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, 20_48), 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							:		Optional[Any]							,      lowerCamelCase							:		int=sys.argv[1:]	)			->				int:
        _UpperCamelCase  :							Tuple	    =	None
        _UpperCamelCase  :							Dict	    =	None
        _UpperCamelCase  :							List[Any]	    =	None
        _UpperCamelCase  :							Dict	    =	1
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	getopt.getopt(lowerCamelCase							,      'i:o:b:s'							,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']	)
        for opt, arg in opts:
               if opt in ("-i", "--inputdir"):
                      _UpperCamelCase  :							Dict	    =	arg
               elif opt in ("-o", "--outfile"):
                      _UpperCamelCase  :							int	    =	arg
               elif opt in ("-b", "--batch_size"):
                      _UpperCamelCase  :							List[str]	    =	int(lowerCamelCase	)
               elif opt in ("-s", "--subset_list"):
                      _UpperCamelCase  :							Dict	    =	arg

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

        _UpperCamelCase  :							Any	    =	CONFIG
        if torch.cuda.is_available():
               _UpperCamelCase  :							List[str]	    =	'cuda'
        _UpperCamelCase  :							int	    =	os.path.realpath(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	os.path.realpath(lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	Preprocess(self.config	)
        _UpperCamelCase  :							Tuple	    =	GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'							,      config=self.config	)
        _UpperCamelCase  :							List[Any]	    =	batch_size if batch_size != 0 else 1
        _UpperCamelCase  :							Any	    =	DEFAULT_SCHEMA

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				int:
        _UpperCamelCase  :							Tuple	    =	int(file.split('.'	)[0].split('_'	)[-1]	)
        _UpperCamelCase  :							List[Any]	    =	os.path.join(self.inputdir							,      lowerCamelCase	)
        return (img_id, filepath)

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							str	    =	[]
        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(lowerCamelCase	)	)
               if len(lowerCamelCase	) == self.batch:
                      _UpperCamelCase  :							Dict	    =	batch
                      _UpperCamelCase  :							List[Any]	    =	[]
                      yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

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

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







def SCREAMING_SNAKE_CASE					(  lowercase_:						str					):
       try:
              _UpperCamelCase  :							Optional[Any]	    =	json.load(lowercase_					)
              try:
                     _UpperCamelCase  :							Union[str, Any]	    =	list(data.keys()					)
              except Exception:
                     _UpperCamelCase  :							Optional[Any]	    =	[d['img_id'] for d in data]
       except Exception:
              try:
                     _UpperCamelCase  :							Any	    =	eval(stream.read()					)
              except Exception:
                     _UpperCamelCase  :							Optional[int]	    =	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)







from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime





class     __a     (       __UpperCamelCase ):
 def __init__(  self							,					*lowerCAmelCase__							,					**lowerCAmelCase__					)   ->							Any:




        '''simple docstring'''
        super().__init__(*lowerCAmelCase__							,					**lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__:  str         =  (
            next_prime(value % self.size_table					)
            if not is_prime(value % self.size_table					)
            else value % self.size_table
        )  # gt = bigger than
        return next_prime_gt - (data % next_prime_gt)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        return (increment * self.__hash_function_a(lowerCAmelCase__							,					lowerCAmelCase__					)) % self.size_table
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__=None					)   ->							str:




        '''simple docstring'''
        lowercase__:  Dict         =  1
        lowercase__:  str         =  self.hash_function(lowerCAmelCase__					)

        while self.values[new_key] is not None and self.values[new_key] != key:
               lowercase__:  List[Any]         =  (
                   self.__hash_double_function(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
                   if self.balanced_factor() >= self.lim_charge
                   else None
               )
               if new_key is None:
                      break
               else:
                      i += 1

        return new_key


