



'''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__  :					Any				= False
A__  :					Optional[int]				= Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
A__  :					List[Any]				= 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__		:
 def __init__(					self		:				List[str]	,			__a		:				Any=sys.argv[1:]   )	->					Optional[Any]:







       '''simple docstring'''
       __snake_case      :					Tuple						=  None
       __snake_case      :					Union[str, Any]						=  None
       __snake_case      :					Dict						=  None
       __snake_case      :					List[str]						=  1
       __snake_case      :					Any						=  getopt.getopt(lowerCAmelCase__	,			'i:o:b:s'	,			['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']   )
       for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                   __snake_case      :					int						=  arg
             elif opt in ("-o", "--outfile"):
                   __snake_case      :					Union[str, Any]						=  arg
             elif opt in ("-b", "--batch_size"):
                   __snake_case      :					Tuple						=  int(lowerCAmelCase__   )
             elif opt in ("-s", "--subset_list"):
                   __snake_case      :					Dict						=  arg

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

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







       '''simple docstring'''
       __snake_case      :					str						=  int(file.split('.'   )[0].split('_'   )[-1]   )
       __snake_case      :					List[Any]						=  os.path.join(self.inputdir	,			lowerCAmelCase__   )
       return (img_id, filepath)
 @property
 def A_					(					self		:				int   )	->					Union[str, Any]:







       '''simple docstring'''
       __snake_case      :					Optional[Any]						=  []
       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(lowerCAmelCase__   )   )
             if len(lowerCAmelCase__   ) == self.batch:
                   __snake_case      :					Union[str, Any]						=  batch
                   __snake_case      :					Optional[Any]						=  []
                   yield list(map(lowerCAmelCase__	,			zip(*lowerCAmelCase__   )   )   )

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





 def __call__(					self		:				Any   )	->					List[str]:







       '''simple docstring'''



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





def    a_					(						_UpperCAmelCase  :	List[Any]							)  ->   Optional[Any]:
      try:
            __snake_case      :					List[Any]						=  json.load(_lowercase							)
            try:
                  __snake_case      :					str						=  list(data.keys()							)
            except Exception:
                  __snake_case      :					Dict						=  [d['''img_id'''] for d in data]
      except Exception:
            try:
                  __snake_case      :					Dict						=  eval(stream.read()							)
            except Exception:
                  __snake_case      :					str						=  stream.read().split('\n'							)
      return data


if __name__ == "__main__":
    A__  :					Union[str, Any]				= Extract(sys.argv[1:])
    extract()
    if not TEST:
        A__  :					int				= datasets.Dataset.from_file(extract.outputfile)
        # wala!
        # print(np.array(dataset[0:2]["roi_features"]).shape)










"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



