



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								=   False
__lowerCamelCase								=   Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__lowerCamelCase								=   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 						_snake_case :





	"""simple docstring"""



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




			"""simple docstring"""







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

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

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




			"""simple docstring"""







			_A          =      int(file.split('''.'''   )[0].split('''_'''   )[-1]   )
			_A          =      os.path.join(self.inputdir		,      a   )
			return (img_id, filepath)
	@property
	def lowercase_						(  self   )		->							Optional[Any]:




			"""simple docstring"""







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

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



	def __call__(  self   )		->							Any:




			"""simple docstring"""







			if not TEST:
					_A          =      datasets.ArrowWriter(features=self.schema		,      path=self.outputfile   )
			# do file generator
			for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
					_A      ,				_A      ,				_A          =      self.preprocess(a   )
					_A          =      self.model(
					    a		,      a		,      scales_yx=a		,      padding='''max_detections'''		,      max_detections=self.config.MAX_DETECTIONS		,      pad_value=0		,      return_tensors='''np'''		,      location='''cpu'''		,      )
					_A          =      output_dict.pop('''normalized_boxes'''   )
					if not TEST:
							_A          =      np.array(a   )
							_A          =      self.schema.encode_batch(a   )
							writer.write_batch(a   )
					if TEST:
							break
					# finalizer the writer
			if not TEST:
					_A      ,				_A          =      writer.finalize()
					print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''   )







def       UpperCAmelCase__	(							__snake_case							)				->	str:
		try:
				_A          =      json.load(__snake_case							)
				try:
						_A          =      list(data.keys()							)
				except Exception:
						_A          =      [d['''img_id'''] for d in data]
		except Exception:
				try:
						_A          =      eval(stream.read()							)
				except Exception:
						_A          =      stream.read().split('''\n'''							)
		return data


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




import warnings

from .generation import FlaxGenerationMixin



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 warnings.warn(
     '''Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'''   ,lowerCamelCase   ,)