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     =     False
_SCREAMING_SNAKE_CASE     =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_SCREAMING_SNAKE_CASE     =     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        UpperCAmelCase_					:




    def __init__(       self	: Optional[int]   ,   lowerCAmelCase	: Tuple=sys.argv[1:] )						->			Tuple:
      a__  		=  None
      a__  		=  None
      a__  		=  None
      a__  		=  1
      a__		,a__  		=  getopt.getopt(lowerCAmelCase   ,   "i:o:b:s"   ,   ["inputdir=", "outfile=", "batch_size=", "subset_list="] )
      for opt, arg in opts:
        if opt in ("-i", "--inputdir"):
          a__  		=  arg
        elif opt in ("-o", "--outfile"):
          a__  		=  arg
        elif opt in ("-b", "--batch_size"):
          a__  		=  int(lowerCAmelCase )
        elif opt in ("-s", "--subset_list"):
          a__  		=  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:
          a__  		=  {self._vqa_file_split()[0] for x in tryload(lowerCAmelCase )}
      else:
        a__  		=  None

      a__  		=  CONFIG
      if torch.cuda.is_available():
        a__  		=  "cuda"
      a__  		=  os.path.realpath(lowerCAmelCase )
      a__  		=  os.path.realpath(lowerCAmelCase )
      a__  		=  Preprocess(self.config )
      a__  		=  GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"   ,   config=self.config )
      a__  		=  batch_size if batch_size != 0 else 1
      a__  		=  DEFAULT_SCHEMA




    def     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: List[Any] )						->			Optional[Any]:
      a__  		=  int(file.split("." )[0].split("_" )[-1] )
      a__  		=  os.path.join(self.inputdir   ,   lowerCAmelCase )
      return (img_id, filepath)




    @property
    def     __lowerCamelCase			(       self	: int )						->			str:
      a__  		=  []
      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:
          a__  		=  batch
          a__  		=  []
          yield list(map(lowerCAmelCase   ,   zip(*lowerCAmelCase ) ) )

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




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

def 					__lowercase						(    lowerCamelCase__       )					->str:

  '''simple docstring'''




  try:
    a__  		=  json.load(lowerCamelCase__       )
    try:
      a__  		=  list(data.keys()       )
    except Exception:
      a__  		=  [d["img_id"] for d in data]
  except Exception:
    try:
      a__  		=  eval(stream.read()       )
    except Exception:
      a__  		=  stream.read().split("\n"       )
  return data


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


def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  if not isinstance(lowerCamelCase__			,					lowerCamelCase__       ):
    raise ValueError("double_factorial() only accepts integral values"       )
  if n < 0:
    raise ValueError("double_factorial() not defined for negative values"       )
  return 1 if n <= 1 else n * double_factorial(n - 2       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

