'''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	:					str					=  False
__SCREAMING_SNAKE_CASE	:					str					=  Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE	:					str					=  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_048), 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						:    Optional[int]						, A						:    Tuple=sys.argv[1:]	):
             _UpperCAmelCase					:  List[str]											=   None
             _UpperCAmelCase					:  Union[str, Any]											=   None
             _UpperCAmelCase					:  Optional[Any]											=   None
             _UpperCAmelCase					:  Any											=   1
             _UpperCAmelCase					:  str											=   getopt.getopt(A						, "i:o:b:s"						, ["inputdir=", "outfile=", "batch_size=", "subset_list="]	)
             for opt, arg in opts:
                    if opt in ("-i", "--inputdir"):
                           _UpperCAmelCase					:  Union[str, Any]											=   arg
                    elif opt in ("-o", "--outfile"):
                           _UpperCAmelCase					:  str											=   arg
                    elif opt in ("-b", "--batch_size"):
                           _UpperCAmelCase					:  Optional[int]											=   int(A	)
                    elif opt in ("-s", "--subset_list"):
                           _UpperCAmelCase					:  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(A	), F"""{outputfile}"""
             if subset_list is not None:
                    with open(os.path.realpath(A	)	) as f:
                           _UpperCAmelCase					:  List[Any]											=   {self._vqa_file_split()[0] for x in tryload(A	)}
             else:
                    _UpperCAmelCase					:  Tuple											=   None

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




      def 					_A     (	self						:    Tuple						, A						:    Optional[int]	):
             _UpperCAmelCase					:  List[str]											=   int(file.split("."	)[0].split("_"	)[-1]	)
             _UpperCAmelCase					:  Dict											=   os.path.join(self.inputdir						, A	)
             return (img_id, filepath)




      @property
      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  Union[str, 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(A	)	)
                    if len(A	) == self.batch:
                           _UpperCAmelCase					:  Dict											=   batch
                           _UpperCAmelCase					:  List[Any]											=   []
                           yield list(map(A						, zip(*A	)	)	)

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





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

def      UpperCamelCase_							( _UpperCAmelCase  :				Any   )   ->   int:


       """simple docstring"""







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


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


'''simple docstring'''




from itertools import compress, repeat
from math import ceil, sqrt

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   list[int]:


       """simple docstring"""







       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       _UpperCAmelCase					:  Optional[int]											=   bytearray(b"\x01"   ) * ((num >> 1) - 1)

       for i in range(3      ,     int(sqrt(_UpperCAmelCase   )   ) + 1      ,     2   ):
              if sieve[(i >> 1) - 1]:
                     _UpperCAmelCase					:  List[Any]											=   i**2
                     _UpperCAmelCase					:  Optional[int]											=   repeat(
                         0      ,     ceil((num - i_squared) / (i << 1)   )   )

       return [2] + list(compress(range(3      ,     _UpperCAmelCase      ,     2   )      ,     _UpperCAmelCase   )   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

