


"""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      __lowerCamelCase	:

     '''simple docstring'''


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

          _a     						=					CONFIG
          if torch.cuda.is_available():
               _a     						=					'''cuda'''
          _a     						=					os.path.realpath(__UpperCAmelCase				)
          _a     						=					os.path.realpath(__UpperCAmelCase				)
          _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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Union[str, Any]:
          _a     						=					int(file.split('''.'''				)[0].split('''_'''				)[-1]				)
          _a     						=					os.path.join(self.inputdir     ,					__UpperCAmelCase				)
          return (img_id, filepath)


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          _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(__UpperCAmelCase				)				)
               if len(__UpperCAmelCase				) == self.batch:
                    _a     						=					batch
                    _a     						=					[]
                    yield list(map(__UpperCAmelCase     ,					zip(*__UpperCAmelCase				)				)				)

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





     def __call__(   self				)							->				str:
          # make writer
          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(__UpperCAmelCase				)
               _a     						=					self.model(
                   __UpperCAmelCase     ,					__UpperCAmelCase     ,					scales_yx=__UpperCAmelCase     ,					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(__UpperCAmelCase				)
                    _a     						=					self.schema.encode_batch(__UpperCAmelCase				)
                    writer.write_batch(__UpperCAmelCase				)
               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 >> 20} mb'				)







def    A_ ( _lowerCAmelCase  :       Dict							):






     """simple docstring"""

     try:
          _a     						=					json.load(_lowerCAmelCase							)
          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__":
   __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"""






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

     if len(_lowerCAmelCase							) == 0:
          return len(_lowerCAmelCase							)

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

          for j, ca in enumerate(_lowerCAmelCase							):
               # Calculate insertions, deletions and substitutions
               _a     						=					previous_row[j + 1] + 1
               _a     						=					current_row[j] + 1
               _a     						=					previous_row[j] + (ca != ca)

               # Get the minimum to append to the current row
               current_row.append(min(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)							)

          # Store the previous row
          _a     						=					current_row

     # Returns the last element (distance)
     return previous_row[-1]


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')