
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 							A__		:
      def __init__(				self   ,  __magic_name__=sys.argv[1:]				):
       lowerCamelCase		:     List[str]          =				None
       lowerCamelCase		:     List[str]          =				None
       lowerCamelCase		:     Dict          =				None
       lowerCamelCase		:     Any          =				1
       lowerCamelCase      ,   lowerCamelCase		:     Any          =				getopt.getopt(__magic_name__   ,  """i:o:b:s"""   ,  ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]				)
       for opt, arg in opts:
        if opt in ("-i", "--inputdir"):
         lowerCamelCase		:     str          =				arg
        elif opt in ("-o", "--outfile"):
         lowerCamelCase		:     Any          =				arg
        elif opt in ("-b", "--batch_size"):
         lowerCamelCase		:     Any          =				int(__magic_name__				)
        elif opt in ("-s", "--subset_list"):
         lowerCamelCase		:     List[str]          =				arg

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

       lowerCamelCase		:     int          =				CONFIG
       if torch.cuda.is_available():
        lowerCamelCase		:     Tuple          =				"""cuda"""
       lowerCamelCase		:     List[str]          =				os.path.realpath(__magic_name__				)
       lowerCamelCase		:     Dict          =				os.path.realpath(__magic_name__				)
       lowerCamelCase		:     Union[str, Any]          =				Preprocess(self.config				)
       lowerCamelCase		:     List[str]          =				GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""   ,  config=self.config				)
       lowerCamelCase		:     Optional[int]          =				batch_size if batch_size != 0 else 1
       lowerCamelCase		:     Optional[int]          =				DEFAULT_SCHEMA
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     List[Any]          =				int(file.split("""."""				)[0].split("""_"""				)[-1]				)
       lowerCamelCase		:     Optional[int]          =				os.path.join(self.inputdir   ,  __magic_name__				)
       return (img_id, filepath)
      @property
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     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(__magic_name__				)				)
        if len(__magic_name__				) == self.batch:
         lowerCamelCase		:     Optional[int]          =				batch
         lowerCamelCase		:     Union[str, Any]          =				[]
         yield list(map(__magic_name__   ,  zip(*__magic_name__				)				)				)

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






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





def    _a (						lowerCamelCase							):
 try:
  lowerCamelCase		:     str          =				json.load(lowerCamelCase							)
  try:
   lowerCamelCase		:     Tuple          =				list(data.keys()							)
  except Exception:
   lowerCamelCase		:     Union[str, Any]          =				[d["""img_id"""] for d in data]
 except Exception:
  try:
   lowerCamelCase		:     Optional[int]          =				eval(stream.read()							)
  except Exception:
   lowerCamelCase		:     List[str]          =				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    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[int]          =				[1 / first_term]
 lowerCamelCase		:     Any          =				1 / first_term
 for _ in range(number_of_terms - 1							):
  first_term += common_difference
  arithmetic_progression.append(lowerCamelCase							)
 lowerCamelCase		:     Optional[Any]          =				[1 / step for step in arithmetic_progression]
 return sum(lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(sum_of_harmonic_progression(1 / 2, 2, 2))




