






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, 2_0_4_8), 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[int]       ,       lowerCAmelCase__			:					str=sys.argv[1:])    ->					Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						None
      SCREAMING_SNAKE_CASE							  =						None
      SCREAMING_SNAKE_CASE							  =						None
      SCREAMING_SNAKE_CASE							  =						1
      SCREAMING_SNAKE_CASE							  =						getopt.getopt(lowerCAmelCase__       ,       'i:o:b:s'       ,       ['inputdir=', 'outfile=', 'batch_size=', 'subset_list='])
      for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
        SCREAMING_SNAKE_CASE							  =						arg
       elif opt in ("-o", "--outfile"):
        SCREAMING_SNAKE_CASE							  =						arg
       elif opt in ("-b", "--batch_size"):
        SCREAMING_SNAKE_CASE							  =						int(lowerCAmelCase__)
       elif opt in ("-s", "--subset_list"):
        SCREAMING_SNAKE_CASE							  =						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:
        SCREAMING_SNAKE_CASE							  =						{self._vqa_file_split()[0] for x in tryload(lowerCAmelCase__)}
      else:
       SCREAMING_SNAKE_CASE							  =						None

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





     def   __UpperCamelCase		(						self			:					Optional[int]       ,       lowerCAmelCase__			:					Dict)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						int(file.split('.')[0].split('_')[-1])
      SCREAMING_SNAKE_CASE							  =						os.path.join(self.inputdir       ,       lowerCAmelCase__)
      return (img_id, filepath)





     @property
     def   __UpperCamelCase		(						self			:					int)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						[]
      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:
        SCREAMING_SNAKE_CASE							  =						batch
        SCREAMING_SNAKE_CASE							  =						[]
        yield list(map(lowerCAmelCase__       ,       zip(*lowerCAmelCase__)))

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





     def __call__(						self			:					Tuple)    ->					Dict:

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





def 		A_			(		lowercase_	)							->Any:







 """simple docstring"""

 try:
  SCREAMING_SNAKE_CASE							  =						json.load(lowercase_	)
  try:
   SCREAMING_SNAKE_CASE							  =						list(data.keys()	)
  except Exception:
   SCREAMING_SNAKE_CASE							  =						[d['img_id'] for d in data]
 except Exception:
  try:
   SCREAMING_SNAKE_CASE							  =						eval(stream.read()	)
  except Exception:
   SCREAMING_SNAKE_CASE							  =						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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      				=							{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      				=							[
          "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "VisualBertForMultipleChoice",
          "VisualBertForPreTraining",
          "VisualBertForQuestionAnswering",
          "VisualBertForRegionToPhraseAlignment",
          "VisualBertForVisualReasoning",
          "VisualBertLayer",
          "VisualBertModel",
          "VisualBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_visual_bert import (
                VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VisualBertForMultipleChoice,
                VisualBertForPreTraining,
                VisualBertForQuestionAnswering,
                VisualBertForRegionToPhraseAlignment,
                VisualBertForVisualReasoning,
                VisualBertLayer,
                VisualBertModel,
                VisualBertPreTrainedModel,
            )


else:
      import sys

      __UpperCAmelCase      				=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




