





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			:List[str]	 =     False
__lowerCAmelCase			:Dict	 =     Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__lowerCAmelCase			:Tuple	 =     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, 20_48), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)






class 							_a:







		def __init__(     self			,    __snake_case=sys.argv[1:]					)   ->   Optional[int]:


							'''simple docstring'''

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

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

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







		def 							lowercase							(     self			,    __snake_case					)   ->   Dict:


							'''simple docstring'''

							_snake_case							:       List[str]												= int(file.split("."					)[0].split("_"					)[-1]					)
							_snake_case							:       Optional[int]												= os.path.join(self.inputdir			,    __snake_case					)
							return (img_id, filepath)







		@property
		def 							lowercase							(     self					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Tuple												= []
							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(__snake_case					)					)
												if len(__snake_case					) == self.batch:
																	_snake_case							:       Dict												= batch
																	_snake_case							:       Optional[int]												= []
																	yield list(map(__snake_case			,    zip(*__snake_case					)					)					)

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







		def __call__(     self					)   ->   List[str]:


							'''simple docstring'''

							if not TEST:
												_snake_case							:       int												= 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							:       int												= self.preprocess(__snake_case					)
												_snake_case							:       Tuple												= self.model(
												    __snake_case			,    __snake_case			,    scales_yx=__snake_case			,    padding="max_detections"			,    max_detections=self.config.MAX_DETECTIONS			,    pad_value=0			,    return_tensors="np"			,    location="cpu"			,    )
												_snake_case							:       List[str]												= output_dict.pop("normalized_boxes"					)
												if not TEST:
																	_snake_case							:       Tuple												= np.array(__snake_case					)
																	_snake_case							:       Optional[int]												= self.schema.encode_batch(__snake_case					)
																	writer.write_batch(__snake_case					)
												if TEST:
																	break
												# finalizer the writer
							if not TEST:
												_snake_case							,			_snake_case							:       Tuple												= writer.finalize()
												print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""					)







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


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






from typing import TYPE_CHECKING

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


__lowerCAmelCase			:Optional[int]	 =     {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase			:Dict	 =     ['MobileNetV1FeatureExtractor']
							__lowerCAmelCase			:List[Any]	 =     ['MobileNetV1ImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase			:Optional[int]	 =     [
							    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MobileNetV1ForImageClassification',
							    'MobileNetV1Model',
							    'MobileNetV1PreTrainedModel',
							    'load_tf_weights_in_mobilenet_v1',
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							__lowerCAmelCase			:Optional[int]	 =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)