
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__a        = {
    'configuration_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = ['DeformableDetrFeatureExtractor']
						__a        = ['DeformableDetrImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeformableDetrForObjectDetection',
						    'DeformableDetrModel',
						    'DeformableDetrPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
												from .image_processing_deformable_detr import DeformableDetrImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deformable_detr import (
												    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeformableDetrForObjectDetection,
												    DeformableDetrModel,
												    DeformableDetrPreTrainedModel,
												)

else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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
__a        = False
__a        = Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__a        = 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 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=sys.argv[1:]	)	->					int:
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = 1
					lowercase_  ,     lowercase_					  = getopt.getopt(SCREAMING_SNAKE_CASE_	,							'''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(SCREAMING_SNAKE_CASE_	)
									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(SCREAMING_SNAKE_CASE_	), f'''{outputfile}'''
					if subset_list is not None:
									with open(os.path.realpath(SCREAMING_SNAKE_CASE_	)	) as f:
													lowercase_					  = {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE_	)}
					else:
									lowercase_					  = None

					lowercase_					  = CONFIG
					if torch.cuda.is_available():
									lowercase_					  = '''cuda'''
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					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 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	)	->					Any:
					lowercase_					  = int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
					lowercase_					  = os.path.join(self.inputdir	,							SCREAMING_SNAKE_CASE_	)
					return (img_id, filepath)
	@property
	def 						_lowercase  (	self  :      List[str]	)	->					List[Any]:
					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(SCREAMING_SNAKE_CASE_	)	)
									if len(SCREAMING_SNAKE_CASE_	) == self.batch:
													lowercase_					  = batch
													lowercase_					  = []
													yield list(map(SCREAMING_SNAKE_CASE_	,							zip(*SCREAMING_SNAKE_CASE_	)	)	)

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




	def __call__(	self  :      Any	)	->					List[str]:
					# 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(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = self.model(
									    SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							scales_yx=SCREAMING_SNAKE_CASE_	,							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(SCREAMING_SNAKE_CASE_	)
													lowercase_					  = self.schema.encode_batch(SCREAMING_SNAKE_CASE_	)
													writer.write_batch(SCREAMING_SNAKE_CASE_	)
									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 >> 2_0} mb'''	)




def     a						(							snake_case__: Optional[Any] ):


				'''simple docstring'''
				try:
								lowercase_					  = json.load(snake_case__ )
								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__":
						__a        = Extract(sys.argv[1:])
						extract()
						if not TEST:
												__a        = datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)




