


'''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_       :     Dict 							=     False
a_       :     List[str] 							=     Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a_       :     str 							=     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 			a  :
 def __init__(			self   ,					__magic_name__=sys.argv[1:]    )       ->				int:
      _a         =				None
      _a         =				None
      _a         =				None
      _a         =				1
      _a   , _a         =				getopt.getopt(__SCREAMING_SNAKE_CASE   ,					'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(__SCREAMING_SNAKE_CASE    )
           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(__SCREAMING_SNAKE_CASE    ), f'{outputfile}'
      if subset_list is not None:
           with open(os.path.realpath(__SCREAMING_SNAKE_CASE    )    ) as f:
                _a         =				{self._vqa_file_split()[0] for x in tryload(__SCREAMING_SNAKE_CASE    )}
      else:
           _a         =				None

      _a         =				CONFIG
      if torch.cuda.is_available():
           _a         =				'cuda'
      _a         =				os.path.realpath(__SCREAMING_SNAKE_CASE    )
      _a         =				os.path.realpath(__SCREAMING_SNAKE_CASE    )
      _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   ,					__magic_name__    )       ->				Union[str, Any]:
      _a         =				int(file.split('.'    )[0].split('_'    )[-1]    )
      _a         =				os.path.join(self.inputdir   ,					__SCREAMING_SNAKE_CASE    )
      return (img_id, filepath)
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      _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(__SCREAMING_SNAKE_CASE    )    )
           if len(__SCREAMING_SNAKE_CASE    ) == self.batch:
                _a         =				batch
                _a         =				[]
                yield list(map(__SCREAMING_SNAKE_CASE   ,					zip(*__SCREAMING_SNAKE_CASE    )    )    )

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




 def __call__(			self    )       ->				List[str]:

      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(__SCREAMING_SNAKE_CASE    )
           _a         =				self.model(
               __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					scales_yx=__SCREAMING_SNAKE_CASE   ,					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(__SCREAMING_SNAKE_CASE    )
                _a         =				self.schema.encode_batch(__SCREAMING_SNAKE_CASE    )
                writer.write_batch(__SCREAMING_SNAKE_CASE    )
           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__	:Tuple		) ->	Dict:




     '''simple docstring'''



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








"""simple docstring"""


def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	int:

 '''simple docstring'''






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

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

 snake_case__     				=					list(range(len(_UpperCAmelCase						) + 1						)						)

 for i, ca in enumerate(_UpperCAmelCase						):
  snake_case__     				=					[i + 1]

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

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

  # Store the previous row
  snake_case__     				=					current_row

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


if __name__ == "__main__":
   a__       :  Optional[int]       		=					input("""Enter the first word:\n""").strip()
   a__       :  int       		=					input("""Enter the second word:\n""").strip()

   a__       :  Union[str, Any]       		=					levenshtein_distance(first_word, second_word)
   print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')

