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






						'''simple docstring'''






						def __init__(					self    ,   _lowercase=sys.argv[1:]      ):

												"""simple docstring"""



												_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 					_lowercase	(					self    ,   _lowercase      ):

												"""simple docstring"""



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




						@property
						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_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      ):

												"""simple docstring"""



												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 >> 20} mb'      )






def A      (__lowerCamelCase      :str		):
						try:
												_lowerCAmelCase  						= json.load(SCREAMING_SNAKE_CASE_		)
												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__":
						_lowercase					    =		Extract(sys.argv[1:])
						extract()
						if not TEST:
												_lowercase					    =		datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)


'''simple docstring'''



from ..utils import DummyObject, requires_backends

class       _a (metaclass=_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     ['torch', 'librosa']






       def __init__(							self				,							*A__				,							**A__					)			->	str:
          requires_backends(self				,							["""torch""", """librosa"""]					)






       @classmethod
       def 				UpperCamelCase (							cls				,							*A__				,							**A__					)			->	Tuple:
          requires_backends(cls				,							["""torch""", """librosa"""]					)






       @classmethod
       def 				UpperCamelCase (							cls				,							*A__				,							**A__					)			->	Optional[Any]:
          requires_backends(cls				,							["""torch""", """librosa"""]					)






class       _a (metaclass=_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     ['torch', 'librosa']






       def __init__(							self				,							*A__				,							**A__					)			->	Dict:
          requires_backends(self				,							["""torch""", """librosa"""]					)






       @classmethod
       def 				UpperCamelCase (							cls				,							*A__				,							**A__					)			->	Optional[Any]:
          requires_backends(cls				,							["""torch""", """librosa"""]					)






       @classmethod
       def 				UpperCamelCase (							cls				,							*A__				,							**A__					)			->	Union[str, Any]:
          requires_backends(cls				,							["""torch""", """librosa"""]					)






