




import torch

from transformers import AutoTokenizer

class   UpperCAmelCase    (      a_ ):






   def __init__(						self :							Optional[int]    ,       UpperCamelCase_ :							Any  )->      str:
     _lowercase:			List[str]      =     AutoTokenizer.from_pretrained(UpperCamelCase_  )






   def 			_a    (						self :							Optional[int]    ,       UpperCamelCase_ :							List[str]  )->      str:

     if isinstance(UpperCamelCase_    ,       UpperCamelCase_  ) and all(isinstance(_x    ,       UpperCamelCase_  ) for _x in x  ):
       _lowercase:			Any      =     None
       for l in x:
         _lowercase:			List[str]      =     self.tokenizer(
             UpperCamelCase_    ,       padding='max_length'    ,       max_length=3_84    ,       truncation=UpperCamelCase_    ,       return_tensors='pt'    ,       )
         _lowercase:			Optional[Any]      =     torch.tensor([len(UpperCamelCase_  )]  )
         if d is not None:
           for k in d.keys():
             _lowercase:			Dict      =     torch.cat((d[k], t[k])    ,       0  )
         else:
           _lowercase:			Optional[int]      =     t

       _lowercase:			Optional[int]      =     torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'  )  )
       _lowercase:			Dict      =     torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'  )  )

     elif isinstance(UpperCamelCase_    ,       UpperCamelCase_  ) and all(isinstance(_x    ,       UpperCamelCase_  ) for _x in x  ):
       _lowercase:			int      =     self.tokenizer(
           UpperCamelCase_    ,       padding='max_length'    ,       max_length=3_84    ,       truncation=UpperCamelCase_    ,       return_tensors='pt'    ,       )

     else:
       raise Exception(
           'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
           ' strings` for supports are supported.'  )

     return d






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							Tuple    ,       UpperCamelCase_ :							Tuple    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							List[str]=0.70  )->      Optional[int]:
     _lowercase:			Union[str, Any]      =     []
     for idx in range(len(W_query['input_ids']  )  ):
       _lowercase:			List[Any]      =     range(p_start.shape[1]  )

       _lowercase:			List[str]      =     []
       for start_id in start_indexes:
         for end_id in end_indexes:
           if start_id < end_id:
             output.append(
                 (
                     start_id,
                     end_id,
                     p_start[idx][start_id].item(),
                     p_end[idx][end_id].item(),
                 )  )

       output.sort(key=lambda UpperCamelCase_  : (tup[2] * tup[3])    ,       reverse=UpperCamelCase_  )
       _lowercase:			Dict      =     []
       for k in range(len(UpperCamelCase_  )  ):
         if output[k][2] * output[k][3] >= thresh:
           _lowercase:			int      =     output[k][0], output[k][1]
           _lowercase:			Optional[Any]      =     self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]  )
           temp.append((decoded, output[k][2] * output[k][3])  )

       final_outputs.append(UpperCamelCase_  )

     return final_outputs

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)



