




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_  						=   False
lowerCamelCase_  						=   Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowerCamelCase_  						=   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       __a					:


     """simple docstring"""


     def __init__(	self			: Optional[int]				,_UpperCamelCase			: Dict=sys.argv[1:]						)		->   Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=1
         SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=getopt.getopt(_UpperCamelCase				,"""i:o:b:s"""				,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]						)
         for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                 SCREAMING_SNAKE_CASE__								=arg
             elif opt in ("-o", "--outfile"):
                 SCREAMING_SNAKE_CASE__								=arg
             elif opt in ("-b", "--batch_size"):
                 SCREAMING_SNAKE_CASE__								=int(_UpperCamelCase						)
             elif opt in ("-s", "--subset_list"):
                 SCREAMING_SNAKE_CASE__								=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:
                 SCREAMING_SNAKE_CASE__								={self._vqa_file_split()[0] for x in tryload(_UpperCamelCase						)}
         else:
             SCREAMING_SNAKE_CASE__								=None

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




     def   __A				(	self			: Optional[int]				,_UpperCamelCase			: Any						)		->   Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=int(file.split("""."""						)[0].split("""_"""						)[-1]						)
         SCREAMING_SNAKE_CASE__								=os.path.join(self.inputdir				,_UpperCamelCase						)
         return (img_id, filepath)




     @property
     def   __A				(	self			: Union[str, Any]						)		->   Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         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:
                 SCREAMING_SNAKE_CASE__								=batch
                 SCREAMING_SNAKE_CASE__								=[]
                 yield list(map(_UpperCamelCase				,zip(*_UpperCamelCase						)						)						)

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




     def __call__(	self			: int						)		->   Tuple:


         '''simple docstring'''





         if not TEST:
             SCREAMING_SNAKE_CASE__								=datasets.ArrowWriter(features=self.schema				,path=self.outputfile						)
         # do file generator
         for i, (img_ids, filepaths) in enumerate(self.file_generator						):
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.preprocess(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=self.model(
                 _UpperCamelCase				,_UpperCamelCase				,scales_yx=_UpperCamelCase				,padding="""max_detections"""				,max_detections=self.config.MAX_DETECTIONS				,pad_value=0				,return_tensors="""np"""				,location="""cpu"""				,)
             SCREAMING_SNAKE_CASE__								=output_dict.pop("""normalized_boxes"""						)
             if not TEST:
                 SCREAMING_SNAKE_CASE__								=np.array(_UpperCamelCase						)
                 SCREAMING_SNAKE_CASE__								=self.schema.encode_batch(_UpperCamelCase						)
                 writer.write_batch(_UpperCamelCase						)
             if TEST:
                 break
             # finalizer the writer
         if not TEST:
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=writer.finalize()
             print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""						)






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


if __name__ == "__main__":
    lowerCamelCase_  						=   Extract(sys.argv[1:])
    extract()
    if not TEST:
        lowerCamelCase_  						=   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()






