
'''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
a__		: Union[str, Any]  							=							False
a__		: str  							=							Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a__		: Dict  							=							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 			UpperCamelCase__	:






 def __init__(      self	:int						,					_A	:List[Any]=sys.argv[1:]      )					->      str:







       '''simple docstring'''



       __A													=      None
       __A													=      None
       __A													=      None
       __A													=      1
       __A						,   __A													=      getopt.getopt(_A						,					'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(_A      )
             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(_A      ), F'{outputfile}'
       if subset_list is not None:
             with open(os.path.realpath(_A      )      ) as f:
                   __A													=      {self._vqa_file_split()[0] for x in tryload(_A      )}
       else:
             __A													=      None

       __A													=      CONFIG
       if torch.cuda.is_available():
             __A													=      'cuda'
       __A													=      os.path.realpath(_A      )
       __A													=      os.path.realpath(_A      )
       __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 						lowercase_    (      self	:str						,					_A	:Tuple      )					->      List[Any]:







       '''simple docstring'''



       __A													=      int(file.split('.'      )[0].split('_'      )[-1]      )
       __A													=      os.path.join(self.inputdir						,					_A      )
       return (img_id, filepath)






 @property
 def 						lowercase_    (      self	:Union[str, Any]      )					->      Any:







       '''simple docstring'''



       __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(_A      )      )
             if len(_A      ) == self.batch:
                   __A													=      batch
                   __A													=      []
                   yield list(map(_A						,					zip(*_A      )      )      )

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






 def __call__(      self	:Dict      )					->      Any:







       '''simple docstring'''



       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						,   __A						,   __A													=      self.preprocess(_A      )
             __A													=      self.model(
                 _A						,					_A						,					scales_yx=_A						,					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(_A      )
                   __A													=      self.schema.encode_batch(_A      )
                   writer.write_batch(_A      )
             if TEST:
                   break
             # finalizer the writer
       if not TEST:
             __A						,   __A													=      writer.finalize()
             print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'      )





def 							snake_case				(		UpperCAmelCase						)->      List[str]:







      """simple docstring"""
      try:
            __A													=      json.load(UpperCAmelCase						)
            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__":
 a__		: Optional[int]  							=							Extract(sys.argv[1:])
 extract()
 if not TEST:
  a__		: Optional[int]  							=							datasets.Dataset.from_file(extract.outputfile)
  # wala!
  # print(np.array(dataset[0:2]["roi_features"]).shape)








'''simple docstring'''





def 							snake_case				(		UpperCAmelCase						)->      list:







      """simple docstring"""
      __A													=      1.3
      __A													=      len(UpperCAmelCase						)
      __A													=      False

      while not completed:
            # Update the gap value for a next comb
            __A													=      int(gap / shrink_factor						)
            if gap <= 1:
                  __A													=      True

            __A													=      0
            while index + gap < len(UpperCAmelCase						):
                  if data[index] > data[index + gap]:
                        # Swap values
                        __A						,   __A													=      data[index + gap], data[index]
                        __A													=      False
                  index += 1

      return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a__		: Optional[Any]  							=							input("Enter numbers separated by a comma:\n").strip()
 a__		: Union[str, Any]  							=							[int(item) for item in user_input.split(",")]
 print(comb_sort(unsorted))






