




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
UpperCamelCase__		=						False
UpperCamelCase__		=						Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
UpperCamelCase__		=						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_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)


class     __SCREAMING_SNAKE_CASE							:

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







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							None
						lowerCamelCase_							:		List[Any]							       =							None
						lowerCamelCase_							:		int							       =							None
						lowerCamelCase_							:		int							       =							1
						lowerCamelCase_       ,      lowerCamelCase_							:		Optional[Any]							       =							getopt.getopt(__lowercase			,	'i:o:b:s'			,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
						for opt, arg in opts:
							if opt in ("-i", "--inputdir"):
								lowerCamelCase_							:		Optional[Any]							       =							arg
							elif opt in ("-o", "--outfile"):
								lowerCamelCase_							:		Optional[Any]							       =							arg
							elif opt in ("-b", "--batch_size"):
								lowerCamelCase_							:		Union[str, Any]							       =							int(__lowercase )
							elif opt in ("-s", "--subset_list"):
								lowerCamelCase_							:		Optional[int]							       =							arg

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

						lowerCamelCase_							:		Optional[int]							       =							CONFIG
						if torch.cuda.is_available():
							lowerCamelCase_							:		Optional[int]							       =							'cuda'
						lowerCamelCase_							:		Dict							       =							os.path.realpath(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							os.path.realpath(__lowercase )
						lowerCamelCase_							:		List[str]							       =							Preprocess(self.config )
						lowerCamelCase_							:		List[str]							       =							GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'			,	config=self.config )
						lowerCamelCase_							:		str							       =							batch_size if batch_size != 0 else 1
						lowerCamelCase_							:		Optional[int]							       =							DEFAULT_SCHEMA

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							int(file.split('.' )[0].split('_' )[-1] )
						lowerCamelCase_							:		Optional[int]							       =							os.path.join(self.inputdir			,	__lowercase )
						return (img_id, filepath)

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							[]
						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(__lowercase ) )
							if len(__lowercase ) == self.batch:
								lowerCamelCase_							:		Optional[int]							       =							batch
								lowerCamelCase_							:		List[str]							       =							[]
								yield list(map(__lowercase			,	zip(*__lowercase ) ) )

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





					def __call__(    self ):







						'''simple docstring'''




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






def  snake_case (							A      )							->							Union[str, Any]:




	'''simple docstring'''


	try:
		lowerCamelCase_							:		str							       =							json.load(A      )
		try:
			lowerCamelCase_							:		Optional[Any]							       =							list(data.keys()      )
		except Exception:
			lowerCamelCase_							:		int							       =							[d['img_id'] for d in data]
	except Exception:
		try:
			lowerCamelCase_							:		List[Any]							       =							eval(stream.read()      )
		except Exception:
			lowerCamelCase_							:		Tuple							       =							stream.read().split('\n'      )
	return data


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






from typing import Callable






def  snake_case (							A      )							->							List[str]:




	'''simple docstring'''


	def wrapper(A      ):
		lowerCamelCase_							:		Dict							       =							function_with_docstring.__doc__
		return target_function

	return wrapper
