






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




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self   ,   _A=sys.argv[1:]			)    ->   Any:
    __a    :	List[Any]									=						None
    __a    :	Optional[int]									=						None
    __a    :	Tuple									=						None
    __a    :	List[str]									=						1
    __a			,    __a    :	Optional[Any]									=						getopt.getopt(_A   ,   'i:o:b:s'   ,   ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']			)
    for opt, arg in opts:
     if opt in ("-i", "--inputdir"):
      __a    :	Optional[int]									=						arg
     elif opt in ("-o", "--outfile"):
      __a    :	Dict									=						arg
     elif opt in ("-b", "--batch_size"):
      __a    :	int									=						int(_A			)
     elif opt in ("-s", "--subset_list"):
      __a    :	Tuple									=						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    :	str									=						{self._vqa_file_split()[0] for x in tryload(_A			)}
    else:
     __a    :	List[Any]									=						None

    __a    :	Tuple									=						CONFIG
    if torch.cuda.is_available():
     __a    :	str									=						'cuda'
    __a    :	Union[str, Any]									=						os.path.realpath(_A			)
    __a    :	str									=						os.path.realpath(_A			)
    __a    :	Tuple									=						Preprocess(self.config			)
    __a    :	List[Any]									=						GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'   ,   config=self.config			)
    __a    :	str									=						batch_size if batch_size != 0 else 1
    __a    :	int									=						DEFAULT_SCHEMA



   def    __magic_name__    ( self   ,   _A			)    ->   str:
    __a    :	List[str]									=						int(file.split('.'			)[0].split('_'			)[-1]			)
    __a    :	List[Any]									=						os.path.join(self.inputdir   ,   _A			)
    return (img_id, filepath)



   @property
   def    __magic_name__    ( self			)    ->   Optional[Any]:
    __a    :	str									=						[]
    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    :	Union[str, Any]									=						batch
      __a    :	int									=						[]
      yield list(map(_A   ,   zip(*_A			)			)			)

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



   def __call__( self			)    ->   Tuple:
    # make writer
    if not TEST:
     __a    :	Dict									=						datasets.ArrowWriter(features=self.schema   ,   path=self.outputfile			)
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator			):
     __a			,    __a			,    __a    :	List[Any]									=						self.preprocess(_A			)
     __a    :	List[Any]									=						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    :	Any									=						output_dict.pop('normalized_boxes'			)
     if not TEST:
      __a    :	Optional[Any]									=						np.array(_A			)
      __a    :	Tuple									=						self.schema.encode_batch(_A			)
      writer.write_batch(_A			)
     if TEST:
      break
     # finalizer the writer
    if not TEST:
     __a			,    __a    :	List[str]									=						writer.finalize()
     print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''			)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):




 try:
  __a    :	Tuple									=						json.load(lowerCamelCase__		)
  try:
   __a    :	str									=						list(data.keys()		)
  except Exception:
   __a    :	int									=						[d['img_id'] for d in data]
 except Exception:
  try:
   __a    :	int									=						eval(stream.read()		)
  except Exception:
   __a    :	Any									=						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__		):
 if len(SCREAMING_SNAKE_CASE__		) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]		),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]		),
  ]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




