

'''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
lowerCAmelCase_     =      False
lowerCAmelCase_     =      Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowerCAmelCase_     =      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, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)







class lowerCamelCase   :

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

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

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							Tuple:
					snake_case							=					int(file.split('.' )[0].split('_' )[-1] )
					snake_case							=					os.path.join(self.inputdir,						A__ )
					return (img_id, filepath)

				@property
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					[]
					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:
							snake_case							=					batch
							snake_case							=					[]
							yield list(map(A__,						zip(*A__ ) ) )

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






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

def      __magic_name__							(			A				)      ->		List[Any]:




	try:
		snake_case							=					json.load(A				)
		try:
			snake_case							=					list(data.keys()				)
		except Exception:
			snake_case							=					[d['img_id'] for d in data]
	except Exception:
		try:
			snake_case							=					eval(stream.read()				)
		except Exception:
			snake_case							=					stream.read().split('\n'				)
	return data


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











def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					str(lowerCamelCase    ).strip()
      if not bin_string:
            raise ValueError('Empty string was passed to the function'    )
      lowerCamelCase__   	=					bin_string[0] == '-'
      if is_negative:
            lowerCamelCase__   	=					bin_string[1:]
      if not all(char in '01' for char in bin_string    ):
            raise ValueError('Non-binary value was passed to the function'    )
      lowerCamelCase__   	=					0
      for char in bin_string:
            lowerCamelCase__   	=					2 * decimal_number + int(lowerCamelCase    )
      return -decimal_number if is_negative else decimal_number


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

      testmod()