




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     : Any      =False
__SCREAMING_SNAKE_CASE     : str      =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE     : 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_048), 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	: List[str]						,	A__	: List[str]=sys.argv[1:]      )					->							Tuple:
           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					1
           lowerCamelCase__	,		lowerCamelCase__   	=					getopt.getopt(A__						,	'i:o:b:s'						,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       lowerCamelCase__   	=					arg
                 elif opt in ("-o", "--outfile"):
                       lowerCamelCase__   	=					arg
                 elif opt in ("-b", "--batch_size"):
                       lowerCamelCase__   	=					int(A__      )
                 elif opt in ("-s", "--subset_list"):
                       lowerCamelCase__   	=					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:
                       lowerCamelCase__   	=					{self._vqa_file_split()[0] for x in tryload(A__      )}
           else:
                 lowerCamelCase__   	=					None

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




     def 			__snake_case (     self	: str						,	A__	: List[str]      )					->							Tuple:
           lowerCamelCase__   	=					int(file.split('.'      )[0].split('_'      )[-1]      )
           lowerCamelCase__   	=					os.path.join(self.inputdir						,	A__      )
           return (img_id, filepath)




     @property
     def 			__snake_case (     self	: Union[str, Any]      )					->							Optional[int]:
           lowerCamelCase__   	=					[]
           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:
                       lowerCamelCase__   	=					batch
                       lowerCamelCase__   	=					[]
                       yield list(map(A__						,	zip(*A__      )      )      )

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




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

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]    )       ->      List[Any]:


      '''simple docstring'''




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


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





def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					str(lowerCamelCase    ).strip()
      if not bin_string:
            raise ValueError('Empty string was passed to the function'    )
      lowerCamelCase__   	=					bin_string[0] == '-'
      if is_negative:
            lowerCamelCase__   	=					bin_string[1:]
      if not all(char in '01' for char in bin_string    ):
            raise ValueError('Non-binary value was passed to the function'    )
      lowerCamelCase__   	=					0
      for char in bin_string:
            lowerCamelCase__   	=					2 * decimal_number + int(lowerCamelCase    )
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
      from doctest import testmod

      testmod()