




'''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, 20_48), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)
class      __UpperCAmelCase							:



     '''simple docstring'''





     def __init__(  self    :	Dict			,					_lowercase    :	Any=sys.argv[1:])						->       Any:
        A_       		=   None
        A_       		=   None
        A_       		=   None
        A_       		=   1
        A_       		=   getopt.getopt(_snake_case			,					'i:o:b:s'			,					['inputdir=', 'outfile=', 'batch_size=', 'subset_list='])
        for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
              A_       		=   arg
           elif opt in ("-o", "--outfile"):
              A_       		=   arg
           elif opt in ("-b", "--batch_size"):
              A_       		=   int(_snake_case)
           elif opt in ("-s", "--subset_list"):
              A_       		=   arg

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

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



     def 						__snake_case	(  self    :	List[str]			,					_lowercase    :	Any)						->       Optional[Any]:
        A_       		=   int(file.split('.')[0].split('_')[-1])
        A_       		=   os.path.join(self.inputdir			,					_snake_case)
        return (img_id, filepath)



     @property
     def 						__snake_case	(  self    :	Any)						->       List[str]:
        A_       		=   []
        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(_snake_case))
           if len(_snake_case) == self.batch:
              A_       		=   batch
              A_       		=   []
              yield list(map(_snake_case			,					zip(*_snake_case)))

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



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





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		Any:
   try:
      A_       		=   json.load(SCREAMING_SNAKE_CASE_    )
      try:
         A_       		=   list(data.keys()    )
      except Exception:
         A_       		=   [d['''img_id'''] for d in data]
   except Exception:
      try:
         A_       		=   eval(stream.read()    )
      except Exception:
         A_       		=   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'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		int:

			if not isinstance(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    ) or number < 0:
						raise ValueError('Input must be a non-negative integer'    )

			A_       		=   number & ~(number - 1)
			A_       		=   0
			while intermediate:
						intermediate >>= 1
						index += 1
			return index - 1


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)




