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
_snake_case			=						False
_snake_case			=						Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
_snake_case			=						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 					_snake_case						:
				def __init__(  self:				Any	,  __lowerCamelCase:				Union[str, Any]=sys.argv[1:]     )    ->				List[Any]:
											__UpperCAmelCase						:			List[Any]         =   None
											__UpperCAmelCase						:			int         =   None
											__UpperCAmelCase						:			List[Any]         =   None
											__UpperCAmelCase						:			str         =   1
											__UpperCAmelCase						:			List[str]         =   getopt.getopt(_lowerCAmelCase	,  "i:o:b:s"	,  ["inputdir=", "outfile=", "batch_size=", "subset_list="]     )
											for opt, arg in opts:
																		if opt in ("-i", "--inputdir"):
																									__UpperCAmelCase						:			str         =   arg
																		elif opt in ("-o", "--outfile"):
																									__UpperCAmelCase						:			str         =   arg
																		elif opt in ("-b", "--batch_size"):
																									__UpperCAmelCase						:			Dict         =   int(_lowerCAmelCase     )
																		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(_lowerCAmelCase     ), f'''{outputfile}'''
											if subset_list is not None:
																		with open(os.path.realpath(_lowerCAmelCase     )     ) as f:
																									__UpperCAmelCase						:			Optional[int]         =   {self._vqa_file_split()[0] for x in tryload(_lowerCAmelCase     )}
											else:
																		__UpperCAmelCase						:			List[str]         =   None

											__UpperCAmelCase						:			List[Any]         =   CONFIG
											if torch.cuda.is_available():
																		__UpperCAmelCase						:			Dict         =   """cuda"""
											__UpperCAmelCase						:			Tuple         =   os.path.realpath(_lowerCAmelCase     )
											__UpperCAmelCase						:			List[Any]         =   os.path.realpath(_lowerCAmelCase     )
											__UpperCAmelCase						:			int         =   Preprocess(self.config     )
											__UpperCAmelCase						:			Optional[int]         =   GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"	,  config=self.config     )
											__UpperCAmelCase						:			str         =   batch_size if batch_size != 0 else 1
											__UpperCAmelCase						:			Tuple         =   DEFAULT_SCHEMA
				def    _lowerCamelCase (  self:				Any	,  __lowerCamelCase:				Any     )    ->				Union[str, Any]:
											__UpperCAmelCase						:			int         =   int(file.split("."     )[0].split("_"     )[-1]     )
											__UpperCAmelCase						:			str         =   os.path.join(self.inputdir	,  _lowerCAmelCase     )
											return (img_id, filepath)
				@property
				def    _lowerCamelCase (  self:				Dict     )    ->				Dict:
											__UpperCAmelCase						:			int         =   []
											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						:			str         =   batch
																									__UpperCAmelCase						:			Tuple         =   []
																									yield list(map(_lowerCAmelCase	,  zip(*_lowerCAmelCase     )     )     )

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





				def __call__(  self:				Optional[Any]     )    ->				List[Any]:
											# make writer
											if not TEST:
																		__UpperCAmelCase						:			Optional[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(_lowerCAmelCase     )
																		__UpperCAmelCase						:			int         =   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						:			Union[str, Any]         =   output_dict.pop("normalized_boxes"     )
																		if not TEST:
																									__UpperCAmelCase						:			Any         =   np.array(_lowerCAmelCase     )
																									__UpperCAmelCase						:			str         =   self.schema.encode_batch(_lowerCAmelCase     )
																									writer.write_batch(_lowerCAmelCase     )
																		if TEST:
																									break
																		# finalizer the writer
											if not TEST:
																		__UpperCAmelCase						:			Union[str, Any]         =   writer.finalize()
																		print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''     )




def   _UpperCamelCase   (			snake_case__							)						->   Dict:
							try:
														__UpperCAmelCase						:			Union[str, Any]         =   json.load(__lowerCamelCase							)
														try:
																					__UpperCAmelCase						:			List[Any]         =   list(data.keys()							)
														except Exception:
																					__UpperCAmelCase						:			Optional[int]         =   [d["""img_id"""] for d in data]
							except Exception:
														try:
																					__UpperCAmelCase						:			Dict         =   eval(stream.read()							)
														except Exception:
																					__UpperCAmelCase						:			Union[str, Any]         =   stream.read().split("\n"							)
							return data


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





from __future__ import annotations




class 					_snake_case						:
					def __init__(  self:				List[Any]	,  __lowerCamelCase:				int = 0     )    ->				None:
												__UpperCAmelCase						:			Dict         =   value
												__UpperCAmelCase						:			Node | None         =   None
												__UpperCAmelCase						:			Node | None         =   None




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Node | None:
							if treea is None:
														return treea
							if treea is None:
														return treea

							__UpperCAmelCase						:			Tuple         =   treea.value + treea.value
							__UpperCAmelCase						:			Optional[Any]         =   merge_two_binary_trees(treea.left,    treea.left							)
							__UpperCAmelCase						:			List[str]         =   merge_two_binary_trees(treea.right,    treea.right							)
							return treea




def   _UpperCamelCase   (			snake_case__							)						->   None:
							if root:
														print(root.value							)
														print_preorder(root.left							)
														print_preorder(root.right							)


if __name__ == "__main__":
	_snake_case			=						Node(1)
	_snake_case			=						Node(2)
	_snake_case			=						Node(3)
	_snake_case			=						Node(4)

	_snake_case			=						Node(2)
	_snake_case			=						Node(4)
	_snake_case			=						Node(6)
	_snake_case			=						Node(9)
	_snake_case			=						Node(5)

	print('''Tree1 is: ''')
	print_preorder(treea)
	print('''Tree2 is: ''')
	print_preorder(treea)
	_snake_case			=						merge_two_binary_trees(treea, treea)
	print('''Merged Tree is: ''')
	print_preorder(merged_tree)




