

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

class        _UpperCamelCase	:


  """simple docstring"""





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


         '''simple docstring'''





         __magic_name__			:		int        =     None
         __magic_name__			:		int        =     None
         __magic_name__			:		Optional[Any]        =     None
         __magic_name__			:		Optional[Any]        =     1
         __magic_name__			:		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"):
                       __magic_name__			:		List[Any]        =     arg
                elif opt in ("-o", "--outfile"):
                       __magic_name__			:		Any        =     arg
                elif opt in ("-b", "--batch_size"):
                       __magic_name__			:		List[str]        =     int(snake_case      )
                elif opt in ("-s", "--subset_list"):
                       __magic_name__			:		Tuple        =     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:
                       __magic_name__			:		Dict        =     {self._vqa_file_split()[0] for x in tryload(snake_case      )}
         else:
                __magic_name__			:		Tuple        =     None

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



  def 							_UpperCAmelCase      ( self					: int       ,       snake_case					: List[str]      )	->			Tuple:


         '''simple docstring'''





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



  @property
  def 							_UpperCAmelCase      ( self					: List[Any]      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		Optional[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(snake_case      )      )
                if len(snake_case      ) == self.batch:
                       __magic_name__			:		Any        =     batch
                       __magic_name__			:		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					: int      )	->			Any:


         '''simple docstring'''





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







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[Any]   )						->     Dict:



       """simple docstring"""







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








"""simple docstring"""



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__)






