'''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
snake_case  =			False
snake_case  =			Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
snake_case  =			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        SCREAMING_SNAKE_CASE   :
  """simple docstring"""







  def __init__(  self   :					Tuple					,					__lowerCAmelCase   :					Union[str, Any]=sys.argv[1:]					):


        """simple docstring"""



        _lowerCAmelCase		   =   None
        _lowerCAmelCase		   =   None
        _lowerCAmelCase		   =   None
        _lowerCAmelCase		   =   1
        _lowerCAmelCase   ,  _lowerCAmelCase		   =   getopt.getopt(__lowerCAmelCase					,					'i:o:b:s'					,					['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']					)
        for opt, arg in opts:
              if opt in ("-i", "--inputdir"):
                    _lowerCAmelCase		   =   arg
              elif opt in ("-o", "--outfile"):
                    _lowerCAmelCase		   =   arg
              elif opt in ("-b", "--batch_size"):
                    _lowerCAmelCase		   =   int(__lowerCAmelCase					)
              elif opt in ("-s", "--subset_list"):
                    _lowerCAmelCase		   =   arg

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

        _lowerCAmelCase		   =   CONFIG
        if torch.cuda.is_available():
              _lowerCAmelCase		   =   'cuda'
        _lowerCAmelCase		   =   os.path.realpath(__lowerCAmelCase					)
        _lowerCAmelCase		   =   os.path.realpath(__lowerCAmelCase					)
        _lowerCAmelCase		   =   Preprocess(self.config					)
        _lowerCAmelCase		   =   GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'					,					config=self.config					)
        _lowerCAmelCase		   =   batch_size if batch_size != 0 else 1
        _lowerCAmelCase		   =   DEFAULT_SCHEMA



  def   a   (  self   :					Optional[int]					,					__lowerCAmelCase   :					List[str]					):


        """simple docstring"""



        _lowerCAmelCase		   =   int(file.split('.'					)[0].split('_'					)[-1]					)
        _lowerCAmelCase		   =   os.path.join(self.inputdir					,					__lowerCAmelCase					)
        return (img_id, filepath)



  @property
  def   a   (  self   :					Optional[int]					):


        """simple docstring"""



        _lowerCAmelCase		   =   []
        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(__lowerCAmelCase					)					)
              if len(__lowerCAmelCase					) == self.batch:
                    _lowerCAmelCase		   =   batch
                    _lowerCAmelCase		   =   []
                    yield list(map(__lowerCAmelCase					,					zip(*__lowerCAmelCase					)					)					)

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



  def __call__(  self   :					int					):


        """simple docstring"""



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







def 			A_   ( _lowerCamelCase			:							int    ):
      try:
            _lowerCAmelCase		   =   json.load(_lowerCamelCase    )
            try:
                  _lowerCAmelCase		   =   list(data.keys()    )
            except Exception:
                  _lowerCAmelCase		   =   [d['img_id'] for d in data]
      except Exception:
            try:
                  _lowerCAmelCase		   =   eval(stream.read()    )
            except Exception:
                  _lowerCAmelCase		   =   stream.read().split('\n'    )
      return data


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






'''simple docstring'''




snake_case  =			{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

snake_case  =			{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}







def 			A_   ( _lowerCamelCase			:							dict  ,  _lowerCamelCase			:							int  ,  _lowerCamelCase			:							list    ):
      _lowerCAmelCase		   =   True
      _lowerCAmelCase		   =   []

      for neighbour in graph[vert]:
            if not visited[neighbour]:
                  connected_verts += dfs(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase    )

      return [vert, *connected_verts]







def 			A_   ( _lowerCamelCase			:							dict    ):
      _lowerCAmelCase		   =   len(_lowerCamelCase    )
      _lowerCAmelCase		   =   graph_size * [False]
      _lowerCAmelCase		   =   []

      for i in range(_lowerCamelCase    ):
            if not visited[i]:
                  _lowerCAmelCase		   =   dfs(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase    )
                  components_list.append(_lowerCamelCase    )

      return components_list


if __name__ == "__main__":
 import doctest

 doctest.testmod()





