




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  :				Union[str, Any]						=				False
_lowerCAmelCase  :				str						=				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCAmelCase  :				Union[str, Any]						=				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      _UpperCamelCase :
      def __init__(     self				:Any	, lowerCamelCase				:Any=sys.argv[1:] )       ->     Union[str, Any]:
       UpperCAmelCase__								=  None
       UpperCAmelCase__								=  None
       UpperCAmelCase__								=  None
       UpperCAmelCase__								=  1
       UpperCAmelCase__	,	UpperCAmelCase__								=  getopt.getopt(lowerCamelCase	, "i:o:b:s"	, ["inputdir=", "outfile=", "batch_size=", "subset_list="] )
       for opt, arg in opts:
        if opt in ("-i", "--inputdir"):
         UpperCAmelCase__								=  arg
        elif opt in ("-o", "--outfile"):
         UpperCAmelCase__								=  arg
        elif opt in ("-b", "--batch_size"):
         UpperCAmelCase__								=  int(lowerCamelCase )
        elif opt in ("-s", "--subset_list"):
         UpperCAmelCase__								=  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:
         UpperCAmelCase__								=  {self._vqa_file_split()[0] for x in tryload(lowerCamelCase )}
       else:
        UpperCAmelCase__								=  None

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

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







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



def   lowerCAmelCase						(						_lowerCAmelCase							:				str		):




 """simple docstring"""







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


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











_lowerCAmelCase  :				dict[str, float]						=				{
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

_lowerCAmelCase  :				dict[str, float]						=				{
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_60_54_01_99E-27,
}



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				float		):




 """simple docstring"""







 if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
  UpperCAmelCase__								=  (
      F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Supported values are: {", ".join(_lowerCAmelCase		)}'''
  )
  raise ValueError(_lowerCAmelCase		)
 return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
  import doctest

  doctest.testmod()





