



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, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)



class _A  :





		def __init__(     self ,       __UpperCAmelCase=sys.argv[1:]				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     None
					__UpperCAmelCase   :      Optional[Any]										=     None
					__UpperCAmelCase   :      Optional[int]										=     None
					__UpperCAmelCase   :      Dict										=     1
					__UpperCAmelCase   :      List[Any]										=     getopt.getopt(__UpperCAmelCase ,       """i:o:b:s""" ,       ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]				)
					for opt, arg in opts:
								if opt in ("-i", "--inputdir"):
											__UpperCAmelCase   :      int										=     arg
								elif opt in ("-o", "--outfile"):
											__UpperCAmelCase   :      Optional[int]										=     arg
								elif opt in ("-b", "--batch_size"):
											__UpperCAmelCase   :      int										=     int(__UpperCAmelCase				)
								elif opt in ("-s", "--subset_list"):
											__UpperCAmelCase   :      List[Any]										=     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:
											__UpperCAmelCase   :      int										=     {self._vqa_file_split()[0] for x in tryload(__UpperCAmelCase				)}
					else:
								__UpperCAmelCase   :      int										=     None

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





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     List[str]:

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





		@property
		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     []
					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:
											__UpperCAmelCase   :      Optional[int]										=     batch
											__UpperCAmelCase   :      Tuple										=     []
											yield list(map(__UpperCAmelCase ,       zip(*__UpperCAmelCase				)				)				)

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





		def __call__(     self				)       ->     str:

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


def   lowercase_	(   lowerCAmelCase__	:  str ):

			"""simple docstring"""





			try:
						__UpperCAmelCase   :      List[Any]										=     json.load(lowerCAmelCase__ )
						try:
									__UpperCAmelCase   :      Tuple										=     list(data.keys() )
						except Exception:
									__UpperCAmelCase   :      Dict										=     [d["""img_id"""] for d in data]
			except Exception:
						try:
									__UpperCAmelCase   :      Dict										=     eval(stream.read() )
						except Exception:
									__UpperCAmelCase   :      List[str]										=     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)









'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_UpperCamelCase											=			logging.get_logger(__name__)



class _A  (    __SCREAMING_SNAKE_CASE    ):





		def __init__(     self ,       *__UpperCAmelCase ,       **__UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					warnings.warn(
					    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
					    """ Please use MobileNetV2ImageProcessor instead.""" ,       __UpperCAmelCase ,       )
					super().__init__(*__UpperCAmelCase ,       **__UpperCAmelCase				)




