




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_			:       Union[str, Any]						       =				False
lowercase_			:       int						       =				Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
lowercase_			:       Optional[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, 20_48), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)






class 					__lowerCAmelCase				:







			def __init__( self					:    Dict					,					snake_case__					:    List[Any]=sys.argv[1:]   ):




									"""simple docstring"""


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

									_UpperCAmelCase									=  CONFIG
									if torch.cuda.is_available():
															_UpperCAmelCase									=  '''cuda'''
									_UpperCAmelCase									=  os.path.realpath(_snake_case   )
									_UpperCAmelCase									=  os.path.realpath(_snake_case   )
									_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 UpperCamelCase    ( self					:    int					,					snake_case__					:    Union[str, Any]   ):




									"""simple docstring"""


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







			@property
			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""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(_snake_case   )   )
															if len(_snake_case   ) == self.batch:
																					_UpperCAmelCase									=  batch
																					_UpperCAmelCase									=  []
																					yield list(map(_snake_case					,					zip(*_snake_case   )   )   )

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







			def __call__( self					:    int   ):




									"""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									=  self.preprocess(_snake_case   )
															_UpperCAmelCase									=  self.model(
															    _snake_case					,					_snake_case					,					scales_yx=_snake_case					,					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(_snake_case   )
																					_UpperCAmelCase									=  self.schema.encode_batch(_snake_case   )
																					writer.write_batch(_snake_case   )
															if TEST:
																					break
															# finalizer the writer
									if not TEST:
															_UpperCAmelCase									=  writer.finalize()
															print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""   )




def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						try:
												_UpperCAmelCase									=  json.load(snake_case_		)
												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_			:       Any						       =				Extract(sys.argv[1:])
	extract()
	if not TEST:
		lowercase_			:       Dict						       =				datasets.Dataset.from_file(extract.outputfile)
		# wala!
		# print(np.array(dataset[0:2]["roi_features"]).shape)










def 							__SCREAMING_SNAKE_CASE						(    snake_case_ = 99_8001		):

						'''simple docstring'''


						for number in range(n - 1			,					9999			,					-1		):
												_UpperCAmelCase									=  str(snake_case_		)

												# checks whether 'str_number' is a palindrome.
												if str_number == str_number[::-1]:
																		_UpperCAmelCase									=  999

																		# if 'number' is a product of two 3-digit numbers
																		# then number is the answer otherwise fetch next number.
																		while divisor != 99:
																								if (number % divisor == 0) and (len(str(number // divisor		)		) == 3.0):
																														return number
																								divisor -= 1
						raise ValueError("That number is larger than our acceptable range."		)


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




