

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						:Any            = False
__a						:int            = Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__a						:Any            = 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      _a				:




      """simple docstring"""

      def __init__(      self       :							Optional[Any]	, UpperCAmelCase       :							str=sys.argv[1:]  ):
            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 		__A  (      self       :							Dict	, UpperCAmelCase       :							int  ):
            A_	     =							int(file.split("."  )[0].split("_"  )[-1]  )
            A_	     =							os.path.join(self.inputdir	, UpperCAmelCase  )
            return (img_id, filepath)





      @property
      def 		__A  (      self       :							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(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       :							Union[str, Any]  ):
            # 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 			__snake_case	(   __UpperCamelCase  :   str						):



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


def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

      A_	     =							len(__UpperCamelCase						)
      A_	     =							[0] * length
      A_	     =							[[False for i in range(__UpperCamelCase						)] for j in range(__UpperCamelCase						)]
      for i, c in enumerate(__UpperCamelCase						):
            A_	     =							i
            for j in range(i + 1						):
                  if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
                        A_	     =							True
                        A_	     =							min(__UpperCamelCase							,0 if j == 0 else (cut[j - 1] + 1)						)
            A_	     =							mincut
      return cut[length - 1]


if __name__ == "__main__":
      __a						:Union[str, Any]            = input('Enter the string: ').strip()
      __a						:Tuple            = find_minimum_partitions(s)
      print(F"Minimum number of partitions required for the '{s}' is {ans}")