


'''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
__lowercase:		Optional[int]    						=  False
__lowercase:		Union[str, Any]    						=  Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
__lowercase:		Tuple    						=  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_048), 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],       a_			:						Optional[Any]=sys.argv[1:]       ):





    """simple docstring"""



    UpperCamelCase__			    =    None
    UpperCamelCase__			    =    None
    UpperCamelCase__			    =    None
    UpperCamelCase__			    =    1
    UpperCamelCase__    ,		UpperCamelCase__			    =    getopt.getopt(a_,       "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(a_       )
       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(a_       ), f'{outputfile}'
    if subset_list is not None:
       with open(os.path.realpath(a_       )       ) as f:
          UpperCamelCase__			    =    {self._vqa_file_split()[0] for x in tryload(a_       )}
    else:
       UpperCamelCase__			    =    None

    UpperCamelCase__			    =    CONFIG
    if torch.cuda.is_available():
       UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    os.path.realpath(a_       )
    UpperCamelCase__			    =    os.path.realpath(a_       )
    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 		lowercase_    (			self			:						Dict,       a_			:						Optional[int]       ):





    """simple docstring"""



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

 @property
 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    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(a_       )       )
       if len(a_       ) == self.batch:
          UpperCamelCase__			    =    batch
          UpperCamelCase__			    =    []
          yield list(map(a_,       zip(*a_       )       )       )

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

 def __call__(			self			:						Tuple       ):





    """simple docstring"""



    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(a_       )
       UpperCamelCase__			    =    self.model(
           a_,       a_,       scales_yx=a_,       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(a_       )
          UpperCamelCase__			    =    self.schema.encode_batch(a_       )
          writer.write_batch(a_       )
       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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any )		->       Union[str, Any]:





   '''simple docstring'''







   try:
      UpperCamelCase__			    =    json.load(_UpperCamelCase )
      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__":
       __lowercase:		str    						=  Extract(sys.argv[1:])
       extract()
       if not TEST:
              __lowercase:		Any    						=  datasets.Dataset.from_file(extract.outputfile)
              # wala!
              # print(np.array(dataset[0:2]["roi_features"]).shape)


"""simple docstring"""




def   __SCREAMING_SNAKE_CASE		(		lowercase__ = 30 ):
					"""simple docstring"""
					A     =   exponent + 2
					A     =   (1 + 5**0.5) / 2
					A     =   (phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(lowercase__ )


if __name__ == "__main__":
	print(f"""{solution() = }""")





