

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, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)







class 		__magic_name__     :

   '''simple docstring'''




   def __init__(							self,       lowercase_=sys.argv[1:]			)				->       Any:






     """simple docstring"""






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

     a__    =CONFIG
     if torch.cuda.is_available():
       a__    ='''cuda'''
     a__    =os.path.realpath(lowercase_			)
     a__    =os.path.realpath(lowercase_			)
     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 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






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



   @property
   def 					_UpperCAmelCase					(							self			)				->       Optional[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(lowercase_			)			)
       if len(lowercase_			) == self.batch:
         a__    =batch
         a__    =[]
         yield list(map(lowercase_,       zip(*lowercase_			)			)			)

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



   def __call__(							self			)				->       Optional[int]:






     """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(lowercase_			)
       a__    =self.model(
           lowercase_,       lowercase_,       scales_yx=lowercase_,       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(lowercase_			)
         a__    =self.schema.encode_batch(lowercase_			)
         writer.write_batch(lowercase_			)
       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      UpperCAmelCase__							(     _A :					str				):





  '''simple docstring'''







  try:
    a__    =json.load(_A				)
    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__":
       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      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					Optional[int]				):





  '''simple docstring'''







  sequence.append(_A				)

  a__    =0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(_A				):
    return None

  return index


if __name__ == "__main__":
       lowerCamelCase 	=   input('''Enter numbers separated by comma:\n''').strip()
       lowerCamelCase 	=   [int(item) for item in user_input.split(''',''')]

       lowerCamelCase 	=   input('''Enter a single number to be found in the list:\n''')
       lowerCamelCase 	=   int(target_input)
       lowerCamelCase 	=   sentinel_linear_search(sequence, target)
       if result is not None:
              print(f"""{target} found at positions: {result}""")
       else:
              print('''Not found''')
