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




    @property
    def 						__lowerCamelCase (					self  :   Any   )	-> Optional[Any]:
      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  :   List[Any]   )	-> Tuple:
      # 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__					)				->Tuple:

  '''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)


_SCREAMING_SNAKE_CASE   						=    [0.485, 0.456, 0.406]
_SCREAMING_SNAKE_CASE   						=    [0.229, 0.224, 0.225]
_SCREAMING_SNAKE_CASE   						=    [0.5, 0.5, 0.5]
_SCREAMING_SNAKE_CASE   						=    [0.5, 0.5, 0.5]
_SCREAMING_SNAKE_CASE   						=    [0.48145466, 0.4578275, 0.40821073]
_SCREAMING_SNAKE_CASE   						=    [0.26862954, 0.26130258, 0.27577711]

