

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__      =		False
a__      =		Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
a__      =		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, 20_48), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)







class 						snake_case  :



   '''simple docstring'''






   def __init__(   self							:     Dict			,						lowerCAmelCase							:     str=sys.argv[1:])			->			List[Any]:

    """simple docstring"""







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

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

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

   def        UpperCamelCase_					(   self							:     Optional[int]			,						lowerCAmelCase							:     Optional[Any])			->			Dict:

    """simple docstring"""







    _snake_case      :					Any					=   int(file.split(""".""")[0].split("""_""")[-1])
    _snake_case      :					str					=   os.path.join(self.inputdir			,						__snake_case)
    return (img_id, filepath)

   @property
   def        UpperCamelCase_					(   self							:     Any)			->			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(__snake_case))
     if len(__snake_case) == self.batch:
      _snake_case      :					List[Any]					=   batch
      _snake_case      :					str					=   []
      yield list(map(__snake_case			,						zip(*__snake_case)))

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


   def __call__(   self							:     str)			->			Tuple:

    """simple docstring"""







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



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		str			)					->       List[Any]:
 try:
  _snake_case      :					str					=   json.load(_lowerCAmelCase			)
  try:
   _snake_case      :					Optional[int]					=   list(data.keys()			)
  except Exception:
   _snake_case      :					int					=   [d['''img_id'''] for d in data]
 except Exception:
  try:
   _snake_case      :					Union[str, Any]					=   eval(stream.read()			)
  except Exception:
   _snake_case      :					int					=   stream.read().split("""\n"""			)
 return data


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





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       Optional[int]:
 _snake_case      :					Any					=   [701, 301, 132, 57, 23, 10, 4, 1]
 for gap in gaps:
  for i in range(SCREAMING_SNAKE_CASE__			,    len(SCREAMING_SNAKE_CASE__			)			):
   _snake_case      :					List[Any]					=   collection[i]
   _snake_case      :					List[str]					=   i
   while j >= gap and collection[j - gap] > insert_value:
    _snake_case      :					int					=   collection[j - gap]
    j -= gap
   if j != i:
    _snake_case      :					Tuple					=   insert_value
 return collection


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		input("""Enter numbers separated by a comma:\n""").strip()
  a__      =		[int(item) for item in user_input.split(""",""")]
  print(shell_sort(unsorted))
