





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 				_UpperCamelCase       :

       def __init__(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Any=sys.argv[1:]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	1
              UpperCamelCase_							,			UpperCamelCase_ 							=	getopt.getopt(_SCREAMING_SNAKE_CASE       , "i:o:b:s"       , ["inputdir=", "outfile=", "batch_size=", "subset_list="]		)
              for opt, arg in opts:
                     if opt in ("-i", "--inputdir"):
                            UpperCamelCase_ 							=	arg
                     elif opt in ("-o", "--outfile"):
                            UpperCamelCase_ 							=	arg
                     elif opt in ("-b", "--batch_size"):
                            UpperCamelCase_ 							=	int(_SCREAMING_SNAKE_CASE		)
                     elif opt in ("-s", "--subset_list"):
                            UpperCamelCase_ 							=	arg

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

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

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	int(file.split("."		)[0].split("_"		)[-1]		)
              UpperCamelCase_ 							=	os.path.join(self.inputdir       , _SCREAMING_SNAKE_CASE		)
              return (img_id, filepath)

       @property
       def lowercase			(		self:			Union[str, Any]		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	[]
              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(_SCREAMING_SNAKE_CASE		)		)
                     if len(_SCREAMING_SNAKE_CASE		) == self.batch:
                            UpperCamelCase_ 							=	batch
                            UpperCamelCase_ 							=	[]
                            yield list(map(_SCREAMING_SNAKE_CASE       , zip(*_SCREAMING_SNAKE_CASE		)		)		)

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



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




              """simple docstring"""


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




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Tuple:
       try:
              UpperCamelCase_ 							=	json.load(UpperCamelCase_	)
              try:
                     UpperCamelCase_ 							=	list(data.keys()	)
              except Exception:
                     UpperCamelCase_ 							=	[d["img_id"] for d in data]
       except Exception:
              try:
                     UpperCamelCase_ 							=	eval(stream.read()	)
              except Exception:
                     UpperCamelCase_ 							=	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_tokenizers_available, is_torch_available


UpperCAmelCase_       : List[Any]							=    {
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    ["""RetriBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    [
          """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RetriBertModel""",
          """RetriBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
      from .tokenization_retribert import RetriBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_retribert_fast import RetriBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_retribert import (
                RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RetriBertModel,
                RetriBertPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_       : Tuple							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
