




'''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
_lowerCamelCase							:						int							     =  False
_lowerCamelCase							:						Any							     =  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_lowerCamelCase							:						List[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,  __UpperCamelCase=sys.argv[1:]			):
							_snake_case     	=	None
							_snake_case     	=	None
							_snake_case     	=	None
							_snake_case     	=	1
							_snake_case	,   _snake_case     	=	getopt.getopt(__UpperCamelCase,  "i:o:b:s",  ["inputdir=", "outfile=", "batch_size=", "subset_list="]			)
							for opt, arg in opts:
									if opt in ("-i", "--inputdir"):
											_snake_case     	=	arg
									elif opt in ("-o", "--outfile"):
											_snake_case     	=	arg
									elif opt in ("-b", "--batch_size"):
											_snake_case     	=	int(__UpperCamelCase			)
									elif opt in ("-s", "--subset_list"):
											_snake_case     	=	arg

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

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






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






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	[]
							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(__UpperCamelCase			)			)
									if len(__UpperCamelCase			) == self.batch:
											_snake_case     	=	batch
											_snake_case     	=	[]
											yield list(map(__UpperCamelCase,  zip(*__UpperCamelCase			)			)			)

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






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






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		try:
				_snake_case     	=	json.load(lowercase_  )
				try:
						_snake_case     	=	list(data.keys()  )
				except Exception:
						_snake_case     	=	[d["img_id"] for d in data]
		except Exception:
				try:
						_snake_case     	=	eval(stream.read()  )
				except Exception:
						_snake_case     	=	stream.read().split("\n"  )
		return data


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











'''simple docstring'''







def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5) + 32						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) + 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5) + 491.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) - 32) * 5 / 9						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(((float(lowercase_  ) - 32) * 5 / 9) + 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) + 459.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) - 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(((float(lowercase_  ) - 273.15) * 9 / 5) + 32						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) - 491.67) * 5 / 9						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) - 459.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 5 / 9)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 1.25 + 273.15)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 2.25 + 32)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 1.25)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 2.25 + 32 + 459.67)						,							lowercase_  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





