



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 	lowercase		:






	"""simple docstring"""


	def __init__(			self			:		Optional[Any]		,						__UpperCAmelCase			:		Optional[Any]=sys.argv[1:]  )    ->				Optional[Any]:
							UpperCAmelCase_=     None
							UpperCAmelCase_=     None
							UpperCAmelCase_=     None
							UpperCAmelCase_=     1
							UpperCAmelCase_=     getopt.getopt(__a		,						"""i:o:b:s"""		,						["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]  )
							for opt, arg in opts:
													if opt in ("-i", "--inputdir"):
																			UpperCAmelCase_=     arg
													elif opt in ("-o", "--outfile"):
																			UpperCAmelCase_=     arg
													elif opt in ("-b", "--batch_size"):
																			UpperCAmelCase_=     int(__a  )
													elif opt in ("-s", "--subset_list"):
																			UpperCAmelCase_=     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:
																			UpperCAmelCase_=     {self._vqa_file_split()[0] for x in tryload(__a  )}
							else:
													UpperCAmelCase_=     None

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		List[str]  )    ->				Dict:
							UpperCAmelCase_=     int(file.split("""."""  )[0].split("""_"""  )[-1]  )
							UpperCAmelCase_=     os.path.join(self.inputdir		,						__a  )
							return (img_id, filepath)




	@property
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Optional[Any]:
							UpperCAmelCase_=     []
							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:
																			UpperCAmelCase_=     batch
																			UpperCAmelCase_=     []
																			yield list(map(__a		,						zip(*__a  )  )  )

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




	def __call__(			self			:		Any  )    ->				List[str]:





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



def  __a  (					lowerCAmelCase_     :      Optional[Any]       )   ->				Optional[int]:






						'''simple docstring'''



						try:
												UpperCAmelCase_=     json.load(_SCREAMING_SNAKE_CASE       )
												try:
																		UpperCAmelCase_=     list(data.keys()       )
												except Exception:
																		UpperCAmelCase_=     [d['img_id'] for d in data]
						except Exception:
												try:
																		UpperCAmelCase_=     eval(stream.read()       )
												except Exception:
																		UpperCAmelCase_=     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)









from .prime_check import Test

Test()




