
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:					str     =False
_UpperCamelCase:					Any     =Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_UpperCamelCase:					Optional[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_048), 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					:			int  ,      _lowerCAmelCase					:			Union[str, Any]=sys.argv[1:]       )					->   Dict:
         _lowerCAmelCase						=		None
         _lowerCAmelCase						=		None
         _lowerCAmelCase						=		None
         _lowerCAmelCase						=		1
         _lowerCAmelCase	,       _lowerCAmelCase						=		getopt.getopt(__lowercase  ,      'i:o:b:s'  ,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']       )
         for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
             _lowerCAmelCase						=		arg
           elif opt in ("-o", "--outfile"):
             _lowerCAmelCase						=		arg
           elif opt in ("-b", "--batch_size"):
             _lowerCAmelCase						=		int(__lowercase       )
           elif opt in ("-s", "--subset_list"):
             _lowerCAmelCase						=		arg

         assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
         assert outputfile is not None and not os.path.isfile(__lowercase       ), F'''{outputfile}'''
         if subset_list is not None:
           with open(os.path.realpath(__lowercase       )       ) as f:
             _lowerCAmelCase						=		{self._vqa_file_split()[0] for x in tryload(__lowercase       )}
         else:
           _lowerCAmelCase						=		None

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			Optional[Any]       )					->   Optional[Any]:
         _lowerCAmelCase						=		int(file.split('.'       )[0].split('_'       )[-1]       )
         _lowerCAmelCase						=		os.path.join(self.inputdir  ,      __lowercase       )
         return (img_id, filepath)



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Optional[Any]:
         _lowerCAmelCase						=		[]
         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(__lowercase       )       )
           if len(__lowercase       ) == self.batch:
             _lowerCAmelCase						=		batch
             _lowerCAmelCase						=		[]
             yield list(map(__lowercase  ,      zip(*__lowercase       )       )       )

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






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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




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


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




'''simple docstring'''

import math

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					math.log(2,					1_0) * number
						__lowerCAmelCase											=					round((log_number - int(lowerCamelCase)),					1_5)

						return difference

def 				__magic_name__(    lowerCamelCase = 6_7_8_9_1_0):
						__lowerCAmelCase											=					9_0
						__lowerCAmelCase											=					0

						__lowerCAmelCase											=					math.log(1.23,					1_0)
						__lowerCAmelCase											=					math.log(1.24,					1_0)
						__lowerCAmelCase											=					0

						while position < number:
												__lowerCAmelCase											=					log_difference(lowerCamelCase)

												if difference >= upper_limit:
																		power_iterator += 9_3

												elif difference < lower_limit:
																		power_iterator += 1_9_6

												else:
																		__lowerCAmelCase											=					power_iterator
																		power_iterator += 1_9_6
																		position += 1

						return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(f"""{solution() = }""")

