






"""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
_UpperCamelCase						:    Optional[int]		=						False
_UpperCamelCase						:    List[str]		=						Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_UpperCamelCase						:    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, 2_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)

class 			a	:







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

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







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			int(file.split('.'			)[0].split('_'			)[-1]			)
      lowercase				       =			os.path.join(self.inputdir				,						_lowerCamelCase			)
      return (img_id, filepath)







    @property
    def      UpperCamelCase_ (     self			):
      lowercase				       =			[]
      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				       =			batch
          lowercase				       =			[]
          yield list(map(_lowerCamelCase				,						zip(*_lowerCamelCase			)			)			)

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







    def __call__(     self			):
      # make writer
      if not TEST:
        lowercase				       =			datasets.ArrowWriter(features=self.schema				,						path=self.outputfile			)
      # do file generator
      for i, (img_ids, filepaths) in enumerate(self.file_generator			):
        lowercase       ,	lowercase       ,	lowercase				       =			self.preprocess(_lowerCamelCase			)
        lowercase				       =			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				       =			output_dict.pop('normalized_boxes'			)
        if not TEST:
          lowercase				       =			np.array(_lowerCamelCase			)
          lowercase				       =			self.schema.encode_batch(_lowerCamelCase			)
          writer.write_batch(_lowerCamelCase			)
        if TEST:
          break
        # finalizer the writer
      if not TEST:
        lowercase       ,	lowercase				       =			writer.finalize()
        print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'			)


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str       ):





  '''simple docstring'''





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


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







'''simple docstring'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE = 1000     ):
		return sum(i for i in range(SCREAMING_SNAKE_CASE     ) if i % 3 == 0 or i % 5 == 0     )


if __name__ == "__main__":
							print(f"""{solution() = }""")
