
'''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
lowerCAmelCase_			:			Tuple			   =     False
lowerCAmelCase_			:			Tuple			   =     Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCAmelCase_			:			int			   =     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 	__lowerCAmelCase    :



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

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



     def     snake_case_ (self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Union[str, Any]         =    int(file.split("""."""	)[0].split("""_"""	)[-1]	)
          _UpperCAmelCase					:  List[Any]         =    os.path.join(self.inputdir						,	lowerCAmelCase__	)
          return (img_id, filepath)



     @property
     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[int]         =    []
          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:
                    _UpperCAmelCase					:  int         =    batch
                    _UpperCAmelCase					:  List[str]         =    []
                    yield list(map(lowerCAmelCase__						,	zip(*lowerCAmelCase__	)	)	)

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



     def __call__(self	):
          # make writer
          if not TEST:
               _UpperCAmelCase					:  List[Any]         =    datasets.ArrowWriter(features=self.schema						,	path=self.outputfile	)
          # do file generator
          for i, (img_ids, filepaths) in enumerate(self.file_generator	):
               _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Dict         =    self.preprocess(lowerCAmelCase__	)
               _UpperCAmelCase					:  Optional[Any]         =    self.model(
                   lowerCAmelCase__						,	lowerCAmelCase__						,	scales_yx=lowerCAmelCase__						,	padding="""max_detections"""						,	max_detections=self.config.MAX_DETECTIONS						,	pad_value=0						,	return_tensors="""np"""						,	location="""cpu"""						,	)
               _UpperCAmelCase					:  Dict         =    output_dict.pop("""normalized_boxes"""	)
               if not TEST:
                    _UpperCAmelCase					:  Dict         =    np.array(lowerCAmelCase__	)
                    _UpperCAmelCase					:  int         =    self.schema.encode_batch(lowerCAmelCase__	)
                    writer.write_batch(lowerCAmelCase__	)
               if TEST:
                    break
               # finalizer the writer
          if not TEST:
               _UpperCAmelCase  ,     _UpperCAmelCase					:  Dict         =    writer.finalize()
               print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"	)







def __A  (    lowerCAmelCase_       ):
     try:
          _UpperCAmelCase					:  Union[str, Any]         =    json.load(lowerCAmelCase_       )
          try:
               _UpperCAmelCase					:  Optional[Any]         =    list(data.keys()       )
          except Exception:
               _UpperCAmelCase					:  int         =    [d["""img_id"""] for d in data]
     except Exception:
          try:
               _UpperCAmelCase					:  int         =    eval(stream.read()       )
          except Exception:
               _UpperCAmelCase					:  List[str]         =    stream.read().split("""\n"""       )
     return data


if __name__ == "__main__":
   lowerCAmelCase_			:			Tuple			   =     Extract(sys.argv[1:])
   extract()
   if not TEST:
      lowerCAmelCase_			:			Dict			   =     datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)



"""simple docstring"""


from __future__ import annotations





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					int | float:




						'''simple docstring'''



						if len(__lowerCAmelCase				) == 0:
												raise ValueError("find_max() arg is an empty sequence"				)
						lowerCamelCase__					=nums[0]
						for x in nums:
												if x > max_num:
																		lowerCamelCase__					=x
						return max_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)


