


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
__UpperCAmelCase      			=  False
__UpperCAmelCase      			=  Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__UpperCAmelCase      			=  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 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE=sys.argv[1:])					->						str:







         """simple docstring"""







         UpperCamelCase__ :			int 						=None
         UpperCamelCase__ :			Dict 						=None
         UpperCamelCase__ :			Any 						=None
         UpperCamelCase__ :			Union[str, Any] 						=1
         UpperCamelCase__ :			Optional[int] 						=getopt.getopt(__SCREAMING_SNAKE_CASE   ,			"i:o:b:s"   ,			["inputdir=", "outfile=", "batch_size=", "subset_list="])
         for opt, arg in opts:
            if opt in ("-i", "--inputdir"):
               UpperCamelCase__ :			int 						=arg
            elif opt in ("-o", "--outfile"):
               UpperCamelCase__ :			List[Any] 						=arg
            elif opt in ("-b", "--batch_size"):
               UpperCamelCase__ :			Union[str, Any] 						=int(__SCREAMING_SNAKE_CASE)
            elif opt in ("-s", "--subset_list"):
               UpperCamelCase__ :			Optional[int] 						=arg

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

         UpperCamelCase__ :			Tuple 						=CONFIG
         if torch.cuda.is_available():
            UpperCamelCase__ :			Optional[int] 						="""cuda"""
         UpperCamelCase__ :			int 						=os.path.realpath(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Any 						=os.path.realpath(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Union[str, Any] 						=Preprocess(self.config)
         UpperCamelCase__ :			str 						=GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"   ,			config=self.config)
         UpperCamelCase__ :			str 						=batch_size if batch_size != 0 else 1
         UpperCamelCase__ :			str 						=DEFAULT_SCHEMA




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







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




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







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=[]
         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(__SCREAMING_SNAKE_CASE))
            if len(__SCREAMING_SNAKE_CASE) == self.batch:
               UpperCamelCase__ :			str 						=batch
               UpperCamelCase__ :			Optional[int] 						=[]
               yield list(map(__SCREAMING_SNAKE_CASE   ,			zip(*__SCREAMING_SNAKE_CASE)))

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




      def __call__(		self)					->						str:







         """simple docstring"""







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




def 	_lowerCamelCase  (		A_  :	Optional[int]      )			->			Tuple:

   '''simple docstring'''






   try:
      UpperCamelCase__ :			Any 						=json.load(A_      )
      try:
         UpperCamelCase__ :			Any 						=list(data.keys()      )
      except Exception:
         UpperCamelCase__ :			Union[str, Any] 						=[d["""img_id"""] for d in data]
   except Exception:
      try:
         UpperCamelCase__ :			Union[str, Any] 						=eval(stream.read()      )
      except Exception:
         UpperCamelCase__ :			Any 						=stream.read().split("\n"      )
   return data


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





'''simple docstring'''
import math



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					math.log(2    ,       10     ) * number
       snake_case__					:	int          =					round((log_number - int(UpperCAmelCase     ))    ,       15     )

       return difference



def 				lowerCAmelCase__	(       UpperCAmelCase = 67_8910     ):






       """simple docstring"""





       snake_case__					:	str          =					90
       snake_case__					:	Dict          =					0

       snake_case__					:	List[str]          =					math.log(1.2_3    ,       10     )
       snake_case__					:	Optional[Any]          =					math.log(1.2_4    ,       10     )
       snake_case__					:	Any          =					0

       while position < number:
              snake_case__					:	Tuple          =					log_difference(UpperCAmelCase     )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     snake_case__					:	Union[str, Any]          =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(f"""{solution() = }""")




