
'''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
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, 2048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class       UpperCAmelCase_ :





 def __init__(			self       ,				lowercase_=sys.argv[1:]):
     snake_case_       :			Any   	=  None
     snake_case_       :			Any   	=  None
     snake_case_       :			Optional[Any]   	=  None
     snake_case_       :			str   	=  1
     snake_case_	,       snake_case_       :			List[str]   	=  getopt.getopt(_UpperCamelCase       ,				"i:o:b:s"       ,				["inputdir=", "outfile=", "batch_size=", "subset_list="])
     for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
             snake_case_       :			Optional[Any]   	=  arg
         elif opt in ("-o", "--outfile"):
             snake_case_       :			int   	=  arg
         elif opt in ("-b", "--batch_size"):
             snake_case_       :			str   	=  int(_UpperCamelCase)
         elif opt in ("-s", "--subset_list"):
             snake_case_       :			str   	=  arg

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

     snake_case_       :			List[str]   	=  CONFIG
     if torch.cuda.is_available():
         snake_case_       :			Union[str, Any]   	=  "cuda"
     snake_case_       :			Optional[Any]   	=  os.path.realpath(_UpperCamelCase)
     snake_case_       :			str   	=  os.path.realpath(_UpperCamelCase)
     snake_case_       :			Tuple   	=  Preprocess(self.config)
     snake_case_       :			Dict   	=  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 				snake_case__						(			self       ,				lowercase_):
     snake_case_       :			List[Any]   	=  int(file.split(".")[0].split("_")[-1])
     snake_case_       :			List[str]   	=  os.path.join(self.inputdir       ,				_UpperCamelCase)
     return (img_id, filepath)





 @property
 def 				snake_case__						(			self):
     snake_case_       :			Dict   	=  []
     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(_UpperCamelCase))
         if len(_UpperCamelCase) == self.batch:
             snake_case_       :			str   	=  batch
             snake_case_       :			Tuple   	=  []
             yield list(map(_UpperCamelCase       ,				zip(*_UpperCamelCase)))

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





 def __call__(			self):





     if not TEST:
         snake_case_       :			List[str]   	=  datasets.ArrowWriter(features=self.schema       ,				path=self.outputfile)
     # do file generator
     for i, (img_ids, filepaths) in enumerate(self.file_generator):
         snake_case_	,       snake_case_	,       snake_case_       :			List[str]   	=  self.preprocess(_UpperCamelCase)
         snake_case_       :			str   	=  self.model(
             _UpperCamelCase       ,				_UpperCamelCase       ,				scales_yx=_UpperCamelCase       ,				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_       :			str   	=  np.array(_UpperCamelCase)
             snake_case_       :			Dict   	=  self.schema.encode_batch(_UpperCamelCase)
             writer.write_batch(_UpperCamelCase)
         if TEST:
             break
         # finalizer the writer
     if not TEST:
         snake_case_	,       snake_case_       :			List[Any]   	=  writer.finalize()
         print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb')


def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    try:
        snake_case_       :			int   	=  json.load(__UpperCamelCase				)
        try:
            snake_case_       :			int   	=  list(data.keys()				)
        except Exception:
            snake_case_       :			List[Any]   	=  [d["img_id"] for d in data]
    except Exception:
        try:
            snake_case_       :			Tuple   	=  eval(stream.read()				)
        except Exception:
            snake_case_       :			str   	=  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 	UpperCAmelCase_    (	__UpperCamelCase	):
    if not isinstance(__UpperCamelCase,				__UpperCamelCase	):
        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()






