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


  """simple docstring"""





  def __init__(					self	,		a__=sys.argv[1:]		):
    _lowerCamelCase      =						None
    _lowerCamelCase      =						None
    _lowerCamelCase      =						None
    _lowerCamelCase      =						1
    _lowerCamelCase    ,    _lowerCamelCase      =						getopt.getopt(a__	,		'''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(a__		)
      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(a__		), f'''{outputfile}'''
    if subset_list is not None:
      with open(os.path.realpath(a__		)		) as f:
        _lowerCamelCase      =						{self._vqa_file_split()[0] for x in tryload(a__		)}
    else:
      _lowerCamelCase      =						None

    _lowerCamelCase      =						CONFIG
    if torch.cuda.is_available():
      _lowerCamelCase      =						'''cuda'''
    _lowerCamelCase      =						os.path.realpath(a__		)
    _lowerCamelCase      =						os.path.realpath(a__		)
    _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     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						int(file.split('''.'''		)[0].split('''_'''		)[-1]		)
    _lowerCamelCase      =						os.path.join(self.inputdir	,		a__		)
    return (img_id, filepath)


  @property
  def     _UpperCAmelCase  (					self		):
    _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(a__		)		)
      if len(a__		) == self.batch:
        _lowerCamelCase      =						batch
        _lowerCamelCase      =						[]
        yield list(map(a__	,		zip(*a__		)		)		)

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


  def __call__(					self		):


    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(a__		)
      _lowerCamelCase      =						self.model(
          a__	,		a__	,		scales_yx=a__	,		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(a__		)
        _lowerCamelCase      =						self.schema.encode_batch(a__		)
        writer.write_batch(a__		)
      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 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  try:
    _lowerCamelCase      =						json.load(lowercase__  )
    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__":
  _UpperCAmelCase    			=					Extract(sys.argv[1:])
  extract()
  if not TEST:
    _UpperCAmelCase    			=					datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)









class lowerCAmelCase_       :







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase=None       ):



				'''simple docstring'''


				a__					=  data
				a__					=  None







	def __repr__(	self       ):



				'''simple docstring'''


				a__					=  """"""
				a__					=  self
				while temp:
							string_rep += f'<{temp.data}> ---> '
							a__					=  temp.next
				string_rep += "<END>"
				return string_rep




def    UpperCAmelCase					(  lowercase__						:					Optional[int]	):



			'''simple docstring'''





			if not elements_list:
						raise Exception("""The Elements List is empty"""	)

			# Set first element as Head
			a__					=  Node(elements_list[0]	)
			a__					=  head
			# Loop through elements from position 1
			for data in elements_list[1:]:
						a__					=  Node(lowercase__	)
						a__					=  current.next
			return head


_lowercase				:					List[Any]     					=[1, 3, 5, 32, 44, 12, 43]
print(f'''List: {list_data}''')
print("""Creating Linked List from List.""")
_lowercase				:					Dict     					=make_linked_list(list_data)
print("""Linked List:""")





print(linked_list)

