


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  lowerCamelCase__  :





     def __init__(self						,	UpperCAmelCase=sys.argv[1:]			)	->  Any:
      _lowercase				 =None
      _lowercase				 =None
      _lowercase				 =None
      _lowercase				 =1
      _lowercase ,       _lowercase				 =getopt.getopt(UpperCAmelCase						,	'''i:o:b:s'''						,	['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']			)
      for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
        _lowercase				 =arg
       elif opt in ("-o", "--outfile"):
        _lowercase				 =arg
       elif opt in ("-b", "--batch_size"):
        _lowercase				 =int(UpperCAmelCase			)
       elif opt in ("-s", "--subset_list"):
        _lowercase				 =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:
        _lowercase				 ={self._vqa_file_split()[0] for x in tryload(UpperCAmelCase			)}
      else:
       _lowercase				 =None

      _lowercase				 =CONFIG
      if torch.cuda.is_available():
       _lowercase				 ='''cuda'''
      _lowercase				 =os.path.realpath(UpperCAmelCase			)
      _lowercase				 =os.path.realpath(UpperCAmelCase			)
      _lowercase				 =Preprocess(self.config			)
      _lowercase				 =GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''						,	config=self.config			)
      _lowercase				 =batch_size if batch_size != 0 else 1
      _lowercase				 =DEFAULT_SCHEMA





     def 	__A   (self						,	UpperCAmelCase			)	->  Any:
      _lowercase				 =int(file.split('''.'''			)[0].split('''_'''			)[-1]			)
      _lowercase				 =os.path.join(self.inputdir						,	UpperCAmelCase			)
      return (img_id, filepath)





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



def 							UpperCAmelCase_   (				__snake_case		)	->     Optional[Any]:







 """simple docstring"""


 try:
  _lowercase				 =json.load(__snake_case		)
  try:
   _lowercase				 =list(data.keys()		)
  except Exception:
   _lowercase				 =[d['''img_id'''] for d in data]
 except Exception:
  try:
   _lowercase				 =eval(stream.read()		)
  except Exception:
   _lowercase				 =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)





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


UpperCAmelCase__        =				logging.get_logger(__name__)

UpperCAmelCase__        =				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__        =				{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase__        =				{
    '''microsoft/mpnet-base''': 512,
}

UpperCAmelCase__        =				{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__      =     PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__      =     PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE__      =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__      =     MPNetTokenizer
     SCREAMING_SNAKE_CASE__      =     ['''input_ids''', '''attention_mask''']





     def __init__(self						,	UpperCAmelCase=None						,	UpperCAmelCase=None						,	UpperCAmelCase=True						,	UpperCAmelCase="<s>"						,	UpperCAmelCase="</s>"						,	UpperCAmelCase="</s>"						,	UpperCAmelCase="<s>"						,	UpperCAmelCase="[UNK]"						,	UpperCAmelCase="<pad>"						,	UpperCAmelCase="<mask>"						,	UpperCAmelCase=True						,	UpperCAmelCase=None						,	**UpperCAmelCase						,	)	->  Optional[int]:
      super().__init__(
          UpperCAmelCase						,	tokenizer_file=UpperCAmelCase						,	do_lower_case=UpperCAmelCase						,	bos_token=UpperCAmelCase						,	eos_token=UpperCAmelCase						,	sep_token=UpperCAmelCase						,	cls_token=UpperCAmelCase						,	unk_token=UpperCAmelCase						,	pad_token=UpperCAmelCase						,	mask_token=UpperCAmelCase						,	tokenize_chinese_chars=UpperCAmelCase						,	strip_accents=UpperCAmelCase						,	**UpperCAmelCase						,	)

      _lowercase				 =json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
      if (
          pre_tok_state.get('''lowercase'''						,	UpperCAmelCase			) != do_lower_case
          or pre_tok_state.get('''strip_accents'''						,	UpperCAmelCase			) != strip_accents
      ):
       _lowercase				 =getattr(UpperCAmelCase						,	pre_tok_state.pop('''type'''			)			)
       _lowercase				 =do_lower_case
       _lowercase				 =strip_accents
       _lowercase				 =pre_tok_class(**UpperCAmelCase			)

      _lowercase				 =do_lower_case





     @property
     def 	__A   (self			)	->  str:
      if self._mask_token is None:
       if self.verbose:
        logger.error('''Using mask_token, but it is not set yet.'''			)
       return None
      return str(self._mask_token			)





     @mask_token.setter
     def 	__A   (self						,	UpperCAmelCase			)	->  Optional[int]:
      _lowercase				 =AddedToken(UpperCAmelCase						,	lstrip=UpperCAmelCase						,	rstrip=UpperCAmelCase			) if isinstance(UpperCAmelCase						,	UpperCAmelCase			) else value
      _lowercase				 =value





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase=None			)	->  List[str]:
      _lowercase				 =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
       return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None			)	->  List[int]:
      _lowercase				 =[self.sep_token_id]
      _lowercase				 =[self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep			) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None			)	->  Tuple[str]:
      _lowercase				 =self._tokenizer.model.save(UpperCAmelCase						,	name=UpperCAmelCase			)
      return tuple(UpperCAmelCase			)

