




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__						:       Union[str, Any]							=    False
UpperCAmelCase__						:       List[Any]							=    Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
UpperCAmelCase__						:       Union[str, 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      UpperCAmelCase						:






   def __init__(	self   :       int						,					UpperCamelCase_   :       str=sys.argv[1:]     )->			Optional[int]:
     _lowercase: List[Any]						     =    None
     _lowercase: Union[str, Any]						     =    None
     _lowercase: Union[str, Any]						     =    None
     _lowercase: List[Any]						     =    1
     _lowercase: str						     =    getopt.getopt(__a						,					'i:o:b:s'						,					['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']     )
     for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
         _lowercase: List[str]						     =    arg
       elif opt in ("-o", "--outfile"):
         _lowercase: Any						     =    arg
       elif opt in ("-b", "--batch_size"):
         _lowercase: Optional[Any]						     =    int(__a     )
       elif opt in ("-s", "--subset_list"):
         _lowercase: Any						     =    arg

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

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






   def  _a						(	self   :       Tuple						,					UpperCamelCase_   :       List[str]     )->			List[str]:
     _lowercase: Optional[int]						     =    int(file.split('.'     )[0].split('_'     )[-1]     )
     _lowercase: int						     =    os.path.join(self.inputdir						,					__a     )
     return (img_id, filepath)






   @property
   def  _a						(	self   :       List[Any]     )->			Tuple:
     _lowercase: Optional[Any]						     =    []
     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(__a     )     )
       if len(__a     ) == self.batch:
         _lowercase: List[Any]						     =    batch
         _lowercase: str						     =    []
         yield list(map(__a						,					zip(*__a     )     )     )

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






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





     if not TEST:
       _lowercase: Any						     =    datasets.ArrowWriter(features=self.schema						,					path=self.outputfile     )
     # do file generator
     for i, (img_ids, filepaths) in enumerate(self.file_generator     ):
       _lowercase: Tuple						     =    self.preprocess(__a     )
       _lowercase: Union[str, Any]						     =    self.model(
           __a						,					__a						,					scales_yx=__a						,					padding='max_detections'						,					max_detections=self.config.MAX_DETECTIONS						,					pad_value=0						,					return_tensors='np'						,					location='cpu'						,					)
       _lowercase: str						     =    output_dict.pop('normalized_boxes'     )
       if not TEST:
         _lowercase: List[str]						     =    np.array(__a     )
         _lowercase: List[Any]						     =    self.schema.encode_batch(__a     )
         writer.write_batch(__a     )
       if TEST:
         break
       # finalizer the writer
     if not TEST:
       _lowercase: Union[str, Any]						     =    writer.finalize()
       print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'     )


def 		_lowerCamelCase				(					UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  try:
    _lowercase: List[Any]						     =    json.load(_SCREAMING_SNAKE_CASE							)
    try:
      _lowercase: Optional[Any]						     =    list(data.keys()							)
    except Exception:
      _lowercase: List[Any]						     =    [d['img_id'] for d in data]
  except Exception:
    try:
      _lowercase: List[str]						     =    eval(stream.read()							)
    except Exception:
      _lowercase: Union[str, Any]						     =    stream.read().split('\n'							)
  return data


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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__						:       Union[str, Any]							=    {'configuration_bert_generation': ['BertGenerationConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       str							=    ['BertGenerationTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Tuple							=    [
       'BertGenerationDecoder',
       'BertGenerationEncoder',
       'BertGenerationPreTrainedModel',
       'load_tf_weights_in_bert_generation',
   ]


if TYPE_CHECKING:
   from .configuration_bert_generation import BertGenerationConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_generation import BertGenerationTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert_generation import (
          BertGenerationDecoder,
          BertGenerationEncoder,
          BertGenerationPreTrainedModel,
          load_tf_weights_in_bert_generation,
      )

else:
   import sys

   UpperCAmelCase__						:       List[Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
