


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	:							int           =  False
__SCREAMING_SNAKE_CASE	:							Any           =  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        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    int	,							lowerCamelCase_     :    Optional[int]=sys.argv[1:]					):
								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=1
								_lowerCAmelCase						,							_lowerCAmelCase  						=getopt.getopt(lowerCamelCase_	,							"""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(lowerCamelCase_					)
												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(lowerCamelCase_					), F"{outputfile}"
								if subset_list is not None:
												with open(os.path.realpath(lowerCamelCase_					)					) as f:
																_lowerCAmelCase  						={self._vqa_file_split()[0] for x in tryload(lowerCamelCase_					)}
								else:
												_lowerCAmelCase  						=None

								_lowerCAmelCase  						=CONFIG
								if torch.cuda.is_available():
												_lowerCAmelCase  						="""cuda"""
								_lowerCAmelCase  						=os.path.realpath(lowerCamelCase_					)
								_lowerCAmelCase  						=os.path.realpath(lowerCamelCase_					)
								_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        lowerCAmelCase__			(				self     :    Optional[Any]	,							lowerCamelCase_     :    Union[str, Any]					):
								_lowerCAmelCase  						=int(file.split("""."""					)[0].split("""_"""					)[-1]					)
								_lowerCAmelCase  						=os.path.join(self.inputdir	,							lowerCamelCase_					)
								return (img_id, filepath)






				@property
				def        lowerCAmelCase__			(				self     :    Dict					):
								_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(lowerCamelCase_					)					)
												if len(lowerCamelCase_					) == self.batch:
																_lowerCAmelCase  						=batch
																_lowerCAmelCase  						=[]
																yield list(map(lowerCamelCase_	,							zip(*lowerCamelCase_					)					)					)

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






				def __call__(				self     :    Union[str, Any]					):
								# 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(lowerCamelCase_					)
												_lowerCAmelCase  						=self.model(
												    lowerCamelCase_	,							lowerCamelCase_	,							scales_yx=lowerCamelCase_	,							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(lowerCamelCase_					)
																_lowerCAmelCase  						=self.schema.encode_batch(lowerCamelCase_					)
																writer.write_batch(lowerCamelCase_					)
												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 		snake_case_   (				lowercase__     :    Any     ):






				'''simple docstring'''






				try:
								_lowerCAmelCase  						=json.load(lowercase__     )
								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	:							Optional[int]           =  Extract(sys.argv[1:])
		extract()
		if not TEST:
				__SCREAMING_SNAKE_CASE	:							Union[str, Any]           =  datasets.Dataset.from_file(extract.outputfile)
				# wala!
				# print(np.array(dataset[0:2]["roi_features"]).shape)





def 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=53_81
				for x in s:
								_lowerCAmelCase  						=((hash_value << 5) + hash_value) + ord(lowercase__     )
				return hash_value & 0Xffff_ffff

