



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, 2_0_4_8), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)



class 						_snake_case :





 """simple docstring"""



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




   """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		,      a   )		->							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   )		->							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(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   )		->							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 >> 2_0} mb'''   )







def       UpperCAmelCase__	(							__snake_case							)				->	str:
  try:
    _A          =      json.load(__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__":
    __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)




import importlib.metadata
import subprocess
import sys




def       UpperCAmelCase__	(							__snake_case = False							)				->	List[Any]:
  _A          =      False
  if "IPython" in sys.modules:
    _A          =      '''google.colab''' in str(sys.modules['''IPython'''].get_ipython()							)

  if in_colab:
    if upgrade:
      _A          =      ['''pip''', '''install''', '''-U''', '''torch''']
      subprocess.run(__snake_case  ,			check=__snake_case							)
    # get the current version of torch
    _A          =      importlib.metadata.version('''torch'''							)
    _A          =      torch_version[: torch_version.rindex('''.'''							)]
    _A          =      F'''https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl'''
    _A          =      ['''pip''', '''install''', xla_wheel]
    subprocess.run(__snake_case  ,			check=__snake_case							)
  else:
    raise RuntimeError('''`install_xla` utility works only on google colab.'''							)