



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
_lowercase				:					Dict     					=False
_lowercase				:					Optional[Any]     					=Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_lowercase				:					List[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, 20_48), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)
class lowerCAmelCase_       :







	'''simple docstring'''




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



				'''simple docstring'''


				a__					=  None
				a__					=  None
				a__					=  None
				a__					=  1
				a__				,       a__					=  getopt.getopt(lowerCamelCase   ,       """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(lowerCamelCase       )
							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(lowerCamelCase       ), f'{outputfile}'
				if subset_list is not None:
							with open(os.path.realpath(lowerCamelCase       )       ) as f:
										a__					=  {self._vqa_file_split()[0] for x in tryload(lowerCamelCase       )}
				else:
							a__					=  None

				a__					=  CONFIG
				if torch.cuda.is_available():
							a__					=  """cuda"""
				a__					=  os.path.realpath(lowerCamelCase       )
				a__					=  os.path.realpath(lowerCamelCase       )
				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   ,       lowerCamelCase       ):



				'''simple docstring'''


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

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				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(lowerCamelCase       )       )
							if len(lowerCamelCase       ) == self.batch:
										a__					=  batch
										a__					=  []
										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:
							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(lowerCamelCase       )
							a__					=  self.model(
							    lowerCamelCase   ,       lowerCamelCase   ,       scales_yx=lowerCamelCase   ,       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(lowerCamelCase       )
										a__					=  self.schema.encode_batch(lowerCamelCase       )
										writer.write_batch(lowerCamelCase       )
							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    UpperCAmelCase					(  lowercase__						:					str	):



			'''simple docstring'''





			try:
						a__					=  json.load(lowercase__	)
						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__":
	_lowercase				:					Tuple     					=Extract(sys.argv[1:])
	extract()
	if not TEST:
		_lowercase				:					Any     					=datasets.Dataset.from_file(extract.outputfile)
		# wala!
		# print(np.array(dataset[0:2]["roi_features"]).shape)






from .stack import Stack






def 			UpperCAmelCase__     (     lowercase__ )			->   bool:
						__lowercase											=					Stack()
						__lowercase											=					{"""(""": """)""", """[""": """]""", """{""": """}"""}
						for bracket in parentheses:
												if bracket in bracket_pairs:
																		stack.push(lowercase__ )
												elif bracket in (")", "]", "}"):
																		if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
																								return False
						return stack.is_empty()


if __name__ == "__main__":
			from doctest import testmod

			testmod()

			UpperCamelCase__			=    ["((()))", "((())", "(()))"]
			print("Balanced parentheses demonstration:\n")
			for example in examples:
						UpperCamelCase__			=    "" if balanced_parentheses(example) else "not "
						print(F"""{example} is {not_str}balanced""")





