


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









from __future__ import annotations




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__ = 0       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=value
									__UpperCamelCase :   Node | None									=None
									__UpperCamelCase :   Node | None									=None






def 				A	(			a_				,a_   )	->		Node | None:
					if treea is None:
										return treea
					if treea is None:
										return treea

					__UpperCamelCase :   Dict									=treea.value + treea.value
					__UpperCamelCase :   Any									=merge_two_binary_trees(treea.left				,treea.left   )
					__UpperCamelCase :   Union[str, Any]									=merge_two_binary_trees(treea.right				,treea.right   )
					return treea






def 				A	(			a_   )	->		None:
					if root:
										print(root.value   )
										print_preorder(root.left   )
										print_preorder(root.right   )


if __name__ == "__main__":
							A_			:List[str]	    =		Node(1)
							A_			:Dict	    =		Node(2)
							A_			:str	    =		Node(3)
							A_			:Union[str, Any]	    =		Node(4)

							A_			:Any	    =		Node(2)
							A_			:Tuple	    =		Node(4)
							A_			:Optional[Any]	    =		Node(6)
							A_			:Optional[Any]	    =		Node(9)
							A_			:str	    =		Node(5)

							print('''Tree1 is: ''')
							print_preorder(treea)
							print('''Tree2 is: ''')
							print_preorder(treea)
							A_			:Optional[Any]	    =		merge_two_binary_trees(treea, treea)
							print('''Merged Tree is: ''')
							print_preorder(merged_tree)

