"""simple docstring"""







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
SCREAMING_SNAKE_CASE						=    False
SCREAMING_SNAKE_CASE						=    Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
SCREAMING_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 			_lowerCamelCase      :







				def __init__(			self     :							Dict      ,       lowerCamelCase_     :							Any=sys.argv[1:]    ):

					"""simple docstring"""






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

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







				def 				__UpperCAmelCase       (			self     :							Any      ,       lowerCamelCase_     :							List[str]    ):

					"""simple docstring"""






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







				@property
				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     List[str]      					=				[]
					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:
							_lowercase			:     Union[str, Any]      					=				batch
							_lowercase			:     Tuple      					=				[]
							yield list(map(lowerCamelCase_      ,       zip(*lowerCamelCase_    )    )    )

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







				def __call__(			self     :							Union[str, Any]    ):

					"""simple docstring"""






					if not TEST:
						_lowercase			:     int      					=				datasets.ArrowWriter(features=self.schema      ,       path=self.outputfile    )
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator    ):
						_lowercase					,   _lowercase					,   _lowercase			:     Optional[Any]      					=				self.preprocess(lowerCamelCase_    )
						_lowercase			:     Union[str, Any]      					=				self.model(
						    lowerCamelCase_      ,       lowerCamelCase_      ,       scales_yx=lowerCamelCase_      ,       padding='max_detections'      ,       max_detections=self.config.MAX_DETECTIONS      ,       pad_value=0      ,       return_tensors='np'      ,       location='cpu'      ,       )
						_lowercase			:     Any      					=				output_dict.pop('normalized_boxes'    )
						if not TEST:
							_lowercase			:     Tuple      					=				np.array(lowerCamelCase_    )
							_lowercase			:     Optional[Any]      					=				self.schema.encode_batch(lowerCamelCase_    )
							writer.write_batch(lowerCamelCase_    )
						if TEST:
							break
						# finalizer the writer
					if not TEST:
						_lowercase					,   _lowercase			:     List[Any]      					=				writer.finalize()
						print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''    )



def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	try:
		_lowercase			:     int      					=				json.load(__UpperCAmelCase      )
		try:
			_lowercase			:     List[str]      					=				list(data.keys()      )
		except Exception:
			_lowercase			:     Tuple      					=				[d['img_id'] for d in data]
	except Exception:
		try:
			_lowercase			:     str      					=				eval(stream.read()      )
		except Exception:
			_lowercase			:     Union[str, Any]      					=				stream.read().split('\n'      )
	return data


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




"""simple docstring"""







from __future__ import annotations

import re


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	def alphanum_key(__UpperCAmelCase      ):
		return [int(__UpperCAmelCase      ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'  ,__UpperCAmelCase      )]

	return sorted(__UpperCAmelCase  ,key=__UpperCAmelCase      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



