
"""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, 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     ,						_lowerCamelCase=sys.argv[1:]					) ->       str:




										'''simple docstring'''
										__lowercase						      =					None
										__lowercase						      =					None
										__lowercase						      =					None
										__lowercase						      =					1
										__lowercase      ,				__lowercase						      =					getopt.getopt(_lowerCamelCase     ,						"i:o:b:s"     ,						["inputdir=", "outfile=", "batch_size=", "subset_list="]					)
										for opt, arg in opts:
													if opt in ("-i", "--inputdir"):
																__lowercase						      =					arg
													elif opt in ("-o", "--outfile"):
																__lowercase						      =					arg
													elif opt in ("-b", "--batch_size"):
																__lowercase						      =					int(_lowerCamelCase					)
													elif opt in ("-s", "--subset_list"):
																__lowercase						      =					arg

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

										__lowercase						      =					CONFIG
										if torch.cuda.is_available():
													__lowercase						      =					"cuda"
										__lowercase						      =					os.path.realpath(_lowerCamelCase					)
										__lowercase						      =					os.path.realpath(_lowerCamelCase					)
										__lowercase						      =					Preprocess(self.config					)
										__lowercase						      =					GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"     ,						config=self.config					)
										__lowercase						      =					batch_size if batch_size != 0 else 1
										__lowercase						      =					DEFAULT_SCHEMA
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					int(file.split("."					)[0].split("_"					)[-1]					)
										__lowercase						      =					os.path.join(self.inputdir     ,						_lowerCamelCase					)
										return (img_id, filepath)
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					[]
										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(_lowerCamelCase					)					)
													if len(_lowerCamelCase					) == self.batch:
																__lowercase						      =					batch
																__lowercase						      =					[]
																yield list(map(_lowerCamelCase     ,						zip(*_lowerCamelCase					)					)					)

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





							def __call__(    self					) ->       Optional[int]:




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





def     lowerCAmelCase__ (		__magic_name__			)	->str:
			try:
						__lowercase						      =					json.load(__magic_name__			)
						try:
									__lowercase						      =					list(data.keys()			)
						except Exception:
									__lowercase						      =					[d["img_id"] for d in data]
			except Exception:
						try:
									__lowercase						      =					eval(stream.read()			)
						except Exception:
									__lowercase						      =					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"""






def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->list:
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					(
			    first_input_list_length
			    if first_input_list_length > second_input_list_length
			    else second_input_list_length
			)
			__lowercase						      =					[]
			for char_count in range(__magic_name__			):
						if char_count < first_input_list_length:
									output_result_list.append(first_input_list[char_count]			)
						if char_count < second_input_list_length:
									output_result_list.append(second_input_list[char_count]			)

			return output_result_list


if __name__ == "__main__":
					print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





